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.
Status of Claims
Claims 1-2, 4-5, 8-12, 14-15, and 18-23 remain pending, and are rejected.
Claims 24-46 have been added, and are rejected.
Claims 3, 6-7, 13, and 16-17 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 9/9/2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered, and are persuasive. Applicant has amended subject matter from dependent claim indicated as free of the prior art in the previous Office Action into the independent claims.
Applicant’s arguments filed on 9/9/2026 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 9/9/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 12-13 of the Applicant’s Remarks, arguments are made that concatenating a feature vector derived from user data with item type embeddings is a feature-level operation on the inputs to the recited model which joins a feature vector derived from the user’s historical transactions with item product type embeddings, which is not a commercial or legal interaction within certain methods of organizing human activity. The Applicant argues that the claims recite a specified sequence of operations that specify the operand, trigger, and condition under which the recited weights are changed. On pages 13-14, arguments are made that the claim recite specifically what is adjusted (the one or more weights associated with an affinity score), on what input (the comparison between two or more cross-pollinating items), and under what condition (when the additional real-time interaction data reflects different interactions associated with the first item and the second item), and ties the manipulated weights to the output of the recited model.
Examiner respectfully disagrees. The concatenating a feature vector derived from user data with item type embeddings is merely a step in the claims within the abstract idea that is recited with a very high level of generality. Furthermore, concatenating a vector from historical user data and feature of item type embeddings is a mathematical feature to perform calculations on data of the abstract idea. The iterative manipulating of the weights associated with an affinity score also does not represent any technical activity. The manipulating of weights merely represents mathematical functions within the abstract algorithm. The affinity score, comparison of two or more cross-pollinating items, and when the additional real-time interaction data reflects different interactions associated with the first and second item are all aspects of the abstract idea of scoring items based user data to recommend items from another category. These operations do not change or improve any underlying technology of machine learning. The specification also does not disclose any technical problems or solutions of any machine learning functionality, such as in paragraphs [0002-0003] of the Applicant’s specification, which discloses the problem of difficulty in deciding which products to recommend to increase gross merchandise value, and the inefficiency of current methods as many products may be irrelevant or not desirable to the customer. The steps of the claims merely utilize machine learning as a calculator to produce scores to determine product recommendations, and merely adjust the mathematical parameters, but do not address any technical functionality of the machine learning itself.
On page 14 of the Applicant’s Remarks, it is argued that even if the claims are directed to an abstract idea, the claims integrate any alleged abstract idea into a practical application, reciting features that allow for the affinity score to contribute to prioritizing of cross-pollinating items only when the interactions for the items are different, allowing for the refining of the model based on instances where the predicted interactions need improvement, which result in reducing model refining operations, and improving model training architectures. On page 15, arguments are made that the retraining causes the model to “continuously adapt and refine”, citing Ex Parte Desjardins for “improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams”.
Examiner respectfully disagrees. Allowing for affinity scores to contribute to prioritizing cross-pollinating items is an abstract idea. Limiting this feature to only when the interactions for the items are different merely furthers that abstract idea. These features are all based around commercial activities of interacting with items and generating scores to determine items of another category. The refining of the model merely uses mathematical iterations in order to tune the mathematical parameters of the abstract algorithm. This is unlike the comparisons to Ex Parte Desjardins, which addressed specific technical abilities of the machine learning, such as the problem of “catastrophic forgetting” encountered in continual learning systems, such as that the model can learn new tasks while protecting knowledge about previous tasks. The present claims do not address such technical features, but merely refine the abstract scoring/weights for this particular abstract idea in response to interactions with the items. The claims merely recite a periodic retraining of the model based on feedback from the user and additional real-time interaction data (data of the abstract idea). In the court decision of Recentive Analytics, the court had decided that the machine learning model to be “iteratively trained” or dynamically adjusted do not represent a technological improvement as they are incident to the very nature of machine learning models.
