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UNITED STATES PATENT AND TRADEMARK OFFICE
`
`UNITED STATES DEPARTMENT OF COMMERCE
`United States Patent and Trademark Office
`Address: COMMISSIONER FOR PATENTS
`P.O. Box 1450
`Alexandria, Virginia 22313-1450
`www.uspto.gov
`
`APPLICATION NO.
`
`11/284,603
`
`FILING DATE
`
`11/21/2005
`
`FIRST NAMED INVENTOR
`
`ATTORNEY DOCKET NO.
`
`CONFIRMATION NO.
`
`Chiranjit Acharya
`
`7114-86640-US
`
`6671
`
`06/19/2012
`7590
`37123
`FITCH EVEN TABIN & FLANNERY, LLP
`120 SOUTH LASALLE STREET
`SUITE 1600
`CHICAGO, IL 60603-3406
`
`EXAMINER
`
`RICHARDSON, JAMES E
`
`ART UNIT
`
`2167
`
`MAIL DATE
`
`06/19/2012
`
`PAPER NUMBER
`
`DELIVERY MODE
`
`PAPER
`
`Please find below and/or attached an Office communication concerning this application or proceeding.
`
`The time period for reply, if any, is set in the attached communication.
`
`PTOL-90A (Rev. 04/07)
`
` Progressive Exhibit 2004
`Liberty Mutual v. Progressive
`CBM2012-00002
`
`

