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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/11/2026 has been entered.
Status of Claims
Claims 1-2, 4-5, 7-12, 14-15, and 17-23 remain pending, and are rejected.
Claims 3, 6, 13, and 16 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 3/11/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 3/11/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on page 9 of the Applicant’s Remarks, arguments are made that the claims are similar to Core Wireless, in which the Federal Circuit determined that the claimed user interfaces allow a user to more quickly access data and applications in electronic devices by a specific manner of displaying a limited set of information to the user, and were an improvement in the functioning of computers. The Applicant also argues that the computer component is required to display the plurality of items to be displayed on the user interface. On page 10 of the Applicant’s Remarks, the Applicant cites In re Desjardins which recites “training of the machine learned model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task”. Specification paragraph [0117] is cited for disclosing how the iterative process creates a dynamic feedback loop, enhancing the model’s precision in personalization by incorporating up-to-date insights from customer behavior.
Examiner respectfully disagrees. The comparisons to Core Wireless are inapposite. In Core Wireless, the specification specifically discusses the technical problems of the interface, such as devices with small screens (technical specifications) where data and functionality needs to be divided into many layers and views, and the navigation of an interface, such as having to scroll around and switch views to find the right data/functionality. The claims contained precise language delimiting a limited set of information to the user, rather than using conventional user interface methods to merely display products that are ordered by their affinity score. The present claims do not recite any technical problem (such as the small screen size), and provide a specific technical solution to the technical problem, but are directed to the steps of determining a plurality of items to display to the user, and merely ranking and presenting these items to the user. Any technical issue of the interface or any specific manner in how a computer displays data at a technical level are not disclosed in the specification or recited in the claims. The computer is any generic computing device that merely provides a general link to a computing environment, and the items being displayed by a computer does not make the claims an inherent computer functionality. In regards to Desjardins, the Appeals Review Panel found the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”. The present claims do not recite any specific changes to how machine learning models operate or solve any technical problems in machine learning. The present claims merely recite a retraining of a model without any technical detail as to how the training or operation of the technology of machine learning is changed/improved, but merely describes the adjustment of the abstract idea, and merely feeding it back into a machine learning model. The underlying technology of the machine learning model remains unchanged, and apply machine learning to the abstract idea, and the retraining merely represents what information of the abstract idea is being used. Similarly, the present specification does not disclose such technical problems or improvements. A dynamic feedback loop that enhances the model’s precision in personalization by incorporating up-to-date insights from customer behavior represents the commercial sales activity. Precision in personalization and incorporating up-to-date insights is abstract as sales activities. Feedback loops are not anything inherently technical as well, it is merely a process of making adjustments to any activity based on results being close to the desired effect.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 3/11/2026 regarding the rejection under 35 U.S.C. 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s arguments necessitated new grounds of rejection.
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.
Claims 1-2, 4-5, 7-12, 14-15, 17-23 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-2, 4-5, 7-10, and 21-23 are directed to a system, which is an apparatus. Claims 11-12, 14-15, and 17-19 are directed to a method, which is a process. Claim 20 is directed to a non-transitory computer readable medium, which is an article of manufacture. Therefore, claims 1-2, 4-5, 7-12, 14-15, and 17-23 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 1 as representative, claim 1 sets forth the following limitation reciting the abstract idea of determining affinity scores for items based on real-time interaction and historical data of a user:
receive a request to display to a user;
receive the historical user data associated with the user and real-time interaction data of the user interacting with a first item associated with a first category;
that includes the historical user data, concatenates features derived from the historical user data and features of item type embeddings;
determine a plurality of items to be displayed based on the real-time interaction data and the historical user data, wherein the plurality of items includes a second item associated with a second category different from the first category;
automatically display the plurality of items to be displayed in an arrangement based on affinity scores of the plurality of items;
receive feedback from the user based on the plurality of items displayed to the user;
generate affinity scores that prioritize the second item associated with the second category when real-time interaction data reflect different interactions associated with the first item and the second item.
