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 .
Status of Claims
This action is in reply to the communications filed on 11/19/2025.
Claims 1, 6, and 11 have been amended and are hereby entered.
Claims 2-3,7-8, and 12-13 have been canceled.
Claims 1-3, 6-8, and 11-13 are currently pending and have been examined.
This action is made Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 6, and 11 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. For instance, in In re Hayes Microcomputer Products, the written description requirement was satisfied because the specification disclosed the specific type of microcomputer used in the claimed invention as well as the necessary steps for implementing the claimed function. The disclosure was in sufficient detail such that one skilled in the art would know how to program the microprocessor to perform the necessary steps described in the specification. In re Hayes Microcomputer Prods., Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, ___ (Fed. Cir. 1992). In the present applicant, independent claims 1, 6, and 11 recite “determining whether a co-efficient value is less or greater than the pre-computed co-efficient value, followed by: enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value; or disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value; and obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data, wherein the observations comprise details of the covenants being recommended and the covenants not recommended; and displaying, via the one or more hardware processors, the at least a subset of sorted predicted covenants to an underwriter obtaining, via the one or more hardware processors, selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model;” where theses limitation recite “determining whether a co-efficient value is less or greater than the pre-computed co-efficient value….enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value; or disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value; and obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data, wherein the observations comprise details of the covenants being recommended and the covenants not recommended; and displaying….the at least a subset of sorted predicted covenants to an underwriter obtaining….selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model.” which are not disclosed within the application’s specification and to show possession of the invention at the time of filing. While one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of determining whether a co-efficient value is less or greater than the pre-computed co-efficient value….enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value…disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value…. obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data…. displaying….the at least a subset of sorted predicted covenants to an underwriter obtaining….selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model. Simply stating or re-stating the claim limitation does not provide enough support to show possession. Since these important details about how the invention operates are not disclosed, it is not readily evident that Applicant has full possession of the invention at the time of filing (i.e., the original disclosure fails to provide adequate written description to support the claimed invention as a whole). Neither the specification nor the drawings disclose in detail the specific steps or algorithm needed to perform the operation. If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112a, for lack of written description must be made. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
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, 6, and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of obtaining and processing an entity’s (entities’) historical details, loan information, and recommended loan covenants without significantly more.
Examiner has identified claim 1 as the claim that represents the claimed invention presented in independent claims 1, 6, and 11.
Claim 1 is directed to a method, which is one of the statutory categories of invention; Claim 6 is directed to a system, which is one of the statutory categories of invention; and Claim 11 is directed to one or more non-transitory machine-readable information storage mediums, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a method, which recites a series of steps, e.g., obtaining, via one or more hardware processors, an input training data comprising historical details of one or more entities, historical loan information corresponding to the one or more entities, and one or more corresponding recommended covenants; performing, via the one or more hardware processors, a data exploration analysis on the input training data to obtain one or more covenants of at least one of a first covenant category and a second covenant category, wherein the first covenant category is a disconnected dependent covenant category, and the second covenant category is a connected dependent covenant category, comprise: associating between each of the covenant among the one or more covenants and providing an outcome as one of a positive correlation, a negative correlation, or a zero correlation between the one or more covenants, based on the one or more associated covenants; training, by using a binary technique via the one or more hardware processors, a first machine learning model based on the input training data and one or more covenants of the first covenant category to obtain a first trained machine learning model; obtaining, via the one or more hardware processors, at least a subset of the input training data; iteratively performing via the one or more hardware processors, for each covenant of the second covenant category, until a number of predicted covenants corresponding to the second covenant category is less than or equal to an iteration count, to obtain a second trained machine learning model, wherein the subset of the input training data serves as a one-time input, comprises: processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, a second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration, wherein the iteration is proportional to a number of connected dependent covenant counts, and the number of connected dependent covenants are dependent on each other that are formed in groups, used to derive the one or more intermediatory machine learning models at each iteration and results in the second trained machine learning model; obtaining, via the one or more hardware processors, a test loan application corresponding to an entity; applying, via the one or more hardware processors, the first trained machine learning model and the second trained machine learning model on the test loan application corresponding to the entity to obtain a plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category; applying, via the one or more hardware processors, a probability technique on the plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category to obtain a set of sorted predicted covenants and an associated probability score thereof, comprises: identifying a plurality of rules; applying each rule, among the plurality of rules, during prediction on the entity and a loan data to calculate the associated probability score for each covenant, wherein the each rule compares attribute values of the entity and the loan data with a pre-computed co-efficient value; determining whether a co-efficient value is less or greater than the pre-computed co-efficient value, followed by: enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value; or disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value; and obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data, wherein the observations comprise details of the covenants being recommended and the covenants not recommended; recommending, via the one or more hardware processors, at least a subset of sorted predicted covenants, in a table, from the set of sorted predicted covenants to a user based on the associated probability score; displaying, via the one or more hardware processors, the at least a subset of sorted predicted covenants to an underwriter obtaining, via the one or more hardware processors, selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model; and modifying, via the one or more hardware processors, the recommended subset of sorted predicted covenants in the table, in accordance with the user feedback. These series of steps describe the abstract idea of obtaining and processing an entity’s (entities’) historical details, loan information, and recommended loan covenants (with the exception of the italicized and bolded terms above), which is mitigating risk of bias at the time of underwriting of a loan by using covenants as the first line of defense while administering a loan; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan application corresponding to an entity to obtain a plurality of predicted covenants that are used while administering a loan, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional limitations of one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models, limitations 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. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Similar arguments can be extended to the other independent claims, claims 6 and 11; and hence, claims 6 and 11 are rejected on similar grounds as claim 1.
