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 .
Response to Amendment
Acknowledgement is made of Applicant’s claim amendments on 01/29/2026. The claim amendments are entered. Presently, claims 1-6, 8, 10-15, 17, and 19-20 remain pending. Claims 1-2, 5, 8, 10-11, 14, 17, and 19-20 have been amended and claims 7, 9, 16, and 18 are cancelled.
Response to Arguments
Regarding the 35 U.S.C 103 rejection, Applicant’s arguments, see pages 17-20 of remarks, filed 01/29/2026, with respect to claims 1, 10, and 19 have been fully considered and are persuasive. The 35 U.S.C 103 rejection of claims 1-6, 8, 10-15, 17, and 19-20 has been withdrawn. In particular, the cited references to not teach or suggest “computing, by the at least one processor, a plurality of at least one causal counterfactual inputs by using the selected list and the determined at least one causal model, wherein the computing of the plurality of causal counterfactual inputs include generating, via the at least one causal model, a plurality of data points based on an output from the selected list of the plurality of model features, and wherein each of the plurality of data points reflect causal relationships between the plurality of causal counterfactual inputs; generating, via the at least one machine learning model executed by the at least one processor, at least one prediction on causal counterfactuals by using the plurality of at least one causal counterfactual inputs input and the at least one model; verifying, by the at least one processor, that the at least one prediction corresponds to the counterfactual outcome, wherein the verifying includes: inputting each of the plurality of causal counterfactual inputs into the at least one machine learning model to determine whether it results in: a flipped causal outcome to indicate an agreement between causally unaware and causally aware outcomes, or a non-flipped causal outcome to indicate a disagreement; and when the disagreement is indicated in the verifying, re-evaluating, via the at least one machine learning model and incorporating one or more constraints, a corresponding model feature among the list of the plurality of model features selected until the disagreement is minimized”.
Regarding the 35 U.S.C 101 rejection, Applicant's arguments filed 01/29/2026 have been fully considered but they are not persuasive.
Applicant argues: The claimed invention provides an improvement in computational performance by verifying a prediction generated by a machine learning model by inputting each of the plurality of causal counterfactual inputs into the at least one machine learning model to determine whether it results in: a flipped causal outcome to indicate an agreement between causally unaware and causally aware outcomes, or a non-flipped causal outcome to indicate a disagreement. Moreover, aspects of the present disclosure provide that when the disagreement is indicated in the verifying, re-evaluating, via the at least one machine learning model and incorporating one or more constraints, a corresponding model feature among the list of the plurality of model features selected until the disagreement is minimized (pages 16-17 of remarks).
Examiner response: Examiner respectfully disagrees. Verifying whether a prediction corresponds to a counterfactual outcome is a mental process. For example, para [0083] of Applicant’s specification describe the counterfactual inputs as datapoints “using "(x, x')", or just "(x')", a concise list of features that explains the decision "y(x')" may be offered” and further describes the counterfactual outcome as “y(x')”. Based on this description of the counterfactual inputs and outcomes, a human can verify whether a prediction from a ML model corresponds to a counterfactual outcome "y(x')". A human can further determine whether the counterfactual inputs result in a flipped causal outcome indicating an agreement between casually unaware outcomes and causally aware outcomes or a non-flipped outcome indicating a disagreement. For example, para [0098]-[0099] of Applicant’s specification describes determining whether a outcome label should be flipped based on a disagreement score. MPEP 2106.05(a)(II) states “[h]owever, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Verifying whether a counterfactual input results in a flipped or non-flipped outcome is an abstract idea. Furthermore, a human can re-evaluate a feature from a list of features until a disagreement is minimized by incorporating a constraint. For example, para [0098] of Applicant’s specification describes the re-evaluating as incorporating a constraint as either a score or penalty such that the penalty will lead the algorithm to re-evaluate the selection of key model features until the disagreement is minimized. Re-evaluating via at least one machine learning model is mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f). In addition, selecting a plurality of model features that explain a counterfactual income is a mental process. Using a ML model to select the features is mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f). Similarly, generating a prediction using counterfactual inputs via a ML model is mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f). Thus, the claim does not recite additional elements that integrate into a practical application and amount to significantly more than the judicial exception. Arguments are not persuasive.
