Notice of Pre-AIA or AIA Status
This Final communication is in response to Application No. 18/500,965 filed on 11/02/2023. 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
The amendments filed 8/3/2026 have been entered. They provide amendments to claims 1, 4, 8, 11, and 15-21. The amendments overcome the 112b rejection.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C § 101 filed 8/3/2026 have been fully considered but they are not persuasive.
Applicant argues the claims do not recite a judicial exception. The examiner respectfully disagrees. MPEP 2106.04(a) states that a claim recites an abstract idea if a limitation(s) falls within one of the categories of abstract idea. The limitations “generate an initial prediction”, “performing explainable AI (XAI) analysis using approximate method to identify influential features”, “determining key features based on the identified influential features”, “generate an initial set of counterfactuals” and “determining whether the prioritized set of counterfactuals has reached convergence by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold” can be performed by a human in their mind or using pen and paper. A human could generate initial prediction. Having an AI model create the predictions does not make it any less a mental process. A human could perform XAI analysis. Claim 4 describes performing the XAI analysis as creating a dataset, making predictions (using an AI model), assigning weights to samples based on similarities, and applying linear regression. All of which a human could do in their mind or with pen and paper. A human looking at identified influential features could determine key features. A human, with pen and paper, could generate counterfactuals. Finally, a human could calculate delta comparison and determine the comparison is below a threshold to determine if the set of counterfactuals has converged. Thus the claims do recite a judicial exception.
Applicant argues claims are integrated into a practical application. The examiner respectfully disagrees. Applicant argues the claims are improving a computer or another technology or technical field. According to MPEP 2106.05(a) the improvement cannot come from the judicial exception alone. The additional elements are “receiving an input query that includes query parameters and auxiliary data;”, “applying the received input query to an Al model”, “applying the determined key features to a modified counterfactual model having a modified counter factual algorithm”, “prioritizing the generated initial set of counterfactuals;” and “outputting an enhanced set of counterfactuals in response to determining that the prioritized set of counterfactuals has reached convergence”. How these limitations alone or in combination show an improvement is unclear.
Applicant argues the claims recite significantly more than the judicial exception. The examiner disagrees. The additional elements mentioned above amount to receiving data, applying the data to an AI model, applying determined features to a modified model (the claims do not describe how the model is modified), prioritizing certain counterfactuals (ordering data), and outputting the result. Even in combination these additional elements amount to applying the judicial exception and insignificant extra solution activity.
Thus, the 101 rejection is maintained.
Applicant’s arguments with respect to 35 U.S.C § 103 filed 8/3/2026 have been fully considered but they are not persuasive.
Applicant argues that prior art reference Mothilal does not teach claims 2, 9, and 16. Mothilal validation is based on validating the counterfactuals. However, Mothilal also teaches adding constraints to the inputs and features. In it broadest reasonable interpretation this is ensuring the input data follows predefined validation rules. Applicant also argues Mothilal does not teach claims 3, 10, and 17. Mothilal teaches validating the generated counterfactuals as shown in sections 4.1 and also 5.1.1 where it is done in the experiments. Spooner teaches the prioritized set of counterfactuals. It would have been obvious to come these to validate the prioritized set of counterfactuals. Applicant also argues Mothilal does not teach claims 5, 12 and 19. Mothilal shows that sparsity is adjustable and can be optimized for different conditions whether that is high or low sparsity.
Applicant argues that Geada does not teach claims 6, 13, 20. The examiner respectfully disagrees. Geada teaches generating counterfactuals based on constraints and boundaries (changing these constraints and boundaries give alternative counterfactuals) that are compliant with General Data Protection Regulation.