Thus, claims 1, 6, and 11 are not patent-eligible.
Response to Arguments
Applicant's arguments filed on 11/19/2025 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of 1-3, 6-8, and 11-13 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “Claims 1, 6, and 11 are not directed to an abstract idea. …. Step 2A: claims are not directed to law of nature, natural phenomenon, or abstract idea.”
Examiner respectfully disagrees.
Under Step 2A: Prong I, as previously discussed in the Final office action-dated 04/11/2025 and Non-Final office action-dated 08/22/2025, Examiner respectfully notes that claims 1, 6, and 11, as amended, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of obtaining and processing an entity’s (entities’) historical details, loan information, and recommended loan covenants; without significantly more. The series of steps recited in claims 1, 6, and 11, as amended, describe the abstract idea of obtaining and processing an entity’s (entities’) historical details, loan information, and recommended loan covenants, which is mitigating risk of bias at the time of underwriting of a loan by using covenants as the first line of defense while administering a loan; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing of a loan application corresponding to an entity to obtain a plurality of predicted covenants that are used while administering a loan, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the system limitations, e.g., one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, and as discussed in the 2024 Updated Guidance on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations: “obtaining, via one or more hardware processors, an input training data comprising historical details of one or more entities, historical loan information corresponding to the one or more entities, and one or more corresponding recommended covenants; performing, via the one or more hardware processors, a data exploration analysis on the input training data to obtain one or more covenants of at least one of a first covenant category and a second covenant category, wherein the first covenant category is a disconnected dependent covenant category, and the second covenant category is a connected dependent covenant category, comprise: associating between each of the covenant among the one or more covenants and providing an outcome as one of a positive correlation, a negative correlation, or a zero correlation between the one or more covenants, based on the one or more associated covenants; training, by using a binary technique via the one or more hardware processors, a first machine learning model based on the input training data and one or more covenants of the first covenant category to obtain a first trained machine learning model; obtaining, via the one or more hardware processors, at least a subset of the input training data; iteratively performing via the one or more hardware processors, for each covenant of the second covenant category, until a number of predicted covenants corresponding to the second covenant category is less than or equal to an iteration count, to obtain a second trained machine learning model, wherein the subset of the input training data serves as a one-time input, comprises: processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, a second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration, wherein the iteration is proportional to a number of connected dependent covenant counts, and the number of connected dependent covenants are dependent on each other that are formed in groups, used to derive the one or more intermediatory machine learning models at each iteration and results in the second trained machine learning model; obtaining, via the one or more hardware processors, a test loan application corresponding to an entity; applying, via the one or more hardware processors, the first trained machine learning model and the second trained machine learning model on the test loan application corresponding to the entity to obtain a plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category; applying, via the one or more hardware processors, a probability technique on the plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category to obtain a set of sorted predicted covenants and an associated probability score thereof, comprises: identifying a plurality of rules; applying each rule, among the plurality of rules, during prediction on the entity and a loan data to calculate the associated probability score for each covenant, wherein the each rule compares attribute values of the entity and the loan data with a pre-computed co-efficient value; determining whether a co-efficient value is less or greater than the pre-computed co-efficient value, followed by: enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value; or disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value; and obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data, wherein the observations comprise details of the covenants being recommended and the covenants not recommended; recommending, via the one or more hardware processors, at least a subset of sorted predicted covenants, in a table, from the set of sorted predicted covenants to a user based on the associated probability score; displaying, via the one or more hardware processors, the at least a subset of sorted predicted covenants to an underwriter obtaining, via the one or more hardware processors, selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model; and modifying, via the one or more hardware processors, the recommended subset of sorted predicted covenants in the table, in accordance with the user feedback” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Applicant argues that “independent amended claims 1, 6, and 11 are patent- eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., identifying association between each of the covenants among the one or more covenants and providing an outcome as one of a positive correlation, a negative correlation, or a zero correlation between the one or more covenants, based on the one or more associated covenants... …… independent amended claims 1, 6, and 11 are patent-eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., the subset of the input training data serves as a one-time input.. .