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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/18/2026 has been entered.
Status of Claims
Claims 1-5, 7-13, 15-20 are currently pending. Claims 6 and 14 are currently cancelled. Claims 21-22 were presented in the Response received on 12/19/2025 but appear to be omitted in the Request for Continued Examination received on 5/18/2026. The examiner believes this to be a typographical error as the claims are not being listed as amended or cancelled, therefore the claims were rejected as presented in the previous response received 12/19/2025.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 9, 15-16 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yeh et al. (US PGPUB No. 2023/0033490; Pub. Date: Feb. 2, 2023) in view of LI et al. (US PGPUB No. 2024/0152732; Pub. Date: May 9, 2024) and Rajagopal et al. (US PGPUB No. 2023/0394115; Pub. Date: Dec. 7, 2023).
Regarding independent claim 1,
Yeh discloses a system, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor to: receive a set of items associated with a completed first post-selection process; See FIG. 1, (Disclosing a system for facilitating purchases by a buyer with a seller and additionally recommending products to a buyer. FIG. 1 illustrates method 100 comprising step 110 of facilitating placing an order by a buyer with a seller followed by step 120 of selecting one or more product recommendations for the buyer using a product recommendation model, i.e. a system, comprising: a processor (e.g. Note [0100] describing computer system 900 comprising processor 902); and a non-transitory memory storing instructions (e.g. Note [0101] processor 902 retrieves program instructions from memory 904 to execute the method) that, when executed, cause the processor to: receive a set of items associated with a completed first post-selection process (e.g. step 120 selects recommendations following step 110 of facilitating placement of an order);)
generate a set of updated items including the set of items and the at least one item by adding the at least one item from the set of ranked contextually relevant items to the set of items associated with the completed first post-selection process; See FIG. 1 & Paragraphs [0038] & [0041], (Method 100 comprises step 130 wherein the transaction processing system may display a post-purchase support interface including interactive elements corresponding to selected product recommendations. Users may select a recommended product by interacting with the one or more interactive elements, which causes the system to automatically facilitate an order for the selected product at step 160, i.e. generate a set of updated items including the set of items and the at least one item by adding the at least one item from the set of ranked contextually relevant items to the set of items associated with the completed first post-selection process; (e.g. following the purchase at step 110, a user may interact with a recommended item screen (e.g. step 130) to automatically purchase a recommended item (e.g. step 160).)
The examiner notes that while Yeh discloses presenting a plurality of interactive elements on the post-purchase interface corresponding to different recommended products, the recommended products are not explicitly described as being “ranked contextually”.
and implement a second post-selection process for the set of updated items to provide for processing of the set of items and the at least one item, See FIG. 1 & Paragraph [0041], (Method 100 comprises step 160 of automatically facilitating a purchase of a recommended product that is displayed in response to a first purchase at step 110, i.e. implement a second post-selection process (e.g. by providing tracking data for the first order & additional purchase options for recommended products) for the set of updated items to provide for processing of the set of items and the at least one item (e.g. the post-purchase support interface may display tracking information for the order placed at step 110 as well as recommended items that a user may interact with to make further purchases).)
Yeh does not disclose the step wherein the system may generate a set of contextually relevant items based on the set of items using at least one graph neural network;
wherein the at least one graph neural network is optimized for first post-selection process application.
LI discloses the step wherein the system may generate a set of contextually relevant items based on the set of items using at least one graph neural network; See Paragraph [0024], (Disclosing a training method of a hybrid graph neural network model. A hybrid graph neural network model is used to predict a degree of interest of a user in a to-be-recommended product by using several products that have been clicked on by the user in the past.) See Paragraph [0120], (The hybrid graph neural network may evaluate a degree of matching between a user and an object and recommends an object to a user based on the degree of matching between said user and object, i.e. generate a set of contextually relevant items based on the set of items using at least one graph neural network (e.g. recommended products are presented based on a degree of interest to a user by the hybrid graph neural network);)
wherein the at least one graph neural network is optimized for first post-selection process application. See Paragraph [0024], (The hybrid graph neural network predicts a degree of interest of a user in a to-be-recommended product using products that have been clicked on by the user in the past, i.e. wherein the at least one graph neural network is optimized for first post-selection process application (e.g. the hybrid graph neural network generates outputs based on previous user selections).)
