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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/15/2025 has been entered.
Response to Arguments
Applicant's arguments filed 05/16/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC 101 rejection of claims 6-10 and 12-23 are applied in light of Applicant's amendments.
The Applicant argues “The system integrates machine learning, behavioral modeling and model selection, into a practical application that enables intervention actions based on officer-specific risk predictions.” (Remarks 09/15/2025)
In response, the Examiner respectfully disagrees. The Examiner has thoroughly reviewed and analyzed the claims, arguments, and specification. The Examiner attempted to find eligible subject matter in the claims and/or specification, but was unsuccessful; and thus, is unable to provide any suggestions/Examiner’s amendment to overcome the 101 rejection.
The claimed subject matter, is directed to an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts grouping.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding risk scores. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract ideas (mathematical concepts). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for risk scores, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding purchase information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
The use of Artificial Intelligence (AI), machine learning (ML) models, and/or artificial neural networks (ANN) fall within the realm of abstract ideas. They are, at their core, mathematical algorithms implemented on a computer. As highlighted in Examples 47-49 of the 2024 Patent Subject Matter Eligibility Guidance, the USPTO has consistently viewed claims directed to such models as being drawn to abstract ideas. These examples illustrate claims that, while couched in the language of specific applications, ultimately boil down to mathematical relationships and calculations.
For instance, Applicant claims "wherein the probability value is generated by executing a neural network." While this claim appears to have a practical application, a closer examination reveals that the core of the invention is the underlying mathematical model and its training process. Furthermore, even if the claim recites specific steps related to data collection, preprocessing, or post-processing, these steps often represent well-understood, conventional activities. As demonstrated in Examples 47-49, adding such conventional elements to a claim directed to an abstract idea does not necessarily transform it into a patent-eligible application. These examples illustrate situations where the additional steps were deemed insufficient to provide an "inventive concept" that meaningfully narrowed the scope of the abstract idea. In the context of machine learning, simply collecting and preparing data for input into a model, or applying the model's output to a particular problem, falls into this category of conventional activity. The Applicant has not created a new learning algorithm, but rather optimizing existing algorithm(s) or the application of known techniques to a new dataset. Such incremental advancements, while potentially valuable for business, do not automatically confer patent eligibility or a technological improvement. As highlighted in the Alice framework, the mere recitation of known components or processes does not necessarily amount to an inventive concept.
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 6-10 and 12-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 6-10 and 12-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is 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.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 18-23) and system (claims 6-17) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One of 2019 PEG, it is next noted that the claims recite an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” group within the enumerated groupings of abstract ideas set. The mere nominal recitation of a generic computer does not take the claim limitation out of the mathematical concepts grouping. .
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The limitations reciting the abstract idea (mathematical concepts), as set forth in exemplary claim 6, are: collecting and storing historical data about a plurality of characteristics and events relating to a plurality of police officers, …a first aggregate
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&owhether the risk score exceeds a threshold risk level and when the risk score exceeds the threshold risk level, initiate an intervention action for the officer for reducing the risk score of the officer to generate a predicted risk score for the officer…Independent claim18 recites the method for performing the system of independent claim 6 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to A machine-learning based system configured to generate a risk score that predicts whether an officer is at risk of involvement in an adverse event, comprising: a preprocessing module collecting and storing historical data … and creating and storing a first-level residual feature of the officer wherein the preprocessing module is configured to generate [[Y1 =]]…;a machine learning module for…,an alerter module configured to… wherein the display renders an interface displaying the risk score for the officer and the predicted risk score of the officer with groupings of adverse events, and the display renders a reduction or boost of adverse risk for the officer as depicted based on the risk score for the officer and the predicted risk score of the officer…; A computer-implemented method… a preprocessing module… (as recited in claims 6 and 18). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: A machine-learning based system configured to generate a risk score that predicts whether an officer is at risk of involvement in an adverse event, comprising: a preprocessing module collecting and storing historical data …;a machine learning module for…; A computer-implemented method… a preprocessing module… (as recited in claims 6 and 18) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0081]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (7-17 and 19-23) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 7-17, “Y1 =#l + Vunit+E1 where Vunit = * Uniti = A vector of coefficients that represent units the plurality of police officers work in and whether the individual officer works in that unit (the coefficient is the difference between that unit's first aggregate statistic and the average, holding all else constant; wherein the first-level residual feature is created based on additional historical data as follows:Y1=#l + Vgeo+E1 where Vgeo=pgeoi * Geoi = A vector of coefficients that represent the geographies the plurality of police officers are deployed to and whether the individual police officer works in that individual geography (the coefficient is the difference between that geography's first aggregate statistic and the average, holding all else constant); a risk-revising module for revising the officer’s risk score based on data other than data used to determine the initial risk score; an alerter module for displaying possible interventions to be taken for the officer based on the officer’s risk score; wherein the data to which the model is applied includes one or more of: aggregate data, preprocessed residuals, social structure residuals and latent class residuals; the preprocessing module is also for creating and storing a second-level residual feature, based on the first-level residual, as follows: where: Y2 =a second aggregate statistic, different from the first aggregate statistic, selected from the group consisting of number of arrests, number of traffic stops, number of dispatches, number of uses of force, number of vehicle pursuits, number of complaints, number of citations written, number of traffic stops, number of field interviews, secondary employment, officer training, existing flags, general neighborhood data, number of vehicle accidents, number of rule-of-conduct violations, number of injuries, number of raids conducted, results or rulings of complaints, whether events were justified or not, and whether some events were preventable or not, fB, = the average second aggregate statistic for the officer's department e2 = the officer's second-level residual feature and the machine level module is also for constructing and storing a plurality of models that predict the risk of an adverse event and generating a risk score, each of the models including the second-level residual feature; wherein the first-level residual feature is created based on additional historical data as follows: Y2 => Bo + Vunit + E2 where; Vunit = Yunit;=0 Punit, * Unit; = A vector of coefficients that represent units the plurality of police officers work in and whether the individual officer works in that unit (the coefficient is the difference between that unit's second aggregate statistic and the average, holding all else constant); wherein the first-level residual feature is created based on additional historical data as follows: Y2 => Bo + Vgeo + E2 where Vgeo = yj c0)=0 Bgeo, * Geo; = A vector of coefficients that represent the geographies the plurality of police officers are deployed to and whether the individual police officer works in that individual geography (the coefficient is the difference between that geography’s second aggregate statistic and the average, holding all else constant); Y1 => Bo + Viime + E2 where Viime = Ytime;=0 Prime, * Time; = A vector of coefficients that represent the time- periods during the day when an the individual police officer is deployed and whether the individual officer works in that time period (the coefficient is the difference between that time-period’s second aggregate statistic and the average, holding all else constant) and the average, holding all else constant); wherein the data to which the model is applied includes one or more of: aggregate data, preprocessed residuals, social structure residuals and latent class residuals”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (19-23) recite the method for performing the system of claims 7-17. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Shah; Tapan. Model Management System, .U.S. Patent 11010702 Different types of enterprises employ one or more models to evaluate risk associated with various aspects of each enterprise's business dealings. Some types of businesses, such as financial institutions, health care organizations, and insurance institutions, are subject to governmental regulations. In those instances, government regulators periodically evaluate the risk positions to ensure compliance with regulatory law. Depending on the size of the enterprise, tens, hundreds or thousands of models may be simultaneously in use.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/Primary Examiner, Art Unit 3625
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