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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-12 and 18-20 in the reply filed on 5/15/2026 is acknowledged. While Applicant has made claims 13-17 dependent on claim 1 and state that the claims “fall within the elected group and do not impart any undue burden in search and/or examination”, these claims are still directed towards non-elected subject matter and do indeed impart a search burden for the reasons provided in the Restriction Requirement dated 3/26/2026. Therefore, Claims 13-17 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, this claim recites the limitation “the neutral-epsilon agent”. There is insufficient antecedent basis for this limitation in the claim language. Claims 2-12 inherit this rejection through dependency from claim 1.
Regarding claims 2, 3, these claims recite the limitation “the deep learning agent”. There is insufficient antecedent basis for this limitation in the claim language.
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 1-12 and 18-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, the claim recites, in part, constructing an interaction matrix from historical user interactions with incentive offers; applying a non-negative matrix factorization to the interaction matrix to obtain an approximation of the interaction matrix including dense interaction data, the dense interaction data including user context features and offer features; applying a [model] across a plurality of users and a plurality of offers to generate, a matrix of expected rewards across the plurality of offers for each of the plurality of users, wherein the [model] employs to: across a plurality of iterations, select an offer from among the plurality of offers and approximate an expected reward for the offer; and learn from observed rewards generated by an environment to train a model to approximate an expected reward for each offer; based on the matrix of expected rewards associated with the plurality of users and the plurality of offers, select, for at least some of the plurality of users, one or more offers; and displaying at least one of the one or more offers to the given user.
Regarding claim 18, the claim recites, in part, a sparse interaction matrix captured from customer interaction data associated with a plurality of customers and a plurality of offers, wherein the sparse interaction matrix is stored and includes, for each combination of a customer and an offer, a binary representation of whether the customer interacted with the offer; a set of customer features and a set of offer features extracted from the sparse interaction matrix by non-negative matrix factorization; selecting an offer from among the plurality of offers; predicting an expected reward associated with a customer for the selected offer based on the set of customer features and the set of offer features; communicate one or more combinations of a customer and a selected offer to be presented to a customer.
The limitations, as drafted and detailed above, recites determining offers from a plurality of reward offers based on historical user interactions and displaying the offers to a given user, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and specifically commercial interactions including advertising, marketing or sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of contextual multi-armed bandit (claims 1, 18, algorithm that merely implies machine learning), epsilon greedy agent (claims 1, 18, algorithm that merely implies machine learning), deep learning model (claims 1, 18, merely used in an “apply it” manner), memory (claim 18), publishing service (claim 18, a “service” is not necessarily structural), campaign manager (claim 18), and retail server (claim 18). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of constructing, applying, selecting, learning, displaying, predicting, and communicating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using contextual multi-armed bandit (claims 1, 18, algorithm that merely implies machine learning), epsilon greedy agent (claims 1, 18, algorithm that merely implies machine learning), deep learning model (claims 1, 18, merely used in an “apply it” manner), memory (claim 18), publishing service (claim 18, a “service” is not necessarily structural), campaign manager (claim 18), and retail server (claim 18) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 00102-00107 and Figure 13); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 2-12, 19, and 20 appear to merely limit a deep learning agent including a customer “network” and an offer “network”, a deep learning agent including a common tower “network”, receiving interaction data with offers, specifics of the user features, updating weights in the deep learning model, utilizing a loss function, specifics of the expected reward, utilization of a hypervolume scalarization process, specifics of the plurality of offers, specifics of the interaction matrix data, and adding of offers to the plurality of offers, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The contextual multi-armed bandit (claims 1, 18, algorithm that merely implies machine learning), epsilon greedy agent (claims 1, 18, algorithm that merely implies machine learning), deep learning model (claims 1, 18, merely used in an “apply it” manner), memory (claim 18), publishing service (claim 18, a “service” is not necessarily structural), campaign manager (claim 18), and retail server (claim 18) are each functional generic computer components that perform the generic functions of constructing, applying, selecting, learning, displaying, predicting, and communicating, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the contextual multi-armed bandit (claims 1, 18, algorithm that merely implies machine learning), epsilon greedy agent (claims 1, 18, algorithm that merely implies machine learning), deep learning model (claims 1, 18, merely used in an “apply it” manner), memory (claim 18), publishing service (claim 18, a “service” is not necessarily structural), campaign manager (claim 18), and retail server (claim 18) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-12 and 18-20 are not patent eligible.
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, 4, 5, 10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048).
