IN THE UNITED STATES PATENT AND TRADEMARK OFFICE BEFORE THE PATENT TRIAL AND APPEAL BOARD HULU, LLC Petitioner v. Chinook Licensing DE, LLC Patent Owner Patent No. 7,047,482 PETITION FOR INTER PARTES REVIEW UNDER 35 U.S.C. 311, 37 C.F.R. 42.100 ET SEQ.
TABLE OF CONTENTS PETITIONER S LIST OF EXHIBITS...iii II. MANDATORY NOTICES, STANDING, AND FEES... 1 III. OVERVIEW OF CHALLENGE AND RELIEF REQUESTED... 2 A. Publications Relied Upon... 2 B. Grounds for Challenge... 3 IV. Background of the Technology... 4 A. Recommendation Systems... 4 B. The MIT Media Lab in the 1990s... 7 V. OVERVIEW OF THE 482 PATENT... 7 A. Overview of the 482 Patent... 7 B. The 482 Patent Prosecution History... 8 C. The Iron Dome Petition... 8 VI. Claim Construction... 9 A. Level of Skill in the Art... 10 B. Contemporaneous Terms... 11 C. Directory... 11 VII. A REASONABLE LIKELIHOOD EXISTS THAT THE CHALLENGED CLAIMS ARE UNPATENTABLE.... 12 A. Ground 1: Claims 1-7, and 9-20 are Anticipated Under 35 -i-
U.S.C. 102 by Sheth.... 12 B. Ground 2: Claim 20 Is Obvious Under 35 U.S.C. 103 by Sheth in View of WebCompass... 38 C. Ground 3: Claims 1-20 Are Obvious Under 35 U.S.C. 103 by Rucker in View of Sheth... 40 VIII. CONCLUSION... 59 -ii-
PETITIONER S LIST OF EXHIBITS Ex. Description 1001 U.S. Patent No. 7,047,482 1002 File History for U.S. Patent No. 7,047,482 1003 A Learning Approach to Personalized Information Filtering by Beerud Sheth, Massachusetts Institute of Technology (February 1994) ( Sheth ) 1004 U.S. Patent No. 6,845,374 ( Oliver I ) 1005 U.S. Patent No. 7,158,986 ( Oliver II ) 1006 James Rucker and Marcos Polanco, Siteseer: Personalized Navigation for the Web, COMMUNICATIONS OF THE ACM, Vol. 40 No. 3 (March 1997) ( Rucker ) 1007 Chinook Licensing DE, LLC s Disclosure of Initial Infringement Contentions for the matter of Chinook Licensing DE, LLC v. Hulu, LLC, Case No. 1:14-cv-00074-LPS ( Infringement Contentions ) 1008 Declaration of Henry Lieberman ( Lieberman Decl. ) 1009 Scott Wilson, WEBCOMPASS USER GUIDE (Quarterdeck Corp. 1996) 1010 October 10, 2014 Decision Denying Institution of Inter Partes Review for the 482 Patent, IPR2014-00674 1011 Evolving Agents For Personalized Information Filtering by Beerud Sheth and Pattie Maes, IEEE (1993). 1012 Amalthaea: Information Filtering and Discovery Using a Multiagent Evolving System by Alexandros G. Moukas, Massachusetts Institute of Technology (June 1997) ( Moukas ) 1013 Complaint for the matter of Chinook Licensing DE, LLC v. Hulu, LLC, Case No. 1:14-cv-00074-LPS -iii-
Ex. Description 1014 Ed Bott, et al., USING MICROSOFT WINDOWS 95 WITH INTERNET EXPLORER 4.0, Special Ed. (Que Corporation 1998) 1015 Webmate: A Personal Agent for Browsing and Searching, Proceedings for the Second Int l Conference on Autonomous Agents (Katia P. Sycara & Michael Woolridge, eds. 1998). 1016 Letizia: An Agent that Assist Web Browsing, Proceedings of the Fourteenth Int l Joint Conference on Artificial Intelligence, Vol. I (Chris S. Mellish, ed. 1995). 1017 Iron Dome Petition for Inter Partes Review for the 482 Patent, IPR2014-00674. 1018 Declaration of Marilyn McSweeney, Massachusetts Institute of Technology from IPR2014-00562. 1019 Library of Congress catalog entry for Using Microsoft Windows 95 with Internet Explorer 4.0 1020 Library of Congress catalog entry with MARC tags for Using Microsoft Windows 95 with Internet Explorer 4.0 1021 Library of Congress catalog entry for WebCompass 1022 Library of Congress catalog entry with MARC tags for WebCompass 1023 Discovery of Shared Topics Networks among People A Simple Approach to Find Community Knowledge from WWW Bookmarks by Hideaki Takeda et al., National Institute of Informatics (2000). 1024 RFC 1036 Standard for Interchange of USENET Messages 1025 Barton MIT Library Catalog entry with MARC tags of A learning approach to personalized information filtering by Beerud Dilip Sheth 1026 Barton MIT Library Catalog entry with MARC tags of Amalthaea-- -iv-
Ex. Description information filtering and discovery using a multiagent evolving system by Alexandros G. Moukas 1027 Scott Wilson, WEBCOMPASS USER GUIDE (Quarterdeck Corp. 1996) (Library of Congress stamped copy) 1028 Marko Balabanovic, Exploring versus Exploiting when Learning User Models for Text Recommendation, USER MODELING AND USER- ADAPTED INTERACTION, vol. 8 at 71 102 (Feb. 1998) 1029 Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation, by Marko Balabanovic, Stanford University (1998) -v-
Pursuant to 35 U.S.C. 311 and 37 C.F.R. 42.100 et seq., Hulu, LLC ( Petitioner and real party in interest), hereby petitions for inter partes review of claims 1-20 of U.S. Pat. No. 7,047,482 ( the 482 Patent ), issued to Gary Odom. Petitioner hereby asserts there is a reasonable likelihood that at least one of the challenged claims is unpatentable and respectfully requests review of, and judgment against, claims 1-20 as unpatentable under 102 or 103. II. MANDATORY NOTICES, STANDING, AND FEES Real Party in Interest: Petitioner Hulu, LLC is the real party in interest. The 482 Patent was asserted against the Petitioner in Case No. 1:14-cv-00074-LPS, Chinook Licensing DE, LLC v. Hulu, LLC, pending in the U.S. District Court for the District of Delaware. See Ex. 1013, Complaint. Related Matters: Case No. 1:14-cv-00074-LPS is currently pending in the U.S. District Court for the District of Delaware. The complaint was filed on January 20, 2014 and served on January 27, 2014. An independent third-party (Iron Dome LLC) filed a petition for inter partes review of the 482 Patent in April 2014. Ex. 1017. The third-party petition was denied. IPR2014-00674, Ex. 1010. Lead Counsel and Request for Authorization: Pursuant to 37 C.F.R. 42.8(b)(3) and 42.10(a), Petitioner designates the following: Lead Counsel is Eliot D. Williams (Reg. No. 50,822) of Baker Botts L.L.P.; Back-up Counsel is Harper S. Batts (Reg. No. 56,160) of Baker Botts L.L.P. -1-
Service Information: Service information is as follows: Baker Botts L.L.P., 1001 Page Mill Road, Building One, Suite 200, Palo Alto, CA 94304; Tel. (650) 739-7500; Fax (650) 739-7609. Petitioner consents to service by electronic mail at eliot.williams@bakerbotts.com and harper.batts@bakerbotts.com. A Power of Attorney is filed concurrently herewith under 37 C.F.R. 42.10(b). Certification of Grounds for Standing: Petitioner certifies under 37 C.F.R. 42.104(a) that the 482 Patent is available for inter partes review. Petitioner is not barred or estopped from requesting inter partes review of any claim of the 482 Patent on the grounds set forth herein. Fees: Under 37 C.F.R. 42.103(a), the Office is authorized to charge the fee set forth in 37 C.F.R. 42.15(a) to Deposit Account No. 02-0384, Ref. No. 082838.0102, as well as any additional fees due in connection with this Petition. III. OVERVIEW OF CHALLENGE AND RELIEF REQUESTED Petitioner challenges claims 1-20 of the 482 Patent. A. Publications Relied Upon Petitioner relies upon the following patents and publications: Exhibit 1003 A Learning Approach to Personalized Information Filtering by Beerud Dilip Sheth ( Sheth ), published and entered into OCLC (a national bibliographic utility) by the Massachusetts Institute of Technology in 1994, and available as prior art under 35 U.S.C. 102(b). See Ex. 1008 39-40. -2-
Sheth was not previously presented to the PTO in the context of the 482 Patent. Exhibit 1006 Siteseer: Personalized Navigation for the Web by James Rucker, et al. ( Rucker ), published at least by March 1997 in vol. 40 of the Communications of the ACM, and available as prior art under 35 U.S.C. 102(b). See Ex. 1008 38, 42. Rucker has not been previously presented to the PTO in the context of the 482 Patent. Exhibit 1009 WebCompass User Guide by Scott Wilson ( WebCompass ), published by at least 1996 and available as prior art under 35 U.S.C. 102(b). Ex. 1008 38, 43. WebCompass has not been previously presented to the PTO in the context of the 482 Patent. B. Grounds for Challenge Petitioner requests cancellation of the claims on the following grounds: GROUND 1: Claims 1-7, 9-20 are anticipated under 102(b) by Sheth. GROUND 2: Claim 20 is obvious under 103 by Sheth in view of WebCompass. GROUND 3: Claims 1-20 are obvious under 103 by Rucker in view of Sheth. Sheth anticipates all elements of claims 1-7 and 9-20. To the extent Sheth does not expressly or inherently disclose dependent claim 20, Ground 2 provides an additional ground for invalidity for that claim. Ground 3 has been provided as -3-
