DETAILED ACTION
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 responsive to the original application filed on 12/21/2023. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/439,815 filed on 1/18/2023.
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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
generating, …, a latent representation of the input query: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a latent representation of an input query or question, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally generate a latent representation of a question or query by paraphrasing the question or query.
transforming, …, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of transforming a latent representation of an input query or question to generate a counterfactual, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally generate a counterfactual or “what if” example in response to a paraphrased version of a question, query, or input.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “receiving, by a trained generative machine learning model, an input query, wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations” and “by the trained generative machine learning model”.
The additional elements of “by a/the trained generative machine learning mode” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic generative ML model is broadly used to receive a query, generate a latent representation of the query, and transform the latent representation to generate a counterfactual. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving, by a trained generative machine learning model, an input query” is an insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
The additional element of “wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application, and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “by a/the trained generative machine learning mode” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic generative ML model is broadly used to receive a query, generate a latent representation of the query, and transform the latent representation to generate a counterfactual. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving, by a trained generative machine learning model, an input query” is an insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
The additional element of “wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the trained generative machine learning model comprises an autoencoder” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
applying a plausibility adjustment to the generated counterfactual to generate an adjusted, generated counterfactual: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of applying a plausibility adjustment to a counterfactual, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally adjust counterfactual information.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
transforming the latent representation using a Nearest Unlike Neighbor (NUN) technique: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of transforming a latent representation using a nearest unlike neighbor technique, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally transform data using a particular technique.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
transforming the latent representation using a latent interpolation technique: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of transforming a latent representation using a latent interpolation technique, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally transform data using a particular technique.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
transforming the latent representation using a gradient-based technique: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of transforming a latent representation using a gradient-based technique, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally transform data using a particular technique.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining, …, if a generated candidate for a counterfactual meets the predefined outcome criteria to determine if the generated candidate comprises a valid counterfactual: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining if a counterfactual meets outcome criteria, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “using a trained predictor” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
evaluating the generated counterfactual using one or more counterfactual evaluation measures: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating a counterfactual, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more counterfactual evaluation measures include at least one of: proximity, plausibility and validity” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the plurality of input observations comprises a plurality of Reinforcement Learning (RL) agent's observations” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 11-19
Claims 11-19 recite a system (step 1: a machine) using processing circuitry and storage media to perform the steps of claims 1-9, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-9, respectively.
Claim 20
Claim 20 recites non-transitory computer-readable storage media (step 1: a manufacture) using processing circuitry to perform the steps of claim 1, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and is thus rejected for the same reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 102
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 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1, 2, 6-9, 11, 12, and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (Yang et al., “Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation”, Jan. 18, 2021, arXiv:2101.06930v1, pp. 1-10, hereinafter “Yang”).