The recited limitations above set forth the process for determining affinity scores for items based on real-time interaction and historical data of a user. These limitations amount to certain methods of organizing human activity, including commercial or legal transactions (e.g. agreements in the form of contracts, advertising, marketing or sales activities or behaviors, etc.). The claims are directed to using historical and real-time interaction data to determine a plurality of items of a second category, display the items to a user, and receive feedback and prioritize an item based on different interactions (see specification [0002-0003] disclosing the problem of inefficient recommendations to entice additional purchases due to many products being irrelevant to the customer), which is an advertising and marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 1 recites additional elements, such as:
a database storing historical customer data associated with a plurality of customers;
a computing device comprising at least one processor in communication with the database;
a user interface;
train a machine learning model using first training data;
retrain the machine learning model based on the feedback from the user and retrain the machine learning model using incremental training at a regularly set time period to continuously adapt and refine the model based on the real-time interaction data.
Taken individually and as a whole, representative claim 1 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a database and a computing device comprising at least one processor, these elements are recited with a very high level of generalization, and are only recited as to performing the steps of the abstract idea. As disclosed in paragraph [0040] of the specification, the computing devices can be any suitable computing device that includes hardware of hardware and software combination for processing and handling information. The processors can be any one or more FGPAs, ASICs, state machines, digital circuitry, or any other suitable circuitry. Specification paragraph [0051] discloses the database as any remote storage device, sch as a cloud-server, a disk, a memory device on another application server, or any other suitable remote storage. The specification also discloses the machine learning models include, but are not limited to Heuristics, Univariate based techniques, Multivariate, control limit, etc. (specification: [0074]). As such, it is clear that the machine learning models are not any particular model, and the claims are not directed to any machine learning functionality, merely utilizing machine learning to provide an output of data. It is evident that the additional elements are any generic computing component performing in a generic manner, and only serve to provide a general link to a computing environment, and the claims are directed to the abstract idea of determining what the customer wants based on interaction data.
In view of the above, under Step 2A (Prong 2), representative claim 1 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to representative claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 1 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 11 (method): Claim 11 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 11 is rejected under at least similar rationale as provided above regarding claim 1.
Regarding Claim 20 (non-transitory computer readable medium): Claim 20 recites at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 20 is rejected under at least similar rationale as provided above regarding claim 1.
Dependent claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the algorithm of determining affinity scores for items based on real-time interaction and historical data of a user, and do not recite any further additional elements. Thus, each of claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 rely on at least similar elements as recited in claim 1. Further additional elements are also acknowledged, however, the additional elements of claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 1. Thus, dependent claims 2, 4-5, 7-10, 12, 14-15, 17-19, and 21-23 do not add “significantly more” to the abstract idea.
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.
Claims 1, 8, 10-11, 18, 20, and 23 are rejected under 35 U.S.C. 103 as being unpatentable by Li (US 20240354812 A1) in view of Bhatia (US 20200104697 A1), in further view of Kumar (US 20210019805 A1).
Regarding Claim 1: Li discloses a system comprising:
a database storing historical user data associated with a plurality of users; (Li: [0028] – “The data collection module 200 collects customer data, which is information or data that describe characteristics, attributes, or other types of information associated with a customer. Customer data may include a customer's name, address, shopping preferences, favorite items, favorite retailers, stored payment instruments, dietary preferences (e.g., vegetarian, gluten-free, etc.), and demographic information (e.g., age, gender, etc.). Customer data also may include historical information associated with a customer”).
a computing device comprising: a processor and a non-transitory memory storing instructions; (Li: [0103] – “a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described”).
receive a request to display a user interface to a user; (Li: [0044] – “receives a request from a customer client device 100 to access a brand page for a brand when a customer associated with the customer client device 100 interacts with an object (e.g., an advertisement presented by the online system 140 or an external website) associated with the brand”).
