Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to a patent application filed on March 24th, 2023. Claims 1-20 are pending in the current application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards a process an abstract idea without significantly more.
Regarding claim 1 Under Step 1 of the Subject Matter Eligibility Test of Products and
Processes, the claim is directed towards a process, which is one of the four statutory categories.
Next, under a Step 2A Prong 1 Analysis, the following limitations in the claim recites an abstract idea.
generating an initial ground set based on a first candidate set of outer-If conditions (SD), and a second candidate set used for selecting Inner-If or Then conditions (RL); (mental process)
evaluating a fixed number of triples and forming a new ground set that provides a recourse accuracy level above a reference threshold, wherein the fixed number of triples included in the new ground set is less than a number of triples included in the initial ground set; (mental process)
sorting the new ground set by recourse accuracy; (mental process)
selecting a predetermined number of triples based on corresponding recourse accuracies indicated in the sorting; (mental process)
performing calculation based on the selected number of triples. (mental process)
Therefore, we have to examine the claim under Step 2A prong 2, which considers the additional elements within the claim. The claim does not contain any additional elements. Therefore there are no additional elements that integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Under a Step 2B analysis, since the claim does not contain any additional elements, there is nothing that amounts to significantly more than the judicial exception as explained above in Step 2A prong 2. Therefore, the claim is ineligible.
Regarding claim 11 Under Step 1 of the Subject Matter Eligibility Test of Products and
Processes, the claim is directed towards a machine, which is one of the four statutory categories.
Next, under a Step 2A Prong 1 Analysis, the following limitations in the claim recites an abstract idea.
generating an initial ground set based on a first candidate set of outer-If conditions (SD), and a second candidate set used for selecting Inner-If or Then conditions (RL); (mental process)
evaluating a fixed number of triples and forming a new ground set that provides a recourse accuracy level above a reference threshold, wherein the fixed number of triples included in the new ground set is less than a number of triples included in the initial ground set; (mental process)
sorting the new ground set by recourse accuracy; (mental process)
selecting a predetermined number of triples based on corresponding recourse accuracies indicated in the sorting; (mental process)
performing calculation based on the selected number of triples. (mental process)
Therefore, we have to examine the claim under Step 2A prong 2, which considers the additional elements within the claim. The claim’s additional elements are:
at least one processor
at least one memory
at least one communication circuit
The limitations, as drafted, is interpreted to be “generally linked” to the abstract idea, as the processor, memory, and circuit are merely used to perform the abstract idea. (See MPEP 2106.05(h)) Therefore, the additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Under a Step 2B analysis, the claim' s additional elements do not amount to significantly more than the judicial exception as explained above in Step 2A prong 2. Therefore, the claim is ineligible.
Regarding claims 2 and 12, the claims recite “generating of the ground set is performed by iterating over the second candidate set in O(n) time and computing feature combinations, before removing any items that contain a feature combination that only occurs once, for yielding a new RL with size an, and wherein α is greater than or equal to 0 and less than or equal 1.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 3 and 13, the claims recite “generating of the ground set is performed by filtering a dataset based on the outer-If or the inner-If conditions, and separately deploying a method for generating Then conditions.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 4 and 14, the claims recite “each triple includes an outer-If condition, an inner-If condition, and a Then condition.” The limitation, as drafted, is interpreted to be, further details of the judicial exception and the claims do not recite any further additional elements. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 5 and 15, the claims recite “the selecting of the predetermined number of triples includes selecting highest-performing triples within the new ground set.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 6 and 16, the claims recite “each triple forming the new ground set increases the recourse accuracy level.” The limitation, as drafted, is interpreted to be, further details of the judicial exception and the claims do not recite any further additional elements. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 7 and 17, the claims recite “one or more constraints are applied during the generating of the initial ground set.” The limitation further details the judicial exception and the claims do not recite any further additional elements. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 8 and 18, the claims recite “the initial ground set removes a feature combination that only occurs once.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 9 and 19, the claims recite “an upper bound defined as acc(R) < acc(V) is reached before an algorithm for providing the global counterfactual explanation has completed execution, wherein acc(R) is a percentage of instances in Xaff that are provided with a successful recourse, wherein Xaff is a set of individuals with an unfavorable prediction from a model, and wherein acc(v) is a recourse accuracy.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Regarding claims 10 and 20, the claims recite “the algorithm is terminated prior to its completion when the upper bound for saturation is reached.” The limitation, as drafted, is interpreted to be, under the broadest reasonable interpretation, a “mental process”. Therefore, the claims are rejected on the same basis as claims 1 and 11 respectively.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-7, 9, 11, 13-17, and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rawal and Lakkaraju. (Herein referred to as Rawal) (Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses)