….. independent amended claims 1, 6, and 11 are patent-eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., identifying a plurality of rules and applying each rule during prediction on the entity and a loan data to calculate the associated probability score for each covenant. Each rule, among the plurality of rules, compares attribute values of the entity and the loan data with a pre-computed co-efficient value. …… independent amended claims 1, 6, and 11 are patent-eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a))….. the claimed subject matter is patent-eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., selection of underwriter is also sent back to model (e.g., machine learning (ML) model(s)) for further tuning for better learning/training of the machine learning (ML) models…..independent amended claims 1, 6, and 11 are patent-eligible as they integrate a judicial exception into a practical application in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(1) and 2106.05(a)) i.e., first covenant category is a disconnected dependent covenant category, and the second covenant category is a connected dependent covenant category. …. Along similar lines the claimed subject matter discloses deriving one or more intermediatory machine learning models at each iteration and results in the second trained machine learning model…..Therefore, Applicant believes that the judicial exception is integrated into a practical application, and claims 1, 6, and 11 are not directed to a judicial exception and are patent-eligible.”
Examiner respectfully disagrees.
Under Step 2A: Prong II, Examiner respectfully notes that there is no improved technology in simply obtaining, inputting, analyzing, training , processing, associating, providing, determining, enabling, disabling, displaying, predicting, counting, comparing, testing, applying, recommending, sorting, modifying, and outputting data (i.e., historical details of one or more entities, historical loan information, recommended covenants/agreement conditions, user feedback, covenant data, agreement conditions, etc.). The disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “obtaining, via one or more hardware processors, an input training data comprising historical details of one or more entities, historical loan information corresponding to the one or more entities, and one or more corresponding recommended covenants; performing, via the one or more hardware processors, a data exploration analysis on the input training data to obtain one or more covenants of at least one of a first covenant category and a second covenant category, wherein the first covenant category is a disconnected dependent covenant category, and the second covenant category is a connected dependent covenant category, comprise: associating between each of the covenant among the one or more covenants and providing an outcome as one of a positive correlation, a negative correlation, or a zero correlation between the one or more covenants, based on the one or more associated covenants; training, by using a binary technique via the one or more hardware processors, a first machine learning model based on the input training data and one or more covenants of the first covenant category to obtain a first trained machine learning model; obtaining, via the one or more hardware processors, at least a subset of the input training data; iteratively performing via the one or more hardware processors, for each covenant of the second covenant category, until a number of predicted covenants corresponding to the second covenant category is less than or equal to an iteration count, to obtain a second trained machine learning model, wherein the subset of the input training data serves as a one-time input, comprises: processing, by using a classification technique via the one or more hardware processors, the at least the subset of the input training data and one or more covenants of the second covenant category to obtain a set of predicted covenants; performing a comparison of (i) number of covenants of the second covenant category comprised in the set of predicted covenants, and (ii) the iteration count; and training, via the one or more hardware processors, a second machine learning model based on the comparison to obtain the second trained machine learning model, wherein the second trained machine learning model is obtained based on one or more intermediary machine learning models being trained at each iteration, wherein the iteration is proportional to a number of connected dependent covenant counts, and the number of connected dependent covenants are dependent on each other that are formed in groups, used to derive the one or more intermediatory machine learning models at each iteration and results in the second trained machine learning model; obtaining, via the one or more hardware processors, a test loan application corresponding to an entity; applying, via the one or more hardware processors, the first trained machine learning model and the second trained machine learning model on the test loan application corresponding to the entity to obtain a plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category; applying, via the one or more hardware processors, a probability technique on the plurality of predicted covenants corresponding to at least one of the first covenant category and the second covenant category to obtain a set of sorted predicted covenants and an associated probability score thereof, comprises: identifying a plurality of rules; applying each rule, among the plurality of rules, during prediction on the entity and a loan data to calculate the associated probability score for each covenant, wherein the each rule compares attribute values of the entity and the loan data with a pre-computed co-efficient value; determining whether a co-efficient value is less or greater than the pre-computed co-efficient value, followed by: enabling recommendation of a predicted covenant in response to determining that the co-efficient value is less than the pre-computed co-efficient value; or disabling recommendation of the predicted covenant in response to determining that the co-efficient value is greater than the pre-computed co-efficient value; and obtaining the associated probability score for the predicted covenant based on total number of predicted covenants, and observations from the input training data, wherein the observations comprise details of the covenants being