Yeh and LI are analogous art because they are in the same field of endeavor, product recommendation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh to include the method of recommending products using a graph neural network as disclosed by LI. Paragraph [0117] of LI discloses that the hybrid graph neural network model may provide a more targeted service by assessing a category of a user and processing a service based on the determined category in order to generate tailored recommendations to users by applying different service procedures for users of different categories.
Yeh-Li does not disclose the step wherein the system may generate a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items;
generate instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order;
receive a selection of at least one item from the set of ranked contextually relevant items;
Rajagopal discloses the step wherein the system may generate a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items; See Paragraph [0102], (Disclosing a system for automatic intelligent electronic data processing system for multi-faced data pattern recognition and ranking. The system may generate a recommendation list of data items to a user based on multi-facet data pattern recognition and ranking, i.e. generating a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items (e.g. the ranking process generates a list of recommended items based on a target entity's contextual information).)
generate instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order; See Paragraph [0053], (Recommendation engine 224 may generate a ranking of at least one data item from a data item pool such that highly ranked data items may be recommended to a particular entity.) See Paragraphs [0022] & [0100], (A client side interface is employed for allowing users to electronically select order items and placing orders. Personalized recommendations of data items may be provided via interactive user interface 208, i.e. generate instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order;)
receive a selection of at least one item from the set of ranked contextually relevant items; See Paragraph [0071], (The system may detect recommendation feedback corresponding to a user selection of a recommended data item of the recommendation list, i.e. receiving a selection of at least one item from the set of ranked contextually relevant items;) Yeh, Li and Rajagopal are analogous art because they are in the same field of endeavor, product recommendation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh-Li to include the method of generating a ranked list of recommendations for products as disclosed by Rajagopal. Paragraph [0018] of Rajagopal discloses that the system may apply self-learning models to improve the adaptability of the system to recommend information to users based on historical search data.
Regarding independent claim 9,
Yeh discloses a computer-implemented method, comprising: receiving a closed set of items associated with a completed first post-selection process; See FIG. 1, (Disclosing a system for facilitating purchases by a buyer with a seller and additionally recommending products to a buyer. FIG. 1 illustrates method 100 comprising step 110 of facilitating placing an order by a buyer with a seller followed by step 120 of selecting one or more product recommendations for the buyer using a product recommendation model, i.e. receiving a closed set of items associated with a completed first post-selection process; (e.g. the order fulfilled at step 110 comprises a set of items purchased by a buyer).)
updating the closed set of items to include the at least one item by adding the at least one item from the set of ranked contextually relevant items to the set of items associated with the completed first post-selection process; See FIG. 1 & Paragraphs [0038] & [0041], (Method 100 comprises step 130 wherein the transaction processing system may display a post-purchase support interface including interactive elements corresponding to selected product recommendations. Users may select a recommended product by interacting with the one or more interactive elements, which causes the system to automatically facilitate an order for the selected product at step 160, i.e. updating the closed set of items to include the at least one item (e.g. a user's purchased items would include those purchased at step 110 and newly purchased recommended product facilitated at step 160) by adding the at least one item from the set of ranked contextually relevant items to the set of items associated with the completed first post-selection process; (e.g. following the purchase at step 110, a user may interact with a recommended item screen (e.g. step 130) to automatically purchase a recommended item (e.g. step 160).)
and implementing a second post-selection process for the closed set of items including the at least one item to provide for processing of the set of items and the at least one item, See FIG. 1 & Paragraph [0041], (Method 100 comprises step 160 of automatically facilitating a purchase of a recommended product that is displayed in response to a first purchase at step 110, i.e. implementing a second post-selection process (e.g. by providing tracking data for the first order & additional purchase options for recommended products) for the closet set of items including the at least one item to provide for processing of the set of items and the at least one item (e.g. the post-purchase support interface may display tracking information for the order placed at step 110 as well as recommended items that a user may interact with to make further purchases).)