receive the historical user data associated with the user and real-time interaction data of the user interacting with a first item associated with a first category; (Li: [0028] – “The data collection module 200 collects customer data, which is information or data that describe characteristics, attributes, or other types of information associated with a customer. Customer data may include a customer's name, address, shopping preferences, favorite items, favorite retailers, stored payment instruments, dietary preferences (e.g., vegetarian, gluten-free, etc.), and demographic information (e.g., age, gender, etc.). Customer data also may include historical information associated with a customer. For example, customer data may describe historical interaction information associated with a customer, such as a search or a browsing history of the customer, and historical order information associated with the customer, such as information describing previous orders placed by the customer”); Li: [0018] – “the picker client device 110 transmits to the online system 140 or the customer client device 100 which items the picker has collected in real time as the picker collects the items”).
train a machine learning model using first training data that includes the historical user data, wherein the machine learning model concatenates features derived from the historical user data and features of item type embeddings; (Li: [0073] – “The machine learning training module 230 may train the conversion model via supervised learning based at least in part on attributes of items presented to customers, attributes of the customers, and historical information (e.g., historical interaction information or historical order information) for the customers”).
automatically display, on the user interface, the plurality of items to be displayed on the user interface in an arrangement based on affinity scores of the plurality of items generated by the machine learning model; (Li: [0036] – “the content presentation module 210 presents a catalog of all items that are available to the customer, which the customer can browse to select items to order. The content presentation module 210 also may identify items that the customer is most likely to order and present those items to the customer. For example, the content presentation module 210 may score items and rank the items based on their scores. In this example, the content presentation module 210 then displays the items with scores that exceed some threshold”).
Li does not explicitly teach a system comprising:
determine, using the machine learning model, a plurality of items to be displayed on the user interface based on the real-time interaction data and the historical user data, wherein the plurality of items includes a second item associated with a second category different from the first category;
receive feedback from the user based on the plurality of items displayed on the user interface;
retrain the machine learning model based on the feedback from the user and retrain the machine learning model using incremental training at a regularly set time period to continuously adapt and refine the model based on the real-time interaction data including retraining the machine learning model to generate affinity scores that prioritize the second item associated with the second category when real-time interaction data reflect different interactions associated with the first item and the second item.
Notably, however, Li does disclose using machine learning models to determine the items (Li: [0105]), and determining items to display to the user (Li: [0036-0037]).
To that accord, Bhatia does teach generate affinity scores that prioritize the second item associated with the second category when real-time interaction data reflect different interactions associated with the first item and the second item. (Bhatia: [0074] – “However, by prioritizing a content item group based on interaction strength, the difference between two adjacent entries with different interaction types is minimized. In addition, when dealing with large amounts of data with voluminous entries for each interaction type, the edge case example provided above will generally occur infrequently. Further, for content items with a large number of possible interaction types, the difference in interaction strength between two adjacent prioritized interaction types may be minor (e.g., one interaction type with an interaction strength of 0.25 adjacent to another interaction type with an interaction strength of 0.23)”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Li disclosing the system for determining items to display to a user by training a machine learning model with historical user data and item data with the prioritizing an item based on different interactions as taught by Bhatia. One of ordinary skill in the art would have been motivated to do so in order to make the difference between interaction strengths significant (Bhatia: [0074]).
Li in view of Bhatia does not explicitly teach a system comprising:
determine, using the machine learning model, a plurality of items to be displayed on the user interface based on the real-time interaction data and the historical user data, wherein the plurality of items includes a second item associated with a second category different from the first category;
receive feedback from the user based on the plurality of items displayed on the user interface;
retrain the machine learning model based on the feedback from the user and retrain the machine learning model using incremental training at a regularly set time period to continuously adapt and refine the model based on the real-time interaction data.
Notably, however, Li does disclose using machine learning models to determine the items (Li: [0105]), and determining items to display to the user (Li: [0036-0037]).