recommended and the covenants not recommended; recommending, via the one or more hardware processors, at least a subset of sorted predicted covenants, in a table, from the set of sorted predicted covenants to a user based on the associated probability score; displaying, via the one or more hardware processors, the at least a subset of sorted predicted covenants to an underwriter obtaining, via the one or more hardware processors, selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model; and modifying, via the one or more hardware processors, the recommended subset of sorted predicted covenants in the table, in accordance with the user feedback.” as previously discussed in the Final office action-dated 04/11/2025 and Non-Final office action-dated 08/22/2025, and unlike Examples 47-49 of the 2024 updated Guidance on Subject Matter Eligibility, the recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (loan application data), and no technical solution or improvement has been disclosed. Furthermore, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of obtaining and processing an entity’s (entities’) historical details, loan information, and recommended loan covenants. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. In addition, all uses of the recited judicial exceptions require such data gathering and outputting; and therefore, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Moreover, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Furthermore, these steps, as amended, are recited as being performed by one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models. The additional elements: one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Similar to 2024 Guidance Update on Patent Subject Matter Eligibility: Example 47 (claim 2), amended claims 1, 6, and 11 recite one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models, which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong 1, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Hence, unlike Example 39, Claims 1, 6, and 11, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Applicant argues that “Step 2B: claims recite additional element (s) that amount to significantly more than the judicial exception (s) ….. Applicant believes that the subject matter of claims 1, 6, and 11 achieves significantly more in terms of obtaining associated probability score for the predicted covenant based on a total number of predicted covenants, and observations from the input training data. The observations include which of the covenants are being recommended and which of the covenants are not recommended. Enabling recommendation of a predicted covenant in response to determining a co-efficient value is less than the pre-computed co-efficient value, and disabling recommendation of the predicted covenant in response to determining the co-efficient value is greater than the pre-computed co-efficient value.…... Referring to Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc. (Fed. Cir. 2018)….Along similar lines as the Federal Circuit decision (summary window), the claimed subject matter presents/displays at least a subset of sorted predicted covenants to an underwriter, and obtain selection of the one or more covenants in the table from the underwriter, and sent back as an user feedback to the first trained machine learning model and the second trained machine learning model. …… Therefore, taking all the claim elements individually, or in combination, the claim as a whole amounts to “significantly more” than an abstract idea of itself (Step 2B: Yes). Applicant requests the Examiner to consider the above mentioned arguments and submissions on merits. By means of the aforementioned submissions, Applicant respectfully submits that the subject matter claimed does not merely constitute an abstract idea and constitutes significantly more than an abstract idea. Accordingly, Applicant respectfully requests withdrawal of the rejection of claims 1, 6, and 11 under 35 U.S.C § 101.”
Examiner respectfully disagrees.
Under Step 2B, as previously discussed in the Final office action-dated 04/11/2025 and Non-Final office action-dated 08/22/2025, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models 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. Hence, unlike the referenced case, Core Wireless, the additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Applying the 2024 Guidance Update on Patent Subject Matter Eligibility here, and as explained with respect to Step 2A, Prong II, the additional elements: one or more hardware processors, first machine learning model, first trained machine learning model, second trained machine learning model, second machine learning model, and one or more intermediary machine learning models, are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations, as amended, amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. (Step 2B: NO).
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1, 6, and 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Bjonerud (U.S. Patent Application Publication No. US 2019/0102835 A1) “Artificial intelligence derived anonymous marketplace”
Cella (U.S. Patent Application Publication No. US 2020/0184556 A1) “Adaptive intelligence and shared infrastructure lending transaction enablement platform responsive to crowd sourced information”
Silberman (U.S. Patent Application Publication No. US 2021/0056569 A1) “Detecting and reducing bias (including discrimination) in an automated decision making process”
Koo (U.S. Patent No. US 11,663,660 B1) “Information display and decision making”
Moghadam (U.S. Patent Application Publication No. US 2020/0334569-A1) “Using hyperparameter predictors to improve accuracy of automatic machine learning model selection”
Lesner (U.S. Patent Application Publication No. US 2021/0406780-A1) “Training an ensemble of machine learning models for classification prediction”
Datta (U.S. Patent Application Publication No. US 2022/0012613-A1) “System and method for evaluating machine learning model behavior over data segments”
Prendki (U.S. Patent Application Publication No. US 2022/0138561-A1) “Data valuation using meta-learning for machine learning programs”
Cella (U.S. Patent Application Publication No. US 2020/0273100-A1) “System and method that varies the terms and conditions of a subsidized loan”
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693