CTNF 18/374,812 CTNF 87600 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gharibi et al. (US Pub. 20230300115) . Referring to claim 1, Gharibi discloses A federated learning apparatus comprising at least one processor [fig. 16; par. 162; a computer device that can be used in connection with any of the disclosed systems comprises processor 1610] , the at least one processor carrying out: a training process of training a first prediction model that predicts an evaluation value corresponding to a combination of a user and an evaluation target with respect to which the evaluation value is not obtained [pars. 149-151; a client device trains their local matrix factorization model using their user vector U and an item-vector matrix V shared by a server; there are n customers and m items at the client device, where each customer rated only a subset of all available items; a recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items] , with use of a first training data set including (i) evaluation values each of which is given to one of a part or all of evaluation targets in an evaluation target list and each of which indicates evaluation by one of users in a user list [pars. 149-151; note the item ratings] and (ii) target attribute values each of which is possessed by one of a part or all of the evaluation targets in the evaluation target list and each of which relates to one of target attributes in a target attribute list [pars. 149-151; note the item vector matrix V] ; a parameter information transmitting process of transmitting, to a server apparatus, first parameter information which indicates at least a part of the first prediction model [pars. 149-151; the client device shares a set of gradients of the item-vector matrix V with the server] ; a parameter information obtaining process of obtaining, from the server apparatus, integrated parameter information obtained by integrating the first parameter information and second parameter information that indicates at least a part of a second prediction model trained with use of a second training data set which is configured similarly to the first training data set and which differs from the first training data set in at least a part of the evaluation target list, the user list, the target attribute list, the evaluation values, and the target attribute values [pars. 149-151 and 153-160; the server also transmits the item-vector matrix V to other client devices (i.e., a second client device) in a set of client devices, where each client device in the set of client devices trains a local matrix factorization model using a respective user vector U and the item-vector matrix V to generate a respective set of gradients; the respective set of gradients are shared with the server, and the server aggregates the respective set of gradients to generate an updated item-vector matrix V] ; and an updating process of updating the first prediction model by replacing the first parameter information with the integrated parameter information [pars. 149-151 and 153-160; the server downloads the updated item-vector matrix V to the client device, so that the client device can train the local matrix factorization model using a stochastic gradient descent method (i.e., based on the updated item-vector matrix V)] . Referring to claim 2, Gharibi discloses The federated learning apparatus as set forth in claim 1, wherein: the first training data set further includes user attribute values each of which is possessed by one of a part or all of the users in the user list and each of which relates to one of user attributes in a user attribute list; and in the training process, the at least one processor trains the first prediction model with use of the first training data set [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data] . Referring to claim 3, Gharibi discloses The federated learning apparatus as set forth in claim 2, wherein: the user list included in the first training data set and the user list included in the second training data set include a mutually common user; the evaluation target list included in the first training data set and the evaluation target list included in the second training data set include a mutually common evaluation target; the target attribute list included in the first training data set and the target attribute list included in the second training data set include a mutually common target attribute; and the user attribute list included in the first training data set and the user attribute list included in the second training data set include a mutually common user attribute [pars. 146 and 149-151; the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items]. Referring to claim 4, Gharibi discloses The federated learning apparatus as set forth in claim 1, wherein: in the training process, the at least one processor uses the first training data set as a multidimensional array in which a part of evaluation values each of which corresponds to a combination of one of the users and one of the evaluation targets is missing [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items] , and determines, as the first prediction model, a plurality of vectors obtained by decomposing the multidimensional array; and in the parameter information transmitting process, the at least one processor transmits at least a part of the plurality of vectors as the first parameter information [pars. 149-151; note the local matrix factorization (i.e., decomposition) model and the set of gradients (i.e., vectors) shared with the server] . Referring to claim 5, Gharibi discloses The federated learning apparatus as set forth in claim 4, wherein, in the training process, the at least one processor determines the plurality of vectors so that a component which is included in a product of the plurality of vectors and which corresponds to the other of