Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 6/5/2026.
Claims 1-15, 17-20 are currently pending and have been examined. Claim 16 stands cancelled. Claims 1, 9, 14, 17 have been amended.
Claim Rejection - 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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-8 are directed to a process. Therefore, claims 1-8 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 1 recites at least the following limitations that are believed to recite an abstract idea:
receiving by a recommendation system, from a user, a first input response in reply to a first item bundle comprising a first set of items,
wherein the recommendation system comprises a conversation process, a bundling process, and a question process, wherein the conversation process, the bundling process, and the question process are based on an architecture, and wherein the conversation process, bundling process, and the question process are generated trained jointly in a training process using historical interaction data, the training process comprising:
Training the bundling process and the question process with a first-level reward to encourage recommendations and questions; and
Training the conversation process with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward;
updating a state model associated with the user to reflect the first input response in reply to the first item bundle;
applying the conversation process to the state model to determine an action type in response to receiving the first input response to the first item bundle;
based on the action type, applying the bundling method to the state model to generate a second item bundle different than the first item bundle;
providing the second item bundle to the user;
receiving, from the user, a second input response in reply to the second item bundle;
updating the state model based on the second input response; and
applying the conversation process to the state model to determine a second action type in response to receiving the second input response to the second item bundle;
based on the second action type, applying the question process to the state model to generate a question related to an attribute of an item in the second item bundle;
receiving a third input response in response to the question; and
updating the state model based on the third input response.
The above limitations recite the concept of item bundle recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-8 an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A user device
Machine-learning modules
Training the ML modules
A self-attentive encoder-decoder architecture
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. For example, claims 2-3 and 5-8 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claim 4, these claims are similar to the independent claims except that they recite the further additional elements of fine-tuning the ML modules. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. Therefore the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A user device
Machine-learning modules
Training the ML modules
A self-attentive encoder-decoder architecture
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claims 9-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 9-13 are directed to a machine. Therefore, claims 9-13 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 9 recites at least the following limitations that are believed to recite an abstract idea:
storing a state model comprising an item candidate pool and an attribute candidate pool;
a recommendation system comprising a conversation process, a bundling process, a question process, and a modeling process, wherein the conversation process, the bundling process, and the question process are based on an architecture, and wherein the conversation process, the bundling process, and the question process are trained jointly in a training process using historical interaction data, the training processes comprising:
training the bundling process and the question process with a first-level reward to encourage recommendations and questions; and
training the conversation process with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward;
the bundling process for generating a first item bundle comprising a set of items;
the modeling process for updating the state model to reflect an input response to the first item bundle;
the conversation process for:
determining, based on the state model, an action type in response to receiving the input response to the first item bundle; and
triggering the bundling process based on the action type being a recommendation action;
the bundling process further for generating and outputting, based on the action type, a second item bundle different than the first item bundle;
the modeling process for updating the state model to reflect a second input response to the second item bundle; and
the conversation process for determining a second action type based on the state model;
the question process generating a question related to an attribute of an item in the second item bundle based on the second action type; and
the modeling process updating the state model to reflect a third input response.
The above limitations recite the concept of item bundle recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 9-13 an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
a memory component
machine-learning modules comprising program code
training the ML modules
a self-attentive encoder-decoder architecture
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. For example, claims 10-13 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. Therefore the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
a memory component
machine-learning modules comprising program code
training the ML modules
a self-attentive encoder-decoder architecture
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claims 14-15; 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 14-15; 17-20 are directed to a process. Therefore, claims 14-15; 17-20 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claim 14 recites at least the following limitations that are believed to recite an abstract idea:
receiving by a recommendation system from a user, a first input response in reply to a first item bundle comprising a set of items,
wherein the recommendation system comprises a conversation process, a bundling process, and a question process, wherein the conversation process, the bundling process, and the question process are based on an architecture, and wherein the conversation process, the bundling process, and the question process are trained jointly in a training process using historical interaction data, the training process comprising:
training the bundling process and the question process with a first-level reward to encourage recommendations and questions; and
training the conversation process with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward;
updating a state model associated with the user to reflect the first input response in reply to the first item bundle;
applying the conversation process to the state model to determine a first action type in response to receiving the first input response to the first item bundle;
based on the first action type, applying the question process to the state model to generate a question related to one or more items in the first item bundle;
providing the question to the user;
updating the state model to reflect a second input response received from the user in reply to the question; and
applying the conversation process to the state model to determine a second action type in response to receiving the second input response;
based on the second action type, applying the bundling process to the state model to generate a second item bundle different from the first item bundle;
receiving, from the user, a second input response in reply to the second item bundle; and
updating the state model based on the second input response.
The above limitations recite the concept of item bundle recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 14-15, 17-20 an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A user device
Machine-learning modules
Training the machine learning modules
A self-attentive encoder-decoder architecture
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. For example, claims 15 & 18-20 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 17 these claims are similar to the independent claims except that they recite the further additional elements of fine-tuning the ML modules. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. Therefore the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A user device
Machine-learning modules
Training the machine learning modules
A self-attentive encoder-decoder architecture
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Interpretation
Examiner notes that the machine-learning modules recited in the claims are interpreted in light of Applicant’s Specification, e.g. [0021], [0001] to be machine learning models.
Allowable over Prior Art of Record
Claims 1-15, 17-20 are allowable over prior art though rejected on other grounds [e.g. 35 USC §101] as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1-15, 17-20 would be allowable if rewritten to overcome the rejections under 35 USC §101 as set forth in this Office Action, and to include all of the limitations of the base claim and any intervening claims.
Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1-15, 17-20 are allowable over prior art. The most related prior art patent of record is Parker et al (US 20200302506 A1), hereinafter Parker, Sadeh et al (US 20190108353 A1), hereinafter Sadeh, and Wuebker et al (US 10878201 B1), hereinafter Wuebker.