Yeh does not disclose the step of generating a set of contextually relevant items based on the closed set of items using at least one graph neural network;
wherein the at least one graph neural network and the listwise ranker comprise[s] a post- selection recommendation model, See Paragraph [0024], (The hybrid graph neural network predicts a degree of interest of a user in a to-be-recommended product using products that have been clicked on by the user in the past, i.e. wherein the at least one graph neural network (e.g. the hybrid graph neural network generates outputs based on previous user selections).)
The examiner notes that LI does not disclose a “listwise ranker”
Yeh and LI are analogous art because they are in the same field of endeavor, product recommendation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh to include the method of recommending products using a graph neural network as disclosed by LI. Paragraph [0117] of LI discloses that the hybrid graph neural network model may provide a more targeted service by assessing a category of a user and processing a service based on the determined category in order to generate tailored recommendations to users by applying different service procedures for users of different categories.
Yeh-LI does not disclose the step of generating a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items;
generating instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order;
receiving a selection of at least one item from the set of ranked contextually relevant items;
Rajagopal discloses the step of generating a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items; See Paragraph [0102], (Disclosing a system for automatic intelligent electronic data processing system for multi-faced data pattern recognition and ranking. The system may generate a recommendation list of data items to a user based on multi-facet data pattern recognition and ranking, i.e. generating a set of ranked contextually relevant items by applying a listwise ranker to the set of contextually relevant items (e.g. the ranking process generates a list of recommended items based on a target entity's contextual information).)
generate instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order; See Paragraph [0053], (Recommendation engine 224 may generate a ranking of at least one data item from a data item pool such that highly ranked data items may be recommended to a particular entity.) See Paragraphs [0022] & [0100], (A client side interface is employed for allowing users to electronically select order items and placing orders. Personalized recommendations of data items may be provided via interactive user interface 208, i.e. generate instructions that cause a user device to display an interface including at least a portion of the set of ranked contextually relevant items in ranked order;)
receiving a selection of at least one item from the set of ranked contextually relevant items; See Paragraph [0071], (The system may detect recommendation feedback corresponding to a user selection of a recommended data item of the recommendation list, i.e. receiving a selection of at least one item from the set of ranked contextually relevant items;)
wherein the listwise ranker comprise a post-selection recommendation model, See Paragraph [0053], (Recommendation engine 224 may generate a ranking of at least one data item from a data item pool such that highly ranked data items may be recommended to a particular entity.) See Paragraph [0030], (Recommendation engine 224 may utilize processed datasets and trained AI models to provide recommended data items based on recognized inter and intra-correlations and pattens in data selection lists. Note [0063] wherein data selection lists are associated with orders, i.e. wherein the listwise ranker comprises a post-selection recommendation model.) Yeh, Li and Rajagopal are analogous art because they are in the same field of endeavor, product recommendation. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh-Li to include the method of generating a ranked list of recommendations for products as disclosed by Rajagopal. Paragraph [0018] of Rajagopal discloses that the system may apply self-learning models to improve the adaptability of the system to recommend information to users based on historical search data.
Regarding independent claim 16,
The claim is analogous to the subject matter of independent claim 9 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 21,
As discussed above with claim 1, Yeh-Li-Rajagopal discloses all of the limitations.
Yeh further discloses the step wherein the post-first process application comprises an application after a checkout process, an order completion process, or a fulfillment process. See FIG. 1, (Method 100 comprises step 110 of facilitating placement of an order by a buyer with a seller. Steps 120-160 are part of a post-purchase service interface, i.e. wherein the post-first process application comprises an application after a checkout process.)