the evaluation values that is not missing in the multidimensional array approximates to the other of the evaluation values [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items; also note the stochastic gradient descent method that calculates estimates based on a subset of the data] . Referring to claim 6, Gharibi discloses The federated learning apparatus as set forth in claim 4, wherein: in the multidimensional array, a part of target attribute values each of which corresponds to a combination of one of the target attributes and one of the evaluation targets is further missing, in addition to missing of the part of the evaluation values; and in the training process, the at least one processor determines the plurality of vectors so that a component which is included in a product of the plurality of vectors and which corresponds to the other of the target attribute values that is not missing in the multidimensional array approximates to the other of the target attribute values [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items; also note the stochastic gradient descent method that calculates estimates based on a subset of data] . Referring to claim 7, Gharibi discloses The federated learning apparatus as set forth in claim 4, wherein: the first training data set further includes user attribute values each of which is possessed by one of a part or all of the users in the user list and each of which relates to one of user attributes in a user attribute list; in the multidimensional array, a part of user attribute values each of which corresponds to a combination of one of the user attributes and one of the users is further missing, in addition to missing of the part of the evaluation values; and in the training process, the at least one processor determines the plurality of vectors so that a component which is included in a product of the plurality of vectors and which corresponds to the other of the user attribute values that is not missing in the multidimensional array approximates to the other of the user attribute values [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items; also note the local matrix factorization (i.e., decomposition) model and the set of gradients (i.e., vectors); further note the stochastic gradient descent method that calculates estimates based on a subset of data] . Referring to claim 8, Gharibi discloses The federated learning apparatus as set forth in claim 7, wherein: in the multidimensional array, a part of relevance values each of which corresponds to a combination of one of the target attributes and one of the user attributes and each of which indicates relevance of the combination is further missing, in addition to missing of the part of the evaluation values; and in the training process, the at least one processor determines the plurality of vectors so that a component which is included in the product of the plurality of vectors and which corresponds to the other of the relevance values that is not missing in the multidimensional array approximates to the other of the relevance values [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items and hence to predict the next item the customer is interested in (i.e., the next relevant item); also note the local matrix factorization (i.e., decomposition) model and the set of gradients (i.e., vectors); further note the stochastic gradient descent method that calculates estimates based on a subset of data] . Referring to claim 9, Gharibi discloses The federated learning apparatus as set forth in claim 8, wherein, in the training process, the at least one processor calculates the each of the relevance values based on a value obtained by dividing a product of one of the user attribute values and one of the target attribute values by one of the evaluation values which corresponds to the one of the user attribute values and the one of the target attribute values [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items and hence to predict the next item the customer is interested in (i.e., the next relevant item); also note the local matrix factorization (i.e., decomposition) model and the set of gradients (i.e., vectors); further note the stochastic gradient descent method that calculates estimates based on a subset of data] . Referring to claim 10, Gharibi discloses The federated learning apparatus as set forth in claim 1, wherein, in the parameter information transmitting process, parameter information obtained based on, among information indicating the first prediction model, information common to the first training data set and the second training data set is transmitted as the first parameter information [pars. 146 and 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items; thus, respective sets of gradients may include common information] . Referring to claim 11, Gharibi discloses The federated learning apparatus as set forth in claim 10, wherein the information common to the first training data set and the second training data set includes information indicating a common evaluation target [pars. 146 and 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items; thus, respective sets of gradients may include common information] . Referring to claim 12, Gharibi discloses The federated learning apparatus as set forth in claim 10, wherein the information common to the first training data set and the second training data set includes information indicating a common user [pars. 146 and 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items; thus, respective sets of gradients may include common information] . Referring to claim 13, Gharibi discloses The federated learning apparatus as set forth in claim 10, wherein the information common to the first training data set and the second training data set includes information indicating a common target attribute [pars. 146 and 