`

`UNITED STATES PATENT AND TRADEMARK OFFICE
`________________
`
`BEFORE THE BOARD OF PATENT APPEALS
`AND INTERFERENCES
`________________
`
`Ex parte CHIRANJIT ACHARYA
`________________
`
`Appeal 2010-003919
`Application 11/284,603
`Technology Center 2100
`________________
`
`Before ROBERT E. NAPPI, KRISTEN L. DROESCH, and JOHN G.
`NEW, Administrative Patent Judges.
`
`NEW, Administrative Patent Judge.
`
`DECISION ON APPEAL
`
`Appellant appeals under 35 U.S.C. § 134(a) from the Examiner’s
`
`rejection of claims 1-18, which stand rejected under 35 U.S.C. § 103(a) as
`being unpatentable over U.S. Patent Publication No. 2004/0054572 A1 to
`Oldale et al. (“Oldale”), in view of U.S. Patent No. 6,981,040 B1 to Konig
`et al. (“Konig”), and also in view of Lyle H. Ungar, et al., A Formal
`Statistical Approach to Collaborative Filtering, Conference on Automated
`Learning and Discovery, 1-6 (1998) (“Ungar”).
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`We reverse.
`
`STATEMENT OF THE CASE
`Appellant describes the present invention, entitled User's Preference
`
`Prediction from Collective Rating Data as follows:
`A computer-implemented method includes receiving a dataset
`representing a plurality of users, a plurality of items, and a
`plurality of ratings given to items by users; clustering the
`plurality of users into a plurality of user-groups such that at
`least one user belongs to more than one user-group; clustering
`the plurality of items into a plurality of item-groups such that at
`least one item belongs to more than one item-group; inducing a
`model describing a probabilistic relationship between the
`plurality of users, items, ratings, user-groups, and item-groups,
`the induced model defined by a plurality of model parameters;
`and predicting a rating of a user for an item using the induced
`model.
`
`Abstract.
`
`
`
`Independent claim 1 is representative1:
`
`A computer-implemented method, comprising:
`
`obtaining a dataset representing a plurality of users, a plurality
`of items, and a plurality of ratings given to items by users;
`
`clustering the plurality of users into a plurality of user-groups
`such that at least one user belongs to more than one user-group;
`
`clustering the plurality of items into a plurality of item-groups
`such that at least one item belongs to more than one item-group;
`
`
`1 Appellant and Examiner agree that the Examiner’s rejection of
`independent claims 1 and 10 were based upon the same reasoning.
`Appellant’s Brief (App. Br.) 18; Examiner’s Answer (Ex. Ans.) 4-8 and 12-
`15. Consequently, we choose claim 1 as representative.
`
`2
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`inducing a model describing a probabilistic relationship
`between the plurality of users, items, ratings, user-groups, and
`item-groups, the induced model defined by a plurality of model
`parameters; and
`
`predicting a rating of a user for an item using the induced
`model.
`
`Claims 2-9 depend from claim 1 and claims 11-18 depend from claim
`
`10. Appellant admits that, for purposes of the instant appeal, the applicant
`is content to rely upon the arguments raised with respect to claims 1 and 10
`for all of the claims.
`
`ISSUES
`
`Claims 1 and 10
`The Examiner concludes that the claims are unpatentable as obvious
`under 35 U.S.C. § 103(a) over the combination of prior art references
`Oldale, Konig, and Ungar. Specifically, the Examiner concludes that it
`would have been obvious for an artisan of ordinary skill to combine the
`teachings of Oldale with the teachings of Konig by modifying Oldale such
`that when customers of Oldale are sorted into groups or clusters based on
`profile similarity, a user is sorted into multiple clusters based on similarities
`to multiple groups as in Konig. Ex. Ans. 6.
`Furthermore, the Examiner finds, although neither Oldale nor Konig
`specifically disclose inducing a model describing a probabilistic
`relationship between the plurality of user-groups, and item-groups, Ungar
`discloses inducing a model describing a probabilistic relationship between a
`plurality of user-groups and item-groups. Ex. Ans. 7. The Examiner
`concludes, at the time of invention it would have been obvious to a person
`having ordinary skill in the art to combine the teachings of Oldale and
`
`3
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`Konig with the teachings of Ungar. Ex. Ans. 7. The motivation for so
`doing would have been to allow the combined system of Oldale and Konig
`to include a probabilistic model in which there are link probabilities
`between clusters of users and items. Ex. Ans. 8. Did the Examiner err in
`concluding that it would have been obvious to a person of ordinary skill in
`the art to combine the teachings of Oldale, Konig, and Ungar, thereby
`rendering Appellant’s claimed invention obvious at the time of invention?
`ANALYSIS
`For the Examiner to establish a prima facie case of obviousness in
`
`view of a combination of prior art references, a proper analysis under § 103
`requires, inter alia, consideration of two factors: (1) whether the prior art
`would have suggested to those of ordinary skill in the art that they should
`make the claimed composition or device, or carry out the claimed process;
`and (2) whether the prior art would also have revealed that in so making or
`carrying out, those of ordinary skill would have a reasonable expectation of
`success. See In re Dow Chemical Co., 837 F.2d 469, 473 (Fed. Cir. 1988).
`Because the Examiner has failed to meet at least one of these requirements,
