DETAILED ACTION
This non-final rejection is responsive to communication filed June 18, 2024. Claims 1-20 are pending in this application.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 16, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 2 and 14 are objected to because of the following informalities: There appears to be a typo in the claims. The examiner believes the language “the balanced fair prediction as part of a recommendation” should be “the balanced fair prediction is part of a recommendation” Appropriate correction is required.
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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the “computer readable medium” is not limited to non-transitory embodiments. The broadest reasonable interpretation of a claim drawn to a computer readable medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable medium, particularly when the specification is silent. Because Applicant’s definition in the specification is open-ended (i.e. uses the language “such as for example”), it is not limited to non-transitory embodiments. The examiner suggests amending the claims to recite “a non-transitory computer readable medium.”
Claims 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed system does not include at least one hardware device, which is required to be a statutory system/machine. The examiner suggests adding a processor and memory to the claims.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12 and 13 recite: obtaining sensitivity representations of the sensitive-correlated information from the data; deriving a learned representation from the sensitivity representations; and generating a balanced fair prediction based on the learned representation. The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally, or manually with the aid of pen and paper, obtain sensitivity representations, derive a learned representation, and generate a balanced fair prediction.
This judicial exception is not integrated into a practical application. The step of “receiving data comprising sensitive-correlated information” is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. The limitations of obtaining by “using a plurality of neural networks trained in relation to a set of predetermined context features”; generating “from the recommendation system”; and “a processor of a computing system” are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Further, the limitation “using a plurality of neural networks trained…” also merely indicates a field of use or technological environment (neural networks) in which the judicial exception is performed and thus fails to add an inventive concept to the claims. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the recitations of “receiving data comprising sensitive-correlated information” is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed above, the recitations of a processor, a plurality of neural networks, and a recommendation system to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Further, the limitation “using a plurality of neural networks trained…” also merely indicates a field of use or technological environment (neural networks) in which the judicial exception is performed and thus fails to add an inventive concept to the claims. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Claims 2 and 14 recite the additional elements: wherein the balanced fair prediction as part of a recommendation provided by the recommendation system to a user. The judicial exception is not integrated into a practical application because the step of “wherein the balanced fair prediction as part of a recommendation provided by the recommendation system to a user” is mere data output recited at a high level of generality, and thus is insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation “wherein the balanced fair prediction as part of a recommendation provided by the recommendation system to a user” amounts to receiving or transmitting data over a network or presenting offers and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claims 3 and 15 recite the additional element: incorporating the balanced fair prediction as part of the data received by the recommendation system as the method is iterated. The judicial exception is not integrated into a practical application because the step of “incorporating the balanced fair prediction as part of the data received by the recommendation system as the method is iterated” is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation “incorporating the balanced fair prediction as part of the data received by the recommendation system as the method is iterated” amounts to receiving or transmitting data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claims 4 and 16 recite the additional elements: wherein the plurality of neural networks comprises a first neural network representative of user sensitive features, a second neural network representative of item sensitive features, and a third neural network representative of non-sensitive features. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the plurality of neural networks are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer. Further, the limitation “wherein the plurality of neural networks comprises a first neural network representative of user sensitive features, a second neural network representative of item sensitive features, and a third neural network representative of non-sensitive features” also merely indicates a field of use or technological environment (neural networks) in which the judicial exception is performed and thus fails to add an inventive concept to the claims. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer.
Claims 5 and 17 recite the additional element: wherein the first, second, and third neural networks comprise an identifiable variational autoencoder architecture. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the identifiable variational autoencoder architecture is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer. Further, the limitation “wherein the first, second, and third neural networks comprise an identifiable variational autoencoder architecture” also merely indicates a field of use or technological environment (neural networks) in which the judicial exception is performed and thus fails to add an inventive concept to the claims. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer.
Claims 6 and 18 recite the additional elements: wherein the set of predetermined context features is collected from a recommendation system. The judicial exception is not integrated into a practical application because the step of “wherein the set of predetermined context features is collected from a recommendation system” is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitation “wherein the set of predetermined context features is collected from a recommendation system” amounts to receiving or transmitting data over a network and is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Even when considered in combination, this additional element represents insignificant extra-solution activity, which does not provide an inventive concept.
Claims 7 and 19 recite the additional element: wherein the plurality of neural networks is configured to extract the sensitive-correlated information from the set of predetermined context features, wherein the sensitive-correlated information is represented by the sensitivity representations. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitation “wherein the plurality of neural networks is configured to extract the sensitive-correlated information from the set of predetermined context features, wherein the sensitive-correlated information is represented by the sensitivity representations” is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer. Further, the limitation “wherein the plurality of neural networks is configured to extract the sensitive-correlated information from the set of predetermined context features, wherein the sensitive-correlated information is represented by the sensitivity representations” also merely indicates a field of use or technological environment (neural networks) in which the judicial exception is performed and thus fails to add an inventive concept to the claims. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer.
