DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This office action is in response to applicant's communication of December 4, 2025. The rejections are stated below. Claims 1-20 are pending and have been examined.
Response to Amendment/Arguments
2. Applicant’s arguments concerning 35 U.S.C. 101 have been considered but is not persuasive. Applicant's arguments have been considered but are not persuasive. The controlling legal framework for this rejection is the Supreme Court's decision in Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014), and its two step analysis.
In step one, the claims are directed to the abstract idea of collecting, analyzing, weighting, and scoring data to assess risk and control access. This is a fundamental economic or business practice, specifically a form of risk assessment and mitigation. The claims recite collecting raw data on user interactions, analyzing that data to identify anomalies, applying mathematical models to generate scores, weighting and aggregating those scores, and then using the result to control access. These steps are directed to the abstract concept of evaluating risk based on data analysis.
3. Applicant argues the claims recite a specific hybrid architecture that provides a technical improvement. However, the claimed steps of receiving data, analyzing it with models, weighting results, aggregating scores, and controlling access based on those scores, are functional concepts that can be performed in the human mind or with pen and paper. The recitation of an ensemble of unsupervised learning models and binary point anomalies describes mathematical and statistical techniques. The claims are directed to the application of these abstract mathematical and data analysis techniques to the concept of user risk assessment. Regarding step two, the claims do not recite an inventive concept sufficient to transform the abstract idea into a patent eligible application. The additional elements, when considered individually and as an ordered combination, do not add significantly more to the abstract idea. The claims require a processor, storage, and a network, which are computer components used to perform the abstract process. The steps of generating machine learning scores using an ensemble of models, weighting aggregated data with a pre-configured vector, and aggregating values are part of the data analysis itself and do not impose a meaningful limitation beyond the abstract idea.
4. The claimed controlling of access to a network service, described as blocking a user or reducing service parameters, is the intended outcome of the abstract risk assessment process. It is the application of the abstract idea in a technological environment, which is insufficient to confer eligibility. The claim does not recite a specific technological mechanism for controlling access beyond the high level outcome.
5. Applicant cites Enfish and McRO. These cases are distinguishable. In Enfish, the claims were directed to a specific self-referential data table that improved database functionality. In McRO, the claims recited specific rules that altered automated animation techniques. The instant claims do not recite a specific improvement to computer functionality or a specific technical solution. Instead, they recite the use of computers as tools to execute an abstract data analysis and risk assessment process. The claims do not detail how the computer functionality itself is improved.
6. Applicant also references Ex parte Desjardins. That decision is not binding precedent. Furthermore, the claims in that case were found to address a specific technical problem in machine learning known as catastrophic forgetting through a recited sequence of training steps. The instant claims recite applying existing data analysis techniques, ensemble models and binary point anomaly detection, to the field of user risk scoring. They do not solve a technical problem in the operation of computers or machine learning models themselves. The August 2025 memorandum does not alter the Alice analysis. The memorandum reiterates existing precedent and examination procedure. The analysis here correctly applies the Alice framework. The claims are directed to an abstract idea and the additional elements do not integrate that idea into a practical application that amounts to significantly more.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of assigning risk scores to users associated with data records without significantly more.
9. Claim 1 is directed to a method which is one of the four statutory categories of invention (Step 1: YES).
10. Claim 1 recites “a method comprising: receiving, by a …, raw data representing interactions of a set of users stored in a …;
analyzing, by the …, the raw data to identify an aggregate number of binary point anomalies (BPAs) for each user in the set of users;
generating, by the …, … scores for each user in the set of users using an ensemble of … models applied to the raw data;
weighting, by the …, the aggregate number of BPAs for each user in the set of users using a pre-configured weighting vector;
generating a set of weighted BPA values for each user;
aggregating, by the …, each set of weighted BPA values and the machine learning scores for each user to generate corresponding total scores for each user;
displaying, by the …, the corresponding total scores; and
…, by the …, access to a … service for a given user based on a corresponding total score; and
controlling …, access to a network service for a given user based on a corresponding total score, wherein controlling access comprises at least one of … blocking the given user from the network service or reducing available service parameters for the given users”.