Regarding dependent claim 22,
As discussed above with claim 9, Yeh-Li-Rajagopal discloses all of the limitations.
Yeh further discloses the step wherein the post-selection recommendation model comprises a model for use after a checkout process, an order completion process, or a fulfillment process. See FIG. 1, (Method 100 comprises step 110 of facilitating placement of an order by a buyer with a seller. Steps 120-160 are part of a post-purchase service interface, where step 120 comprises selecting one or more product recommendations based on a product recommendation model, i.e. wherein the post-selection recommendation model comprises a model for use after a checkout process.)
Claim(s) 2, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yeh in view of LI and Rajagopal as applied to claim 1 above, and further in view of SHEN et al. (US PGPUB No. 2024/0177006; Pub. Date: May 30, 2024).
Regarding dependent claim 2,
As discussed above with claim 1, Yeh-LI-Rajagopal discloses all of the limitations.
Yeh-LI-Rajagopal does not disclose the step wherein the at least one graph neural network comprises a heterogenous graph.
SHEN discloses the step wherein the at least one graph neural network comprises a heterogenous graph. See Paragraphs [0045] & [0091], (Disclosing a data processing method including a heterogeneous conversion graph. The system comprises server 200 that may train a prediction network in combination a heterogeneous conversion graph. The prediction network may include a graph neural network which may effectively propagate information between nodes of the heterogeneous graph, i.e. wherein the at least one graph neural network comprises a heterogenous graph.)
Yeh, LI, Rajagopal and SHEN are analogous art because they are in the same field of endeavor, data recommendation via machine learning techniques. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh-LI-Rajagopal to include the method of using a heterogeneous graph to provide recommendations as disclosed by SHEN. Paragraph [0091] of SHEN discloses that the use of a graph neural network which may effectively propagate information between nodes of a heterogeneous graph. Additionally, Paragraph [0046] describes the use of the heterogeneous conversion graph as improving accuracy of the trained prediction network and improves accuracy of predicting the conversion index of the user for delivered content, in this case an advertisement.
Regarding dependent claim 10,
The claim is analogous to the subject matter of dependent claim 2 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 10 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 3-4, 11-12 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yeh in view of LI and Rajagopal as applied to claim 1 above, and further in view of Petrescu et al. (US PGPUB No. 2018/0075146; Pub. Date: Mar. 15, 2018).
Regarding dependent claim 3,
As discussed above with claim 1, Yeh-LI-Rajagopal discloses all of the limitations.
Yeh-LI-Rajagopal does not disclose the step wherein prior to generating instructions that cause the user device to display the interface, re-rank the set of ranked contextually relevant items using a generative model that receives the set of ranked contextually relevant items and the at least one current session signal.
Petrescu discloses the step wherein prior to generating instructions that cause the user device to display the interface, re-rank the set of ranked contextually relevant items using a generative model that receives the set of ranked contextually relevant items and the at least one current session signal. See FIG. 7 & Paragraph [0094], (Disclosing an online system configured to provide a continuous feed of content items to a client device. FIG. 7 illustrates method 700 comprising step 710 of receiving content items ranked in an order. At step 750, the order of content items is modified based on session scores associated with a user session. At step 760, the content items are displayed on a content device in the modified order. Note [0039] wherein the system comprises machine learning module 245 which uses machine learning techniques to train content ranking models to rank content items generated by content item generator 235 or stored in content item store 240, i.e. prior to generating instructions that cause the user device to display the interface (e.g. results are only displayed at step 750 after the re-ranking occurs as in FIG. 7), re-rank the set of ranked contextually relevant items using a generative model that receives the set of ranked contextually relevant items and the at least one current session signal (e.g. a machine learning model is used to rank content, which would include re-ranking based on session data as in FIG. 7).)
