DETAILED ACTION
This action is responsive to the amendment filed on 04/13/2026. Claims 2-3, 5-8, 12-13 and 15-18 have been cancelled and claims 1, 9, 11 and 19, have been amended. Claims 1, 4, 9-11, 14, 19-20 have been examined and are pending.
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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/03/2026 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 1 and 11 objected to because of the following informalities: the following minor typo: training an reconstructive model should read training a reconstructive model. Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 1 and 11 recites the limitation a reconstruction error of same said particular inference. There is insufficient antecedent basis for same said particular inference referenced in this limitation of the claims.
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, 4, 9-11, 14, 19-20 are rejected under 35 U.S.C. 101 because claims are directed to an abstract idea.
Regarding claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: inferring … a particular inference for a new tuple to explain that is based on the plurality of features which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user judging making a determination and inference of a tuple that was generated with a different value in one of the dimensions than other tuples. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: generating a plurality of random integers in a range that is based on a count of the plurality of training tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user creating a certain number of random integers. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: randomly selecting, based on the plurality of random integers, a plurality of perturbed values from values of the feature in the plurality of training tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user selecting perturbed values at random. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: generating a plurality of perturbed tuples…wherein each perturbed tuple of the plurality of perturbed tuples is based on same said new tuple to explain and a respective perturbed value of the plurality of perturbed values which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user generates with the aid of pen and paper a plurality of modified or changed tuples that are based on original fourth dimensional tuple data and the respective perturbed value for data interpretation or comparison. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: …inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining an outcome based on a tuple. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: measuring a respective difference between a reconstructive error of each perturbed inference of the plurality of perturbed tuples and a reconstruction error of same said particular inference which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining the difference between two outcomes. See 2106.04.(a)(2).III.C. Additionally, the BRI of the limitation encompasses Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C)).
The claim recites for each feature of the plurality of features, calculating a respective importance of the feature based on the differences measured for the feature which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining the relevance of each dimension of two tuple based comparing two respective outcomes of the two tuples. See 2106.04.(a)(2).III.C.
The claim recites generating, based on the importance of at least one numeric feature of the plurality of features…a local explanation of the particular inference for the new tuple to explain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses a user evaluating and generating an explanation (mental process of evaluate and/or explain) of an outcome where the explanation is how relevant each dimension of a tuple was to the outcome. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Training a reconstructive(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
with a plurality of training tuples that are based on a plurality of features; (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
the reconstructive model (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
performed by one or more computers (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
including concurrently generating two perturbed tuples of the plurality of perturbed tuples(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
and displaying(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (c) (d) and (f) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) (d) (e) and (f) in claim 1 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the local explanation comprises a ranking of at least two features of the plurality of features based on the importances of the at least two features which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses a user evaluating dimensional relevance to output of tuples and deciding the most important tuples via ranking said evaluated dimensional relevance of tuples. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 4 does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
the reconstructive model is unsupervised, and the plurality of training tuples are unlabeled(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Regarding claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
two execution contexts performing said concurrently generating (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Regarding claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: inferring … a particular inference for a new tuple to explain that is based on the plurality of features which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user judging making a determination and inference of a tuple that was generated with a different value in one of the dimensions than other tuples. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: generating a plurality of random integers in a range that is based on a count of the plurality of training tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user creating a certain number of random integers. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: randomly selecting, based on the plurality of random integers, a plurality of perturbed values from values of the feature in the plurality of training tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitations encompass a user selecting perturbed values at random. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: generating a plurality of perturbed tuples…wherein each perturbed tuple of the plurality of perturbed tuples is based on same said new tuple to explain and a respective perturbed value of the plurality of perturbed values which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user generates with the aid of pen and paper a plurality of modified or changed tuples that are based on original fourth dimensional tuple data and the respective perturbed value for data interpretation or comparison. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: …inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining an outcome based on a tuple. See 2106.04.(a)(2).III.C.