To that accord, Kumar does teach a system comprising:
determine, using the machine learning model, a plurality of items to be displayed on the user interface based on the real-time interaction data and the historical user data, wherein the plurality of items includes a second item associated with a second category different from the first category; (Kumar: [0116] – “the computing device may determine a second item to recommend for inclusion with the first item in the current transaction. For example, the second item may be an item that is offered by the first merchant, as determined based on the list of items offered by the first merchant, and may be an item that the current buyer has purchased with the first item in the past more frequently than any other items purchased with the first item.”; Kumar: claim 2 – “a cross-selling recommendation for the buyer to purchase an additional item in addition to an item the buyer has already selected for purchase, wherein the additional item is associated with a different item category than the item”; Kumar: [0025] – “the service provider may send to the merchant device a recommendation that the merchant offer one or more additional items to the particular customer based at least in part on the first item that the customer has already selected. For example, suppose that a customer has selected nail polish as an item for purchase. The transaction history for the current merchant may be obtained from a plurality of buyer profiles of buyers that have conducted at least one transaction with the merchant”). In summary, Kumar discloses providing cross-selling recommendations for items of a different category based on a items already selected (real-time interaction data) and the history of previous transactions.
receive feedback from the user based on the plurality of items displayed on the user interface; (Kumar: [0099] – “the first buyer profile 128(1) may provide an indication as to whether the first buyer is receptive to incentive offers, such as being more willing to purchase additional items if offered at a discount or as part of a bundle, such as based on whether the first buyer has accepted such deals in the past”).
retrain the machine learning model based on the feedback from the user and retrain the machine learning model using incremental training at a regularly set time period to continuously adapt and refine the model based on the real-time interaction data. (Kumar: [0062] – “The statistical model may be initially trained using a set of training data, checked for accuracy, and then used for matching transactions with particular buyer profiles by determining confidence scores, and associating a particular transaction with a particular buyer profile when a confidence score exceeds a specified threshold of confidence. The statistical model may be periodically updated and re-trained based on new training data to keep the model up to date”; Kumar: [0103] – “the item recommendation module 130 may periodically send updated predetermined recommendations to the particular merchant such as when the particular merchant changes the items 622 offered by the particular merchant, or when the predetermined recommendations otherwise change due to changes in buyer purchase habits”). In summary, the model is retrained based on changes in buyer profile purchase habits, such as the buyer receptiveness to the offers (feedback) as disclosed above.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Li in view of Bhatia disclosing the system for determining items to display to a user by training a machine learning model with historical user data and item data with the determining items to display based on real-time interaction data and historical data, receiving feedback, and retraining the machine learned model periodically based on the feedback as taught by Kumar. One of ordinary skill in the art would have been motivated to do so in order to help merchants cross sell additional items to items already selected by customers (Kumar: [0018]).
Regarding Claim 8: Li in view of Bhatia and Kumar discloses the limitations of claim 1 above.
Li does not explicitly teach calculating a cross-pollinating intent score that is a probability that the user interacts with a second item associated with a second category different than the first category. Notably, however, Li does disclose displaying items on the user interface ranked by a score based on how likely the user is to order the item (Li: [0036-0037]).
To that accord, Bhatia does teach wherein the cross-pollinating intent score is a probability that the customer interacts with a second product associated with a second category different than the first category. (Bhatia: [0129] – “the researchers selected predicting the probability that a user will interact (e.g., select) a content item given the user's past behavioral traits (i.e., content item interactions) and given the time a content item is provided to the user. For example, the researchers tested the accuracy of predicting the probability that a user would select a content item sent via an email at a particular time based on utilizing the user embeddings learned from one or more of the embodiments disclosed herein
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Li disclosing the system for tracking a customer journey to identify intents of the customer with the score being a probability that the customer interacts with a second product associated with a second category different from the first category as taught by Bhatia. One of ordinary skill in the art would have been motivated to do so in order to effectively and efficiently compare users based on their interactions and provide accurate analytics of users (Bhatia: [0005-0006]).
Regarding Claim 10: Li in view of Bhatia and Kumar discloses the limitations of claim 1 above.
Li further discloses wherein the historical user data includes profile data associated with the journey data. (Li: [0028] – “The data collection module 200 collects customer data, which is information or data that describe characteristics, attributes, or other types of information associated with a customer. Customer data may include a customer's name, address, shopping preferences, favorite items, favorite retailers, stored payment instruments, dietary preferences (e.g., vegetarian, gluten-free, etc.), and demographic information (e.g., age, gender, etc.). Customer data also may include historical information associated with a customer. For example, customer data may describe historical interaction information associated with a customer, such as a search or a browsing history of the customer, and historical order information associated with the customer, such as information describing previous orders placed by the customer. Customer data further may include information describing retailers (e.g., names, types, geographical locations of retailer locations operated by the retailers, etc.) and items (e.g., names, types, prices, etc.) with which a customer interacted (e.g., by searching for the items, clicking on them, adding them to a shopping list, etc.)”).