Yeh, LI, Rajagopal and Petrescu are analogous art because they are in the same field of endeavor, data retrieval using machine learning models. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh-LI-Rajagopal to include the method of using machine learning techniques to suggest content of users according to user interaction metrics as disclosed by Petrescu. Paragraph [0065] of Petrescu discloses that the use of a trained session ranking model allows the system to determine more accurate session scores, which results in the system providing users with more relevant content thereby improving the user experience.
Regarding dependent claim 4,
As discussed above with claim 3, Rajagopal-RATH-Petrescu discloses all of the limitations.
Petrescu further discloses the step wherein the generative model utilizes persona-based re-ranking. See Paragraph [0055], (Session ranking module 290 considers session scores for e-ranking during the corresponding session during which the session scores were generated. A session score indicates a likelihood that a user of client device 110 is interested in a content item or the likelihood that the user will interact with said content item, i.e. wherein the generative model utilizes persona-based re-ranking.)
The examiner notes that Paragraph [0038] of Applicant’s Specification describes the "persona-based re-ranking process" as comprising a process wherein an LLM utilizes a set of items and current session signals to re-rank items. The method of Petrescu uses session scores representing user activity to re-rank content to be displayed to a user's client device. Therefore, Petrescu describes functionality that is analogous to the description of the "persona-based re-ranking process" of Paragraph [0038].
Rajagopal, RATH and Petrescu are analogous art because they are in the same field of endeavor, data retrieval using machine learning models. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Rajagopal-RATH to include the method of using machine learning techniques to suggest content of users according to user interaction metrics as disclosed by Petrescu. Paragraph [0065] of Petrescu discloses that the use of a trained session ranking model allows the system to determine more accurate session scores, which results in the system providing users with more relevant content thereby improving the user experience.
Regarding dependent claim 11,
The claim is analogous to the subject matter of dependent claim 3 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 4 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 11 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 19,
The claim is analogous to the subject matter of dependent claim 12 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 5, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajagopal in view of RATH as applied to claim 1 above, and further in view of FILONOV et al. (USPGPUB No. 2020/0192920; Pub. Date: Jun. 18, 2020).
Regarding dependent claim 5,
As discussed above with claim 1, Rajagopal-RATH discloses all of the limitations.
Rajagopal-RATH does not disclose the step wherein the listwise ranker applies cross-entropy normalized discounted cumulative gain (NDCG).
FILONOV discloses the step wherein the listwise ranker applies cross-entropy normalized discounted cumulative gain (NDCG). See Paragraphs [0197] & [0200], (Disclosing a system for selecting documents for inclusion into a search engine search index executed by a machine learning algorithm (MLA). The MLA may implement a listwise ranking algorithm such as the LambdaMART algorithm. LambdaMART is described as being based on the RankNet ranging algorithm which is a pairwise approach using gradient descent to update model parameters in order to minimize a cost function. Another cost function or evaluation metric may be used for evaluating a final ranking quality such as nDCG, i.e., i.e. wherein the listwise ranker applies cross-entropy (LambdaMART is describes as being based on the RankNet algorithm which minimizes a cost function (e.g. Cross-entropy)) normalized discounted cumulative gain (NDCG) (e.g. a second cost function may be nDCG).
Rajagopal, RATH and FILONOV are analogous art because they are in the same field of endeavor, data retrieval via machine learning techniques. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Rajagopal-RATH to include the method of processing and ranking documents as disclosed by FILONOV. Paragraph [0018] of FILONOV discloses that the system allows for managing a limited amount of processing power and storage that can be allocated for indexation by selectively indexing documents in the search index of the search engine. This represents savings in storage resources, bandwidth and computational time.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 5 directed to a method or process and is rejected under similar rationale.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 13 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 7-8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yeh in view of Li and Rajagopal, as applied to claim 1 above, and further in view of RATH et al. (US PGPUB No. 2024/0037631; Pub. Date: Feb. 1, 2024).