Regarding claim 1, Li teaches constructing an interaction matrix from historical user interactions with incentive offers (Paragraphs 0100, 0103-0104, historical contextual information and historical reward information); applying a contextual multi-armed bandit across a plurality of users and a plurality of offers to generate, a matrix of expected rewards across the plurality of offers for each of the plurality of users (Paragraphs 0041-0042, 0084-0088, MAB applied to users and offers/recommendations with rewards), wherein the contextual multi-armed bandit employs a deep learning model (Paragraphs 0041-0044, adaptive learning structure) to: across a plurality of iterations, select an offer from among the plurality of offers and approximate an expected reward for the offer (Paragraphs 0080-0081, different expected rewards for different actions); and learn from observed rewards generated by an environment to train a deep learning model to approximate an expected reward for each offer (Paragraphs 0051, 0066, 0083, 0132, training and updating the model parameters); based on the matrix of expected rewards associated with the plurality of users and the plurality of offers, select, for at least some of the plurality of users, one or more offers (Paragraphs 0065-0066, 0081-0082, 0107-0109, selection of a recommended action, which is an offer); and displaying at least one of the one or more offers to the given user (Paragraphs 0065, 0082, 0109, displaying the recommended action to a user).
Li does not appear to specify applying a non-negative matrix factorization to the interaction matrix to obtain an approximation of the interaction matrix including dense interaction data, the dense interaction data including user context features and offer features. However, Gharibi teaches applying a matrix factorization to the interaction matrix to obtain an approximation of the interaction matrix including dense interaction data, the dense interaction data including user context features and offer features (Abstract, Paragraphs 0149-0153, interaction matrix using interaction data of users and item purchases). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use matrix factorization since matrix factorization is an old and well known efficient tool used by recommender systems and machine learning for data analysis. While Gharbi does not specifically state that the disclosed matrix factorization is non-negative, non-negative matrix factorization is merely one type of matrix factorization that has been old and well known long before the filing of Applicant’s invention. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use non-negative matrix factorization since non-negative matrix factorization makes the data analysis simpler with less clutter while focusing on stronger data features.
Li does not appear to specify use of an epsilon-greedy agent. However, Yoon teaches using an epsilon-greedy algorithm in a recommender system using a multi-armed bandit approach (Paragraphs 0114, 0140). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use an epsilon-greedy agent since the epsilon-greedy is an old and well-known strategy that adds beneficial randomization into reinforcement learning problems.
Regarding claim 4, Li teaches receiving user interaction data with the at least one of the one or more offers as part of a subsequent set of historical interactions with one or more incentive offers (Paragraph 0100, all historical centric contextual information is used, meaning as subsequent interactions are added, they become historical interactions and are used).
Regarding claim 5, Li teaches the user features include at least one of: historical basket size, digital engagement level, and offer interaction history (Paragraphs 0071, 0087).
Regarding claim 10, Li teaches the plurality of offers includes a plurality of loyalty offers (Paragraph 0064, enticing a user to repeat business with a company represents a loyalty offer).
Regarding claim 12, Li teaches adding one or more offers to the plurality of offers, and wherein after the one or more offers are added, the contextual multi-armed bandit explores the one or more offers (Paragraph 0042, example given of an item-to-purchase, as new items are added for purchase and recommendation, the system would naturally explore these recommendations).
Li does not appear to specify use of an epsilon-greedy agent. However, Yoon teaches using an epsilon-greedy algorithm in a recommender system using a multi-armed bandit approach (Paragraphs 0114, 0140). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use an epsilon-greedy agent since the epsilon-greedy is an old and well-known strategy that adds beneficial randomization into reinforcement learning problems.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048), and further in view of Tang (U.S. Pub No. 2025/0156641).
Regarding claim 2, Li does not appear to specify the deep learning agent includes a customer network that handles processing of customer context features and an offer network processing offer features specific to each offer of the plurality of offers. However, Tang teaches the deep learning agent includes a customer network that handles processing of customer context features and an offer network processing offer features specific to each offer of the plurality of offers (Abstract). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to have a customer network and a offer network since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048), and further in view of Li-2 (U.S. Pub No. 2023/0153857).
Regarding claim 6, Li does not appear to specify updating weights within the deep learning model to improve subsequent approximations of expected rewards. However, Li-2 teaches updating weights within the deep learning model to improve subsequent approximations of expected values (Paragraph 0127). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to adjust weights since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Li does not appear to specify updating the weights utilizes a loss function representing a difference between the expected reward for the offer and an observed reward for the offer. However, Li-2 teaches updating the weights utilizes a loss function representing a difference between the expected reward for the offer and an observed reward for the offer (Paragraph 0127). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a loss function since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048), and further in view of Anon (Anonymous authors. "Optimal Scalarizations for Provable Multiobjective Optimization". 2023).