Regarding Claims 11 and 20: Claims 11 and 20 recite substantially similar limitations as claim 1. Therefore, claims 11 and 20 are rejected under the same rationale as claim 1 above.
Regarding Claim 18: Claim 18 recites substantially similar limitations as claim 8. Therefore, claim 18 is rejected under the same rationale as claim 8 above.
Regarding Claim 23: Li in view of Bhatia and Kumar discloses the limitations of claim 1 above.
Li in view of Bhatia does not explicitly teach wherein training the machine learning model further comprises utilizing the feedback information received via the user interface to repeatedly provide one or more of retrained cross-pollination models, retrained affinity models, retrained product models, or retrained recommendation models. Notably, however, Li does disclose using machine learning models to determine the items (Li: [0105]), and determining items to display to the user (Li: [0036-0037]).
To that accord, Kumar does teach wherein training the machine learning model further comprises utilizing the feedback information received via the user interface to repeatedly provide one or more of retrained cross-pollination models, retrained affinity models, retrained product models, or retrained recommendation models. Examiner notes that Applicant recites one or more of in the claim. (Kumar: [0062] – “The statistical model may be initially trained using a set of training data, checked for accuracy, and then used for matching transactions with particular buyer profiles by determining confidence scores, and associating a particular transaction with a particular buyer profile when a confidence score exceeds a specified threshold of confidence. The statistical model may be periodically updated and re-trained based on new training data to keep the model up to date”; Kumar: [0099] – “the first buyer profile 128(1) may provide an indication as to whether the first buyer is receptive to incentive offers, such as being more willing to purchase additional items if offered at a discount or as part of a bundle, such as based on whether the first buyer has accepted such deals in the past”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Li in view of Bhatia disclosing the system for determining items to display to a user by training a machine learning model with historical user data and item data with the retraining of the recommendation model as taught by Bhatia. One of ordinary skill in the art would have been motivated to do so in order to help merchants cross sell additional items to items already selected by customers (Kumar: [0018]).
Claims 2, 5, 9, 12, 14-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Li (US 20240354812 A1), Bhatia (US 20200104697 A1), and Kumar (US 20210019805 A1), in view of Agrawal (US 20210034635 A1).
Regarding Claim 2: The combination of Li, Bhatia, and Kumar discloses the limitations of claim 1 above.
Li does not explicitly teach a system comprising:
identify, based on the journey data, a plurality of cross-pollinating items, the cross-pollinating items being in a second category different than the first category;
generate an affinity score for each of the plurality of cross-pollinating items;
prioritize each of the plurality of cross-selling items based on their respective affinity scores;
display, on a user interface, based on the prioritization, the plurality of cross-pollinating items in a specific arrangement.
Notably, however, Li in view of Kumar does disclose using machine learning models to determine the items (Li: [0105]), and determining items to display to the user (Li: [0036-0037]).
To that accord, Kumar does teach identify, based on the journey data, a plurality of cross-pollinating items, the cross-pollinating items being in a second category different than the first category; (Kumar: [0116] – “the computing device may determine a second item to recommend for inclusion with the first item in the current transaction. For example, the second item may be an item that is offered by the first merchant, as determined based on the list of items offered by the first merchant, and may be an item that the current buyer has purchased with the first item in the past more frequently than any other items purchased with the first item.”; Kumar: claim 2 – “a cross-selling recommendation for the buyer to purchase an additional item in addition to an item the buyer has already selected for purchase, wherein the additional item is associated with a different item category than the item”; Kumar: [0025] – “the service provider may send to the merchant device a recommendation that the merchant offer one or more additional items to the particular customer based at least in part on the first item that the customer has already selected. For example, suppose that a customer has selected nail polish as an item for purchase. The transaction history for the current merchant may be obtained from a plurality of buyer profiles of buyers that have conducted at least one transaction with the merchant”). In summary, Kumar discloses providing cross-selling recommendations for items of a different category based on a items already selected (real-time interaction data) and the history of previous transactions.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Li in view of Bhatia disclosing the system for determining items to display to a user by training a machine learning model with historical user data and item data with the items being in a different category as taught by Kumar. One of ordinary skill in the art would have been motivated to do so in order to help merchants cross sell additional items to items already selected by customers (Kumar: [0018]).
