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 office action is in response to Applicant’s RCE filed November 06, 2025 in which claims 1, 10 and 19 are amended. Claims 8 and 17 were previously canceled. Thus, claims 1-7, 9-16 and 18-22 are pending in the application.
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/06/2025 has been entered.
Claim Rejections - 35 USC § 101
3. 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 1-7, 9-16 and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Examiner has identified independent Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claims 10 and 19.
The claims 1-7 and 9 are directed to a method, claims 10-16 and 18 are directed to a system and claims 19-22 are directed to a computer program product embodied in a non-transitory computer readable medium which are one of the statutory categories of invention (Step 1: YES).
The claim 1 recites : generating, by a predictive data analysis engine and using the predictive analysis machine learning model, an entity score for the entity, wherein the entity comprises a plurality of candidate features; in response to the entity score failing to satisfy a determination decision threshold, selecting, by a contribution determination engine and using the predictive analysis machine learning model, a reference entity from a plurality of candidate reference entities, wherein a reference entity score associated with the reference entity satisfies the determination decision threshold; evaluating, by the contribution determination engine, a set of extrapolation feature scores for a first feature by evaluating output from the predictive analysis machine learning model, at a finite set of points comprising the entity and the reference entity as input; evaluating, by the contribution determination engine and using a Baseline-Shapley decomposition function, a per-candidate feature contribution score based on the set of extrapolation feature scores, the reference entity, and the entity score, wherein the per-candidate feature contribution score corresponds to the first feature of the plurality of candidate features; determining that the per-candidate feature contribution score is greater than or equal to a second per-candidate feature contribution score corresponding to a second candidate feature; and generating, by the contribution determination engine, the predictive contribution report based at least in part on the per-candidate feature contribution score, wherein the predictive contribution report comprises the first feature associated with the per-candidate feature contribution score and an indication that the entity score does not satisfy the determination decision threshold. These limitations (with the exception of italicized portions), when considered collectively as an ordered combination, is a process that covers Mental Processes as these limitations relate to concepts performed in the human mind (including an observation, evaluation, judgment, opinion and use of a pen and paper). Determining whether the entity score satisfies the determination decision threshold is a mental process. In addition, the claims can also be classified under Mathematical concepts. Using a machine learning model to determine the entity score involve mathematical relationships and computations. The claim also recites a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine which do not necessarily restrict the claim from reciting an abstract idea. That is, other than, a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine, nothing in the claim precludes the steps from being performed as Mental processes and mathematical concepts. If the claim limitations, under the broadest reasonable interpretation, covers the concepts that can be performed in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Mathematical concepts” grouping of abstract ideas, respectively. Accordingly, the claim 1 recites an abstract idea (Step 2A: Prong 1: YES).
This judicial exception is not integrated into a practical application. The additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine result in no more than simply applying the abstract idea using generic computer elements. The specification describes the additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine, to be generic computer elements (see Fig. 1, [0059]). Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims as a whole are not integrated into a practical application. Therefore, the claim 1 is directed to an abstract idea (Step 2A - Prong 2: NO).
The claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The additional elements, when considered separately and as an ordered combination, does not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of the instant underlying process, when taken in combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone. Thus, claim 1 is not patent eligible (Step 2B: NO).
Similar arguments can he extended to other independent claims 10 and 19 and hence the claims 10 and 19 are rejected on similar grounds as claim 1. In addition, claim 10 also recites a processor and a memory and claim 19 recites a non-transitory computer-readable storage medium that amounts to generic computer implementation.
Dependent claims 2-7, 9, 11-16 and 18-22 are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only narrow the abstract idea further and thus correspond to “Mental Processes” and “Mathematical concepts” and hence are abstract for the reasons presented above. Claims 2-7, 9, 11-16 and 18-22 do not recite any new additional elements that are not present in independent claims 1, 10 and 19.
Viewing the claim limitations as a combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as a combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claim(s) 1-7, 9-16 and 18-22 are ineligible.
No Prior Art Rejections
4. Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of claims 1-7, 9-16 and 18-22. While some individual features of claims 1-7, 9-16 and 18-22 may be shown in the prior art of record, no known reference, alone or in combination, would provide the invention of claims 1-7, 9-16 and 18-22. The prior art most closely resembling the applicant’s claimed invention are :
1) Miroshnikov (US 12,050,975 B2) – This invention relates to machine learning. More specifically, the embodiments set forth below describe systems and methods for generating adverse action reason codes based on analysis of machine learning models.
2) Merrill (US 2015/0019405 A1) - This invention relates more particularly to the field of credit scoring methods and systems. Preferred embodiments of the present invention provide systems and methods for building and validating a credit scoring function based on a creditor's target information from non-traditional sources using specific algorithms.