The claim recites for each feature of the plurality of features, for each training tuple in the plurality of training tuples, performing without further training: measuring a respective difference between a reconstructive error of each perturbed inference of the plurality of perturbed tuples and a reconstruction error of same said particular inference which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining the difference between two outcomes. See 2106.04.(a)(2).III.C. Additionally, the BRI of the limitation encompasses Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C)).
The claim recites for each feature of the plurality of features, calculating a respective importance of the feature based on the differences measured for the feature which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses the user determining the relevance of each dimension of two tuple based comparing two respective outcomes of the two tuples. See 2106.04.(a)(2).III.C.
The claim recites generating, based on the importance of at least one numeric feature of the plurality of features…a local explanation of the particular inference for the new tuple to explain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind with or without a physical aid. The limitation encompasses a user evaluating and generating an explanation (mental process of evaluate and/or explain) of an outcome where the explanation is how relevant each dimension of a tuple was to the outcome. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Training a reconstructive (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
with a plurality of training tuples that are based on a plurality of features; (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
the reconstructive model (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
including concurrently generating two perturbed tuples of the plurality of perturbed tuples(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
and displaying(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (c) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Additional elements (b) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) (b) (c) (d) and (e) in claim 11 do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Claim 14 is rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claim 4 found in claim 14.
Claims 19-20 are rejected under that same 101 claim analysis due to the substantially similarity of the limitations and additional elements of claims 9-10 found in claims 19-20 respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 9-11, 14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Parker et al(US20190122135A1 henceforth referred to as Parker) in view of Santa (StackOverflow answer, https://stackoverflow.com/questions/9470104/select-random-java-variable-is-this-possible) and further in view of Dalli et al(US20210133630A1 henceforth referred to as Dalli)
Regarding claim 1:
Parker teaches training a reconstructive model(Parker, [0082] “From block 316, process 300 moves to block 320, where the selected feature of the datapoint is modified to the new value, so as to generate a first synthetic datapoint from the selected original input datapoint” where the model of Parker constructing synthetic datapoints from parts of an original datapoint plus modified featured values corresponds to a reconstructive model(See also [0068], “As noted, in a practical system, there may be thousands of features and thousands of synthetic points, and thus machine learning is used to implement processes 100, 200 and 300”)) with a plurality of training tuples(Parker, [0143], “Each of data source 602 and input data 604 supplies data to dataset generator 610, which generates a training set 611 with which to train the model” where the training set generated is considered training tuples that is used to train the model) that are based on a plurality of features(Parker, [0091], “In some embodiments, detailed summary statistics for each feature in the data set used to train the model m are available. Specifically, given a set of training data, histograms may be constructed for each feature of the training data representing a discrete approximation of the distribution of each feature” where the training data with features is considered training tuples with features (See Also: Parker, Figure 6, 610 DATASET GENERATOR and 611 TRAINING DATASET))
Parker teaches inferring, by the reconstructive model after said training, a particular inference for a new tuple to explain(Parker, [0018], “…a prediction m(P) of m for P” where inference is understood to refer to the prediction or classification a machine learning model gives given an input and a prediction m(P) is considered an inference of a tuple. See [0104] for an example “Thus, the input data point P for this applicant is P=[100,000, 40,000, 5]” and Figure 6, 604 INPUT DATA) that is based on the plurality of features, (Parker, [0018],“ input data point P to m, P including one or more features,” where input data point P is a tuple of features which is considered a new tuple to explain that is based on the plurality of features)
Parker teaches for each feature of the plurality of features, for each training tuple in the plurality of training tuples(Parker, Figure 3 operational flow of a process for generating synthetic datapoints showing it can repeat over additional features of the selected datapoint as well as figure 6 with 626 to optionally iterate(See also Parker, [0081], “From block 310, process 300 moves to block 312, where either an input datapoint of the input dataset, or a feature of the input datapoint, or both, are selected. Initially both an input datapoint *and* a feature of that datapoint are selected. However, once that has occurred, and process 300 has moved through blocks 314, 316 and 320 to select a new value for the first selected feature, process 300 may return to block 312, to continue to select different features of the selected input datapoint, and proceed to select new values for those different features” and Parker, [0047-0052] , where the input vector(applicants training tuple) [48, M, 35] is input and each feature of the input vector are changed to create synthetic datapoints)) performing without further training(Parker, Figures 1-3 and 6 that show there is no further training done after the model is trained before or after execution of each block)
Parker teaches randomly selecting… a plurality of perturbed values(Parker, [0092], “With these histograms, in embodiments, a function draw(h[i]) may be specified, which draws a histogram bin with a probability corresponding to its frequency, and outputs a value in that bin (..for example…a random value within the bin)” where the randomly selecting from a value from a histogram bin to generate a set of perturbed input data points corresponds to randomly selecting…a plurality of perturbed values(See Also: Parker, Figure 6, 610 DATASET GENERATOR and 611 TRAINING DATASET as the input to 613 HISTOGRAMS and Parker, Figure 3, 308, 310 and 312 of selecting an input datapoint)) from values of the feature in the plurality of training tuples (Parker, [0046], “…to generate these modifications (synthetic datapoints), a system may, for example, leverage histogram-style summaries of the distributions for each of the features in the point (such as, for example, those shown in FIG. 4). Thus, to select a “different” value for a given feature, in embodiments, the system selects a value from the histogram bin other than the one containing the current value” where the selection of the perturbed values being chosen from a summary of distributions of each feature created from training data(See: [0091] “… given a set of training data, histograms may be constructed for each feature of the training data representing a discrete approximation of the distribution of each feature”) and selecting a value from the histogram bin randomly for one that is NOT the current value is considered randomly selecting perturbed values of features of the training tuples)
Parker teaches generating a plurality of perturbed tuples…wherein each perturbed tuple of the plurality of perturbed tuples (Parker, [0018], “create a set of perturbed input data points (Pk) from P by changing the value of at least one feature of P for each perturbed input data point” where creating a set of perturbed input data points (Pk) from P is considered generating a plurality of perturbed tuples based on a new tuple to explain; See also: Figure 3, illustrates an overview of the operational flow of a process for generating one or more synthetic datapoints, based on a single input datapoint) is based on same said new tuple to explain and a respective perturbed value of the plurality of perturbed values(Parker Figure 3, where the loop of 312 to 324 shows the generation of synthetic data based on a single input dataset (of tuples) P where the new/perturbed value is based on summaries of features of the input)
Parker teaches the reconstructive model inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples(Parker, [0018], “obtain a prediction m(Pk) for each of the perturbed input data points” where each of the predictions of (Pk) is considered an inference and finding a prediction m(Pk) for each of the perturbed input data points is considered inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples)
Parker teaches measuring a respective difference between a reconstruction error of each perturbed inference of the plurality of perturbed tuples and a reconstruction error of same said particular inference(Parker, [0089], “Measure the difference between the predictions m(p) and m(p_i) for each p_i” where the BRI of reconstruction error is the difference between an original input an it’s reconstructed version and measuring the difference between the predictions m(p) and m(p_i) for each p_i corresponds to measuring the difference(distance) between an original input(m(p)) and it’s reconstructed version(m(p_i)) in the prediction-space as m(p) is the models prediction of the original input p and m(p_i) is the model’s prediction for the perturbed/reconstructed with perturbed inputs p_i)
Parker teaches for each feature of the plurality of features, calculating a respective importance of the feature based on the differences measured for the feature; (Parker, [0035], “based on the analysis, it is determined which feature(s) is or are important to, or influential of, the model's prediction outcome”)
Parker teaches and generating, based on the importance of at least one numeric feature of the plurality of features, and displaying(Parker, Fig. 5 and [0099], “FIG. 5 illustrates an image of an example output prediction explanation for a sample prediction made by an example credit risk model, in accordance with various embodiments….The next column to the right of column 510 is “Importance” 505. This column depicts a bar that graphically demonstrates the importance of that feature to the model in making a credit risk prediction. At the far right of the bar, in column 507, a numerical percentage is provided that corresponds to the length of the bar” where Fig. 5 corresponds to GUI interface displaying a numeric and graphical representation of the important of features with a features that include a numeric) a local explanation of the particular inference for the new tuple to explain(Parker, [0035], “…where a report is generated and output to a user indicating which features were most influential in the model's prediction results” where a report that indicates which features were most influential to the model’s prediction is considered a local explanation)
Parker teaches wherein the method is performed by one or more computers(Parker, Abstract, “A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system”)
Parker does not teach, however Dalli discloses including concurrently generating two perturbed tuples of perturbed tuples(Dalli, [0051], “In an alternative embodiment, the fit 511 may be reconfigured to implement the inner functions in parallel or as an atomic…multiple local models which represent the individual partitions may be fitted concurrently” where 511 has the perturbation node, 514, and Dalli implementing 511 with parallelism and concurrently corresponds with concurrently generating perturbed tuples (511See also: Parker, [0028], “Still referring to exemplary FIG. 4, the illustrated system may take into account a variety of factors. For example, in the illustrated system, these factors may include a…number of concurrent applications”))
References Parker and Dalli are analogous art because they are from the field of endeavor directed towards explainable artificial intelligence techniques and explaining black-box machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Parker and Dalli before him or her, to modify the perturbation from an input data as disclosed of Parker to include the concurrency and parallel of Dali enhanced benefit of concurrency and parallelism as it allows multiple executions or simultaneous runs. The suggestion/motivation for doing so would have been, as Dalli states, Dalli, [0009], “A combination of multiple local models may be used in a global model creation process” and Dalli, [0051], “…multiple local models which represent the individual partitions may be fitted concurrently”
Parker does not teach, however Santa discloses generating a plurality of random integers in a range that is based on a count of the plurality of training tuples and randomly selecting based on the plurality of random integers (Santa, code for function getRandomString(), where r.nextInt()%3 is generating a random integer based on the count of the list and switch(i) is selecting a case based on the random integer generated)
References Parker and Santa are analogous art because they are from the field of endeavor of using technology and creating a list with random selection.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Parker and Santa before him or her, to modify the selection of the input data point of Parker to include the random selection with a list of Santa as it is a simple implementation with low complexity. The suggestion/motivation for doing so would have been, as Santa states, a simple method to randomly select to implement.
Regarding claim 4:
Parker-Santa teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Parker further teaches wherein the local explanation comprises a ranking of at least two features of the plurality of features based on the importances of the at least two features(Parker, [0018], “analyze the predictions m(Pk) for the perturbed input data points to determine which features are most influential to the prediction; and output the analysis results to a user” See also: Parker, [0099], where [0099] explains Figure 5’s breakdown of an example with 505 and 507 showing how important each of the four features 501 are.))
Regarding claim 9:
Parker-Santa-Dalli teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Parker further teaches wherein at least one selected from the group consisting of: the reconstructive model is unsupervised, and the plurality of training tuples are unlabeled(Parker, [0084], “… may even be an unsupervised model, such as a latent topic model”)
Regarding claim 10:
Parker-Santa-Dalli teaches the method of claim 1 (and thus the rejection of claim 1 is incorporated).