The combination does not explicitly teach a system comprising:
generate an affinity score for each of the plurality of cross-pollinating items;
prioritize each of the plurality of cross-selling items based on their respective affinity scores;
display, on a user interface, based on the prioritization, the plurality of cross-pollinating items in a specific arrangement.
Notably, however, Li does disclose presenting the items ranked by their scores (Li: [0036]).
To that accord, Agrawal does teach a system comprising:
generate an affinity score for each of the plurality of cross-pollinating items; (Agrawal: [0068] – “process 300 identifies a plurality of content items based on the cohort definition criteria of said cohort. In an embodiment, the cross-cohort optimization model scoring service 115 retrieves from the content delivery system 120 the plurality of content items that are to be presented to the particular user”; Agrawal: [0069] – “process 300 uses a machine-learned model to generate a score for the selected cohort. In an embodiment, the cross-cohort optimization model scoring service 115 may use cross-cohort optimization model algorithm to score the selected cohort”).
prioritize each of the plurality of cross-selling items based on their respective affinity scores; (Agrawal: [0071] – “generates a ranking of the plurality of cohorts based on the score generated for each cohort of the plurality of cohorts. In an embodiment, the cross-cohort optimization model scoring service 115 ranks each of the cohorts according to their respective scores. The scores may be ranked in descending order, where the highest scoring cohort is assigned the first position. the second highest scoring cohort is assigned the second position, and so on”).
display, on a user interface, based on the prioritization, the plurality of cross-pollinating items in a specific arrangement. (Agrawal: [0072] – “process 300 causes the plurality of content items of each cohort of the plurality of cohorts to be displayed concurrently, based on the ranking, on a computing device. In an embodiment, cross-cohort optimization system 105 sends the plurality of cohorts, including their respective rankings and their respective content items, to the content delivery system 120. The content delivery system 120 then uses the publisher system 140 to cause display of the plurality of content items on client device”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Li, Bhatia, and Kumar disclosing the system for determining items to display to a user by training a machine learning model with historical user data and item data with the affinity score for each products, prioritizing the products by their scores, and displaying the products in a specific arrangement as taught by Agrawal. One of ordinary skill in the art would have been motivated to do so in order to present information that is likely to increase user engagement (Agrawal: [0003]).
Regarding Claim 5: The combination of Li, Bhatia, and Kumar, in view of Agrawal, discloses the limitations of claim 2 above.
The combination does not explicitly teach wherein the affinity score is generated by a machine learning model that is evaluated and refined. Notably, however, Li does disclose using machine learning models to determine the items (Li: [0105])
To that accord, Agrawal does teach wherein the affinity score is generated by a machine learning model that is evaluated and refined. (Agrawal: [0079] – “In operation 420, process 400 re-estimates regression coefficients for the machine-learning model using the estimated latent intent values and historical data from users. In an embodiment, the cross-cohort optimization model generation service 110 may use the estimated latent intent values from operation 415 and the historical data from users as training data to re-estimate the regression coefficients”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Li, Bhatia, and Kumar disclosing the system for tracking a customer journey to identify intents of the customer with the evaluation and refining of a machine learning model as taught by Agrawal. One of ordinary skill in the art would have been motivated to do so in order to train a more accurate model (Agrawal: [0079]).
Regarding Claim 9: The combination of Li, Bhatia, and Kumar discloses the limitations of claim 1 above.
The combination does not explicitly teach a user interface configured to display a plurality of cross-pollinating items in a prioritized arrangement. Notably, however, Li does disclose determining items to display to the user (Li: [0036-0037]).