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items; thus, respective sets of gradients may include common information] . Referring to claim 14, Gharibi discloses The federated learning apparatus as set forth in claim 10, wherein: the first training data set further includes user attribute values each of which is possessed by one of a part or all of the users in the user list and each of which relates to one of user attributes in a user attribute list; and the information common to the first training data set and the second training data set includes information indicating a common user attribute [pars. 146 and 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data (i.e., training data); the local matrices of the client devices in the set of client devices may include mutually common attribute values if the same customers visit different stores in a chain of stores and purchase the same items; thus, respective sets of gradients may include common information] . Referring to claim 15, Gharibi discloses The federated learning apparatus as set forth in claim 1, wherein the at least one processor further carries out a predicting process of predicting the evaluation value corresponding to the combination of the user and the evaluation target with respect to which the evaluation value is not obtained, with use of the first prediction model [pars. 149-151; the local matrix is an interaction matrix M that captures relationships between customers and purchased (or rated) items, where attribute values include customer purchases, customer ratings, purchase history, customer demographics, or other data; note that there are n customers and m items at the client device, where each customer rated only a subset of all available items; the recommendation system’s task is to find (i.e., predict) the ratings for each customer for all items] . Referring to claim 16, see at least the rejection for claim 1. Gharibi further discloses A server apparatus comprising at least one processor [fig. 16; par. 162; a computer device that can be used in connection with any of the disclosed systems comprises processor 1610] , the at least one processor carrying out: a parameter information obtaining process of obtaining a plurality of pieces of first parameter information from a respective plurality of federated learning apparatuses each of which functions as the federated learning apparatus recited in claim 1;an integrating process of generating integrated parameter information by integrating the plurality of pieces of first parameter information; and a parameter information transmitting process of transmitting the integrated parameter information to each of the plurality of federated learning apparatuses [pars. 32, 149-151, and 153-160; disclosed processes can occur both from a server point of view or a client device point of view] . Referring to claim 17, see at least the rejections for claims 1 and 16. Gharibi further discloses A federated learning system comprising: a plurality of federated learning apparatuses each of which functions as the federated learning apparatus recited in claim 1 [pars. 32, 149-151, and 153-160; note the set of client devices] ; and a server apparatus [pars. 32, 149-151, and 153-160; note the server] . Referring to claim 18, see the rejection for claim 1, which incorporates the claimed method. Referring to claim 19, see at least the rejection for claim 1. Gharibi further discloses A non-transitory recording medium in which a program for causing a computer to operate as the federated learning apparatus recited in claim 1 is recorded, the program causing the computer to carry out the training process, the parameter information transmitting process, the parameter information obtaining process, and the updating process [fig. 16, storage device 1630, services 1632-1636] . Referring to claim 20, see the rejection for claim 17, which incorporates the claimed method. Conclusion 07-96 The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Luo et al. (US Pub. 20230419123) discloses a federated learning recommendation system that predicts user ratings or preferences associated with items. Matsuoka et al. (US Pub. 20200167834) discloses a federated learning recommendation system that, given input data descriptive of a number of products purchased or rated highly by a user, a recommendation system can output a suggestion or recommendation of an additional product that the user might enjoy or wish to purchase. Shamir et al. (US Pub. 20250077934) discloses a federated learning recommendation system that predicts ratings or other scores for each item in a set of items based on one or more metrics related to the items, their intended use, past performance in that use, their users, etc., and utilize those ratings to provide recommendations. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE PARK whose telephone number is (571)270-7727. The examiner can normally be reached M-F 8AM-5PM. 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, TAMARA KYLE can be reached at (571)272-4241. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Grace Park/Primary Examiner, Art Unit 2144 Application/Control Number: 18/374,812 Page 2 Art Unit: 2144 Application/Control Number: 18/374,812 Page 3 Art Unit: 2144 Application/Control Number: 18/374,812 Page 4 Art Unit: 2144 Application/Control Number: 18/374,812 Page 5 Art Unit: 2144 Application/Control Number: 18/374,812 Page 6 Art Unit: 2144 Application/Control Number: 18/374,812 Page 7 Art Unit: 2144 Application/Control Number: 18/374,812 Page 8 Art Unit: 2144 Application/Control Number: 18/374,812 Page 9 Art Unit: 2144 Application/Control Number: 18/374,812 Page 10 Art Unit: 2144 Application/Control Number: 18/374,812 Page 11 Art Unit: 2144 Application/Control Number: 18/374,812 Page 12 Art Unit: 2144
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