Regarding claim 1, Yang discloses [a] method for generating counterfactuals, the method comprising: (Abstract; “we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP)”; and §3.1; and Figure 1)
receiving, by a trained generative machine learning model, an input query, (§3.1; “Given a query instance x0, 𝐹𝝓(x0) = y0 outputs a one-hot vector. To effectively generate a valid counterfactual sample x∗ ∈ R𝑑 that can flip the 𝐹𝝓 decision to y∗ ∈ {1,··· ,𝐶} as desired, a generative model is trained to achieve this in the framework”, wherein x0 is the received input query and is input into the generative model’s encoder)
wherein the generative machine learning model is trained by jointly encoding a plurality of input observations and a plurality of outcome variables based on the plurality of input observations; (§3.1; “In our designed framework, data encoding is conducted to map the input data space to a low dimension attribute-informed latent space, which is formulated as ) AIP Counterfactual Sample a joint embedding space for both raw features and data attributes. In this way, each data sample x can be effectively encoded through the function 𝐺𝑒𝑛𝑐 𝝍 :R𝑑 →R𝑘 ⊕R𝑡,… Although𝐺𝑒𝑛𝑐 𝝍 and𝐺𝑑𝑒𝑐 𝝎 typically have two different focuses, they are jointly trained as a whole generative model in an end-to-end manner” (emphasis added), which discloses the generative machine learning model that is trained by jointly encoding a plurality of input observations or raw features and outcome variables or data attributes based on the input observations/raw features; and §4.1.1; the section defines “data attributes” or outcome variables as labels or annotations “collected either from labels or annotations” of the raw samples, which means that they are variables based on or derived from the observations)
generating, by the trained generative machine learning model, a latent representation of the input query; and (§3.1; “Assuming 𝐺𝑒𝑛𝑐 𝝍 (x0) = z0 ⊕ a0 (z0 ∈ R𝑘,a0 ∈ R𝑡), AIP method can jointly update z0 and a0, so as to minimize the corresponding loss counter factually”, wherein “z0 ⊕ a0” is interpreted as the latent representation of the input query x0)
transforming, by the trained generative machine learning model, the latent representation of the input query to generate a counterfactual related to the received input query, wherein the generated counterfactual meets a predefined outcome criteria (§3.1; “Assuming 𝐺𝑒𝑛𝑐 𝝍 (x0) = z0 ⊕ a0 (z0 ∈ R𝑘,a0 ∈ R𝑡), AIP method can jointly update z0 and a0, so as to minimize the corresponding loss counter factually”; and §3.3, Equation 5; “x∗=𝐺𝑑𝑒𝑐𝝎 argmin z∈R𝑘,a∈R𝑡 𝐿𝑐(z,a,z0,a0,y∗)”, where the predefined outcome criterion is the target label y∗; and Algorithm 1, Lines 4 and 10; “while𝐹𝝓(𝐺𝑑𝑒𝑐𝝎 (z,a))≠y∗or𝑛≤𝑛maxdo”, which discloses that the algorithm iterates until the criterion is satisfied, thus transforming the latent representation of the input query or x0 to generate a counterfactual).
Regarding claim 11, it is a system claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claim 20, it is a non-transitory computer-readable storage media claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claims 2 and 12, the rejection of claims 1 and 11 are incorporated and Yang further discloses wherein the trained generative machine learning model comprises an autoencoder (§4.2.1; “We employ a transformer based VAE to conduct the relevant data modeling”).
Regarding claims 6 and 16, the rejection of claims 1 and 11 are incorporated and Yang further discloses wherein transforming the latent representation of the input query further comprises transforming the latent representation using a gradient-based technique (§3.3; “the proposed AIP method utilizes an Iterative gradient-based optimization algorithm with dynamic step sizes”; and Algorithm 1; and Equation 6).
Regarding claims 7 and 17, the rejection of claims 1 and 11 are incorporated and Yang further discloses determining, using a trained predictor, if a generated candidate for a counterfactual meets the predefined outcome criteria to determine if the generated candidate comprises a valid counterfactual (Algorithm 1, Lines 4 and 9-12).
Regarding claims 8 and 18, the rejection of claims 1 and 11 are incorporated and Yang further discloses evaluating the generated counterfactual using one or more counterfactual evaluation measures (§4.3.1-4.3.3; Yang evaluates generated counterfactuals using the Flipping ratio, Latent Perturbation ratio, and generation time).
Regarding claims 9 and 19, the rejection of claims 1, 8, 11, and 18 are incorporated and Yang further discloses wherein the one or more counterfactual evaluation measures include at least one of: proximity, plausibility and validity (§4.3.1-4.3.2; Yang evaluates generated counterfactuals using the Flipping ratio (validity), Latent Perturbation ratio (proximity)).
Claim Rejections - 35 USC § 103
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 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
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 3 and 13 are rejected under 35 USC § 103 as being obvious over Yang in view of Van Looveren et al. (Van Looveren et al., “Interpretable Counterfactual Explanations Guided by Prototypes”, Feb. 18, 2020, arXiv:1907.02584v2, pp. 1-17, hereinafter “Van Looveren”).
Regarding claims 3 and 13, the rejection of claims 1 and 11 are incorporated and Yang fails to explicitly disclose but Van Looveren discloses applying a plausibility adjustment to the generated counterfactual to generate an adjusted, generated counterfactual (§4.2.2; “The aim of LAE in loss function B is not to speed up convergence towards a counterfactual instance, but to have xcf respect the training data distribution”, which discloses LAE or a plausibility-increasing adjustment that is applied to the generated counterfactual candidate to produce an adjusted generated counterfactual; and §3.1).