an obviousness combination for dependent claim 8, which is not addressed by Grounds 1 or 2. Ground 3 invalidates all other claims of the 482 Patent as well. IV. BACKGROUND OF THE TECHNOLOGY A. Recommendation Systems The growth of the Internet led to an explosion of research in the 1990s regarding methods for providing relevant information to users out of vast bodies of online information. Ex. 1008 at 21. Initial development in the 1990s focused on search engines that commonly used keyword analysis. Id. at 22. The next generation of information systems on the Internet included recommendation systems that personalized the information delivered to users according to their individual interests. Id. at 23. Recommendation systems in the mid 1990 s were able to collect information from disparate sources, such as newsgroups, news feeds, web pages, or ontologies. Id. Recommendation systems generated a profile for the user s interests and used that profile to find articles, media, or other types of information that would have been potentially of interest to the user. Id.; Ex. 1012 at 23. There were multiple ways to generate a user profile. One way to generate a user profile was to do so in real time while the user interacts with the system, for example by actively monitoring the user s actions, or reacting to live user feedback. Ex. 1008 24. For example, Henry Lieberman (who provides a declaration supporting this petition) created the Letizia system in 1995-4-
with the goal of providing live recommendations to a user while they browsed Web articles. Id. Letizia monitored a user s browsing behavior, updated the profile of the user s interests in real time, and recommended documents based on the user profile while the user was interacting with the system. Id. Another method for generating a user profile was to do so offline, based on the user s past activities or input. Ex. 1008 25. For example, offline systems could discern user interests based on the user s browsing history, bookmarks/favorites folders, or past feedback for documents. Id. The user s past activities or input would not be contemporaneous with the generation or updating of the user s profile. Id. To take advantage of the fact that they did not require contemporaneous user input, offline profile generation and updating would often be performed in the background, or scheduled to run during off-peak hours. Id. Off-line profile generation commonly used sample documents for which the user had previously indicated interest to generate user profiles using keyword analysis. Ex. 1008 at 26; see also Ex. 1003 at 22; Ex. 1004 at 9; Ex. 1012 at 38. Recommendation systems would then use those profiles to search for similar documents based on those keywords. Ex. 1008 26. Another source of sample documents was a user s Internet bookmarks or favorites folders. Ex. 1008 27. A user s bookmarks or favorites folder is, essentially, a collection of documents in which the user previously expressed interest. Id. The prior art recognized numerous -5-
advantages to using bookmarks. See id. at 28-29. Several recommendation engines generated user profiles based on the user s bookmarks/favorites folders. Ex. 1008 28; see, e.g., Ex. 1006 at 1; 1012 at 16; 1023 at 1. For example, Takeda treated bookmark folders as interested topics, and recommended pages were added back to the same bookmark folder. See Ex. 1023 at 7 (Fig. 4); Ex. 1008 28. Moukas used the user s bookmarks as a bootstrapping mechanism for generating a user s profile. See Ex. 1012 at 16; Ex. 1008 29. Because bookmarks represent information the user has previously collected, recommendation engines can utilize bookmarks in background processes without requiring additional user input, or even for the user to be present at all. See Ex. 1006 at 1. Automated, offline recommendation systems were disclosed by numerous prior art patents, including several issued to Mailfrontier, Inc. Ex. 1008 30. For example, U.S. Patent No. 6,845,374 to Oliver et al. disclosed a system that was invoked automatically by a software program to develop a recommended set for existing clients not currently logged on. Ex. 1004 at 2:53-64. The system assembled pre-existing user data from numerous sources, such as shopping history, email, and prior Internet searches, into an interest set. See, e.g., Ex. 1004 at 6:4-20, Fig. 3, 4. Keywords were extracted from the interest set, and those keywords were used to recommend similar documents. See, e.g., Ex. 1004 at 6:4-20, Fig. 3, 4, 8, and 9. Similarly, U.S. Patent No. 7,158,986 to Oliver et al. disclosed the use -6-
of an interest folder for collecting information about the user s interests, such as the history of the user s Internet viewing, recommendations for the user, [or] a summary of the user s purchases. See Ex. 1005 at 14:29-47. Recommendation software used the user s interest folder to identify similar items of interest, which were written back to the same interest folder. See id. at 14:66-15:7, Figs. 6-7. B. The MIT Media Lab in the 1990s The MIT Media Lab conducted prolific research into the field of Human- Computer Interaction (HCI) and the subfield of Intelligent User Interfaces (IUI). Ex. 1008 34. A number of graduate students came through the MIT Media Lab in the 1990s, including Beerud Sheth, the author of the Sheth reference, and Alex Moukas, whose thesis was a follow-up to Sheth that added, among things, new autonomous agent algorithms and the ability to build a user profile using the user s bookmarks folder or browser history. Ex. 1008 35-36; Ex. 1012 at 16, 26, 31. V. OVERVIEW OF THE 482 PATENT A. Overview of the 482 Patent The 482 Patent was filed February 28, 2001. The 482 Patent is directed to offline information filtering. The alleged invention automatically finds, saves, and displays links to documents topically related to a set of documents without a user having to search or specify search terms. Ex. 1001 at 2:34-38. During prosecution, the applicant repeatedly differentiated the prior art as interactive user searching, as opposed to the alleged automatic search of the alleged invention. -7-
See, e.g., Ex. 1002 at 219; Ex. 1008 45. B. The 482 Patent Prosecution History The Examiner initially rejected all pending claims (i.e., pending claims 9-25) based on several prior art references. The applicant traversed the rejections, but the Examiner issued a final rejection of all pending claims. Applicant appealed on February 18, 2005, but the Board held the appeal brief was defective. After the undocketed appeal was returned to the Examiner, the applicant filed a request for continued examination and amended all claims to include elements requiring accessing [a document] without contemporaneous user selection and searching additional documents without contemporaneous user input of a search location. Ex. 1002 at 57-62. A notice of allowance issued one month later. Ex. 1002 at 18. C. The Iron Dome Petition Independent third-party Iron Dome LLC filed a petition for inter partes review of the 482 Patent. Ex. 1017 ( the Iron Dome Petition ). That petition was denied because it failed to address the limitation of accessing [a document] without contemporaneous user selection. Ex. 1010 at 9-10. The Iron Dome petition relied on two prior art references: a paper by Liren Chen ( Chen, Ex. 1015) and a paper by Henry Lieberman ( Lieberman, Ex. 1016). Both references monitored the user s web browsing while user was interacting with the system, and both generated profiles for the user in real-time. Ex. 1008 56. Lieberman -8-
disclosed the Letizia system, which was discussed above. See supra, Section IV( A). Letizia monitored the user s web browsing and provided the user with live recommendations while they surfed the Web. Ex. 1008 at 24, 56. Similarly, Chen disclosed a proxy server and an applet controller to monitor a user s browsing and searching activities and learn from them. Id.; Ex. 1015 at 11. Chen updated the profile in real-time, whenever a user indicated that they liked a particular document. Ex. 1008 at 24, 56; Ex. 1015 at 12. In contrast to the real-time systems from the Iron Dome Petition, the prior art references relied on here use off-line profile generation to target a usage model that does not depend on user interaction. Ex. 1008 57. These off-line references discern user interests using past user feedback or already-existing bookmarks. Id. Because the prior art relied on in this petition operated without user interaction, those references clearly satisfy the elements of the 482 Patent relating to accessing [a document] without contemporaneous user selection [or input]. Id. VI. CLAIM CONSTRUCTION Pursuant to 42.100(b), and solely for purposes of this review, Petitioner construes the claim language such that claim terms are given their broadest reasonable interpretation ( BRI ). For terms not specifically listed and construed below, and in the absence, to date, of detailed arguments from the Applicant indicating a need for construction or a disagreement regarding the -9-
