Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Note
In regards to the 35 USC § 103 rejection has been withdrawn in light of the instant amendments
to the claim because none of the references of record alone or in combination disclose or suggest
the limitations found within the amended independent claim(s).
Response to argument
Applicant's arguments filed 03/12/2026 ("Arguments/Remarks") have been fully considered but they are not persuasive.
In Remarks/Arguments (pg. 4) Applicant contends: ”Especially as amended, claim 1 is directed to a specific technical solution to a technical problem in cross-platform recommender systems; namely, data sparsity/cold- start in newly launched platforms and the lack of anchor links or consistent item attributes that would otherwise allow straightforward transfer. As amended, claim 1 is not merely "using math" in the abstract; it recites a concrete, ordered set of computer operations that implement a particular cross-media recommendation framework and its training/optimization workflow.”
Regarding the above argument, the Examiner notes that the amended claim further recites abstract idea: mathematical concepts. It adds operations involving mathematical relationships, matrix-based representations and mathematical calculations used to model and reconstruct data. Accordingly, the amended claims do not provide specific technical details or improvements to computer functionality, but instead recites an abstract idea implemented on generic computer component.
In Remarks/Arguments (pg. 5) Applicant contends: “Even if certain limitations of the claim involve mathematical operations, the amended claim is not directed to a mathematical concept in the abstract. The claim recites a specific, ordered training procedure for optimizing a constrained objective function having specific variables (shared dictionary, sparse item representations, latent user features, projection matrix, and interaction matrix), including: initializing the variables;….
… The amended claim recites additional elements that meaningfully limit how the optimization is performed, beyond a generic instruction to "apply mathematics." In particular, the claim recites (a) precomputing a graph Laplacian matrix and an MMD matrix and incorporating them into the objective function ([0028]-[0030], [0033], [0046]);…”
Regarding the above argument, the Examiner respectfully disagrees with Applicant’s assertion that amendment is amounts to an improvement to the functioning of a computer or technical field. The limitation does not integrates the recited mathematical into practical application. Specifically, the claims, as amended, lacks sufficient technical details to support a conclusion that they recite a technological improvement. In particular, the claims do not adequately describe how the optimizing of a constrained objective function improves the operation of the recommendation system. Rather, it merely recite mathematical operations performed on user-item rating data and user-user relationship data to generate a result, without specifying any technical mechanism by which the optimization is achieved or how the optimization results an improvement. The recited paragraphs further recite abstract idea mathematical equation, which describes reformulating an objective function, introducing auxiliary variables and performing gradient descent updates according to mathematical equations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 and 4 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, covers
a mental process but for the recitation of generic computer components:
Regarding claim 1:
developing an objective function
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves specifying or formulating an objective function. See (MPEP 2106.04)).
initializing the shared dictionary, the set of sparse item representations, the set of latent user features, the projection matrix, and the interaction matrix;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves defining or initializing multiple matrices that represent relationships among variables. See (MPEP 2106.04)).
pre-computing a graph Laplacian matrix and a maximum mean discrepancy matrix, and including the graph Laplacian matrix and the maximum mean discrepancy matrix in the objective function;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves creating a graph Laplacian matrix and the maximum mean discrepancy matrix, which are numerical arrays derived from graph structure and probability distribution. See (MPEP 2106.04)).
iteratively updating, in a sequential update loop and using a learning rate, the set of sparse item representations using an alternating direction method of multipliers;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves updating the set of sparse items by performing a numerical optimization procedure. See (MPEP 2106.04)).
updating the set of latent user features and the interaction matrix using partial derivatives of the objective function with respect to the set of latent user features and the interaction matrix, respectively
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves computing partial derivatives and updating matrices based on those derivatives. See (MPEP 2106.04)).
updating the projection matrix using a gradient descent optimization procedure while maintaining an orthogonality constraint on the projection matrix,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: which is an optimization technique that updates parameters (the projection matrix) using iterative numerical calculation based on gradients. See (MPEP 2106.04)).
using at least the set of latent user features to generate a friend recommendation output for the social media site.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: metal process: which involves evaluating information about users and selecting or recommending potential friends based on that information. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the Examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
receiving, for a social media site, a user-item rating matrix and a user-user adjacency matrix;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
wherein the objective function comprises a shared dictionary, a set of sparse item representations, a set of latent user features, a projection matrix, and an interaction matrix, the objective function further including (i) a rating reconstruction term based on the user-item rating matrix and (ii) a user-user reconstruction term based on the user-user adjacency matrix;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
executing a dictionary learning method,
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
wherein the dictionary learning method updates the shared dictionary;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
wherein an objective value of the objective function monotonically decreases across iterations and the sequential update loop converges to a local optimum value,
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (III), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II, IV and V), additional elements are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
Regarding limitation (I), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein updating the shared dictionary, the set of sparse item representations, the latent user features, the projection matrix, and the shared interaction matrix until the objective function converges: produces a resultant latent user representation matrix and a resultant item representation matrix,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves producing a resulting user and item representation matrices, which are numerical arrays representing relationships between users and items. See (MPEP 2106.04)).