Applicant’s arguments with respect to newly added limitations and prior art references Spooner and Ribeiro have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly
more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-21 are within the four statutory (a process, machine, manufacture or composition of
matter.) Claims 1-7 describe a process and 8-21 describes a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
generate an initial prediction; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be done manually by an individual.)
performing explainable Al (XAI) analysis using approximate methods to identify influential features; (This is an abstract idea of a "Mental Process." The "performing" & “identifying” step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. Both of these could be done manually by an individual.)
determining key features based on the identified influential features; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be done manually by an individual.)
generate an initial set of counterfactuals; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be done manually by an individual.)
determining whether the prioritized set of counterfactuals has reached convergence by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features t the modified counterfactual model and determining that convergence has been reaches in response to determining that the delta comparison is below a predefined threshold; and (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
receiving, by a processing system including at least one processor, an input query that includes query parameters and auxiliary data; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
applying the received input query to an Al model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
applying the determined key features to a modified counterfactual model having a modified counterfactual algorithm (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
prioritizing the generated initial set of counterfactuals; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
outputting an enhanced set of counterfactuals in response to determining that the prioritized set of counterfactuals has reached convergence. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional elements “receiving…”, “prioritizing…”, and “outputting…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “applying the received input query to an Al model” and “applying the determined key features to a modified counterfactual model” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, recites an additional abstract idea:
performing validation analysis on the query parameters to determine conformance to a standard or constraint or to ensure that the input query and its subsequent results follow predefined validation rules and guidelines. (This is an abstract idea of a "Mental Process." The "performing validation analysis" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: claim 2 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 2 does not recite an additional element.
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, recites an additional abstract idea:
validating the prioritized set of counterfactuals to determine conformance to a standard or constraint or to ensure that the prioritized set of counterfactuals follow predefined validation rules and guidelines. (This is an abstract idea of a "Mental Process." The "validating" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The validation could be made manually by an individual.)
Step 2A Prong 2: claim 3 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 3 does not recite an additional element.
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, recites an additional abstract idea:
applying a linear regression model to the perturbed samples using the assigned weights. (This is an abstract idea of a "Mental Process." The "linear regression model" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
make predictions on the perturbed samples; (This is an abstract idea of a "Mental Process." The "make predictions" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
creating a new dataset by perturbing features of the generated initial predictions that need to be explained; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
using a trained Al model; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
assigning weights to the perturbed samples based on their similarity to the generated initial predictions; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “creating a new dataset…” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “using a trained AI…” and “assigning weights…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to a counterfactual model with relaxed sparsity to generate the initial set of counterfactuals. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, recites an additional abstract idea:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to identify alternative less-sensitive queries and generate a list of alternative queries that are compliant with California Consumer Privacy Act (CCPA) or General Data Protection Regulation (GDPR). (This is an abstract idea of a "Mental Process." The "modified counterfactual algorithm" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: claim 6 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 6 does not recite an additional element.
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 1, recites an additional abstract idea:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to generate counterfactuals with different combinations of categorical data types. (This is an abstract idea of a "Mental Process." The "modified counterfactual algorithm" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: claim 7 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 7 does not recite an additional element.
Therefore, claim 7 is ineligible.
With respect to claim 8:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 8. Therefore, claim 8 is ineligible.
With respect to claim 9:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 9. Therefore, claim 9 is ineligible.
With respect to claim 10:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 10. Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
With respect to claim 19:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible.
With respect to claim 21:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 21. Therefore, claim 21 is ineligible.
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 1, 4, 8, 11, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Spooner (US 2022/0374782 A1) in view of Ribeiro (NPL: ‘ “Why Should I Trust You?” Explaining the Predictions of Any Classifier”(2016)) and Cai (US 2020/0342265 A1).
Regarding claim 1, Spooner teaches:
A method of using artificial intelligence (AI) to make decisions or predictions without direct human intervention, the method comprising: ([0006] “According to an aspect of the present disclosure, a method for generating a counterfactual explanation for an artificial intelligence (AI) regression model is provided.”)
receiving an input query that includes query parameters and auxiliary data; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
applying the received input query to an Al model to generate an initial prediction; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
applying the determined key features to a modified counterfactual model having a modified counterfactual algorithm to generate an initial set of counterfactuals; ([0076] “At step S406, the counterfactual explanations generation module 302 defines a set of candidate counterfactual potential functions. In an exemplary embodiment, the set of candidate counterfactual potential functions is defined based on exponential-polynomial functions of the user-selected input value.”)
prioritizing the generated initial set of counterfactuals; ([0078] “At step S410, an optimum counterfactual explanation is generated based on a result of the optimization. In an exemplary embodiment, the optimum counterfactual explanation corresponds to a result of performing a differentiable continuous mapping between the respective output values of the surrogate model function and a real line over a predetermined subset of the set of real numbers so as to ensure that the designated input value selected by the user corresponds to the maximum value of the user-selected counterfactual potential function.”)
determining whether the prioritized set of counterfactuals has reached convergence by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; and ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
outputting an enhanced set of counterfactuals in response to determining that the prioritized set of counterfactuals has reached convergence. ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
Spooner does not teach:
performing explainable Al (XAI) analysis using approximate methods to identify influential features;
determining key features based on the identified influential features
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold;
Ribeiro teaches:
performing explainable Al (XAI) analysis using approximate methods to identify influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
determining key features based on the identified influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
Spooner and Riberio are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio. One would want to do this to get insight Nito predictions (Riberio conclusion).
Cai teaches:
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; ([0027] “According to the process 100, the computing system determines whether to end the iterations based on whether stopping criteria are met. The stopping criteria includes two parts, which are represented by blocks 108 and 114. At block 108, the computing system determines whether the model performance satisfies a first criteria. According to an example, the computing system determines whether a difference between a validation score calculated at block 106 of the present iteration and a validation score calculated at block 106 of a previous iteration satisfies a first threshold.” And [0029] “If the first criteria is satisfied, at block 112, the computing system updates the convergence value by incrementing the convergence value. Then, at block 114, the computing system determines whether a second criteria is satisfied. According to an example, the computing system uses the updated convergence value to determine whether a number of consecutive iterations for which the first condition (the first threshold) has been met exceeds a second threshold, which is a number of required consecutive iterations (e.g., 0, 1, 2, 3, etc.). The convergence value represents the number of consecutive iterations for which the first condition has been met.”)
Spooner, Riberio and Cai are considered analogous art to the claimed invention because they are in the same field of endeavor being artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the stopping criteria of Cai. One would want to do this to provide a quantitative stopping criterion showing that consecutive iterations are resulting in little change to not waste unnecessary computations.
Regarding claim 4, Spooner in view of Riberio and Cai teaches claim 1 as outlined above. Riberio further teaches:
creating a new dataset by perturbing features of the generated initial predictions that need to be explained; (Section 3.3 page 3 “Thus, in order to learn the local behavior of f as the interpretable inputs vary, we approximate L(f,g,π_x) by drawing samples, weighted by πx. We sample instances around x by drawing nonzero elements of x uniformly at random (where the number of such draws is also uniformly sampled).”)
using a trained Al model to make predictions on the perturbed samples; (Section 3.3 page 3 “Given a perturbed sample z ∈ {0,1}d (which contains a fraction of the nonzero elements of x), we recover the sample in the original representation z ∈ Rd and obtain f(z), which is used as a label for the explanation model. Given this dataset Z of perturbed samples with the associated labels, we optimize Eq. (1) to get an explanation ξ(x).”)
assigning weights to the perturbed samples based on their similarity to the generated initial predictions; and (Section 3.3 page 3 “The primary intuition behind LIME is presented in Figure 3, where we sample instances both in the vicinity of x (which have a high weight due to πx) and far away from x (low weight from πx). Even though the original model may be too complex to explain globally, LIME presents an explanation that is locally faithful (linear in this case), where the locality is captured by πx. It is worth noting that our method is fairly robust to sampling noise since the samples are weighted by πx in Eq. (1).”)
applying a linear regression model to the perturbed samples using the assigned weights. (Section 3.4 describes their sparse linear explanations).
Regarding claim 8, Spooner teaches:
A computing device, comprising: a processing system that includes at least one processor and is configured to: ([0006] “According to an aspect of the present disclosure, a method for generating a counterfactual explanation for an artificial intelligence (AI) regression model is provided.”)
receive an input query that includes query parameters and auxiliary data; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
apply the received input query to an Al model to generate an initial prediction; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
apply the determined key features to a modified counterfactual model having a modified counterfactual algorithm to generate an initial set of counterfactuals; ([0076] “At step S406, the counterfactual explanations generation module 302 defines a set of candidate counterfactual potential functions. In an exemplary embodiment, the set of candidate counterfactual potential functions is defined based on exponential-polynomial functions of the user-selected input value.”)