meaning of the vast majority of terms, Petitioner interprets them for purposes of this review in accordance with their plain and ordinary meaning under the required BRI standard. Because this standard is different from the standard used in U.S. District Court litigation (see In re Am. Acad. of Sci. Tech Ctr., 367 F.3d 1359, 1364, 1369 (Fed. Cir. 2004); see also MPEP 2111), Petitioner expressly reserves the right to argue a different claim construction in litigation. A. Level of Skill in the Art The art at issue in the 482 Patent relates to the field of Human-Computer Interaction ( HCI, also referred to as Human-Computer Interface), which includes information filtering and information retrieval. See, e.g., Ex. 1008 31, 48; Ex. 1012 at 23 ( User modeling can be defined as the effort to create a profile of the user s interersts [sic] and habits and employ the profile in order to improve humancomputer interaction. ). One in the art would be considered to possess ordinary skill in the art with a Bachelor of Science in Computer Science, Electrical Engineering, or Computer Engineering, or a similar amount of coursework. 1 Ex. 1008 48. One of ordinary skill in the art would also have at least two years of experience either working in the field or in post-secondary education. Id. This level of education and work experience would be necessary to read and understand the 1 A Master of Science or Ph.D. in these fields would also have been sufficient. -10-
482 Patent. Id. Someone with less technical education but more experience also meets this standard. B. Contemporaneous Terms Claims 1, 11, and 16 include various terms related to contemporaneous user selection of a document or contemporaneous user input of a search location. The Board has previously construed accessing [a document] without contemporaneous user selection as accessing a document, but not at the same time or period that a user selects that document. See Ex. 1010 at 7. C. Directory The term directory is used numerous times in the claims of the 482 Patent. It appears in claims 1, 2, 5, 6, 8-11, 15, 16, 19, 20 in the elements augmenting a directory, first directory, and results directory. The specification for the 482 Patent states, As depicted in FIG. 2, a directory 3, if not empty, comprises a set of documents 2, or a set of links 1 to documents 2, or a combination of documents 2 and links 1. Ex. 1001 at 3:14-16. In district court, the Patent Owner advanced a broad construction for directory, asserting that the user s viewing history, which includes multiple documents, is a directory. Ex. 1007 at 9. In addition, the Patent Owner argued that [a] recommendation carousel is a directory, referring to graphical, sliding carousels (i.e. rows of videos) in the web page for Hulu s Recommendation Hub. Ex. 1007 at 6, 49, 77. Thus, according -11-
to the Patent Owner, the BRI of directory in the context of the 482 Patent would include a collection of documents and/or links to documents. Ex. 1008 55. VII. A REASONABLE LIKELIHOOD EXISTS THAT THE CHALLENGED CLAIMS ARE UNPATENTABLE. Pursuant to 42.22 and 42.104(b), all of the challenged claims are unpatentable for the reasons set forth in detail below. A. Ground 1: Claims 1-7, and 9-20 are Anticipated Under 35 U.S.C. 102 by Sheth. Sheth discloses a system that finds articles which match the [user] profiles. Ex. 1003 at 32. One method for creating a profile is to provide examples of articles that the agent did not retrieve, which is an example of programming by demonstration. Id. at 26. By showing examples of interesting articles, the user is indirectly programming the agent to get good articles in the future. Id. at 37. Programming by demonstration is particularly useful while training a new agent. Id. When the user gives feedback by providing an example article of interest, the article is not immediately analyzed instead, a link to that article is added to the ArtFeedback directory. See, e.g., Ex. 1003 at 46 ( The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. ), 40 ( The solution adopted in this implementation is that the pointers of documents are stored when feedback is provided, but the -12-
profiles are not immediately modified. ); Ex. 1008 62. Every night, the system accesses the articles listed in the ArtFeedback directory, updates user profiles with keywords from those articles, and searches for similar articles matching those keywords/profiles. See Ex. 1003 at 17-18, 40-46; Ex. 1008 58. Links to topscoring articles are added to a directory in the profile and then presented to the user the next day. See id. Claim 1 1[p]. A computerimplemented method for augmenting a directory without contemporaneous user input comprising: Sheth See, e.g., 20: An information filtering agent assists the user with the task of finding interesting news articles in a particular domain. See, e.g., 17: The proposed idea is to build a set of adaptive autonomous interface agents that inhabit the user s computer and are assigned the goal of being responsive to the information needs of the user. The agents can sense user feedback as well as changes in the information environment. The agents are autonomous as they can take actions relating to news filtering on the user s behalf. See, e.g., 46: Finally, YAIF writes back the updated profile as well as pointers to filtered articles into the profile. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. -13-
As explained below with respect to element 1[f], a profile contains an ArtScores directory for results. See Ex. 1003 at 41, 46. Thus, when the system finds new recommended articles and writes pointers to the filtered articles into the profile (Ex. 1003 at 46), it satisfies the augmenting a directory element (Ex. 1008 60. Because [t]he Information Filtering module (called YAIF, for Yet Another Information Filter) runs offline, every night (Ex. 1003 at 42), it runs without contemporaneous user input (Ex. 1008 60). The automated interface agents of Sheth operate totally autonomously. Ex. 1003 at 16-17. Claim 1 1[a] accessing at least a first document via a first directory without contemporaneous user selection of said first document, said first document comprising at least in part topical textual content; Sheth See, e.g., 40: The list of items labeled 'ArtFeedback' in table 4.1 is the list of pointers to articles which received feedback in the last user session. Next to the pointers is a positive or negative number indicating the feedback for the article. The next time the Information Filtering module filters articles, it re-creates the document representation for all articles and the profile is then modified. See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. See, e.g., 41: See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification -14-
ArtFeedback is a collection of pointers (links) to articles and thus satisfies the BRI of first directory. Ex. 1008 61. When the Information Filtering module runs, it accesses the articles linked to in ArtFeedback (i.e., at least a first document). Ex. 1003 at 40; see also Ex. 1008 61. As explained above, the Information Filtering module (YAIF) runs offline, meaning [t]he process is executed every night (Ex. 1003 at 42), and thus the Information Filtering module accesses the articles linked to in ArtFeedback without contemporaneous user selection (Ex. 1008 61). The articles contain topical textual content as explained below, the articles contain keywords used for content representation based on their presumed importance for text content identification. Ex. 1003 at 21. The articles are newsgroup articles (id. at 41-44), and a person of ordinary skill in the art would recognize that a newsgroup article contains topical textual content (Ex. 1008 61). Thus Sheth discloses all limitations in element 1[b]. See id. Sheth discloses that users can give feedback by providing an example article via programming by demonstration. Ex. 1003 at 26. The system adds a link to the article to the ArtFeedback directory to be processed later, at night. See Id. at 46 ( The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. ); id.at 40: [T]he document representation is generated while scoring the document -15-
and is not stored thereafter. Therefore, when feedback for a retrieved document is provided, its representation is no longer available for modifying the profile. This problem is much more acute in programming by demonstration, where feedback is provided for an article for which no representation has yet been created. The solution adopted in this implementation is that the pointers of documents are stored when feedback is provided, but the profiles are not immediately modified. The list of items labeled 'ArtFeedback' in table 4.1 is the list of pointers to articles which received feedback in the last user session. See also Ex. 1008 62. The next time the Information Filtering module filters articles (at night without contemporaneous user selection), it accesses and recreates the document representation for documents listed in ArtFeedback, which includes the example article (i.e., the first document) provided via programming by demonstration. See Ex. 1003 at 40; Ex. 1008 62. Claim 1 1[b] deriving at least one keyword indicative of at least one topical content from said first document; Sheth See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. (emphasis added) See, e.g., 23: Documents are represented as a set of fields where each field is a term-vector (see Section 3.1.2). The fields of the document representation must be extracted from the document itself.... The keyword field is generated from the text of the article. See, e.g., 41 (Table 4.1: A sample interest profile): keywords: 0.7-16-