To that accord, Agrawal does teach a user interface configured to display a plurality of cross-pollinating items in a prioritized arrangement. (Agrawal: [0072] – “the plurality of content items of each cohort of the plurality of cohorts to be displayed concurrently, based on the ranking, on a computing device. In an embodiment, cross-cohort optimization system 105 sends the plurality of cohorts, including their respective rankings and their respective content items, to the content delivery system 120. The content delivery system 120 then uses the publisher system 140 to cause display of the plurality of content items on client device”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Li, Bhatia, and Kumar disclosing the system for tracking a customer journey to identify intents of the customer with the user interface to display the products in a prioritized arrangement as taught by Agrawal. One of ordinary skill in the art would have been motivated to do so in order to present information that is likely to increase user engagement (Agrawal: [0003]).
Regarding Claim 12: Claim 12 recites substantially similar limitations as claim 2. Therefore, claim 12 is rejected under the same rationale as claim 2 above.
Regarding Claim 15: Claim 15 recites substantially similar limitations as claim 5. Therefore, claim 15 is rejected under the same rationale as claim 5 above.
Regarding Claim 19: Claim 19 recites substantially similar limitations as claim 9. Therefore, claim 19 is rejected under the same rationale as claim 9 above.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Li (US 20240354812 A1), Bhatia (US 20200104697 A1), and Kumar (US 20210019805 A1), in view of Beaurepaire (US 20230129248 A1).
Regarding Claim 22: The combination of Li, Bhatia, and Kumar discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein training the machine learning model further comprises iteratively adjusting a weight of an item price, a weight of an item type, a weight of a calculated relevance value on the feedback from the user, received via the user interface, wherein the feedback comprises user interaction with a displayed item. Notably, however, Li does disclose training the model based on attributes of items and historical information of customers (Li: [0073]), and Kumar does teach retraining the model (Kumar: [0062]).
To that accord, Beaurepaire does teach wherein training the machine learning model further comprises iteratively adjusting a weight of an item price, a weight of an item type, a weight of a calculated relevance value on the feedback from the user. (Beaurepaire: [0043] – “The relevance score may consider the following inputs in determining the relevance score: a distance from the user to a booked shared vehicle or alternative shared vehicle, if a detour needs to be taken to reach the booked shared vehicle or alternative shared vehicle and the length of the detour if necessary, the type of the booked shard vehicle or alternative shared vehicle, a comfort score associated with the booked shared vehicle or alternative shared vehicle, the weather at the location of a user, a distance from the user to the destination, a price of the booked shared vehicle or alternative shared vehicle, routes that the booked shared vehicle or alternative shared vehicle are capable of taking, how and where the user is able to park the booked shared vehicle or alternative shared vehicle at the destination, a safety score associated with the booked shared vehicle or alternative shared vehicle, a user's preference (e.g., a user's preference for a certain type of shared vehicle or that a vehicle include a certain feature), a range of the booked shared vehicle or alternative shared vehicle, and the like”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Li, Bhatia, and Kumar disclosing the system for tracking a customer journey to identify intents of the customer retraining a model to adjust weights with the adjusting of item price, item type, and relevance as taught by Beaurepaire. One of ordinary skill in the art would have been motivated to do so in order to determine suitability for the user (Beaurepaire: [0042]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Li (US 20240354812 A1), Bhatia (US 20200104697 A1), and Kumar (US 20210019805 A1), in view of Spathelf (US 10,636,076 B1).
Regarding Claim 21: The combination of Li, Bhatia, and Kumar discloses the limitations of claim 1 above.
The combination does not explicitly teach display, on the user interface, the second item associated with the second category at a location proximate to items that are added to a cart. Notably, however, Li does disclose customer data including items added to a shopping list (Li: [0028]), and Bhatia does teach identifying items of a different category than the first item (Bhatia: [0074]).