Dalli further discloses further comprising two execution contexts performing said concurrently generating(Dalli, [0051], “In an alternative embodiment, the fit 511 may be reconfigured to implement the inner functions in parallel or as an atomic…multiple local models which represent the individual partitions may be fitted concurrently” where 511 has the perturbation node, 514, and Dalli implementing 511 with parallelism and concurrently corresponds with concurrently generating perturbed tuples (See also: Dalli, [0028], “Still referring to exemplary FIG. 4, the illustrated system may take into account a variety of factors. For example, in the illustrated system, these factors may include a…number of concurrent applications”))
References Parker and Dalli are analogous art because they are from the field of endeavor directed towards explainable artificial intelligence techniques and explaining black-box machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Parker and Dalli before him or her, to modify the perturbation from an input data as disclosed of Parker to include the concurrency and parallel of Dali enhanced benefit of concurrency and parallelism as it allows multiple executions or simultaneous runs. The suggestion/motivation for doing so would have been, as Dalli states, Dalli, [0009], “A combination of multiple local models may be used in a global model creation process” and Dalli, [0051], “…multiple local models which represent the individual partitions may be fitted concurrently”
Regarding claim 11:
Parker teaches One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors(Parker, Abstract, “A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system”)
Parker teaches training a reconstructive model(Parker, [0082] “From block 316, process 300 moves to block 320, where the selected feature of the datapoint is modified to the new value, so as to generate a first synthetic datapoint from the selected original input datapoint” where the model of Parker constructing synthetic datapoints from parts of an original datapoint plus modified featured values corresponds to a reconstructive model(See also [0068], “As noted, in a practical system, there may be thousands of features and thousands of synthetic points, and thus machine learning is used to implement processes 100, 200 and 300”)) with a plurality of training tuples(Parker, [0143], “Each of data source 602 and input data 604 supplies data to dataset generator 610, which generates a training set 611 with which to train the model” where the training set generated is considered training tuples that is used to train the model) that are based on a plurality of features(Parker, [0091], “In some embodiments, detailed summary statistics for each feature in the data set used to train the model m are available. Specifically, given a set of training data, histograms may be constructed for each feature of the training data representing a discrete approximation of the distribution of each feature” where the training data with features is considered training tuples with features (See Also: Parker, Figure 6, 610 DATASET GENERATOR and 611 TRAINING DATASET))
Parker teaches inferring, by the reconstructive model after said training, a particular inference for a new tuple to explain(Parker, [0018], “…a prediction m(P) of m for P” where inference is understood to refer to the prediction or classification a machine learning model gives given an input and a prediction m(P) is considered an inference of a tuple. See [0104] for an example “Thus, the input data point P for this applicant is P=[100,000, 40,000, 5]” and Figure 6, 604 INPUT DATA) that is based on the plurality of features, (Parker, [0018],“ input data point P to m, P including one or more features,” where input data point P is a tuple of features which is considered a new tuple to explain that is based on the plurality of features)
Parker teaches for each feature of the plurality of features, for each training tuple in the plurality of training tuples (Parker, Figure 3 operational flow of a process for generating synthetic datapoints showing it can repeat over additional features of the selected datapoint as well as figure 6 with 626 to optionally iterate(See also Parker, [0081], “From block 310, process 300 moves to block 312, where either an input datapoint of the input dataset, or a feature of the input datapoint, or both, are selected. Initially both an input datapoint *and* a feature of that datapoint are selected. However, once that has occurred, and process 300 has moved through blocks 314, 316 and 320 to select a new value for the first selected feature, process 300 may return to block 312, to continue to select different features of the selected input datapoint, and proceed to select new values for those different features” and Parker, [0047-0052] , where the input vector(applicants training tuple) [48, M, 35] is input and each feature of the input vector are changed to create synthetic datapoints)) performing without further training(Parker, Figures 1-3 and 6 that show there is no further training done after the model is trained before or after execution of each block)