ukraine 0.475 nuclear 0.294... additional 0.158 returned 0.149 washington 0.148 See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification As explained with element 1[a], when the Information Filtering module (YAIF) runs at night, it re-creates the document representation for articles in the ArtFeedback directory, which would include the claimed first document. Ex. 1003 at 40; see also Ex. 1008 63. The document representation includes a keyword field with a vector of keywords obtained through a full text analysis of the documents. Ex. 1003 at 23; see also Ex. 1008 63. Thus, YAIF derives at least one keyword from said first document. Ex. 1008 63. That keyword is indicative of at least one topical content from the first document. Id. Sheth explains that keyword terms are used for content representation and weights are assigned to the terms in proportion to their presumed importance for text content identification. Ex. 1003 at 21. A POSITA would understand that keywords derived from a document are indicative of at least one topical content from that document. Ex. 1008 63. The document representation, including its keyword field, are added to the -17-
interest profile. See Ex. 1003 at 40-41; Ex. 1008 64. Table 4.1 of Sheth shows keywords derived from documents and stored in a profile. See id. Profiles and documents both contain keyword vectors. See Ex. 1003 at 20, 22; Ex. 1008 64. When the user creates a new profile by providing a sample document, the profile contains a list of keywords derived exclusively from the example document (i.e., the first document). Ex. 1008 64-66. When programming by demonstration is used to create a new profile, the system creates a profile which looks like D [the example Document provided by the user]. Ex. 1003 at 27; see also Ex. 1008 64-66. In that instance, the keyword vector in a profile, as illustrated by Table 4.1, would contain more than one keyword indicative of the topical content from the sample document (first document) provided by the user. Ex. 1008 64-66. Claim 1 1[c] searching as a background operation a plurality of documents in storage in at least one computer without contemporaneous user input of a search location, Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 20: An agent is modeled as a population of profile individuals, each of which searches for articles in a small domain.... The profile contains information about where to search for articles and what kinds of articles to filter. See, e.g., 42: The Information Filtering module (called YAIF, for Yet Another Information Filter) is responsible for actually retrieving articles from the database of news articles. YAIF takes the profiles, scores articles with respect to the profiles and selects the high scoring articles to be presented to the user. YAIF is a time-intensive process and is run offline. The process is executed every -18-
night, so that filtered articles are available to every profile in the morning. YAIF searches newsgroup articles (i.e, a plurality of documents in storage). Ex. 1003 at 42-44; see also Ex. 1008 67. The information filtering module YAIF runs offline, at night (i.e., as a background operation). Ex. 1003 at 42; see also Ex. 1008 67. Thus it searches a plurality of documents without contemporaneous user input of a search location. See Ex. 1003 at 20 ( The profile contains information about where to search for articles[.] ); Ex. 1008 67. Newsgroup articles are stored on USENET servers (i.e., at least one computer). See Ex. 1003 at 42 ( On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. ); Ex. 1008 67. Claim 1 1[d] such that said search comprises searching for documents related by said at least one keyword to said first document, thereby accessing a second document; Sheth See, e.g., 23: The keyword field is generated from the text of the article. See, e.g., 17-18: The profiles used by the filtering system consist of terms which are matched with the contents of the documents... Each profile searches for documents that match itself and recommends them to the user. See, e.g., 22: The filtering process consists of translating documents to their vector space representations, finding documents which are similar to the profiles and selecting the top-scoring articles for presentation to the user. See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. -19-
1[e] determining relevance of said second document to said at least one keyword; and See, e.g., 24 (emphasis added): The similarity between a document and a profile is a function of the similarities between the corresponding fields. The field similarities are first computed. Since each field is a term vector, the metric used for measuring similarity between two fields of the same type is just as in equation 3.1. The similarity scores for the corresponding fields in the document and the profile are computed as shown in equation 3.2. The similarity between a complete document and complete profile is computed next. It is the sum of the field similarity scores weighted by field weights of the profile. See supra, element 1[d]. The Information Filtering module YAIF searches for articles that are similar to the user profile. Ex. 1003 at 42; Ex. 1008 68. The profile contains a keyword vector including more than one keyword from the first document (the example article provided by the user. See Ex. 1003 at 20, 22; Ex. 1008 66; supra element 1[b]. The representation of a profile is similar to that of a document. Ex. 1003 at 22. The advantage of using a common vector space for both documents and queries [i.e. profiles] is that a document can also be used as a query itself i.e., one can find documents that are similar to a given document. Id. at 21 (emphasis added). When YAIF searches for documents similar to a profile, it searches for documents related by at least one keyword in the keyword vector. Ex. 1008 68. When programming by demonstration is used to create a new profile, the -20-
system creates a profile which looks like D [the example document provided by the user]. Ex. 1003 at 27; Ex. 1008 64-66, 69. The profile will thus contain keywords from the example document (i.e., the claimed first document). Ex. 1008 65-66, 69. YAIF will score articles based on their similarity to the profile, which is includes measuring the similarities between the keyword vectors. Ex. 1003 at 24-25; Ex. 1008 69. Thus, when YAIF searches for top-scoring articles, it searches for documents similar to (i.e., related) by at least one keyword (as stored in the profile) to the example document provided by the user (i.e., the first document). Ex. 1008 69. Regarding element 1[e], a person of ordinary skill in the art would recognize that the similarity score is indicative of the relevance of the article. Ex. 1008 70. As explained for element 1[d], YAIF calculates a similarity score (i.e., determines the relevance) that includes a calculation of the article s keyword vector and keyword vector in the profile. Ex. 1003 at 24-25; Ex. 1008 70. The keyword vector of the profile includes at least one keyword derived from the first document. See supra, element 1[b]. Thus, YAIF calculates the similarity (i.e., relevance) of searched articles (including the second document) to at least one keyword derived from the first document. Ex. 1008 70. Claim 1 1[f] adding a reference to said second document in a results Sheth See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon -21-
directory. user request. See, e.g., 41 Table 4.1 (sample interest profile).: Sheth further elaborates that YAIF writes back the updated profile as well as pointers to filtered articles into the profile. Ex. 1003 at 46. ArtScores is a directory in the profile: it is a collection of pointers (i.e., links) to recommended/filtered articles. See Ex. 1003 at 41, 46; Ex. 1008 71. Thus, at least one pointer (i.e., a reference) to a recommended article (i.e., the second document) is stored in the ArtScores directory (i.e., the results directory). Ex. 1008 71. A POSITA would understand that pointers, including the pointers to newsgroup articles shown in Table 4.1, are references. Ex. 1008 71. The ArtScores directory is later displayed to the user as required by the additional step[s] of claims 6 and 9 and explained in those respective sections. Id. Claim 2 2. The method according to claim 1, wherein at least part of said storage is on a different computer than the computer storing said first directory. Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 42: On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. -22-
