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 .
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 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 statements (IDS) submitted on 08/02/2024 have been considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
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.
Claim(s) 1 - 20 are directed to statutory computer-readable mediums under Step 1 of the eligibility analysis. However, the claims are further directed toward a judicial exception under Step 2A Prong One of the eligibility analysis, namely an abstract idea. Under Step 2A Prong Two of the eligibility analysis, the claim(s) does/do not include additional elements to integrate the exception into a practical application of that exception. Under Step 2B of the eligibility analysis, the claims are not sufficient to amount to significantly more than the judicial exception because nothing in the asserted claims purports to improve the functioning of the computer itself or effect an improvement in any other technology or technical field. The claim(s) is/are directed to an abstract system and method. This is “organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721”, (see MPEP 2106.04(a)(2)(I)(A)(iv)). “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation”. (see MPEP 2106.04(a)(2)(I)(C)(v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979)). Furthermore, the claim(s) fail to amount to significantly more than the abstract idea itself, (see MPEP 2106.05(f)(i). A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1, 4 – 5, 8, 10, and 13 - 15 are rejected under 35 U.S.C. 102(a)(1)/ (a)(2) [as best understood in view of the 35 USC § 101 above] as being anticipated by Yang (US PgPub No. 2022/0114481).
Regarding claim 1, Yang teaches a method (figure 4) comprising: obtaining, from an input instance dataset, a first input instance and a first class corresponding to the first input instance (figure 4 item 402; receive first query including a plurality of feature columns); calculating, by a distance function generator executing on a computer processor, a probability distance score, based on a set of probability values, to transform the first input instance (figure 4 items 404 - 406 also paragraphs 0029, 0035, and 0051); processing, by a counterfactuals generator executing on the computer processor, the first input instance and the probability distance score, to generate a counterfactual instance (figure 4 items 408 - 410 also paragraphs 0029, 0035, and 0051; generate counterfactual); and executing a machine learning model, on the computer processor, to assign a second class to the counterfactual instance by processing the counterfactual instance, wherein the second class is different from the first class (figure 4 items 412 – 414; output).
Regarding claim 4, as mentioned above in the discussion of claim 1, Yang teaches all of the limitations of the parent claim. Additionally, Yang teaches receiving a counterfactuals generation request with the input instance dataset (figure 1 items 101 - 102); and transmitting the counterfactual instance in response to the counterfactuals generation request (figure 1 item 120).
Regarding claim 5, as mentioned above in the discussion of claim 1, Yang teaches all of the limitations of the parent claim. Additionally, Yang teaches the counterfactuals generator comprises a second machine learning model trained to generate, as output, the counterfactual instance in response to an input comprising an input instance and the probability distance score (figure 4 item 410).
Regarding claim 8, Yang teaches a method comprising: obtaining a first snapshot corresponding to a first timestamp and a second snapshot corresponding to a second timestamp of an input instance dataset from a data repository, wherein the first timestamp is prior to the second timestamp, and wherein the first snapshot and second snapshot are copies of the input instance dataset corresponding to the first timestamp and second timestamp respectively (figure 400 items 404 and 410 at different timings paragraphs 0089 – 0092); extracting, by a feature extraction model executing on a computer processor, feature categories from the input instance dataset to obtain a feature category set (figure 4 item 402; receive first query including a plurality of feature columns); selecting a feature category subset of a subset size from the feature category set (figure 4 item 408; determine optimal features); selecting a previous input instance from the first snapshot and a next input instance from the second snapshot (figure 4 item 408; from identified subset); selecting a previous feature value subset from the previous input instance and a next feature value subset from the next input instance, wherein the previous feature value subset and the next feature value subset correspond to the feature category subset (figure 2 item 412 fine tune); calculating a previous probability value of an occurrence of the previous feature value subset in the first snapshot (figure 4 item 406); and calculating a next probability value of the occurrence of the next feature value subset in the second snapshot (figure 4 items 412 – 414; output).
Regarding claim 10, Yang teaches a system (paragraphs 0019 – 0020 also claim 11 of Yang; systems) comprising: a server computing system (figure 3), comprising: a computer processor (figure 3 item 310); a data repository (figure 3 item 315 and 340), a distance function generator, executing on the computer processor and communicatively coupled to the data repository (figure 4 items 404 - 406 also paragraphs 0029, 0035, and 0051), and a counterfactuals generator (figure 3 item 320), executing on the computer processor and communicatively coupled to the data repository (figure 3), wherein the server computing system is configured to: obtain, from an input instance dataset, a first input instance and a first class corresponding to the first input instance (figure 4 item 402; receive first query including a plurality of feature columns); calculate, by the distance function generator, a probability distance score, based on a set of probability values, to transform the first input instance (figure 4 items 404 - 406 also paragraphs 0029, 0035, and 0051); process, by the counterfactuals generator, the first input instance and the probability distance score, to generate a counterfactual instance (figure 4 items 408 - 410 also paragraphs 0029, 0035, and 0051; generate counterfactual); and execute a machine learning model on the computer processor to assign a second class to the counterfactual instance by processing the counterfactual instance, wherein the second class is different from the first class (figure 4 items 412 – 414; output).
Regarding claim 13, as mentioned above in the discussion of claim 10, Yang teaches all of the limitations of the parent claim. Additionally, Yang teaches receive a counterfactuals generation request from a user computing system (figure 1 items 101 - 102), and transmit the counterfactual instance to the user computing system in response to the counterfactuals generation request (figure 1 item 120).
Regarding claim 14, as mentioned above in the discussion of claim 10, Yang teaches all of the limitations of the parent claim. Additionally, Yang teaches receive a counterfactuals generation request from a developer computing system (figure 1 items 101 - 102), and transmit the counterfactual instance to the developer computing system in response to the counterfactuals generation request (figure 1 item 120).
Regarding claim 15, as mentioned above in the discussion of claim 10, Yang teaches all of the limitations of the parent claim. Additionally, Yang teaches wherein the counterfactuals generator comprises a second machine learning model trained to generate, as output, the counterfactual instance in response to an input comprising an input instance and the probability distance score (figure 4 item 410).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vinov (US PgPub No. 20230025731) teaches a system for processing data with machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Usman A Khan whose telephone number is (571)270-1131. The examiner can normally be reached on M - Th 5:30 AM - 2 PM, F 5:30 AM - Noon.
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Usman Khan
/USMAN A KHAN/Primary Examiner, Art Unit 2637
07/13/2026
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