`we reverse the Examiner’s rejection of the claims.
`
`Claims 1 and 10 both recite “inducing a model describing a
`probabilistic relationship between the plurality of users, items, ratings, user-
`groups, and item-groups, the induced model defined by a plurality of model
`parameters.” The Examiner finds that Ungar discloses “inducing a model
`describing a probabilistic relationship between the plurality of user-groups,
`and item-groups.” Ex. Ans. 7. The Examiner points to Ungar’s teaching of
`Gibbs Sampling as a “‘probabilistic model in which people and the items
`they view or buy are each divided into (unknown) clusters and there are link
`
`4
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`probabilities between these clusters.’” Id. The Examiner further contends
`that, at the time of invention, it would have been obvious for a person of
`ordinary skill in the art to combine the teachings of Oldale and Konig and
`to incorporate the Gibbs sampling taught by Ungar into the model
`describing a probabilistic relationship between the plurality of users, items,
`and ratings, the induced model defined by:
`[A] plurality of model parameters (Oldale, [0017], Lines 8-12)
`of Oldale to allow the system to include a probabilistic model in
`which there are link probabilities between clusters of users and
`items (Ungar, §6 “Summary”, Lines 1-3), and thus allowing the
`system to induce a model describing a probabilistic relationship
`between the plurality of users, items, ratings, user-groups, and
`item-groups. (Ungar, §6 “Summary”, Lines 1-3).
`
`Id. 7-8. The examiner concludes that “[t]he motivation for doing so would
`have been to allow the combined system of Oldale and Konig to include a
`probabilistic model in which there are link probabilities between clusters of
`users and items.” Id. 8.
`
`The Appellant argues it would not have been obvious to an artisan of
`ordinary skill to rely upon the probabilistic Gibbs sampling method taught
`in Ungar, because Ungar teaches away from the method disclosed in
`Appellant’s claims 1 and 10. App. Br. 15. Those claims require, in
`relevant part: “clustering the plurality of users into a plurality of user-
`groups such that at least one user belongs to more than one user-group” and
`also “clustering the plurality of items into a plurality of item-groups such
`that at least one item belongs to more than one item-group.” App. Br. 24.
`Appellant points out that the “Gibbs sampling method disclosed by Ungar
`uses model estimation methods that require random assignment of users or
`
`5
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`items to random groups and using those groups to generate grouping of the
`associated users or items.” App. Br. 14. Furthermore, Appellant notes that
`Ungar teaches that this method requires that users and items are each
`assigned to an individual user- or item-group. Id. (citing Ungar p.3, fn1).
`We find Appellant’s reasoning persuasive. Ungar teaches Gibbs
`sampling as a method of inducing a probabilistic model relating users to
`items. Ungar at 3. Gibbs sampling alternates between two steps: (1)
`Assignment, in which a user or item is chosen at random and assigned it a
`user- or item-group proportionally to probability of the user- or item-group
`generating it and (2) Model estimation, in which one picks the probabilities
`that a (random) user is in that given user-group, that a random item is in
`that given item-group, and the link probability that a user in a given user-
`group is linked to an item in a given item-group. Id.
`
`However, the model taught by Ungar requires that a user or an item
`be assigned randomly to a single user- or item-group. App. Br. 14.
`Assigning an individual user or an item to more than one group destroys the
`functioning of the model. App. Br. 15; see also Ungar 4 (“a person or
`movie is picked at random, and then assigned to a class”). The assignment
`of a user or item to a given user-group or item-group is fundamentally
`incompatible with the requirements of claims 1 and 10 that at least one
`person and one item be assigned to more than one user-group or item-group
`respectively. Thus, we find that the skilled artisan would be discouraged
`from using a probabilistic model in which there are link probabilities
`between clusters of users and items and as such we find Ungar to teach
`away from the combining the teachings as set forth in the Examiner’s
`answer.
`
`6
`
`

`

`Appeal 2010-003919
`Application 11/284,603
`
`Because Ungar teaches away from the methods disclosed by
`
`Appellant’s independent claims 1 and 10, we disagree with the Examiner
`that a person of ordinary skill in the art would combine Ungar with the
`teachings of Oldale and Konig. Consequently we reverse the Examiner’s
`rejection of claims 1, 10 and the claims which depend thereupon.
`
`Appellant has advanced other arguments with respect to the
`obviousness of the disputed claims in light of Oldale and Konig, but we
`need not reach those arguments because, for the reasons stated above, we
`find the Examiner’s erroneous rejection of the claims based on Ungar to be
`dispositive of the case.
`
`CONCLUSION
`Appellant has shown that the Examiner erred in rejecting claims 1
`through 18 under §103(a).
`
`DECISION
`
`The Examiner’s decision rejecting claims 1-18 is reversed.
`No time period for taking any subsequent action in connection with
`this appeal may be extended under 37 C.F.R. § 1.136(a)(1). See 37 C.F.R.
`§ 1.136(a)(1)(iv) (2010).
`
`REVERSED
`
`tsj
`
`7
`
`

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