To that accord, Spathelf does teach display, on the user interface, the second item associated with the second category at a location proximate to items that are added to a cart. (Spathelf: col. 6, ln. 1-14 – “the computer system 104 can analyze the item 112 to determine an attribute associated with the item. This might include whether the item is on sale (e.g., offered at a discounted price), the location of the item, or other features of the item that might not describe physical characteristics of the item directly. However, in some examples, the attribute may help describe the item. As a sample illustration, the item may include a shoe and the attributes may include the size of the shoe, the color of the shoe, whether the shoe is on sale, the location of the shoe, the physical distance between the shoe and the user's location, and the like. Similarly, the category of the shoe may include “shoes” or “men's wearables.””; Spathelf: col. 5, ln. 22-26 – “The item may be placed in the interest queue in response to a second interaction with a second element, including an add-to-cart button, a radio button, or other elements that might enable the item to be placed in and/or associated with the interest queue”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Li, Bhatia, and Kumar disclosing the system for tracking a customer journey to identify intents of the customer with the second item associated with a second category and at a location near the user location interacting with the first item as taught by Spathelf. One of ordinary skill in the art would have been motivated to do so in order to obtain more relevant results for a user (Spathelf: col. 1, ln. 28-32).
Subject Matter Free of Prior Art
The following is a statement of the reasons for indicating subject matter free of the prior art that was previously indicated free of prior art in the Office Action mailed on 12/16/2025.
Claims 4, 7, 14, and 17 are determined to have overcome the prior art of rejection and are free of the prior art, however the claims remain rejected under 35 U.S.C. 101, as set forth above.
Claims 4, 7, 14, and 17 are found to overcome the prior art rejection for the reasons set forth below.
Claims 4 and 14 recites the claimed features of: wherein the affinity score is dependent on a cross-pollinating intent score indicative of a probability that the user interacts with a second item associated with a second category different than the first category.
Claims 7 and 17 recite further configured to:
generate a comparison between two or more cross-pollinating items;
based on the comparison, iteratively manipulate one or more weights associated with the affinity score;
generate an updated affinity score based on the manipulated one or more weights.
The closest prior art was found to be as follows:
Spathelf (US 10,636,076 B2) discloses col. 8, ln. 23-30 – “The process 100 may also comprise determining whether to generate a supplementary information component. In some examples, the determination is based at least in part on a probability of adding the item to an interest queue. For example, previous users may select the item and only two-percent of those users may add the item to an interest queue. Another item may correspond with a 95% add-to-queue probability”; col. 6, ln. 15-27 – “The category, attribute, program, and/or related category may be determined when an interest in item 112 is identified and/or tagged. The interest in item 112 may be identified by an interaction with the item. For example, the computer system 104 may receive a request to provide additional information about item 112. This may be identified when a user selects an item from the search results 106. In some examples, the interest in the item may be identified when the computer system 104 receives a request for information (e.g., a document that details information about the item, an interaction with a document that details information about the item, etc.). Other identifications of interest in an item may be identified throughout the disclosure as well”.
Song (US 20220164851 A1) discloses [0090] – “The learning model may be updated or newly generated by performing at least one of the parameters so as to correspond to the description of FIG. 8. For example, when providing information about one or more items each satisfying a similarity criterion, there is a need to modify a weight, which applied to each of a plurality of attributes, in a case in which a plurality of items satisfying the similarity criterion are identified. According to an example embodiment, it is possible to specify in advance which weight is given to which attribute through a configuration, and a size of the weight may be differently specified according to a section to which the number of attributes according to the item information belongs. For example, as the number of size attributes increases, a weight value for the size attribute may be specified to be high. In this case, at least one of the parameters related to the weight may be modified to reconstruct the learning model”; [0103] – “the vector values of the items may be reconstructed by modifying weight application criteria, thereby affecting a similarity comparison result. For example, when the number of the item information each corresponding to the vector having the similarity value of 90% or more is checked to be 100 or more, the vector values of the items may be reconstructed by lowering or increasing a weight of specific attribute information. In an example embodiment, the weight application criteria may be modified so that the number of item information each corresponding to the vector having the similarity value of 90% or more is derived to be 15 or less”.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the below noted features of Applicant’s invention. To disclose all of the feature of claims 4 and 7, and any intervening claims, would require piecemeal analysis and such reconstruction would only be possible in improper hindsight reasoning.
Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that claims 4, 7, 14, and 17 are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Conclusion
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 6/8/2026
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