Regarding claim 1, Rawal teaches a method for providing a global counterfactual explanation, the method comprising: performing, using a processor and a memory: generating an initial ground set based on a first candidate set of outer-If conditions (SD), and a second candidate set used for selecting Inner-If or Then conditions (RL); (See Figure 1 on pg. 2. While the reference never explicitly mentions utilizing a processor and memory, you would implicitly need those components to run the method of Rawal. In the example given in Figure 1, the Subgroup descriptor corresponds to a first candidate set, with the recourse rules corresponding to second candidate sets.) evaluating a fixed number of triples and forming a new ground set that provides a recourse accuracy level above a reference threshold, wherein the fixed number of triples included in the new ground set is less than a number of triples included in the initial ground set; (“we use the recourse rules outlined by our explanation to prescribe recourses to affected individuals, they should be able to obtain the desired predictions from the black box model upon acting on the recourse. To quantify this notion, we define incorrectrecourse(R) which is defined as the number of instances in Xaff for which acting upon the recourse prescribed by R does not result in the desired prediction (Table 1). Our goal would therefore be to construct explanations that minimize this metric… we evaluate how the recourse accuracy metric (defined above) changes as we vary the size of the two-level recourse set i.e., number of triples of the form (q, c, c0 ) in the two-level recourse set (Section 2.2).”, pg. 4, second paragraph; pg. 8, third paragraph; See also Figure 2 on pg. 8.) (The number of instances in Xaff acting upon the recourse prescribed by R corresponds to recourse accuracy levels. This metric is shown in Figure 2. Figure 2 also shows evaluating the recourse accuracy at different sizes. (Size corresponding to a number of triples.) The previous accuracy at every iteration of size corresponds to an accuracy threshold, as the goal of the algorithm is to find the lowest value for size without compromising accuracy. The set with most optimal values between size and recourse accuracy would then be the new ground set. The lower size values correspond to a number of triples less than the number of triples in the initial ground set.) sorting the new ground set by recourse accuracy; selecting a predetermined number of triples based on corresponding recourse accuracies indicated in the sorting; (“we evaluate how the recourse accuracy metric (defined above) changes as we vary the size of the two-level recourse set i.e., number of triples of the form (q, c, c0 ) in the two-level recourse set (Section 2.2). Results for the same are shown in Figure 2. It can be seen that recourse accuracies converge to their maximum values at explanation sizes of about 10 to 15 rules across all the datasets.” Pg. 8, third paragraph) (They choose 10 to 15 rules to have a reasonable mix of interpretability and accuracy, with the sorting being indicated by the higher accuracies at higher sizes.) and performing calculation based on the selected number of triples. (AReS always had the lowest FCost, and provided recourses that either had the highest Recourse Accuracy or were within 0.5% of the best performing individual recourse generation method… These results clearly demonstrate the necessity and significance of methods which can provide accurate summaries of recourses as opposed to just individual recourses.”) (The triples are used to show the effectiveness of the AReS algorithm, which provides accurate summaries of recourses.)
Regarding claims 3 and 13, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as the generating of the ground set is performed by filtering a dataset based on the outer-If or the inner-If conditions, and separately deploying a method for generating Then conditions. (Figure 1 on pg. 2 shows Subgroup descriptors and recourse rule containing if and then statements which acts as a filter for dataset.)
Regarding claims 4 and 14, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as each triple includes an outer-If condition, an inner-If condition, and a Then condition. (“a two-level recourse set is a set of triples and has the following form: R = {(q1, c11, c0 11),(q1, c12, c0 12)· · ·(q2, c21, c0 21)· · · } where qi corresponds to the subgroup descriptor, and (cij , c0 ij ) together represent the inner if-then recourse rules with cij denoting the antecedent (i.e., the if condition) and c 0 ij denoting the consequent (i.e., the recourse).”, pg. 3, bottom paragraph; See also Figure 1 on pg. 2)
Regarding claims 5 and 15, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as the selecting of the predetermined number of triples includes selecting highest-performing triples within the new ground set. (“we evaluate how the recourse accuracy metric (defined above) changes as we vary the size of the two-level recourse set i.e., number of triples of the form (q, c, c0 ) in the two-level recourse set (Section 2.2). Results for the same are shown in Figure 2. It can be seen that recourse accuracies converge to their maximum values at explanation sizes of about 10 to 15 rules across all the datasets.” Pg. 8, third paragraph) (The triples with the maximum values are selected.)
Regarding claims 6 and 16, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as each triple forming the new ground set increases the recourse accuracy level. (“AReS always had the lowest FCost, and provided recourses that either had the highest Recourse Accuracy or were within 0.5% of the best performing individual recourse generation method.”, pg. 8, second paragraph)
Regarding claims 7 and 17, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as one or more constraints are applied during the generating of the initial ground set. (See Figure 1 on pg.2. The variables within the if statements correspond to constraints that are applied during the generation of ground sets.)