Parker teaches randomly selecting… a plurality of perturbed values(Parker, [0092], “With these histograms, in embodiments, a function draw(h[i]) may be specified, which draws a histogram bin with a probability corresponding to its frequency, and outputs a value in that bin (..for example…a random value within the bin)” where the randomly selecting from a value from a histogram bin to generate a set of perturbed input data points corresponds to randomly selecting…a plurality of perturbed values(See Also: Parker, Figure 6, 610 DATASET GENERATOR and 611 TRAINING DATASET as the input to 613 HISTOGRAMS and Parker, Figure 3, 308, 310 and 312 of selecting an input datapoint)) from values of the feature in the plurality of training tuples (Parker, [0046], “…to generate these modifications (synthetic datapoints), a system may, for example, leverage histogram-style summaries of the distributions for each of the features in the point (such as, for example, those shown in FIG. 4). Thus, to select a “different” value for a given feature, in embodiments, the system selects a value from the histogram bin other than the one containing the current value” where the selection of the perturbed values being chosen from a summary of distributions of each feature created from training data(See: [0091] “… given a set of training data, histograms may be constructed for each feature of the training data representing a discrete approximation of the distribution of each feature”) and selecting a value from the histogram bin randomly for one that is NOT the current value is considered randomly selecting perturbed values of features of the training tuples)
Parker teaches generating a plurality of perturbed tuples…wherein each perturbed tuple of the plurality of perturbed tuples (Parker, [0018], “create a set of perturbed input data points (Pk) from P by changing the value of at least one feature of P for each perturbed input data point” where creating a set of perturbed input data points (Pk) from P is considered generating a plurality of perturbed tuples based on a new tuple to explain; See also: Figure 3, illustrates an overview of the operational flow of a process for generating one or more synthetic datapoints, based on a single input datapoint) is based on same said new tuple to explain and a respective perturbed value of the plurality of perturbed values(Parker Figure 3, where the loop of 312 to 324 shows the generation of synthetic data based on a single input dataset (of tuples) P where the new/perturbed value is based on summaries of features of the input)
Parker teaches the reconstructive model inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples, (Parker, [0018], “obtain a prediction m(Pk) for each of the perturbed input data points” where each of the predictions of (Pk) is considered an inference and finding a prediction m(Pk) for each of the perturbed input data points is considered inferring a respective perturbed inference for each perturbed tuple of the plurality of perturbed tuples)
Parker teaches measuring a respective difference between a reconstruction error of each perturbed inference of the plurality of perturbed tuples and a reconstruction error of same said particular inference(Parker, [0089], “Measure the difference between the predictions m(p) and m(p_i) for each p_i” where the BRI of reconstruction error is the difference between an original input an it’s reconstructed version and measuring the difference between the predictions m(p) and m(p_i) for each p_i corresponds to measuring the difference(distance) between an original input(m(p)) and it’s reconstructed version(m(p_i)) in the prediction-space as m(p) is the models prediction of the original input p and m(p_i) is the model’s prediction for the perturbed/reconstructed with perturbed inputs p_i)
Parker teaches for each feature of the plurality of features, calculating a respective importance of the feature based on the differences measured for the feature; (Parker, [0035], “based on the analysis, it is determined which feature(s) is or are important to, or influential of, the model's prediction outcome”)
Parker teaches and generating, based on the importance of at least one numeric feature of the plurality of features, and displaying(Parker, Fig. 5 and [0099], “FIG. 5 illustrates an image of an example output prediction explanation for a sample prediction made by an example credit risk model, in accordance with various embodiments….The next column to the right of column 510 is “Importance” 505. This column depicts a bar that graphically demonstrates the importance of that feature to the model in making a credit risk prediction. At the far right of the bar, in column 507, a numerical percentage is provided that corresponds to the length of the bar” where Fig. 5 corresponds to GUI interface displaying a numeric and graphical representation of the important of features with a features that include a numeric) a local explanation of the particular inference for the new tuple to explain(Parker, [0035], “…where a report is generated and output to a user indicating which features were most influential in the model's prediction results” where a report that indicates which features were most influential to the model’s prediction is considered a local explanation)
Parker teaches wherein the method is performed by one or more computers(Parker, Abstract, “A non-transitory computer-readable medium including instructions, which when executed by one or more processors of a computing system”)