Regarding claim 8, Li does not appear to specify the expected reward corresponds to a scalarized reward value representative of reward values from a plurality of objectives. However, Anon teaches the expected reward corresponds to a scalarized reward value representative of reward values from a plurality of objectives (Pages 4-6, Section 2). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a scalarized reward since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, Li does not appear to specify the scalarized reward value is obtained from a hypervolume scalarization process. However, Anon teaches the scalarized reward value is obtained from a hypervolume scalarization process (Pages 4-6, Section 2). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a hypervolume scalarization process since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048), and further in view of Raina (U.S. Pub No. 2013/0159100).
Regarding claim 11, Li does not appear to specify the interaction matrix comprises a sparse matrix of customers and offers and includes, for each combination of a customer and an offer, a binary representation of whether the customer interacted with the offer. However, Raina teaches the interaction matrix comprises a sparse matrix of customers and objects s and includes, for each combination of a customer and an object, a binary representation of whether the customer interacted with the object (Paragraph 0048). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a sparse matrix with binary representations since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, Li teaches an interaction matrix captured from customer interaction data associated with a plurality of customers and a plurality of offers (); a deep neural network configured to implement a multi-armed bandit approach by: selecting an offer from among the plurality of offers (); predicting, via the deep neural network, an expected reward associated with a customer for the selected offer based on the set of customer features and the set of offer features (); a publishing service communicatively coupled to a campaign manager and configured to communicate one or more combinations of a customer and a selected offer to the campaign manager to be presented to a customer via a retail server ().
Li does not appear to specify a set of customer features and a set of offer features extracted from the sparse interaction matrix by non-negative matrix factorization. However, Gharibi teaches a set of customer features and a set of offer features extracted from the sparse interaction matrix by matrix factorization (Abstract, Paragraphs 0149-0153, interaction matrix using interaction data of users and item purchases). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use matrix factorization since matrix factorization is an old and well known efficient tool used by recommender systems and machine learning for data analysis. While Gharbi does not specifically state that the disclosed matrix factorization is non-negative, non-negative matrix factorization is merely one type of matrix factorization that has been old and well known long before the filing of Applicant’s invention. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use non-negative matrix factorization since non-negative matrix factorization makes the data analysis simpler with less clutter while focusing on stronger data features.
Li does not appear to specify a sparse interaction matrix captured from customer interaction data associated with a plurality of customers and a plurality of offers, wherein the sparse interaction matrix is stored in memory and includes, for each combination of a customer and an offer, a binary representation of whether the customer interacted with the offer. However, Raina teaches the interaction matrix comprises a sparse matrix of customers and objects s and includes, for each combination of a customer and an object, a binary representation of whether the customer interacted with the object (Paragraph 0048). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a sparse matrix with binary representations since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Li does not appear to specify use of an epsilon-greedy agent. However, Yoon teaches using an epsilon-greedy algorithm in a recommender system using a multi-armed bandit approach (Paragraphs 0114, 0140). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use an epsilon-greedy agent since the epsilon-greedy is an old and well-known strategy that adds beneficial randomization into reinforcement learning problems.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Li (U.S. Pub No. 2022/0198598) in view of Gharibi (U.S. Pub No. 2023/0300115), and further in view of Yoon (U.S. Pub No. 2018/0108048), and further in view of Raina (U.S. Pub No. 2013/0159100), and further in view of Li-2 (U.S. Pub No. 2023/0153857).
Regarding claim 20, Li does not appear to specify the deep neural network is retrained using a loss function corresponding to a difference between an observed reward and the expected reward predicted by the deep neural network. However, Li-2 teaches the deep neural network is retrained using a loss function corresponding to a difference between an observed reward and the expected reward predicted by the deep neural network (Paragraph 0127). It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a loss function since the claimed invention is merely a combination of old elements and the combination of each element merely would have performed the same function as it did separately and a person of ordinary skill in the art would have recognized that the results of the combination were predictable.
Novel/Non-Obvious Subject Matter
Claims 3 and 19 as currently written are allowable over prior art. However, the rejection under 35 U.S.C. 112b as well as the rejection under 35 U.S.C. 101 are currently pending and represent a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references are cited to further show the state of the art with respect to A.I. based content delivery systems:
U.S. Pub No. 2025/0156898 to Crabtree
Gallego, Victor, et al. "Applying machine learning in marketing: an analysis using the NMF and k-means algorithms." Information 15.7 (2024): 368.
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/MICHAEL BEKERMAN/Primary Examiner, Art Unit 3621
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