Parker teaches a recommendation system that recommends outfits consisting of a grouping/bundle of items [0099] that the user can provide feedback on, including about the outfit as a whole or about specific items therein [0025]. The system uses a trained machine learning model for generating the recommendations [0073], and provides the user with questions regarding different contexts of the outfit combination to improve accuracy of further recommendations [0103]. The system uses historical feedback and recommendation data to determine user preferences and train the model [0019/0041], and the user may provide text feedback that can be processed with NLP techniques to extract features [0067] that can be updated into a user profile [0052]. The ML model is applied adaptively to provide combination recommendations based on user feedback for each iteration [0045].
However, Parker does not teach at least that the ML conversation module, the ML bundling module, and the ML question module are based on a self-attentive encoder-decoder architecture and are trained jointly in a training process using historical interaction data, the training process comprising: training the ML bundling module and the ML question module with a first-level reward to encourage recommendations and questions; and training the ML conversation module with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward; or the ability to, based on the second action type, applying the ML question module to the state model to generate a question related to an attribute of an item in the second item bundle; receiving a third input response in response to the question; and Updating the state model based on the third input response.
Sadeh teaches recommendation systems where generating personalized questions to a user based on user responses/feedback to infer more information about the user’s stance on recommendations [0056], wherein an ML model is used to generate the questions [0060]. The recommendations can be presented as a group, allowing the user to modify the group and provide feedback [0055]. The user provides a text response to personalized questions, which is processed by the ML system to update the user-specific model for use in future recommendations [0056].
Wuebker similarly teaches a system for making personalized suggestions to a user, including jointly training all parts of a ML model end-to-end, and using a self-attentive encoder-decoder architecture to process natural language responses and suggestions [Col. 3].
However, these references, in combination, fail to teach at least that the joint training process comprises: training the ML bundling module and the ML question module with a first-level reward to encourage recommendations and questions; and training the ML conversation module with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward
Further relevant prior art includes Semarjian et al (US 20210182934 A1) which teaches machine-learned outfit recommendations, which allows users to provide answers to questions to capture user preferences on recommended items, and trains the ML model to minimize loss/error across multiple rounds to maximize certain outcomes; as well as Reference U (NPL -see attached) which teaches an ML recommendation engine that uses reinforcement learning to train the ML model with different reward levels for different goals/outcomes.
However, each of these references fail to disclose or render obvious at least the limitations of: a recommendation system comprising a machine-learning (ML) conversation module, an ML bundling module, and an ML question module, wherein the ML conversation module, the ML bundling module, and the ML question module are based on a self-attentive encoder-decoder architecture, and wherein the ML conversation module, the ML bundling module, and the ML question module are trained jointly in a training process using historical interaction data, the training process comprising: training the ML bundling module and the ML question module with a first-level reward to encourage recommendations and questions; and training the ML conversation module with a second-level reward to reflect a quality of a multi-round conversation as a whole, wherein the first-level reward is less than the second-level reward.
Ultimately, the particular combination of limitations as claimed, is not anticipated nor rendered obvious in view of the cited references, and the totality of the prior art. While certain references may disclose more general concepts and parts of the claim, the prior art available does not specifically disclose the particular combination of these limitations.
The references, however, do not teach or suggest, alone or in combination the claimed invention. Examiner emphasizes that the prior art/additional art 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 further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art as combining various references from the totality of evidence to reach the combination of features as claimed would be a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
It is thereby asserted by Examiner that, in light of the above and further deliberation over all of the evidence at hand, that the claims are allowable over prior art (though rejected under 35 USC §101) as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
Applicant’s arguments filed 6/5/2026 have been fully considered but are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that “the alleged abstract idea is integrated into a practical application,” specifically arguing that the claims provide “a technical improvement to the technical area of item bundle recommendations by an online platform,” stating that claim 1 includes three ML-based modules which are “trained jointly using historical interaction data for performing respective tasks. The ML bundling module and the ML question module are trained with low-level rewards to encourage useful recommendations and questions.” Applicant argues that “the combination of the claim features regarding the recited three ML modules and the multi-round conversational recommendation approach overcomes the drawbacks of the existing bundle recommendation systems, such as interaction sparsity and large output space, and improves the technology of item bundle recommendation with more accuracy and effectiveness,” with reference to the Specification to argue that a “one-shot approach for generative methods thus does not allow refining a bundle that has been rejected,” and that the invention “address[es] the interaction sparsity problem through the use of multiple machine- learning modules, each trained for a specific task (e.g., conversation, recommendation, or asking questions) so as to model a given user to provide better predictions” and “address[es] the large output space by forming bundles on demand based on refinements made through multiple rounds of conversation using these ML modules.” Applicant contends that the result is a claim that “improves the technology of item bundle recommendation with more accuracy and effectiveness,” with reference to Desjardins.
Examiner respectfully disagrees. The ability to jointly generate a conversational, recommendation/bundling, and questioning technique, or to jointly train entities to perform conversational, bundling, and questioning techniques is part of the abstract idea itself. The alleged improvements to accuracy and effectiveness are at best business improvement rooted solely in the abstract idea, i.e. in the ability to have three different entities perform three different tasks, and to update their training based on new information/feedback; whereas Desjardins provides a specific solution, rooted in computer technology, to a technological problem. Furthermore, the claims’ iterative nature, and its alleged improvement over the “one-shot approach,” is rooted in the abstract idea’s ability to update based on conversation/user feedback. Rather, the additional elements are invoked as mere instructions to apply the abstract idea to a technological environment, creating only a general linking between the abstract idea and computer technology [MPEP 2106.05(f)].
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.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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