Parker does not teach, however Dalli discloses including concurrently generating two perturbed tuples of perturbed tuples(Dalli, [0051], “In an alternative embodiment, the fit 511 may be reconfigured to implement the inner functions in parallel or as an atomic…multiple local models which represent the individual partitions may be fitted concurrently” where 511 has the perturbation node, 514, and Dalli implementing 511 with parallelism and concurrently corresponds with concurrently generating perturbed tuples (511See also: Parker, [0028], “Still referring to exemplary FIG. 4, the illustrated system may take into account a variety of factors. For example, in the illustrated system, these factors may include a…number of concurrent applications”))
References Parker and Dalli are analogous art because they are from the field of endeavor directed towards explainable artificial intelligence techniques and explaining black-box machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Parker and Dalli before him or her, to modify the perturbation from an input data as disclosed of Parker to include the concurrency and parallel of Dali enhanced benefit of concurrency and parallelism as it allows multiple executions or simultaneous runs. The suggestion/motivation for doing so would have been, as Dalli states, Dalli, [0009], “A combination of multiple local models may be used in a global model creation process” and Dalli, [0051], “…multiple local models which represent the individual partitions may be fitted concurrently”
Parker does not teach, however Santa discloses generating a plurality of random integers in a range that is based on a count of the plurality of training tuples and randomly selecting based on the plurality of random integers (Santa, code for function getRandomString(), where r.nextInt()%3 is generating a random integer based on the count of the list and switch(i) is selecting a case based on the random integer generated)
References Parker and Santa are analogous art because they are from the field of endeavor of using technology and creating a list with random selection.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Parker and Santa before him or her, to modify the selection of the input data point of Parker to include the random selection with a list of Santa as it is a simple implementation with low complexity.
The suggestion/motivation for doing so would have been, as Santa states, a simple method to randomly select to implement.
Regarding claim 14:
The rejection of claim 11 incorporated in claim 14, and further, claim 14 is rejected under the same rationale as set forth in the rejection of claim 4.
Regarding claim 19:
The rejection of claim 11 incorporated in claim 19, and further, claim 19 is rejected under the same rationale as set forth in the rejection of claim 9.
Regarding claim 20:
The rejection of claim 11 incorporated in claim 20, and further, claim 20 is rejected under the same rationale as set forth in the rejection of claim 10.
Relevant Art
While not used is the current mapping the following was found to be relevant art:
Riccardo Guidotti, “Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from Latent Representations” and “Black Box Explanation by Learning Image Exemplars in the Latent Feature Space” as they discloses adversarial autoencoder with latent perturbations and feature/saliency explanation based on difference between generated exemplars and the original image.
Shalmali Joshi, “xGEMs: Generating Examplars to Explain Black-Box Models” as they perturb data samples and use exemplars to determine feature importance/saliency
Liat Antwarg, “Explaining Anomalies Detected by Autoencoders Using SHAP”
Response to Arguments
Applicant's arguments filed 04/13/2026 have been fully considered but they are not persuasive. A breakdown of arguments can be found below
103:
Applicant appears to argue on pages 6-7 that Parker does not disclose a reconstruction error or a reconstruction model.
Examiner respectfully disagrees as, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Applicant appears to be interpreting a narrower claim as the current claims do not positively recite the requirements or defining information of a reconstruction error. As the BRI of a reconstruction error is simply the difference between an original input an it’s reconstructed version Parker discloses this as Parker deconstructs an original tuple and constructs synthetic tuples based on the original tuple and compares the differences between the inferences of the original tuple inference and the synthetic tuple inferences Examiner believes this qualifies as a reconstruction error as claimed. See Parker, [0082] “From block 316, process 300 moves to block 320, where the selected feature of the datapoint is modified to the new value, so as to generate a first synthetic datapoint from the selected original input datapoint” that discloses a new synthetic datapoint constructed from an original input
101:
A:
Applicant appears to argue that “accelerated explanation generation” integrates the claims into a practical application through possible applications of interaction of the model(through a display) that could enhance customer experience through use with a support or sales agent.