Claim Objections
Claims 3 and 12 are objected to because of the following informalities: Claims 3 and 12 recite “determining, by the at least one processor, at least one constraint based on the generated combined score, the at least one constraint relating to a penalty that facilitates re-evaluation of feature attribution performance for each of the plurality of model features; and iteratively refining, by the at least one processor, the selection of the list of the plurality of model features by incorporating the determined at least one constraint”. Claim 3 should recite “determining, by the at least one processor, the one or more constraints based on the generated combined score, the the one or more constraints relating to a penalty that facilitates re-evaluation of feature attribution performance for each of the plurality of model features; and iteratively refining, by the at least one processor, the selection of the list of the plurality of model features by incorporating the determined one or more constraints”. 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.
Claims 1-6, 8, 10-15, 17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-6 and 8 are directed to a method and claims 10-15 and 17 are directed to a device comprising at least a processor, and 19-20 is directed to a non-transitory computer readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Claim 1 recites:
Step 2A, prong 1
“determining, by the at least one processor, at least one causal model by using a corresponding causal graph and raw data, the causal graph relating to a description of at least one relationship between a plurality of covariates” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a causal model relating to relationships between covariates based on a causal graph and raw data (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“selecting, via at least one machine learning model executed by the at least one processor, a list of a plurality of model features that explains a counterfactual outcome” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select a list of features (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“computing, by the at least one processor, a plurality of causal counterfactual inputs by using the selected list and the determined at least one causal model, wherein the computing of the plurality of causal counterfactual inputs include generating, via the at least one causal model, a plurality of data points based on an output from the selected list of the plurality of model features, and wherein each of the plurality of data points reflect causal relationships between the plurality of causal counterfactual inputs” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.).
“generating, via the at least one machine learning model executed by the at least one processor, at least one prediction on causal counterfactuals by using the plurality of causal counterfactual inputs” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can make a prediction based on counterfactual inputs and a model (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“verifying, by the at least one processor, that the at least one prediction corresponds to the counterfactual outcome, wherein the verifying includes: inputting each of the plurality of causal counterfactual inputs into the at least one machine learning model to determine whether it results in: a flipped causal outcome to indicate an agreement between causally unaware and causally aware outcomes, or a non-flipped causal outcome to indicate a disagreement” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can verify a prediction corresponds to an outcome. A human can further determine whether a counterfactual input is an agreement or a disagreement (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“when the disagreement is indicated in the verifying, re-evaluating, via the at least one machine learning model and incorporating one or more constraints, a corresponding model feature among the list of the plurality of model features selected until the disagreement is minimized” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can verify a prediction corresponds to an outcome. A human can re-evaluate a model feature using a constraint to minimize a disagreement (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, prong 2
“determining, by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See 2106.05(f).)
“selecting, via at least one machine learning model executed by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See 2106.05(f).)
“computing, by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“generating, via the at least one machine learning model executed by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“verifying, by the at least one processor, …, wherein the verifying includes: inputting each of the plurality of causal counterfactual inputs into the at least one machine learning model to determine…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“when the disagreement is indicated in the verifying, re-evaluating, via the at least one machine learning model,” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The additional elements do not integrate into a practical application.
Step 2B
“determining, by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“selecting, via at least one machine learning model executed by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“computing, by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“generating, via the at least one machine learning model executed by the at least one processor…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“verifying, by the at least one processor, …, wherein the verifying includes: inputting each of the plurality of causal counterfactual inputs into the at least one machine learning model to determine…” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
“when the disagreement is indicated in the verifying, re-evaluating, via the at least one machine learning model,” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 2 recites:
Step 2A, prong 1
“when the disagreement is indicated in the verifying: computing, by the at least one processor, a disagreement score for each of a plurality of causal outcomes, each of the plurality of causal outcomes corresponding to at least one data point” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.).
“aggregating, by the at least one processor, the disagreement score from each of the plurality of causal outcomes to generate a combined score that represents all adversely affected data points” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can combine scores into a single score by summing them (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, prong 2
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The additional elements do not integrate into a practical application.
Step 2B
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 3 recites:
Step 2A, prong 1
“determining, by the at least one processor, at least one constraint based on the generated combined score, the at least one constraint relating to a penalty that facilitates re-evaluation of feature attribution performance for each of the plurality of model features” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine score based on at least one constraint (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“iteratively refining, by the at least one processor, the selection of the list of the plurality of model features by incorporating the determined at least one constraint” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can iteratively update a list of features using a constraint (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, prong 2
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The additional elements do not integrate into a practical application.