The Information Filtering module (YAIF) retrieves newsgroup articles from USENET servers. See Ex. 1003 at 42; Ex. 1008 72. Those servers are different computers than the one running YAIF. Ex. 1008 72. Sheth explains that YAIF only stores pointers to selected articles; it only retrieves those articles from the USENET servers (the different computer) upon user request. See Ex. 1003 at 46. Claim 3 3. The method according to claim 1, further comprising deriving a plurality of keywords. Claim 4 4. The method according to claim 3, further comprising ranking at least two of said plurality of keywords. Sheth See, e.g., 21: A text is then representable as a vector of terms T i =< w ij > where wij represents the weight of term t j in text T i. See supra, element 1[b]. Sheth See, e.g., 23: The weight of a keyword-term is the product of its term frequency and its inverse document frequency. See supra, element 1[b] and claim 3. With respect to claim 3, Sheth discloses a vector of keyword terms as explained above for element 1[b]. The keyword vector includes more than one keyword, and thus includes a plurality of keywords as shown in Table 4.1 (charted for element 1[b]). See also Ex. 1008 74. Specifically, the vector of keyword terms is described in the equation T i =< w ij > which discloses a vector of j terms (i.e., a plurality of keywords) for the text T i. Ex. 1003 at 21. With programming by demonstration, some or all of the keywords in that vector are derived from the sample article (the first document) provided by the user. Ex. 1008 66, 74. -23-
Regarding claim 4, keyword terms are not all equally important for content representation, and thus each keyword term is assigned a weight. Ex. 1003 at 21. The weight of a keyword-term is the product of its term frequency and its inverse document frequency. Id. at 23. The weight of a keyword reflects its importance. Id. at 21 ( [I]mportance factors (or weights) are assigned to the terms ). As explained in element 1[b] and shown in Table 4.1, the keywords terms in a profile are sorted by weight (i.e. ranked) from highest to lowest. Id. at 41; see also Ex. 1008 75. Thus at least two of the plurality of keywords are ranked. Ex. 1008 75. Claim 5 5. The method according to claim 1, further comprising accessing a plurality of documents in said first directory. Sheth See supra, element 1[a]. As shown for element 1[a], the ArtFeedback directory (the first directory), contains a list of articles (a plurality of documents) that are accessed to create document representations when YAIF is run nightly. See also, Ex. 1008 76. Claim 6 6. The method according to claim 1, with the additional step of signifying the relevance of said second document to documents in the first directory when displaying said results directory. Sheth See, e.g., 35 (emphasis added): Clicking on an Agent Icon brings up a window (News Window) for browsing through the news articles retrieved by that agent (see figure 4-2). In the left half of the News Window are the titles of the articles selected by the agent. The color code of the agent which retrieved the articles, is used as the background color for the list of titles. To the left of to [sic] each document title is a small bar graph indicating the score assigned to it by the agent. The titles are sorted in decreasing order of these scores. See, e.g., 34: -24-
See, e.g., 24-25: When the agent collects all the topscoring articles retrieved by the profiles, it is not possible to compare the scores unless they are all on the same scale. Besides, a user would not be able to make sense of the similarity score if the scale is not known. Hence, document scores are constrained to be in the closed interval [-1, 1]. The highest score of 1 would only be assigned when the document and profile representations are identical. As shown in Figure 4-2, the small bars on the left of the recommended article titles are bar graphs indicating the similarity score with the profile (i.e., signifying the relevance). See Ex. 1003 at 34-35; Ex. 1008 77. When using programming by example to create a new profile, the profile will reflect contents and keywords of the example articles provided by the user. See Ex. 1003 at 27; Ex. 1008 64-66, 77. Example articles provided by the user are added to the ArtFeedback directory for processing at night. Ex. 1003 at 40; Ex. 1008 64-66, 77. Therefore, the displayed similarity scores will reflect the relevance of each article (i.e., the second document) to the example articles (i.e., the documents) -25-
listed in the ArtFeedback Directory (i.e., the first directory). Ex. 1008 77. Claim 7 7. The method according to claim 1, with the additional step of comparing the relevance of said second document to a preset threshold. Claim 9 9. The method according to claim 1, with the additional step of displaying said results directory. Claim 10 10. The method according to claim 1, further comprising recognizing a precondition for autonomously augmenting said results directory, prior to accessing said first document. Sheth See, e.g., 25: A third approach is to use a threshold. Any document which scores above the threshold will be selected independent of the profile which scored it. See, e.g., 65 (describing a test scenario): An arbitrary threshold (0.05) for document scores is set. Only documents scoring above the the threshold are retrieved for presentation. To achieve the desired goal of high recall and high precision the similarity scores of relevant documents must lie above the threshold and the scores of irrelevant documents must lie below the threshold. Sheth See, e.g., 20: The articles recommended by each of the profiles are collected together and presented to the user. See supra, claim 6. Sheth See, e.g., 42: The Information Filtering module (called YAIF, for Yet Another Information Filter) is responsible for actually retrieving articles from the database of news articles. YAIF takes the profiles, scores articles with respect to the profiles and selects the high scoring articles to be presented to the user. YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. See, e.g., 16: Autonomous adaptive agents operate totally autonomously and become better over time at achieving its goals. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. Information filtering module YAIF runs every night. Ex. 1003 at 42. Thus, -26-
the system recognizes a precondition (e.g., nightime or a specific time trigger such as midnight) that initiates YAIF. Ex. 1008 81. After YAIF begins to run, it accesses the articles (including the first document) in ArtFeedback (i.e., the first directory). See Ex. 1003 at 42; Ex. 1008 81. YAIF s autonomous agents augment the ArtScores directory (i.e., the results directory) with new recommendations. See Ex. 1003 at 46; Ex. 1008 at 81. Claim 11 11[p]. A computer-implemented method for augmenting a directory comprising: 11[a] autonomously initiating operation based upon a stored precondition; 11[b] accessing at least a first document without contemporaneous user selection, wherein said first document comprises at least in part topical textual content; 11[c] deriving at least one keyword indicative of at least one topical content within said first document; 11[d] as a background operation, searching in storage in at least one computer for documents related by said at least one keyword to said first document, 11[e] wherein at least some of said searched documents are independent and not organized in relation to one another; Sheth See supra, element 1[p]. See supra, claim 10. See supra 1[a]. See supra 1[b]. See supra 1[c] and 1[d]. See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 41 (Table 4.1: A sample interest profile): newsgroups: 0.2 clari.news.cast 1 clari.news.gov.international 1 clari.news.hot.easteurope 1 clari.news.hot.ussr 1 clari.news.top.world 1-27-
USENET is a distributed Internet discussion system. Ex. 1008 84. Newsgroups articles are independent and not organized in relation to one another because articles from one newsgroup are not organized in relation to articles from other newsgroups. Id. Sheth teaches retrieving articles from multiple newsgroups for each profile, as shown in Table 4.1. Ex. 1003 at 42-43; Ex. 1008 84. Even within a newsgroup, articles were not organized in terms of content anyone could post to a newsgroup at any time, and newsgroups were often full of spam. Ex. 1008 84. Therefore, at least some of the searched newsgroup articles (said searched documents) are independent and not organized in relation to one another. Id. Claim 11 11[f] determining relevance of a search-accessed second document to said at least one keyword; and 11[g] adding a reference to said second document in a results directory. Sheth See supra, element 1[e]. See supra, element 1[f]. Claim 12 12. The method according to claim 11, wherein said storage is on a plurality of computers connected to at least one network. Sheth See, e.g., 42-44: For every profile, YAIF retrieves each article from the two sets of newsgroups and scores them with respect to the profile. See, e.g., 42: On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. See, e.g., 46: Only the pointers to the selected articles are stored by YAIF in the profile, as in the ArtScore list in table 4.1. The article is not stored but is retrieved upon user request. USENET is a distributed Internet discussion system with no central server; -28-