Applicant respectfully disagrees as the claims do not positively recite display interaction beyond simple “displaying” and claims appear to recite a generic computer component to perform abstract ideas("apply it on a computer" (see MPEP 2106.05(f))) and fail to integrate into “significantly more” as claims that require a computer may still recite a mental process (please see MPEP 2106.04(a)(2).III.C). Further, displaying a generated local explanation is considered reciting a generic computer(displaying on a monitor) on which to display the focus of the claim’s abstract idea of generating… a local explanation of the particular inference for the new tuple to explain.
B:
Applicant appears to argue appears to argue the use of without further training and use a “reconstruction model” and “reconstruction error” with “measuring a respective difference between a respective reconstruction error of each perturbed inference…and a reconstruction error of the same said particular inference is…a reconstruction error” provides unconventionally performance and cites to the specification for support. Additionally, Applicant appears to argue that the kind of loss used may be “more or less” unconventional after training and the innovation of performing the measurement “without further training” is “innovative by itself as a new way to measure feature importance”.
Examiner respectfully disagrees as the pending claims are directed to a judicial exception due to reciting limitations which fall within the “mental processes” group of abstract ideas and “without further training” is not positively recited how the abstract ideas integrates the abstract ideas into significantly more or reflect how the lack of further training places claims into a practical application or significantly more. Further, the reconstruction error limitation of measuring a respective difference between a reconstructive error of each perturbed inference of the plurality of perturbed tuples and a reconstruction error of same said particular inference can be considered mental or mathematical under the BRI of the claims, thus the claim needs “significantly more” than the exception itself. A statement of the result being achieved by “unconventional means” is not enough and claims must recite the means, operation and how they transform the claim into a patent-eligible application rather than monopolizing the abstract idea(See 2106.05.I.(A).v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d))). The judicial exception is unable to be directed to significantly more than the judicial exception due to the pending claims not including additional elements that contribute to an “inventive concept”. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea.
C- D:
Applicant appears to argue that the exploration of input space instead of output space is unconventional and cites to differences between prior art Parker and Applicants invention.
Examiner respectfully disagrees as claims do not highlight or positively recite how the abstract ideas integrates the abstract ideas into significantly more or reflect how the exploration of input space places claims into a practical application or significantly more. A statement of the result being achieved by “unconventional means” is not enough and claims must recite the means, operation and how they transform the claim into a patent-eligible application rather than monopolizing the abstract idea(See 2106.05.I.(A).v. Adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (see MPEP § 2106.05(d))). The judicial exception is unable to be directed to significantly more than the judicial exception due to the pending claims not including additional elements that contribute to an “inventive concept”. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea.
Further, Applicant arguments are directed towards prior art of Parker rather than the 101 analysis. Arguments concerning the prior art of Parker are not pertinent to 101 analysis and therefor carry no weight as prior art is not taken into account in the Subject Matter Eligibility Test for Products and Process flowchart or MPEP guidelines.
E:
Applicant appears to argue that the use of two errors is innovative and that the reconstruction is a “special characteristic of the nonabstract reconstructive model” that facilitates innovative importance measurements by measuring differences between errors and calculating respective importance of features based on the differences.
Examiner respectfully disagrees as claims do not highlight or positively recite how the abstract ideas integrates the abstract ideas into significantly more or reflect how the use of two errors places the claims into a practical application or significantly more. The judicial exception is unable to be directed to significantly more than the judicial exception due to the pending claims not including additional elements that contribute to an “inventive concept”. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea.
F:
Applicant appears to argue a technological improvement of unconventional speed through use of measuring differences between errors and calculating respective importance of features based on the differences.
Examiner respectfully disagrees as Applicant does not cite limitations that are not mental, mere instructions, insignificant or extra-solution for claim 1 that provide the technological improvement that create a solution to technological problem. As the neural network is cited at a high level it is considered a generic computer component. An improvement in an abstract idea results in an improved abstract idea and not an improvement in technology or computer. Examiner notes MPEP 2106.05(a) which provides the requirements for how an improvement to the functioning of a computer or to any other technology or technical field is evaluated.
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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/C.J.J./ Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122
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