prioritize the generated initial set of counterfactuals; ([0078] “At step S410, an optimum counterfactual explanation is generated based on a result of the optimization. In an exemplary embodiment, the optimum counterfactual explanation corresponds to a result of performing a differentiable continuous mapping between the respective output values of the surrogate model function and a real line over a predetermined subset of the set of real numbers so as to ensure that the designated input value selected by the user corresponds to the maximum value of the user-selected counterfactual potential function.”)
determine whether the prioritized set of counterfactuals has reached convergence by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; and ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
output an enhanced set of counterfactuals in response to determining that the prioritized set of counterfactuals has reached convergence. ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
Spooner does not teach:
performing explainable Al (XAI) analysis using approximate methods to identify influential features;
determining key features based on the identified influential features
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold
Ribeiro teaches:
perform explainable Al (XAI) analysis using approximate methods to identify influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
determine key features based on the identified influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
Spooner and Riberio are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio. One would want to do this to get insight Nito predictions (Riberio conclusion).
Cai teaches:
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; ([0027] “According to the process 100, the computing system determines whether to end the iterations based on whether stopping criteria are met. The stopping criteria includes two parts, which are represented by blocks 108 and 114. At block 108, the computing system determines whether the model performance satisfies a first criteria. According to an example, the computing system determines whether a difference between a validation score calculated at block 106 of the present iteration and a validation score calculated at block 106 of a previous iteration satisfies a first threshold.” And [0029] “If the first criteria is satisfied, at block 112, the computing system updates the convergence value by incrementing the convergence value. Then, at block 114, the computing system determines whether a second criteria is satisfied. According to an example, the computing system uses the updated convergence value to determine whether a number of consecutive iterations for which the first condition (the first threshold) has been met exceeds a second threshold, which is a number of required consecutive iterations (e.g., 0, 1, 2, 3, etc.). The convergence value represents the number of consecutive iterations for which the first condition has been met.”)
Spooner, Riberio and Cai are considered analogous art to the claimed invention because they are in the same field of endeavor being artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the stopping criteria of Cai. One would want to do this to provide a quantitative stopping criterion showing that consecutive iterations are resulting in little change to not waste unnecessary computations.
Regarding claim 11, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 11 recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 15, Spooner teaches:
A non-transitory processor readable media having stored thereon processor- executable instructions configured to cause a processing system that includes at least one processor in a computing device to perform operations comprising: ([0024] “According to yet another exemplary embodiment, a non-transitory computer readable storage medium storing instructions for generating a counterfactual explanation for an artificial intelligence (AI) regression model is provided.”)
receiving an input query that includes query parameters and auxiliary data; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
applying the received input query to an Al model to generate an initial prediction; ([0074] “In process 400 of FIG. 4, at step S402, the counterfactual explanations generation module 302 obtains a mathematical expression that corresponds to an approximation of an artificial intelligence (AI) regression model, also referred to herein as a surrogate model function, together with a first value that corresponds to a query instance for the AI regression model. In an exemplary embodiment, the AI regression model may be designed to receive inputs that correspond to a list of features pertaining to a credit history of a customer and to generate an output that corresponds to a predicted probability of default for that customer. In an exemplary embodiment, the AI regression model may include a neural network model, a logistic regression model, a random forest model, and/or any other suitable type of AI regression model.”)
applying the determined key features to a modified counterfactual model having a modified counterfactual algorithm to generate an initial set of counterfactuals; ([0076] “At step S406, the counterfactual explanations generation module 302 defines a set of candidate counterfactual potential functions. In an exemplary embodiment, the set of candidate counterfactual potential functions is defined based on exponential-polynomial functions of the user-selected input value.”)
prioritizing the generated initial set of counterfactuals; ([0078] “At step S410, an optimum counterfactual explanation is generated based on a result of the optimization. In an exemplary embodiment, the optimum counterfactual explanation corresponds to a result of performing a differentiable continuous mapping between the respective output values of the surrogate model function and a real line over a predetermined subset of the set of real numbers so as to ensure that the designated input value selected by the user corresponds to the maximum value of the user-selected counterfactual potential function.”)