instead, USENET is distributed among a large conglomeration of servers that store and forward messages to one another in so-called news feeds. Ex. 1008 86. Therefore, USENET news feeds are stored on a plurality of servers (i.e., computers) connected to the Internet (i.e., at least one network). Id. Claim 13 Sheth 13[p]. The method according to claim 11, See supra, claim 11. further comprising: 13[a] deriving a plurality of keywords; and See supra, claim 3. 13[b] determining relevance of said second See supra 1[e]. document to said plurality of keywords. Claim 14 Sheth 14. The method according to claim 11, See supra, claim 7. further comprising comparing the relevance of said second document to a preset threshold. Claim 15 Sheth 15. The method according to claim 11, See, e.g., 46: While selecting further comprising conditionally adding articles, care is taken to prevent said reference to said second document presenting the same article twice in depending upon whether said reference to different sessions. The profile keeps said second document already exists in said a list of articles that have been results directory. presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. Claim 16 Sheth 16[p]. A computer-implemented method See supra, element 1[p]. for augmenting a directory comprising: 16[a] accessing a plurality of grouped See supra, element 1[a]. documents without contemporaneous user selection initiating said access; As shown for element 1[a], the ArtFeedback directory in a profile is a -29-
collection of links to articles (i.e., the plurality of grouped documents) that are accessed to create document representations when YAIF is run offline at night (i.e., without contemporaneous user access). See also Ex. 1008 93. Claim 16 16[b] deriving a plurality of keywords indicative of an aggregate content of said grouped documents; Sheth See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. See, e.g., 23: Documents are represented as a set of fields where each field is a term-vector (see Section 3.1.2). The fields of the document representation must be extracted from the document itself.... The keyword field is generated from the text of the article. See, e.g., 46: The profile maintains a list of articles for which feedback was received, if any, and the amount of feedback for each, during the last user session. This can be seen in the items listed under ArtFeedback in table 4.1. See, e.g., 57: Initially, when the profile is empty, the articles retrieved are in a random order.... In that case, the user needs to program the agent by demonstration by providing a few examples of relevant documents. After feedback is provided to the relevant articles, the profile is modified and the search is performed again. See, e.g., 27: The resulting effect is that, for those terms already present in the profile, the term-weights are modified in proportion to the feedback. Terms not already in the profile are added to the profile. See, e.g., 56: -30-
See, e.g., 21: Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification See supra, claim 3. Sheth discloses Scenario 1 where [t]he initial profile is nearly empty and is shown on the left hand side of Table 5.1. Ex. 1003 at 54. Sheth explains that, because the profile is empy, the user needs to program the agent by demonstration by providing a few examples of relevant documents. Id. at 57. When the user provides feedback for documents or provides example documents via programming by demonstration, pointers to those documents are added to (i.e., -31-
grouped together in) the ArtFeedback directory. See id. at 40, 46; Ex. 1008 95; see supra, element 1[a]. The effect of this feedback is shown in Table 5.1 (titled Static user interests: effect of feedback ) where the right-hand side shows the final profile including a cumulative keyword vector derived from (i.e., indicative of the aggregate content of) the example documents provided by the user (the grouped documents). See Ex. 1003 at 56; Ex. 1008 95. Because the initial profile is empty, the profile after one day after the user provided feedback and example documents for programming by demonstration is a keyword vector (a plurality of keywords) with terms derived exclusively from the set of documents in the ArtFeedback directory (the grouped documents). Ex. 1008 96. When feedback for a document is provided for an empty profile, it effectively creates a profile which looks like [the document] D. Id. at 27. After the first document in ArtFeedback is analyzed, feedback for the remaining documents in ArtFeedback are added to the profile. Ex. 1008 96. The resulting effect is that, for those terms already present in the profile, the termweights are modified in proportion to the feedback. Terms not already in the profile are added to the profile. Ex. 1003 at 27; Ex. 1008 96. Thus, the list of keyword terms in the profile are indicative of the aggregate content of the documents grouped together in the ArtFeedback directory. Ex. 1008 96. Claim 16 16[c] prioritizing a Sheth See, e.g., 21: Generally speaking, a term is used for -32-
relative relevance of said keywords; text identification. Since the terms are not all equally important for content representation, importance factors (or weights) are assigned to the terms in proportion to their presumed importance for text content identification See, e.g., 23: The term-vector for the keyword field is obtained through a full text analysis of the documents. The weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. See, e.g., 23: The weight of a keyword-term is the product of its term frequency and its inverse document frequency. See, e.g., 41 (Table 4.1: A sample interest profile): keywords: 0.7 ukraine 0.475 nuclear 0.294... additional 0.158 returned 0.149 washington 0.148 16[d] storing said plurality of keywords with regard to said relevance; See, e.g., 56, Table 5.1 (charted above for element 16[b]). See, e.g., 40: Each profile is stored in a different file. It is stored as an ASCII flatfile in a predetermined format. Table 4.1 shows a sample profile. See supra element 16[c]. As explained previously with elements 1[b] and 3, Sheth teaches that the keyword terms (the plurality of keywords) are weighted according to their importance (i.e., relevance). See also Ex. 1008 99. Specifically, [t]he weight of the term depends on its frequency of occurrence in the text and the number of documents it appears in. Ex. 1003 at 23. Keyword vectors are shown in Tables 4.1-33-
and 5.1, with the keyword terms sorted by weight (i.e., relevance) in descending order with the most relevant terms on top (i.e., prioritized by relative relevance). See Ex. 41, 56; Ex. 1008 99. Sheth provides detailed instruction on the mathematical operations for calculating term weights. See Ex. 1003 at 23; Ex. 1008, 100. With respect to element 16[d], each profile including the keyword vector in every profile is written to a flat ASCII file (i.e., stored). Ex. 1003 at 40. As explained above and shown in Tables 4.1 and 5.1, each profile contains a plurality of keywords prioritized by their importance (i.e., relevance) to the profile, with weights assigned to each keyword based on its importance, and stored along with the rest of the profile in an ASCII file. Ex. 1003 at 40, 56; Ex. 1008 101. Claim 16 16[e] searching as a background operation storage in at least one computer for documents related to said plurality of stored keywords; Sheth See supra, elements 1[c] and 1[d]. As explained previously, the information filtering module YAIF runs offline, at night (i.e., as a background operation). See, e.g., Ex. 1003 at 42; Ex. 1008 103; supra element 1[a]. YAIF searches newsgroup articles (documents) that are stored on USENET servers (storage in at least one computer). See Ex. 1003 at 42 ( On typical USENET servers, the set of articles in a newsgroup changes on a continual basis. ); Ex. 1008 103. YAIF retrieves and scores the newsgroup articles with respect to the user profile. See id. As explained with elements 16[c] and 16[d], -34-
each profile contains a keyword vector, and as explained with element 1[d], scoring of a document against a profile includes comparison of their keyword vectors. See also Ex. 1008 103. When YAIF searches for high scoring articles to be presented to the user, it searches for documents related to the plurality of stored keywords in the profile. See Ex. 1003 at 21 ( The advantage of using a common vector space for both documents and queries is that a document can also be used as a query itself i.e., one can find documents that are similar to a given document. ); Ex. 1008 103. Claim 16 16[f] determining relevance of a found second document to said plurality of stored keywords; 16[g] conditionally adding a reference to said second document in a results directory. Sheth See supra, elements 1[d], 1[e], and claim 3. See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. See, e.g., 20: Profiles search for articles that are similar to itself. Top-scoring articles are retrieved for presentation to the user. The articles recommended by each of the profiles are collected together and presented to the user. See, e.g., 25-26: There are a number of approaches to selecting the final documents: The number documents contributed by each profile is proportional to its fitness.... The number of documents contributed by each -35-