Regarding dependent claim 7,
As discussed above with claim 1, Yeh-Li-Rajagopal discloses all of the limitations.
Yeh-Li-Rajagopal does not disclose the step wherein the at least one graph neural network applies a complementary amendment rule, an indirect amendment rule, an order completion rule, or a combination thereof
RATH discloses the step wherein the at least one graph neural network applies a complementary amendment rule, an indirect amendment rule, an order completion rule, or a combination thereof. See Paragraph [0082], (Recommendation system 114 may utilize session embeddings and item embeddings to limit a search space for determining items to recommend based on which items are most likely to be recommended based on which items are part of an input sequence input by a user selecting items, i.e. wherein the at least one graph neural network applies a complementary amendment rule (e.g. items are recommended based on a likelihood that a certain item will be selected next by a user).)
Yeh, Li, Rajagopal and RATH are analogous art because they are in the same field of endeavor, recommendation systems. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yeh-Li-Rajagopal to include the method of recommending items based on customer purchase data as disclosed by RATH. Paragraph [0083] of RATH discloses that the system may generate real-time recommendations for users using a reduced search space based on a user input sequence to apply a graph neural network. The reduction of the search space reduces computation time and allows for sequence-specific recommendations to be generated quickly in response to receipt of an item sequence.
Regarding dependent claim 8,
As discussed above with claim 7, Yeh-Li-Rajagopal-RATH discloses all of the limitations.
Rajagopal further discloses the step wherein the instructions cause the processor to: receive feedback data based on the set of ranked contextually relevant items; See Paragraph [0071], (The system may detect recommendation feedback corresponding to a user selection of a recommended data item of the recommendation list, i.e. receive feedback data based on the set of ranked contextually relevant items;)
generate an updated post-selection recommendation model based at least in part on the feedback data; See Paragraphs [0071]-[0073], (The personalized ranking score of a data item may be increased by positive feedback. Personalized ranking scores per-item for each entity may be scaled and weighted periodically such that the recommendation ranking of data items may be updated by scaling the ranking score of the plurality of items, i.e. generate an updated post-selection recommendation model based at least in part on the feedback data;)
receive a second set of items associated with a second completed first process; See Paragraph [0102], (The system comprises an interactive online user interface provided to a target entity to place an order by selecting a set of data items from a pool of data items. A subset of data items is displayed and may be presented as a reduced menu as well as a recommendation list.) See Paragraph [0014], (Users may be associated with a historical record of one or more orders and may place additional orders in the present or future, i.e. receive a second set of items associated with a second completed first process (e.g. a user having one or more completed orders may start a new order).)
and generate a second set of ranked contextually relevant items using the updated post-selection recommendation model. See Paragraphs [0071]-[0073], (Rankings for data items may be updated over time for a user by periodically updating the scaling and weighting personalized ranking scores for each data item according to feedback, i.e. generate a second set of ranked contextually relevant items using the updated post-selection recommendation model (e.g. user feedback as in [0071] is used to update the recommendation engine which results in changes to the user's recommendations when placing an order).)
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 8 directed to a method or process and is rejected under similar rationale.
Examiner’s Input
The following references were found to be relevant to the claimed invention but were not relied upon for any of the prior art rejections presented above:
BISWAS et al. (US PGPUB No. 2023/0385607; Pub. Date: Nov. 30, 2023)
BISWAS is directed to a system for generating item recommendations for users. FIGs. 4A-4B illustrate a method for hypergraph-based collaborative filtering recommendations using a collaborative filtering graph 118, graph neural network (GNN) model 114, etc. in order to generate and render item recommendations. FIGs. 4A-4B are relevant to at least claim 1 as currently presented.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 9 and 16 and have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments modify the scope of the claimed invention and therefore necessitated the new grounds of rejection presented in this Office Action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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