Claims 8 and 20 recite: wherein the sensitivity representations comprise representations of user features, item features, and non-sensitive features. This limitation further describes the obtained sensitivity representations, and thus also falls under the grouping of a mental process. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 9 recites: wherein the learned representation comprises a balanced fair objective. This limitation further describes the derived learned representation, and thus also falls under the grouping of a mental process. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 10 recites: applying an adversarial learning strategy consisting of: determining whether the sensitivity representations satisfy at least one balanced fair criterion; and applying a balanced representation function to a subset of sensitivity representations that satisfy said at least one balanced fair criterion to obtain the learned representation, wherein the balanced representation function is configured to remove non-sensitive features and sensitive features from the sensitivity representations not satisfying said at least one balanced fair criterion. The broadest reasonable interpretation of these steps is that they fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally determine whether the sensitivity representations satisfy a balanced fair criterion; apply a balanced representation function to remove non-sensitive features and sensitive features from the sensitivity representations not satisfying criteria. Further, the applying a balanced representation function is a math concept.
The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim 11 recites: wherein said at least one balanced fair criterion is determined by minimizing the balanced fair objective. The broadest reasonable interpretation of this step is that it falls within the mental process groupings of abstract ideas because it covers concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. For example, a user can mentally determine a balanced fair criterion by minimizing the balanced fair objective. Further, this limitation represents a math concept. The judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Klein et al. (US 2022/0019868 A1) (‘Klein’).
With respect to claims 1, 12, and 13, Klein teaches a computer-implemented method, system and computer-readable medium for debiasing data received by or for training a recommendation system, the method comprising:
receiving data comprising sensitive-correlated information (paragraphs 19 and 24);
obtaining sensitivity representations of the sensitive-correlated information from the data using a plurality of neural networks trained in relation to a set of predetermined context features (paragraphs 19, 22, 24, and 42-43);
deriving a learned representation from the sensitivity representations (paragraphs 26, 28, 34, and 42-43); and
generating a balanced fair prediction from the recommendation system based on the learned representation (paragraphs 23, 28, 34, and 43).
With respect to claims 2 and 14, Klein teaches wherein the balanced fair prediction as part of a recommendation provided by the recommendation system to a user (paragraphs 28, 42, and 77).
With respect to claims 3 and 15, Klein teaches further comprising: incorporating the balanced fair prediction as part of the data received by the recommendation system as the method is iterated (paragraphs 41-42).
With respect to claims 4 and 16, Klein teaches, wherein the plurality of neural networks comprises a first neural network representative of user sensitive features, a second neural network representative of item sensitive features, and a third neural network representative of non-sensitive features (paragraphs 12, 16, 22, and 31).
With respect to claims 5 and 17, Klein teaches, wherein the first, second, and third neural networks comprise an identifiable variational autoencoder architecture (paragraphs 22 and 30).
With respect to claims 6 and 18, Klein teaches wherein the set of predetermined context features is collected from a recommendation system (paragraphs 14 and 16-17).
With respect to claims 7 and 19, Klein teaches wherein the plurality of neural networks is configured to extract the sensitive-correlated information from the set of predetermined context features, wherein the sensitive-correlated information is represented by the sensitivity representations (paragraphs 16, 24, 28, and 44).
With respect to claims 8 and 20, Klein teaches wherein the sensitivity representations comprise representations of user features, item features, and non-sensitive features (paragraphs 17 and 18).
With respect to claims 9, Klein teaches wherein the learned representation comprises a balanced fair objective (paragraphs 32, 34, and 40).
With respect to claim 10, Klein teaches wherein said deriving the learned representation from the sensitivity representations, further comprising: applying an adversarial learning strategy consisting of: determining whether the sensitivity representations satisfy at least one balanced fair criterion (paragraphs 30 and 42); and
applying a balanced representation function to a subset of sensitivity representations that satisfy said at least one balanced fair criterion to obtain the learned representation, wherein the balanced representation function is configured to remove non-sensitive features and sensitive features from the sensitivity representations not satisfying said at least one balanced fair criterion (paragraphs 16 and 28-32).
With respect to claim 11, Klein teaches wherein said at least one balanced fair criterion is determined by minimizing the balanced fair objective (paragraphs 22-23 and 50).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALICIA M WILLOUGHBY/ Primary Examiner, Art Unit 2156 August 28, 2026
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