profile is the same.... A third approach is to use a threshold. Any document which scores above the threshold will be selected independent of the profile which scored it.... See supra, element 1[f]. As explained with element 1[f], pointers to recommended articles (i.e., a reference to the second document) are added to the ArtScores directory (the results directory) in the profile. See also Ex. 1008 105. Sheth conditionally adds references to articles to the ArtScores directory if the same article has not already been presented to the user. See Ex. 1003 at 46; Ex. 1008 105. Furthermore, Sheth discloses three approaches for selecting which candidate documents will be added to the ArtScores directory and subsequently presented to the user. See Ex. 1003 at 25-26. Each approach specifies conditions for adding references to the results directory. Ex. 1008 105. Claim 17 17. The method according to claim 16, with the additional step of comparing the relevance of said second document to a preset threshold. Claim 18 18. The method according to claim 16, wherein said storage is on a plurality of computers connected to at least one network. Sheth See supra, claim 7. Sheth See supra, claim 12. Claim 19 19. The method according to claim 16, wherein adding a Sheth See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been -36-
duplicate reference in said results directory is avoided. Claim 20 20. The method according to claim 16, wherein adding a reference that was previously deleted from said results directory is avoided. presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. Sheth See, e.g., 35-36: The user can provide feedback for the articles retrieved by the agent. Under each Agent Icon are small icons bearing the signs '+' and '-' to enable the agent to receive positive and negative feedback respectively. See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. With respect to claim 20, the user can provide negative feedback for articles. Ex. 1003 at 35-36. Articles that have received negative feedback will not be kept in i.e., will be deleted from the ArtScores directory (the results directory). Ex. 1008 109. Sheth keeps a list of articles that have been presented to the user. Ex. 1003 at 46. Any reference previously added to the ArtScores directory will not be added again including those previously added and then deleted. Ex. 1008 109. To the extent it is not explicitly disclosed, Sheth inherently discloses claim 20. Recommended articles must inherently include a mechanism for deleting results from the ArtScores directory. Ex. 1008 110. If Sheth did not have a mechanism for deleting results such as periodically, manually, or automatically once an article has been presented then the ArtScores directory would reach an unmanageable size. Id. Thus, Sheth inherently includes a mechanism for deleting -37-
references from the ArtScores directory (i.e., the results directory). Id. Irrespective of how a reference is deleted, Sheth prevents repetitions of that article from being added to the ArtScores (results) directory. See Ex. 1003 at 46; Ex. 1008 110. B. Ground 2: Claim 20 Is Obvious Under 35 U.S.C. 103 by Sheth in View of WebCompass WebCompass was a software tool that found Webpages and automatically generate[d] summaries and keywords for each document it [found] that matches your search criteria. Ex. 1009 at 6. The user manual ( WebCompass ) discloses remembering when a document has been deleted and not retrieving it again for the same topic. Id. at 19; Ex. 1008 111. For the reasons described below, it would have been obvious for a POSITA to combine this functionality, as disclosed in WebCompass, with the information filtering system of Sheth. Ex. 1008 111. As explained in Ground 1, Sheth explicitly and inherently discloses claim 20. The combination of Sheth with WebCompass also renders claim 20 obvious. Ex. 1008 111. Sheth already kept a list of the articles presented to the user to prevent the same article from being presented twice. See Ex. 1003 at 46; Ex. 1008 111. Thus, it would have been obvious to a POSITA in light of the teachings of WebCompass to add a list of deleted articles to the profiles in Sheth to the extent this functionality was not already present and to compare top-scoring articles to the deleted list before adding them to the results directory. Ex. 1008 111. A POSITA would have been motivated to add this feature of WebCompass -38-
to Sheth because the references are analogous art in the same field of humancomputer interfaces. Ex. 1008 112. Notably, Moukas (the follow-on project to Sheth) cites both Sheth and WebCompass as Related Software Agent Systems. Ex. 1012 at 25-26; Ex. 1008 36, 41, 112. Additionally, Sheth contains teachings that would have motivated a POSITA to combine it with WebCompass. Sheth teaches that repetitions should be avoided; thus, to the extent that functionality was not already present, a POSITA would have been motivated to add a list of deleted articles as taught by WebCompass to the system in Sheth. Ex. 1008 112. Furthermore, the application of WebCompass to Sheth uses a known method to yield a predictable result. Ex. 1008 112. WebCompass teaches a known method for avoiding repetition: the system remembers if an article has been deleted. Id. When added to Sheth, the method yields the predictable result that previously-deleted articles are not presented to the user again. Id. Claim 20 20. The method according to claim 16, wherein adding a reference that was previously deleted from said results directory is avoided. Sheth Sheth: See, e.g., 46: While selecting articles, care is taken to prevent presenting the same article twice in different sessions. The profile keeps a list of articles that have been presented to the user during previous sessions, as in the ArtRead list in table 4.1. The top scoring articles are compared to this list to prevent repetitions. WebCompass: See, e.g., 19: When you delete a document, WebCompass remembers that it has been deleted and will not retrieve it again for the same topic on a subsequent search. -39-
C. Ground 3: Claims 1-20 Are Obvious Under 35 U.S.C. 103 by Rucker in View of Sheth Sheth anticipates all claims of the 482 Patent except claim 8 ( wherein said results directory is said first directory ). Rucker discloses that element, but lacks an explicit disclosure regarding certain other elements, such as when searches are performed, or the use of keyword searching. However, Rucker in view of Sheth renders obvious all claims of the 482 Patent. Rucker discloses a Web-page recommendation system called Siteseer that used an individual s bookmarks in particular, the folders and organization of those bookmarks to predict pages of potential interest. See Ex. 1006 at 1. Rucker treated bookmark folders as a user s personal classification system, and used that information to generate recommendations. Id. Rucker used an almost purely collaborative approach, where its recommendations were based on the bookmarking patterns of other users as opposed to deriv[ing] any semantic value from the contents of the Web pages. See Ex. 1006 at 2-3; Ex. 1008 at 113. To provide the user with context, the recommended pages were added back to the bookmark folder that served as the basis for those recommendations. Ex. 1006 at 3. This usage model, as disclosed in Rucker, was described by the applicant during prosecution of the 482 Patent as an exemplary use-case scenario. Ex. 1002 at 143 ( As an exemplary use-case scenario, a user browses the web, saving topically-related document links in the same web-favorites folder. Once this -40-
precondition is met, the claimed invention software kicks in: deriving keywords from saved documents, thus discerning the topic of interest, then searching for other related documents, resulting in supplementing the directory with newlyfound documents- hence the title of 09/796,235: automatic directory supplementation. ). It would have been obvious to a person of ordinary skill in the art to combine: (a) Rucker s teaching of discerning a user s interests based on the organization of her bookmarks folders, and adding recommendations back into those folders, and (b) Sheth s teaching of creating a profile of user interests and utilizing autonomous software agents to identify similar documents based on keyword analysis. Ex. 1008 115. Specifically, it would have been obvious to use a bookmarks folder, as taught by Rucker, as a starting point for generating an interest profile for the user, as taught by Sheth. Id. Sheth then generates recommendations in the same fashion as explained previously with Ground 1. Id. At night, the system iterates through the links and corresponding Web pages in the bookmarks folder similar to the iteration through the ArtFeedback directory of Sheth and generates a profile based on the keywords of those pages. Id. The process for computing the keywords in a document is the same regardless of whether the document is a Webpage or newsgroup articles. Id. Sheth s autonomous agents then search the Web for pages based on their similarity to the user profile, -41-