wherein the resultant latent user representation matrix and the resultant item representation matrix are respectively used to perform friend recommendation tasks and item recommendation tasks across a source social media site and a target social media site
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Allowable subject matter
Claim(s) 1 and 4 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claim 1. A method, comprising: receiving, for a social media site, a user-item rating matrix and a user-user adjacency matrix;
developing an objective function, wherein the objective function comprises a shared dictionary, a set of sparse item representations, a set of latent user features, a projection matrix, and an interaction matrix, the objective function further including (i) a rating reconstruction term based on the user-item rating matrix and (ii) a user-user reconstruction term based on the user-user adjacency matrix;
executing a dictionary learning method, wherein the dictionary learning method updates the shared dictionary; initializing the shared dictionary, the set of sparse item representations, the set of latent user features, the projection matrix, and the interaction matrix; pre-computing a graph Laplacian matrix and a maximum mean discrepancy matrix, and including the graph Laplacian matrix and the maximum mean discrepancy matrix in the objective function;
iteratively updating, in a sequential update loop and using a learning rate, the set of sparse item representations using an alternating direction method of multipliers;
updating the set of latent user features and the interaction matrix using partial derivatives of the objective function with respect to the set of latent user features and the interaction matrix, respectively; and
updating the projection matrix using a gradient descent optimization procedure while maintaining an orthogonality constraint on the projection matrix, wherein an objective value of the objective function monotonically decreases across iterations and the sequential update loop converges to a local optimum value, and using at least the set of latent user features to generate a friend recommendation output for the social media site.
Closest prior arts:
KARATZOGLOU et al., Pub. No.: US20150187024A1.
KARATZOGLOU teaches a multimedia content recommendation uses a socially enabled collaborative filtering model that directly models social interactions and quantifies influence or trust between users based on implicit feedback data from user and their friends. However, KARATZOGLOU does not teach iteratively update sparse item representations using an alternating direction method of multipliers, updates the latent user features and interaction matrix based on partial derivatives of the object function and updates the projection matrix through gradient descent while maintaining an orthogonality constant. The objective value monotonically decreases across iterations until the sequential update loop converges to a local optimum value, after which at least the latent user features are used to generate a friend recommendation output for social media site.
Li et al., Pub. No.: US20170132509A1.
Li teaches a recommendation system employs a deep collaborative filtering approach that combines deep learning models with matrix factorization collaborative filtering to generate items recommendations. A user-item rating matrix, user side information and item side information are provided as inputs and the system jointly learns user latent factors and item latent factors by decomposing the user-item rating matrix and extracting latent factors from the model. However, Li does not teach iteratively update sparse item representations using an alternating direction method of multipliers, updates the latent user features and interaction matrix based on partial derivatives of the object function and updates the projection matrix through gradient descent while maintaining an orthogonality constant. The objective value monotonically decreases across iterations until the sequential update loop converges to a local optimum value, after which at least the latent user features are used to generate a friend recommendation output for social media site.
Das et al., Pub. No.: US20210263939A1.
Das teaches sequential recommendation using transition regularized non-negative matrix factorization in a collaborative filtering recommender system. The system recommends a next logical or desirable item to a user based on historical sequences of user item preferences and the user’s most recent item interaction. However, Das does not teach iteratively update sparse item representations using an alternating direction method of multipliers, updates the latent user features and interaction matrix based on partial derivatives of the object function and updates the projection matrix through gradient descent while maintaining an orthogonality constant. The objective value monotonically decreases across iterations until the sequential update loop converges to a local optimum value, after which at least the latent user features are used to generate a friend recommendation output for social media site.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to MATIYAS T MARU whose telephone number is (571)270-0902 or via email: matiyas.maru@uspto.gov. The examiner can normally be reached Monday - Friday (8:00am - 4:00pm) EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a
USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to
use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Michelle Bechtold can be reached on (571)431-0762. The fax phone number for the organization were this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from
Patent Center. Unpublished application information in Patent Center is available to registered users.
To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit
https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and
https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional
questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like
assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA)
or 571-272-1000.
/M.T.M./Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights