Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
1. This office action is in response to an amendment received on 3/12/26.
2. Claims 19, 31, 43 are amended.
3. Claims 19, 21-25, 31-38, and 40-43 are pending.
Applicant argues#1
The claims are not directed to a judicial exception.
Initially, Applicant respectfully submits that the claims do not recite a judicial
exception because they do not recite: (i) mathematical concepts, (ii) certain methods of
organizing human activity, or (iii) mental processes.
With regard to (i), the claims do not recite mathematical relationships, mathematical
formulas or equations, or mathematical calculations. With regard to (ii), the claims do not recite methods of organizing human activity such as those relating to: fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of
contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or
instructions).
2019 Guidance at p. 52; see also October 2019 updates: Subject Matter Eligibility at
Section II. In vivid contrast, the instant claims are directed to training predictive sub- models on historical data and generating responsibility profiles for individualized segment- based treatments and therefore involve none of the activity listed in the 2019 Guidance.
With regard to (iii), as acknowledged by the Office (See office action at page 4), the
claims do not recite "concepts performed in the human mind" like "observation,
evaluation, judgement, [or] opinion." Id.
Applicant acknowledges the Examiner's characterization of the claims as relating to
credit scoring. However, as explained below, even if the claims are considered to recite
subject matter falling within a judicial exception, the claims as a whole integrate any such subject matter into a practical application under Step 2A, Prong Two of the 2019 Guidance.
Examiner Response
Examiner respectfully disagrees.
The claims were properly categorized as a fundamental economic practice (steps for scoring of an entity based on predicted data changes).
See the section 101 rejection below.
The rejection is maintained.
Applicant argues#2
The claims are directed to a practical application
i. The claims reflect an improvement to a technology and technical field which is indicative of integrating an alleged judicial exception into a practical application
Even if, arguendo, the claims recite a judicial exception (Applicant is not conceding
that the claims recite a judicial exception), the claims as a whole contain additional elements that integrate the alleged judicial exception into a practical application.
Claim 19, for instance, recites additional elements that integrate the claimed subject
matter into a practical application. These additional elements include:
querying a plurality of databases to collect a training dataset for training
the predictive model, wherein the training dataset characterizes historical
creditworthiness data of a plurality of entities, wherein each data entry of the
training dataset is associated with at least one credit scoring date;
retrieving a first data set corresponding to first changes occurring over a
first period of time, the first changes being from a first state to a second state and
associated with a first entity in the plurality of entities;
retrieving a second data set corresponding to second data changes
occurring over a second period of time, the second changes being from the first
state to the second state and associated with a second entity in the plurality of
entities, wherein the first and second data changes, respectively associated with
the first and second entities, are determined in connection with a plurality of data
attributes;
transforming, based on the plurality of data attributes, the training dataset
into M pre-defined performance behaviors by measuring responsible or
irresponsible acts during a performance period after a credit scoring date, each of
the M pre-defined performance behavior characterizing behavior an entity as
associated with either a responsible state or an irresponsible state during at least
one of the first and second periods of time based on the first changes and/or the
second changes;
performing factor analysis on the M pre-defined performance behaviors to
reduce the M pre-defined performance behaviors into a plurality of N
performance dimensions including a plurality of labels representing an entity's
aptitude, the N performance dimensions being fewer than the M pre-defined
performance behaviors, the N performance dimensions representing a reduced
data set, at least one of the N performance dimensions having a unique variance
and being orthogonal with regard to at least one other performance dimension of
the N performance dimensions, wherein the factor analysis characterizes
variability among observed performance variables in terms of a lower number of
unobserved dimensions, and wherein the observed performance behaviors are
modeled as linear combinations of potential factors plus error terms;
generating, from the factor analysis, a factor loading matrix associating the
M performance behaviors with the N performance dimensions;
assigning a responsibility label to the first entity based on the N
performance dimensions in the reduced data set, instead of using the M pre-
defined performance attributes, for use in training the predictive model;
training, for each of the M pre-defined performance behaviors, a
predictive sub-model using expected data changes over time with respect to one
or more selected data attributes associated with one or more of the first entity and
the second entity, wherein the predictive sub-model is configured to predict
whether an entity exhibits a responsible or irresponsible state for the
corresponding pre-defined performance behavior;
associating matching states of the one or more of the M pre-defined
performance behaviors with one or more of the N performance dimensions, using
the factor loading matrix generated during training, wherein the predictive model
associates matching states of the pre-defined performance behaviors with
matching states of performance dimensions;
(As recited in claim 19, as amended herein.)