Regarding claims 9 and 19, Rawal teaches the method and system according to claims 1 and 11 respectively, as well as an upper bound defined as acc(R) < acc(V) is reached before an algorithm for providing the global counterfactual explanation has completed execution. (“To construct non-negative reward functions, penalty terms (metrics in Table 1) are subtracted from their corresponding upper bound values (U1, U3, U4) which are computed with respect to the sets SD and RL.”, pg. 5, second paragraph of “2.3 Learning Two Level Recourse Sets”) (The upper bounds can be easily configured to be reached before the algorithm has completed execution.) wherein acc(R) is a percentage of instances in Xaff that are provided with a successful recourse, wherein Xaff is a set of individuals with an unfavorable prediction from a model, and wherein acc(v) is a recourse accuracy. (“we define incorrectrecourse(R) which is defined as the number of instances in Xaff for which acting upon the recourse prescribed by R does not result in the desired prediction (Table 1)… we outline the following metrics: 1) Recourse Accuracy: percentage of instances in Xaff for which acting upon the prescribed recourse (e.g., changing the feature values as prescribed by the recourse) obtains the desired prediction”, pg. 4, second paragraph; pg. 7, bottom paragraph)
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 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rawal in view of Manivannan et al. (Herein referred to as Manivannan) (U.S. Patent Application Publication No. US 20230008115 A1)
Regarding claims 2 and 12, Rawal teaches the method and system according to claims 1 and 11 respectively, but does not explicitly teach the generating of the ground set is performed by iterating over the second candidate set in O(n) time and computing feature combinations, before removing any items that contain a feature combination that only occurs once, for yielding a new RL with size an, and wherein a is greater than or equal to 0 and less than or equal 1.
Manivannan teaches the generating of the ground set is performed by iterating over the second candidate set in O(n) time and computing feature combinations, before removing any items that contain a feature combination that only occurs once, for yielding a new RL with size αn, and wherein α is greater than or equal to 0 and less than or equal 1. (“The process of cleaning the data may include fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.”, Paragraph 58) (The removal of duplicate data from a dataset would make it to where the size of the dataset goes from n to αn where α is between 0 and 1.)
Therefore it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the method of Rawal with the data removal of Manivannan. One would have been motivated to combine the two teaching, prior to the filing date of the current application, as data removal allows for the cleaned data to be maintainable, reproducible, and traceable, as disclosed in Manivannan. (“This “clean” form of data ensures that the feature engineering logic is maintainable, the target datasets are reproducible and, sometimes, that the whole pipeline is traceable to the source representations.”, Paragraph 58)
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rawal in view of BANERJEE et al. (Herein referred to as Banerjee) (U.S. Patent Application Publication No. US 20210125207 A1)
Regarding claims 8 and 18, Rawal teaches the method and system according to claims 1 and 11 respectively, but does not explicitly teach the initial ground set removes a feature combination that only occurs once.
Banerjee teaches removing a feature combination that only occurs once. (“data cleaning can be implemented to improve the quality of the data. This includes imputation of missing data and the removal of erroneous values and outliers.”, Paragraph 34) (Removal of erroneous values and outliers corresponds to data that occurs once. Combined with and configured to the removal of the feature combinations of Rawal, teaches the limitation.)
Therefore it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the method of Rawal with the data removal of Banerjee. One would have been motivated to combine the two teaching, prior to the filing date of the current application, as this improved the quality of the data, as disclosed in Banerjee. (“data cleaning can be implemented to improve the quality of the data. This includes imputation of missing data and the removal of erroneous values and outliers.”, Paragraph 34)
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rawal in view of Tesauro et al. (Herein referred to as Tesauro) (U.S. Patent Application Publication No. US 20070203871 A1)
Regarding claims 10 and 20, Rawal teaches the method and system according to claims 9 and 19 respectively, but does not explicitly teach the algorithm is terminated prior to its completion when the upper bound for saturation is reached.
Tesauro teaches an algorithm is terminated prior to its completion when the upper bound for saturation is reached. “training is stopped if an upper bound on the number of training iterations has been reached.” Paragraph 27) (While the stopping criteria of Das is related to specifically a training algorithm, it would be easy to configure the stopping criteria to work with saturation, and the algorithm with upper bounds of Rawal.)
Therefore it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the method of Rawal with the stopping criteria of Tesauro. One would have been motivated to combine the two teaching, prior to the filing date of the current application, as this saves computational resources via dynamic allocation, as disclosed in Tesauro. (“This entails the development of effective policies pertaining to, for example, dynamic allocation of computational resources, performance tuning of system control parameters, dynamic configuration management, automatic repair or remediation of system faults and actions to mitigate or avoid observed or predicted malicious attacks or cascading system failures.” Paragraph 2)
Conclusion
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/T.E.I./ Patent Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122