determining whether the prioritized set of counterfactuals has reached convergence by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; and ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
outputting an enhanced set of counterfactuals in response to determining that the prioritized set of counterfactuals has reached convergence. ([0087] “The algorithm is an iterative scheme where the next input to be fed to the model as a candidate counterfactual is chosen by maximizing the Expected Improvement of the potential function over the input space and the set of all previous inputs are the generated counterfactuals. The generated input points are mathematically guaranteed to maximize the potential function as the number of iterations becomes large. The relevant algorithms to compute each iteration are obtained efficiently by analytically evaluating the Expected Improvement and its gradient for a given input. Gradient-based methods are used to maximize at each iteration of the algorithm. Once the potential value has converged, the best k (chosen by the user) values are returned as counterfactual examples.”)
Spooner does not teach:
performing explainable Al (XAI) analysis using approximate methods to identify influential features;
determining key features based on the identified influential features
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold;
However, Ribeiro does:
performing explainable Al (XAI) analysis using approximate methods to identify influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
determining key features based on the identified influential features; (Section 4 page 5 goes through identifying features and how important they are. We can see in Fig. 5 certain features are identified and then some are more important. “Intuitively, we want I such that features that explain many different instances have higher importance scores. In Figure 5, we show a toy example W, with n = d= 5, where W is binary (for simplicity). The importance function I should score feature f2 higher than feature f1, i.e. I2 > I1, since feature f2 is used to explain more instances.”)
Spooner and Riberio are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio. One would want to do this to get insight Nito predictions (Riberio conclusion).
Cai teaches:
by calculating a delta comparison between prioritized sets of counterfactuals generated during two consecutive iterations of applying the determined key features to the modified counterfactual model and determining that convergence has been reached in response to determining that the delta comparison is below a predefined threshold; ([0027] “According to the process 100, the computing system determines whether to end the iterations based on whether stopping criteria are met. The stopping criteria includes two parts, which are represented by blocks 108 and 114. At block 108, the computing system determines whether the model performance satisfies a first criteria. According to an example, the computing system determines whether a difference between a validation score calculated at block 106 of the present iteration and a validation score calculated at block 106 of a previous iteration satisfies a first threshold.” And [0029] “If the first criteria is satisfied, at block 112, the computing system updates the convergence value by incrementing the convergence value. Then, at block 114, the computing system determines whether a second criteria is satisfied. According to an example, the computing system uses the updated convergence value to determine whether a number of consecutive iterations for which the first condition (the first threshold) has been met exceeds a second threshold, which is a number of required consecutive iterations (e.g., 0, 1, 2, 3, etc.). The convergence value represents the number of consecutive iterations for which the first condition has been met.”)
Spooner, Riberio and Cai are considered analogous art to the claimed invention because they are in the same field of endeavor being artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the stopping criteria of Cai. One would want to do this to provide a quantitative stopping criterion showing that consecutive iterations are resulting in little change to not waste unnecessary computations.
Regarding claim 19, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 11 recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Claims 2-3, 5, 7, 9-10, 12, 14, 16-17, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Spooner in view of Ribeiro, Cai and Mothilal (NPL: ‘ Explaining machine Learning Classifiers through Diverse Counterfactual Explanations (2020)).
Regarding claim 2, Spooner in view of Riberio and Cai teaches claim 1 as outlined above. Neither Spooner nor Riberio teaches:
performing validation analysis on the query parameters to determine conformance to a standard or constraint or to ensure that the input query and its subsequent results follow predefined validation rules and guidelines.
However, Mothilal does:
performing validation analysis on the query parameters to determine conformance to a standard or constraint or to ensure that the input query and its subsequent results follow predefined validation rules and guidelines. (Section 4.1 Page 5 “Validity. Validity is simply the fraction of examples returned by a method that are actually counterfactuals. That is, they correspond to a different outcome than the original input. Here we consider only unique examples because a method may generate multiple examples that are identical to each other.”)
Spooner, Riberio and Mothilal are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the validation method of Mothilal. One would want to do this to guarantee you are generating valid counterfactuals.
Regarding claim 3, Spooner in view of Riberio and Cai teaches claim 1 as outlined above. Neither Spooner nor Riberio teaches:
validating the prioritized set of counterfactuals to determine conformance to a standard or constraint or to ensure that the prioritized set of counterfactuals follow predefined validation rules and guidelines.