Step 2B
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 4 recites:
Step 2A, prong 1
“wherein the at least one causal model corresponds to a functional expression for joint distribution of all variables that factorize into a plurality of marginals, the at least one causal model including a plurality of marginal probability equations that are determined by using the causal graph based on at least one parameter that is estimated from the raw data” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.).
Step 2A, Prong 2 & 2B
This claim does not recite any additional elements.
Claim 5 recites:
Step 2A, prong 1
“computing, by the at least one processor using at least one type of machine learning algorithm, the plurality of models based on the raw data” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.).
“identifying, by the at least one processor for each of the plurality of models, at least one input data point from the raw data that is associated with the counterfactual outcome” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can identify input data point associated with a counterfactual outcome for each model (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“determining, by the at least one processor using at least one predetermined feature attribution procedure, at least one model explanation for each of the at least one input data point, the at least one model explanation including the plurality of model features” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine an explanation for a data point comprising a plurality of model features (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“selecting, by the at least one processor using the at least one predetermined feature attribution procedure, the list of the plurality of model features from the at least one machine learning model according to at least one criterion” (This step is a recitation of a mental process that is practical to perform in the human mind. A select a list of features using a feature attribution procedure (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
Step 2A, prong 2
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The additional elements do not integrate into a practical application.
Step 2B
“by the at least one processor” (mere instructions to apply the judicial exception using a generic computer component. See MPEP 2106.05(f).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 6 recites:
Step 2A, prong 1
Claim 6 recites at least the abstract idea identified above in claim 5.
Step 2A, prong 2
“wherein the at least one criterion includes at least one from among a predetermined stopping criterion and at least one determined constraint” (Linking the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).)
The additional elements do not integrate into a practical application.
Step 2B
“wherein the at least one criterion includes at least one from among a predetermined stopping criterion and at least one determined constraint” (Linking the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 8 recites:
Step 2A, prong 1
Claim 8 recites at least the abstract idea identified above in claim 1.
Step 2A, prong 2
“wherein the output from the selected list of the plurality of model features is used as potential interventions” (Linking the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).)
The additional elements do not integrate into a practical application.
Step 2B
“wherein the output from the selected list of the plurality of model features is used as potential interventions” (Linking the judicial exception to a particular technological environment or field of use. See MPEP 2106.05(h).)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 10 recites: See the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “a computing device configured to implement an execution of a method for identifying causal recourse for explanations of a plurality of models, the computing device comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory” (mere instructions to apply the exception using a generic computer component).
Claim 11 recites: See the rejection of claim 2 above. Same rationale applies.
Claim 12 recites: See the rejection of claim 3 above. Same rationale applies.
Claim 13 recites: See the rejection of claim 4 above. Same rationale applies.
Claim 14 recites: See the rejection of claim 5 above. Same rationale applies.
Claim 15 recites: See the rejection of claim 6 above. Same rationale applies.
Claim 17 recites: See the rejection of claim 8 above. Same rationale applies.
Claim 19 recites: See the rejection of claim 1 above. Same rationale applies.
Claim 20 recites:
Step 2A, prong 1
“when the disagreement is indicated during the verification: compute a disagreement score for each of a plurality of causal outcomes, each of the plurality of causal outcomes corresponding to at least one data point” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.).
“aggregate the disagreement score from each of the plurality of causal outcomes to generate a combined score that represents all adversely affected data points” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can combine scores into a single score by summing them (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“determine at least one constraint based on the generated combined score, the at least one constraint relating to a penalty that facilitates re-evaluation of feature attribution performance for each of the plurality of model features” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine score based on at least one constraint (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
“iteratively refine the selection of the list of the plurality of model features by incorporating the determined at least one constraint” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can iteratively update a list of features (i.e. observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.).
2A Prong 2 & 2B
The claim does not recite any additional elements.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY K NGUYEN whose telephone number is (571)272-0217. The examiner can normally be reached Mon - Fri 7:00am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at 5712723768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HENRY NGUYEN/Examiner, Art Unit 2121
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