particularly the keyword vector of the profile. Id. Links to pages whose similarity scores exceed a threshold, as taught by Sheth, are added back to the bookmarks folder that started the process, as taught by Rucker. See id.; Ex. 1006 at 3. A POSITA by at least January 2000 would have easily combined Rucker and Sheth in this way. Ex. 1008 116. The combination has a clean demarcation between each reference s functionality: Rucker supplies the input to Sheth s recommendation system, and the output of Sheth is written back to the Favorites folder as taught by Rucker. Id. This minimizes changes to either system. Id. A POSITA would not have to invent any new technology, because by at least January 2000, the techniques disclosed in Rucker and Sheth were well-known in the art and could be implemented using known methods with predictable results. Id. 1. Motivation to Combine As an initial matter, the hypothetical question of whether a POSITA could have or would have found it obvious to combine Rucker and Sheth is answered by the reality that a POSITA in fact combined key elements of the combination described above. Ex. 1008 117. In June 1997, Alexandros Moukas, a Ph.D. student in the MIT Media Lab, published a thesis regarding a system called Amalthaea, which was a follow-on project to Sheth. Id.; Ex. 1012 at 1. At the time, Mr. Moukas would have been a POSITA, having a Masters of Science in Artificial Intelligence and two years of post-secondary education in the MIT Ph.D. -42-
program. Ex. 1008 117; Ex. 1012 at 1. Mr. Moukas added a number of features to Sheth, including the ability to acquire[] the user s interests by submit[ting] a bookmark list with favorite sites/documents to provide a starting point for the system. Ex. 1012 at 16; Ex. 1008 117. Based on that information, the system will use search engines to find other similar documents. Ex. 1012 at 16. Like the combination of Rucker and Sheth, Moukas added the ability to use bookmarks to initialize autonomous agents for information filtering. Ex. 1012 at 31 ( The user submits a list of his favorite bookmarks or documents. This is usually the bookmarks list. Each of the sites in the list is examined and for each site an Information Filtering Agent is created[.] ); Ex. 1008 118. Moukas retained the keyword analysis techniques of Sheth, using weighted keyword vectors and TF- IDF to measure the similarity of documents. Ex. 1012 at 37 ( The basic representation of the Information Filtering Agents and the parsed HTML files is the weighted keyword vector. When the HTML files are processed, a clear-text version is generated, the text is decomposed into its keywords, which are weighted and compose the keyword vector. ); id. at 38 ( Finally, each keyword is weighted by producing its tfidf measure. ); Ex. 1008 118. To support the use of Web pages, Moukas also added an engine for retrieving documents from the WWW. Ex. 1012 at 35; Ex. 1008 118. Rucker also teaches that its Siteseer architecture may be enhanced by -43-
expanding beyond a purely collaborative approach. Ex. 1006 at 3 ( While a novel recommendation and categorization system, Siteseer has intrinsic limitations because of its almost purely collaborative approach. ); Ex. 1008 119. Rucker explains that its collaborative approach has difficulties creating new categories or recommending new material for which there is no collective experience to leverage. Ex. 1006 at 3; Ex. 1008 119. This is known in the art as the cold start problem. Ex. 1008 119. Therefore, such users must first arrive at sites or pages by some other means, such as a search service or editorially built Web directory. Ex. 1006 at 3. Sheth addresses the problem of discovering new material by providing a serendipity knob for the genetic algorithms of its autonomous agents. Ex. 1003 at 68 ( [H]ow would the system be able to recommend relevant articles that the user could not possibly have known to ask for in the first place? The genetic algorithm approach provides at least a partial solution to the serendipity problem.... a user can turn the serendipity knob higher, if she really likes to continually receive information about different topics. ); Ex. 1008 119. In addition, the keyword analysis of Sheth allows the discovery of new material, does not require collaborative input to the information filtering system, and mitigates the cold start problem. Ex. 1008 119. Thus, based on the teachings of Rucker, a POSITA would have been motivated to compliment the collaborative approach of Rucker with the keyword analysis and autonomous agents of Sheth. Id. -44-
Sheth also suggests a motivation to combine. Ex. 1008 120. Sheth discloses that [t]he amount of user interaction required can be further reduced by use of more intelligent interfaces. Ex. 1003 at 72. Rucker provides a method for reducing the amount of user interaction required for generating a profile of the user s interests. Ex. 1008 120. Rucker teaches that Bookmarks... are a desirable mechanism for gathering preference information as they are already maintained by the user, and thus require no additional behavior for the purpose of informing the recommendation system. Ex. 1006 at 1. In addition, Rucker and Sheth are analogous art that have been cited together by other references. Ex. 1008 121. For example, a 1998 paper by Marko Balabanovic of Stanford regarding recommendation systems cited both Rucker and Sheth 2 on the same page. Ex. 1028 at 31; see also Ex. 1008 121; Ex. 1029 at 196-97. Rucker and Sheth are both directed to the same field of human-computer interaction, and more specifically, to similar information filtering applications, and they have similar goals of delivering personalized recommendations for online content. See Ex. 1008 121; Ex. 1006 at 1 ( Siteseer then delivers personalized recommendations of online content, Web pages, organized according to each 2 The citation was to an IEEE paper by Sheth presenting the same research and system (Newt) that was the subject of his thesis. See Ex. 1011; Ex. 1008 9. -45-
user s folders. ); Ex. 1003 at 2 ( This thesis presents the basic framework for personalized information filtering agents, and describes an implementation, Newt, built using the framework. ). It would have been obvious for a POSITA to combine the bookmarking capabilities of Rucker with the interest profiles and autonomous agents of Sheth, because doing so would have merely applied known methods to address the known problem of filtering vast amounts of information into personalized recommendations. Ex. 1008 122. The use of bookmarks and Favorites folders for discerning user interests was known in the art of recommendation systems. See id.; Ex. 1023 at 7; Ex. 1012 at 6. Using Favorites folders as the starting point for the information filtering system of Sheth would achieve (and did achieve) the predictable result of creating profiles reflecting the keyword content of the Webpages bookmarked in those folders. Ex. 1008 122. In addition, the use of autonomous agents and keyword analysis were also known in the art, particularly the term-frequency, inverse-document frequency keyword analysis method used in Sheth, which has used in the field since before the advent of the Internet. See id.; Ex. 1003 at 23; Ex. 1004 at 9; Ex. 1012 at 38. Combining the autonomous agents and keyword analysis of Sheth with the bookmarks-based architecture of Rucker would achieve (and did achieve) the predictable result of retrieving Webpages with similar keyword content to the Webpages bookmarked in the source bookmarks -46-
folders. Ex. 1008 122. 2. Obviousness Claim 1 1[p]. A computerimplemented method for augmenting a directory without contemporaneous user input comprising: Rucker in view of Sheth Rucker: See, e.g., 1: Siteseer is a Web-page recommendation system that uses an individual s bookmarks and the organization of bookmarks within folders for predicting and recommending relevant pages. See, e.g., 2: See, e.g., 3: Siteseer contextualizes its recommendations by delivering them in the folder served as the basis for the discovery. Thus, in our example, recommendations coming from users in John s Vacation Spots neighborhood will be delivered within the context of his Vacation Spots folder. Sheth: See, e.g., 42: YAIF is a time-intensive process and is run offline. The process is executed every night, so that filtered articles are available to every profile in the morning. Rucker discloses that folders in the user s bookmarks are used to generate -47-