On pages 15-16 of the Applicant’s Remarks, arguments are made that the claims recite significantly more than the alleged abstract idea by reciting novel and non-obvious subject matter. It is also argued that the features of “retraining the machine learning model based on the feedback from the user and additional real-time interaction data using incremental training at a regularly set time period to continuously adapt and refine the machine learning model to generate additional affinity scores that prioritize the second item associated with the second category when the additional real-time interaction data reflect different interactions associated with the first and the second item” reduces the amount of retraining the machine learning model may otherwise undergo.
Examiner respectfully disagrees. Whether the claims are novel and non-obvious have no bearing on the eligibility of the claims. The novel and non-obvious subject matter is pertinent to the prior art rejections of 35 U.S.C. 102 and 103, but address very different concepts of the claims. The recited step of “retraining the machine learning model based on the feedback from the user and additional real-time interaction data using incremental training at a regularly set time period to continuously adapt and refine the machine learning model to generate additional affinity scores that prioritize the second item associated with the second category when the additional real-time interaction data reflect different interactions associated with the first and the second item” also merely define what data is used in the retraining, and performing the retraining at a regularly set time period. As discussed above, this is merely a generic feature of machine learning models, and the claim only defines what data and what output of the abstract idea is used. There is no particular change or improvement to the underlying technology in the training of machine learning itself, but merely defines the type of information for this particular application.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
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, 8-12, 14-15, 18-26 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;
transmit for display the plurality of items to be displayed in an arrangement based on affinity scores of the plurality of items;
generate a comparison between two or more cross-pollinating items;
based on the comparison, iteratively manipulate one or more weights associated with an affinity score associated with each of the two or more cross-pollinating items;
generate an updated affinity score based on the manipulated one or more weights;
receive feedback from the user based on the plurality of items displayed to the user;
generate additional affinity scores that prioritize the second item associated with the second category when the additional real-time interaction data reflect different interactions associated with the first item and the second item;
transmit for display the plurality of items in a second arrangement that prioritizes the second item associated with the second category.
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 additional real-time interactions data using incremental training at a regularly set time period to continuously adapt and refine the machine learning model;
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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 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, 8-10, 12, 14-15, 18-19, and 21-26 do not add “significantly more” to the abstract idea.
Subject Matter Free of Prior Art
Claims 1-2, 4-5, 8-12, 14-15, and 18-26 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 1-2, 4-5, 8-12, 14-15, and 18-26 are found to overcome the prior art rejection for the reasons set forth below.
Claim 1recites the claimed features of:
generate a comparison between two or more cross-pollinating items;
based on the comparison, iteratively manipulate one or more weights associated with an affinity score associated with each of the two or more cross-pollinating items;
generate an updated affinity scores based on the manipulated one or more weights;
The closest prior art was found to be as follows:
Li (US 20240354812 A1) discloses [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”
However, Li does not disclose retraining the machine learning based on feedback from the user and real-time interaction data, or generating a comparison between two or more cross-pollinating items to iteratively manipulate one or more weights associated with an affinity score, and generating an updated affinity score based on the manipulated one or more weights.
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”.
However, Spathelf does not disclose any training or retraining of models, or generating a comparison between two or more cross-pollinating items to iteratively manipulate one or more weights associated with an affinity score, and generating an updated affinity score based on the manipulated one or more weights.
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”.
However, while Song discloses the modifying weight application criteria, Song does not disclose comparing cross-pollinating items, and iteratively manipulating weights associated with an affinity score based on the comparison.
Zhang (US 20220374805 A1) discloses [0043] – “the value 312 is representative of a market value for items 310 included in the category nodes 308, such that a category node 308 having a high value 312 is representative of items 310 having generally higher prices than items 310 included in a category nodes 308 corresponding to a lower value 312. The item categorization module 306 is configured to periodically update the value 312 for each category nodes 308, such that the graph generation module 202 is provided with real-time information regarding a current market valuation for items represented by category nodes”.
While Zhang discloses periodically updating values of category nodes, this is not based on a comparison of cross-pollinating items, and is not used in generating an updated affinity score.
PTO Reference U (see PTO-892 Reference U mailed on 7/21/2025) discloses a method of analyzing customer journeys with cross-media exposure, and its effects on customer purchase decisions.
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 claim 1, 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 1-2, 4-5, 8-12, 14-15, and 18-26 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 8:30am - 7:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 10/13/2026
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