Yang and Van Looveren are analogous art because both are concerned with generating counterfactuals and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in counterfactual generation and machine learning to combine the plausibility adjustment of Van Looveren and the counterfactual generation method of Yang to yield to the predictable result of applying a plausibility adjustment to the generated counterfactual to generate an adjusted, generated counterfactual. The motivation for doing so would be to improve the time to find a counterfactual instance (Van Looveren; §4.2.2).
Claims 4 and 14 are rejected under 35 USC § 103 as being obvious over Yang in view of Keane et al. (Keane et al., “Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)”, Oct. 30, 2020, Lecture Notes in Computer Science ((LNAI,volume 12311)), pp. 1-15, hereinafter “Keane”).
Regarding claims 4 and 14, the rejection of claims 1 and 11 are incorporated and Yang fails to explicitly disclose but Keane discloses wherein transforming the latent representation of the input query further comprises transforming the latent representation using a Nearest Unlike Neighbor (NUN) technique (Page 4, ¶1; “Stated simply, this prolixity is reduced by using methods that find the minimal changes to the features of the test case that flip the prediction (i.e., the nearest unlike neighbor)”; and §2; “Intuitively, counterfactual explanations seem to provide better explanations than factual ones; in CBR-ese, nearest-unlike-neighbor (NUN) explanations are better than nearest like-neighbor (NLN) explanations”; and see generally §2).
Yang and Keane are analogous art because both are concerned with generating counterfactuals and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in counterfactual generation and machine learning to combine the nearest unlike neighbors technique of Keane and the counterfactual generation method of Yang to yield to the predictable result of wherein transforming the latent representation of the input query further comprises transforming the latent representation using a Nearest Unlike Neighbor (NUN) technique. The motivation for doing so would be to explain how a prediction might be changed (Keane; §1).
Claims 5 and 15 are rejected under 35 USC § 103 as being obvious over Yang in view of Barr et al. (Barr et al., “Counterfactual Explanations via Latent Space Projection and Interpolation”, Dec. 2, 2021, arXiv:2112.00890v1, pp. 1-21, hereinafter “Barr”).
Regarding claims 5 and 15, the rejection of claims 1 and 11 are incorporated and Yang fails to explicitly disclose but Barr discloses wherein transforming the latent representation of the input query further comprises transforming the latent representation using a latent interpolation technique (Abstract; “we introduce SharpShooter, a method for binary classification that starts by creating a projected version of the input that classifies as the target class. Counterfactual candidates are then generated in latent space on the interpolation line between the input and its projection”; and §3).
Yang and Barr are analogous art because both are concerned with generating counterfactuals and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in counterfactual generation and machine learning to combine the latent interpolation technique of Barr and the counterfactual generation method of Yang to yield to the predictable result of wherein transforming the latent representation of the input query further comprises transforming the latent representation using a latent interpolation technique. The motivation for doing so would be to provide for a gradient or optimization-based method that is three orders of magnitude faster than other optimization methods (Barr; Abstract).
Claim 10 is rejected under 35 USC § 103 as being obvious over Yang in view of Olson et al. (Olson et al., “Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning”, Jan. 29, 2021, arXiv:2101.12446v1, pp. 1-62, hereinafter “Olson”).
Regarding claim 10, the rejection of claim 1 is incorporated and Yang fails to explicitly disclose but Olson discloses wherein the plurality of input observations comprises a plurality of Reinforcement Learning (RL) agent's observations (Abstract; “we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action”; and Page 9; “Our generative model is trained using a training dataset � �1 𝒂1 𝒔𝑁 𝒂𝑁 of 𝑁 state-action pairs, where the action vectors 𝒂𝑖 are action distributions obtained from the trained agent as it executes its learned policy”).
Yang and Olson are analogous art because both are concerned with generating counterfactuals and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in counterfactual generation and machine learning to combine the reinforcement learning of Olson and the counterfactual generation method of Yang to yield to the predictable result of wherein the plurality of input observations comprises a plurality of Reinforcement Learning (RL) agent's observations. The motivation for doing so would be to generate counterfactual explanations for deep reinforcement learning (RL) agents (Olson; Abstract).
Conclusion
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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