The above additional elements collectively integrate the subject matter into a
practical application by at least providing improvements in a technical field, for example,
through the operation of transforming historical creditworthiness data into pre-
defined performance behaviors measured during a performance period after a
credit scoring date, and performing factor analysis that characterizes variability
among observed performance variables in terms of a lower number of unobserved
dimensions, wherein the observed performance behaviors are modeled as linear
combinations of potential factors plus error terms, and wherein the predictive model
associates matching states of performance behaviors with matching states of
performance dimensions via a factor loading matrix. The system also trains predictive
sub-models for the M pre-defined performance behaviors using historical data changes
over time, enabling the generation of individualized responsibility profiles and segment-
based treatment recommendations for credit decisioning.
The Examiner's rejection treats the factor analysis and factor loading matrix as mere
mathematical tools. However, as recited in the amended claims, these elements impose
structural constraints on how the predictive model represents, associates, and processes behavioral states, thereby altering the operation of the predictive model itself rather than merely producing an abstract result.
Examiner Response
Examiner respectfully disagrees.
The limitations (querying a plurality of databases to collect a dataset, the dataset characterizes historical creditworthiness data of a plurality of entities, wherein each data entry of the training dataset is associated with at least one credit scoring date; retrieving a first data set corresponding to first changes occurring over a first period of time, the first changes being from a first state to a second state and associated with a first entity in the plurality of entities; retrieving a second data set corresponding to second data changes occurring over a second period of time, the second changes being from the first state to the second state and associated with a second entity in the plurality of entities, wherein the first and second data changes, respectively associated with the first and second entities, are determined in connection with a plurality of data attributes;
transforming, based on the plurality of data attributes, the dataset into M pre-defined performance behaviors by measuring responsible or irresponsible acts during a performance period after a credit scoring, each of the M pre-defined performance behavior characterizing behavior an entity as associated with either a responsible state or an irresponsible state during at least one of the first and second periods of time based on the first changes and/or the second changes; to reduce the M pre-defined performance behaviors into a plurality of N performance dimensions including a plurality of labels representing an entity’s aptitude, the N performance dimensions being fewer than the M pre-defined performance behaviors, the N performance dimensions representing a reduced data set, at least one of the N performance dimensions having a unique variance and being orthogonal with regard to at least one other performance dimension of the N performance dimensions
; assigning a responsibility label to the first entity based on the N performance dimensions in the reduced data set, instead of using the M pre- defined performance attributes; to predict whether an entity exhibits a responsible or irresponsible state for the corresponding pre-defined performance behavior) is part of the identified abstract idea.
The additional elements (the processor executing the predictive model comprising predictive sub-model) are recited at a high level of generality, all operating in their ordinary capacity (the predictive model is being used to determine a future outcome), and thus are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
The training of the predictive sub-model to model expected data changes and matching different states of data, performing factor analysis on the M pre-defined behaviors, generating, from the factor analysis (where factor analysis is a commonly used statistical technique to reduce large number of correlated variables into a smaller, more manageable data set), and further the factor loading matrix is generally linking the abstract idea of to a particular technological environment (predictive modelling). See MPEP 2106.05(h).
Therefore, there are no additional elements that are indicative of integration into a practical application.
Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (determining risk of an entity based on credit data) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology
The rejection is maintained.
Applicant argues#3
ii. The claims are analogies to Example 40.
The approach recited in the present Claim 19 is analogous to the approach in Example
40 of the 2019 PEG. In Example 40, the claims address the challenge of network monitoring by collecting baseline traffic data (e.g., delay, loss, jitter), comparing it to a threshold, and then selectively collecting higher-granularity NetFlow data only upon detecting an abnormal condition. This transforms raw traffic data into actionable monitoring decisions, thereby reducing unnecessary data collection and improving network performance.
Similarly, Claim 19 transforms large-scale historical creditworthiness data-
characterized by multiple predefined performance behaviors-by applying factor analysis to derive a reduced set of N orthogonal performance dimensions. It then uses these dimensions, along with trained predictive sub-models, to generate individualized responsibility profiles and assign consumers to specific risk-based segments. This enables conditional downstream system actions based on a reduced and orthogonal representation of behavioral states, analogous to how Example 40 conditions subsequent data collection and processing on transformed network metrics. Just as Example 40 integrates a threshold comparison into a practical application to improve network monitoring efficiency, Claim 19 integrates advanced data transformation and modeling techniques into a practical application that improves the efficiency and precision of consumer credit risk management.
Examiner Response
Examiner respectfully disagrees.
The claims of the instant application are unlike hypothetical example 40.
In Example 40, the explanation states on the bottom of page 11, “the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring.
In the claims of the instant invention there is no improvement to a prior art system, and there is no improvement to any improvement to monitoring of network data.
Therefore the claims are unlike claim 1 of hypothetical example 40.
The rejection is maintained
Applicant argues#4
B. The claims include additional elements that amount to significantly more than a judicial exception
Applicant submits that the claims are patent eligible under Step 2B because the
additional elements or their combination do not constitute well-understood, routine,
conventional activity.
Applicant further submits that the claims are patent-eligible under Step 2B because
the additional elements recited in the amended claims are not shown to be well-understood, routine, or conventional. In particular, the Examiner has not identified any evidence demonstrating that modeling observed performance behaviors as linear combinations of latent factors plus error terms, or associating matching behavioral states with matching performance-dimension states via a factor loading matrix as part of a predictive model, were conventional techniques in the context of credit scoring systems at the time of the invention.
At a minimum, the amended claims raise a factual dispute regarding whether the claimed combination was well-understood, routine, or conventional, precluding a rejection under §101 at this stage.
For at least these reasons, Applicant respectfully request withdrawn of the subject
matter eligibility rejection.
Examiner Response
Examiner respectfully disagrees.
Applicant misapprehends when a Berkheimer analysis is required under current examination policy. Simply put, Examiner is not required under current Examination policy to evaluate under Step 2B, whether additional elements constitute “well-understood, routine, and conventional activities,” [“WURC activities”] unless an additional element(s) were found to be insignificant extra-solution activity in Step 2A, Prong 2. MPEP § 2106.05(d)(I). Here, the condition precedent was not met and the Non-Final Office Action determined the additional elements were no more than mere instructions to apply the abstract idea exception using a computer. MPEP § 2106.05(f). Thus, Examiner was not required to determine a Berkheimer analysis. MPEP § 2106.05(d)(I). (See Section 101 rejection below).
Claim Rejections- 35 U.S.C § 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.
1. Claims 19, 21-25, 31-38, and 40-43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 19, 21-26, 28 are directed to method, claims 31-38, 40-42 are directed to a product, claim 43 is directed to a system, which are one of the statutory categories of invention. (Step 1: YES).