3) Gao (US 2014/0365356 A1) – The current subject matter provides models that enable a projection of credit scores at a specified future date as well as an estimation of a date when a credit score will reach a certain level.
Response to Arguments
5. Applicant's arguments filed dated 11/06/2025 have been fully considered but they are not persuasive due to the following reasons:
6. With respect to the rejection of all claims under 35 U.S.C. 101 with regards to Step 2A, Prong 1 (pages 10-11), Applicant argues that, “claims do not recite a judicial exception.”
Examiner respectfully disagrees and notes that as explained in the 101 analysis above, the steps of the claim, when collectively as an ordered combination, is a process that, under their broadest reasonable interpretation, covers Mental Processes as these limitations relate to concepts performed in the human mind (including an observation, evaluation, judgment, opinion and use of a pen and paper). Determining whether the entity score satisfies the determination decision threshold is a mental process. Examiner notes that according to the MPEP 2106.04, a claim that requires a computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. The fact that the computer is faster or more efficient does not make an abstract business process eligible. In addition, the claims can also be classified under Mathematical concepts. Using a machine learning model to determine the entity score involve mathematical relationships and computations. The claim also recites a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine which do not necessarily restrict the claim from reciting an abstract idea. That is, other than, a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine, nothing in the claim precludes the steps from being performed as Mental processes and mathematical concepts. If the claim limitations, under the broadest reasonable interpretation, covers the concepts that can be performed in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Mathematical concepts” grouping of abstract ideas, respectively. Accordingly, the claim 1 recites an abstract idea.
7. With respect to the rejection of all claims under 35 U.S.C. 101 with regards to Step 2A, Prong 2 (pages 11-13), Applicant argues that, “the claims integrate any allegedly recited judicial exception into a practical application.”
The Examiner respectfully disagrees. The Examiner would like to point out that according to 2019 Patent Eligibility Guidelines (2019 PEG), limitations that are indicative of integration into a practical application include:
• Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
• Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
• Effecting a transformation or reduction of a particular article to a different state or thing -see MPEP 2106.05(c)
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The amended limitations of the claims do not result in computer functionality improvement or technical/technology improvement when the underlying abstract idea is implemented using technology. The amendments to the claims only further define the data being used however a specific abstract idea is still an abstract idea. Predictive analysis using machine learning model is simply using the generic technology to implement the abstract idea. All the features in the Applicant’s claims can at best be considered an improvement in the abstract idea. Reduction in the computational complexity of runtime operations may improve the abstract idea, not a technical improvement. The advantages over conventional systems are directed towards improving the abstract idea. The specification describes the additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine to be generic computer elements (see Fig. 1, [0059]). Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine, are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, the claims as a whole are not integrated into a practical application.
8. Applicant argues that (pages 10-13), “the combinations of features recited in the claims amount to "significantly more" than any allegedly recited judicial exception, and the claims are eligible at Step 2B.”
One of the guidelines issued by the Office to determine if the claims recite additional elements which are not well understood, routine or conventional and hence, amount to significantly more than an abstract idea, is the USPTO guidelines of April 19, 2018 incorporating the Berkheimer memo (Berkheimer memo, hereinafter).
According to the Berkheimer memo,
In a step 2B analysis, an additional element (or combination of elements) is not well understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
1. A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).
2. A citation to one or more of the court decisions discussed in MPEP § 2106.05(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s).
3. A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).
4. A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional elements). This option should
be used only when the examiner is certain, based upon his or her personal knowledge, that the additional elements) represents well-understood, routine, conventional activity engaged in by those in the relevant art, in that the additional elements are widely prevalent or in common use in the relevant field, comparable to the types of activity or elements that are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).
The claim simply applies the abstract idea using generic computer elements as a tool (see MPEP 2106.05(f)). The additional elements in the claim are the predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine. As per the rejection above, the specification describes the additional elements of a predictive data analysis engine, the predictive analysis machine learning model and the contribution determination engine to be generic computer elements (see Fig. 1, [0059]). Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. There is no indication in Applicants’ claims that any specialized hardware or other inventive computer components are required. The fact that a general purpose computing system, suitably programmed, may be used to perform the claimed method and the fact that the claims at issue do not require any nonconventional computer, network, or other components, or even a “non-conventional and non-generic arrangement of known, conventional pieces” but merely call for performance of the claimed functions “on a set of generic computer components, satisfies the Berkheimer memo requirement that the additional elements are conventional elements (as outlined in criterion 1 of the Berkheimer memo). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone.
For these reasons and those discussed in the rejection, the rejections under 35 U.S.C. 101 are maintained.
Examiner Request
9. The Applicant is request to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BHAVIN D SHAH whose telephone number is (571)272-2981. The examiner can normally be reached on 8:00-5:00 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bennett Sigmond can be reached on 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.D.S./Examiner, Art Unit 3694
December 03, 2025
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694