However, Mothilal does:
validating the prioritized set of counterfactuals to determine conformance to a standard or constraint or to ensure that the prioritized set of counterfactuals follow predefined validation rules and guidelines. (Section 4.1 Page 5 “Validity. Validity is simply the fraction of examples returned by a method that are actually counterfactuals. That is, they correspond to a different outcome than the original input. Here we consider only unique examples because a method may generate multiple examples that are identical to each other.”)
Spooner, Riberio and Mothilal are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the validation method of Mothilal. One would want to do this to guarantee you are generating valid counterfactuals.
Regarding claim 5, Spooner in view of Riberio and Cai teaches claim 1 as outlined above. Neither Spooner nor Riberio teaches:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to a counterfactual model with relaxed sparsity to generate the initial set of counterfactuals.
However, Mothilal does:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to a counterfactual model with relaxed sparsity to generate the initial set of counterfactuals. (Page 5 section 4.1 left column “Sparsity. While proximity quantifies the average change between a CF example and the original input, we also measure another related property, sparsity, that captures the number of features that are different. We define sparsity as the number of changes between the original input and a generated counterfactual.”)
Spooner, Riberio and Mothilal are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the sparsity method of Mothilal. One would want to do this to guarantee you are generating more sparse counterfactuals.
Regarding claim 7, Spooner in view of Riberio and Cai teaches claim 1 as outlined above. Neither Spooner nor Riberio teaches:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to generate counterfactuals with different combinations of categorical data types.
However, Mothilal does:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to generate counterfactuals with different combinations of categorical data types. (page 4 Section 3.3 right column “For categorical features, we convert each feature using one-hot encoding and consider it as a continuous variable between 0 and 1. Also, to enforce the one-hot encoding in the learned counterfactuals, we add a regularization term with high penalty for each categorical feature to force its values for different levels to sum to 1. At the end of the optimization, we pick the level with maximum value for each categorical feature.”)
Spooner, Riberio and Mothilal are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the different types of data of Mothilal. One would want to do this to be able to handle different categories of data.
Regarding claim 9, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 9 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 10, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 10 recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 12, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 12 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 14, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 14 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 16, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 16 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 17, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 17 recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 19, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 19 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 21, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 21 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Spooner in view of Ribeiro, Cai and Geada (US 2024/0135237 A1).
Regarding claim 6, Spooner in view of Riberio teaches claim 1 as outlined above. Neither Spooner nor Riberio teaches:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to identify alternative less-sensitive queries and generate a list of alternative queries that are compliant with California Consumer Privacy Act (CCPA) or General Data Protection Regulation (GDPR).
However, Geada does:
applying the determined key features to a modified counterfactual model to generate the initial set of counterfactuals comprises applying the determined key features to the modified counterfactual algorithm to identify alternative less-sensitive queries and generate a list of alternative queries that are compliant with California Consumer Privacy Act (CCPA) or General Data Protection Regulation (GDPR). ([0002] “The field of Explainable Artificial Intelligence (XAI) seeks to investigate the intuition of black-box models, which may include predictive systems whose inner workings are either inaccessible or so complex as to be conventionally uninterpretable. Common examples of such black-box models are neural networks or random forests. In some cases, explanations of these models may be driven by a variety of regulations. For example, the General Data Protection Regulation (GDPR) of the European Union envisions that subjects of automated decisions may ask for an explanation of the decision-making process that led to said decisions. Furthermore, the behavior of these models may be investigated to ensure they are compliant with regulations and business ethics, for example, verifying the models are not basing their decisions on protected attributes such as race or gender if such things are not relevant to the decision.
Spooner, Riberio and Geada are considered analogous art to the claimed invention because they are in the same field of endeavor being explainable artificial intelligence. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the counterfactual generation of Spooner with explainable artificial intelligence of Riberio and the regulation compliance of Geada. One would want to do this to eliminate model bias.
Regarding claim 13, Spooner in view of Riberio and Cai teaches claim 8 as outlined above. Claim 13 recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 20, Spooner in view of Riberio and Cai teaches claim 15 as outlined above. Claim 20 recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