Claim 19 recites the limitations of:
A method for providing a treatment using a predictive model comprising at least one predictive sub-model, wherein the predictive model is stored in one or more non-transitory data storage media, the method implemented as logic code executed on one or more processors to cause a computer to perform operations comprising:
querying a plurality of databases to collect a training dataset for training the predictive model, wherein the training dataset characterizes historical creditworthiness data of a plurality of entities, wherein each data entry of the training dataset is associated with at least one credit scoring date;
retrieving a first data set corresponding to first data changes occurring over a first period of time, the first changes being from a first state to a second state and associated with a first entity in the plurality of entities;
retrieving a second data set corresponding to second data changes occurring over a second period of time, the second changes being from the first state to the second state and associated with a second entity in the plurality of entities, wherein the first and second data changes, respectively associated with the first and second entities, are determined in connection with a plurality of data attributes;
transforming, based on the plurality of data attributes, the training dataset into M pre-defined performance behaviors by measuring responsible or irresponsible acts during a performance period after a credit scoring date, each of the M pre-defined performance behavior characterizing behavior an first entity as associated with either a responsible state or an irresponsible state during at least one of the first and second periods of time based on the first changes and/or the second changes;
performing factor analysis on the M pre-defined performance behaviors to reduce the M pre-defined performance behaviors into a plurality of N performance dimensions including a plurality of labels representing an entity’s aptitude, the N performance dimensions being fewer than the M pre-defined performance behaviors, the N performance dimensions representing a reduced data set, at least one of the N performance dimensions having a unique variance and being orthogonal with regard to a least one other performance dimension of the N performance dimensions, wherein the factor analysis characterizes variability among observed performance variables in terms of a lower number of unobserved diemensions, and wherein the observed performance behaviors are modeled as linear combinations of potential factors plus error terms;
generating, from the factor analysis, a factor loading matrix associating the M performance behaviors with the N performance dimensions;
assigning a responsibility label to the first entity based on the N performance dimensions in the reduced data set, instead of using the M pre- defined performance attributes for use in training the predictive model;
training, for each of the M pre-defined performance behaviors, a predictive sub-model using expected data changes over time with respect to one or more selected data attributes associated with one or more of the first entity and the second entity, wherein the predictive sub-model is configured to predict whether an entity exhibits a responsible or irresponsible state for the corresponding pre-defined performance behavior;
using at least one of the trained predictive sub-models to predict, based on the first data changes for the first entity and the second data changes for the second entity, third data changes for a third entity during at least a third period of time, the third entity characterized as associated with at least one of the one or more selected attributes;
associating matching states of the one or more of the M pre-defined performance behaviors with one or more of the N performance dimensions, using the factor loading matrix generated during training, wherein the predictive model associates matching states of the predefined performance behaviors with matching states of performance dimensions;
generating, for the third entity, a partial score associated with the third entity quantifying predicted third data changes for the third entity, wherein the partial score is based at least in part on a responsibility score for the corresponding pre-defined performance behavior from the M-predefined performance behavior; and
generating a score at least in part by aggregating the partial score;
generating a responsibility profile for the third entity by processing historical creditworthiness data associated with the third entity using the factor loading matrix and the N performance dimensions, wherein the responsibility profile comprises a composite representation across the N performance dimensions;
placing the third entity into at least one or more of a first, second, third, or fourth segment based at least in part on the responsibility profile; and
providing a treatment to the third entity,
wherein the treatment is a balance transfer if the third entity is placed into the first segment,
wherein the treatment is a penalty pricing if the third entity is placed into the second segment,
wherein the treatment is an early collection if the third entity is placed into the third segment, and
wherein the treatment is a credit limit increase or a promotional annual percentage rate (APR) if the third entity is placed into the fourth segment.
The claim elements that are in bold above, (e.g., querying a plurality of databases to collect a dataset, wherein the dataset characterizes historical creditworthiness data of a plurality of entities, wherein each data entry of the training dataset is associated with at least one credit scoring date; retrieving a first data set corresponding to first data changes occurring over a first period of time, the first changes being from a first state to a second state and associated with a first entity in the plurality of entities; retrieving a second data set corresponding to second data changes occurring over a second period of time, the second changes being from the first state to the second state and associated with a second entity in the plurality of entities, wherein the first and second data changes, respectively associated with the first and second entities, are determined in connection with a plurality of data attributes; transforming, based on the plurality of data attributes, the training dataset into M pre-defined performance behaviors by measuring responsible or irresponsible acts during a performance period after a credit scoring date, each of the M pre-defined performance behavior characterizing behavior an first entity as associated with either a responsible state or an irresponsible state during at least one of the first and second periods of time based on the first changes and/or the second changes; to reduce the M pre-defined performance behaviors into a plurality of N performance dimensions including a plurality of labels representing an entity’s aptitude, the N performance dimensions being fewer than the M pre-defined performance behaviors, the N performance dimensions representing a reduced data set, at least one of the N performance dimensions having a unique variance and being orthogonal with regard to a least one other performance dimension of the N performance dimensions; assigning a responsibility label to the first entity based on the N performance dimensions in the reduced data set, instead of using the M pre- defined performance attributes; to predict whether an entity exhibits a responsible or irresponsible state for the corresponding pre-defined performance behavior; to predict, based on the first data changes for the first entity and the second data changes for the second entity, third data changes for a third entity during at least a third period of time, the third entity characterized as associated with at least one of the one or more selected attributes; associating matching states of the one or more of the M pre-defined performance behaviors with one or more of the N performance dimensions, using the factor loading matrix generated during training; generating, for the third entity, a partial score associated with the third entity quantifying predicted third data changes for the third entity, wherein the partial score is based at least in part on a responsibility score for the corresponding pre-defined performance behavior from the M-predefined performance behavior; and generating a score at least in part by aggregating the partial score; generating a responsibility profile for the third entity by processing historical creditworthiness data associated with the third entity using the factor loading matrix and the N performance dimensions, wherein the responsibility profile comprises a composite representation across the N performance dimensions; placing the third entity into at least one or more of a first, second, third, or fourth segment based at least in part on the responsibility profile; and providing a treatment to the third entity, wherein the treatment is a balance transfer if the third entity is placed into the first segment, wherein the treatment is a penalty pricing if the third entity is placed into the second segment, wherein the treatment is an early collection if the third entity is placed into the third segment, and wherein the treatment is a credit limit increase or a promotional annual percentage rate (APR) if the third entity is placed into the fourth segment), under its broadest reasonable interpretation, covers performance of the limitation(s) as a fundamental economic practice of risk mitigation (steps for scoring an entity based on predicted data changes).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Fundamental Economic Practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly claim 19 recites an abstract idea.
Claims 31,43 recite substantially the same subject matter as claim 19, and are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 19, 31, 43 includes the following additional elements:
- A predictive model comprising at least one predictive sub-model
- A computer
- One or more processors
- Training the predictive sub-model of the at least one predictive sub-model by incorporating into the predictive sub- model, expected data changes over time with respect to the one or more selected attributes for each of the first and second entities.
-A programmable processor
-A machine readable medium
-Performing factor analysis on the M pre-defined behaviors
-Generating, from the factor analysis a factor loading matrix
-The predictive model matches states of the predetermined performance behaviors with matching states of performance dimensions
The training of the predictive sub-model to model expected data changes and matching different states of data, performing factor analysis on the M pre-defined behaviors, wherein the factor analysis characterizes variability among observed performance variables in terms of a lower number of unobserved dimensions, and wherein the observed performance behaviors are modelled as linear combinations of potential factors plus error terms and generating, from the factor analysis a factor loading matrix is generally linking the abstract idea of to a particular technological environment (predictive modelling). See MPEP 2106.05(h).
The computer, one or more processors, programmable processor, machine readable medium are recited at a high level of generality, are all operating in their ordinary capacity are executing the predictive model comprising at least one predictive sub-model (predictive models are designed to determine a future outcome by matching predefined behavior with matching states of performance dimensions), and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Therefore, there are no additional elements in the claim that amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
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. Therefore claims 19, 31, 43 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 19, 31, 43 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 21-25, 32-38, 40-42 which further define the abstract idea that is present in their respective independent claims 19, 31, 43 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Claim 21 further defines the identified abstract idea (wherein the predictive model is configured to apply a ratio analysis or factor analysis to generate results).
Applying a ratio analysis and factor analysis with the predictive model is using the predictive model, which is recited at a high level of generality and operating in its ordinary capacity, as a tool to implement the steps of the identified abstract idea.
Claim 38 further defines the identified abstract idea (wherein the predictive model uses a score card methodology).
The use of a score card methodology by a predictive model is using the predictive model, which is recited at a high level of generality and operating in its ordinary capacity, as a tool to implement the steps of the identified abstract idea.
The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims 21-25, 32-38, 40-42 are directed to an abstract idea. Thus, claims 19, 21-25, 31-38, and 40-43 are not patent-eligible.
CONCLUSION
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BENNETT SIGMOND can be reached at 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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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 5/28/2026
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