11. These limitations describe an abstract idea of assigning risk scores to users associated with data records and corresponds to Certain Methods of Organizing Human Activity (managing personal behavior or relationships or interactions between people) and Mathematical Concepts (mathematical calculations). See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (assigning risk scores to users associated with data records) does not render the claim non-abstract”) (MPEP 2106.04 A II 2).
12. Accordingly, the claim 1 recites an abstract idea (Step 2A: Prong 1: YES).
13. This judicial exception is not integrated into a practical application. The additional elements, e.g., “processor, database, machine learning, unsupervised learning, controlling, by the processor” do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. As a result, the claims do not provide a practical application. And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field. Therefore, claim 1 is directed to an abstract idea without a practical application (Step 2A - Prong 2: NO).
14. Further, as the additional elements of claim 1 do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field. Thus, claim 1 is not patent eligible (Step 2B: NO).
15. Claim 2 recites “further comprising … access to a … service for a given user based on a corresponding total score in the corresponding total scores”. Therefore, the claim recites providing access to a service which further describes the abstract idea of assigning risk scores to users associated with data records. Claim 2 also includes additional elements such as “controlling, by a processor” and “network”. However, these do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. As a result, the claims do not provide a practical application. And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
16. Claim 3 recites “wherein analyzing the raw data to identify an aggregate number of binary point anomalies comprises computing a vector for each user, the vector having a dimensionality equal to the number of BPAs and the values within the vector comprising a count of how many times a given user is associated with a respective BPA” is drawn to mathematical concepts such as computing vectors which further defines the abstract idea.
17. Claim 4 recites “wherein prior to weighting the aggregate number of BPAs, the method further comprises normalizing the aggregate number of BPAs for each user” which represents the mathematical concept of normalization and further defines the abstract idea.
18. Claim 5 recites “wherein prior to output the corresponding total scores, the method further comprises normalizing the corresponding total scores” which represents the mathematical concept of normalization and further defines the abstract idea.
19. Claim 6 recites “for a given user in the set of users, combining a corresponding total score with a … score generated using a subset of the raw data and processed features associated with the given user, the … score …” is drawn to mathematical concepts and further define the abstract idea. The claim includes “machine learning” and “generated using an unsupervised learning algorithm” as additional elements. However, the claim lacks technological details regarding what generating a score using “machine learning” and “unsupervised learning” comprises. Therefore, they are no more than “apply it” (MPEP 2106.05(f)(1)). And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
20. Claim 7 recites “wherein the ML score is generated by scoring a set of features generated based on the subset of the raw data and processed features and averaging the scores to generate the ML score”. However, generating a score is a mathematical concept. Therefore, the claim does no more than further describe the abstract idea.
21. Claims 8 and 15 also recite the abstract idea of assigning risk scores to users associated with data records corresponds to Certain Methods of Organizing Human Activity (fundamental economic principles including insurance). Claim 8 includes the additional elements of “non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, receiving, by the computer processor, stored in a database, analyzing, by the computer processor, weighting, by the computer processor, generating, by the computer processor, aggregating, by the computer processor, and displaying by the processor”. Claim 15 includes the additional elements of “a device comprising a processor, a storage medium for tangibly storing thereon logic for execution by the processor, the logic comprising instructions, stored in a database”. The additional elements of claims 8 and 15 do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. There is no improvement to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a).
22. Claims 9 and 16 each recite “further comprising … access to a … service for a given user based on a corresponding total score in the corresponding total scores”. Therefore, the claim recites providing access to a service which further describes the abstract idea of assigning risk scores to users associated with data records. Therefore, it is no more than using a computer as a tool to further describe the abstract idea by including determining whether a user is eligible for a service (i.e. “apply it” (MPEP 2106.05(f)(1))). As a result, controlling does not provide a practical application. And, as the additional element does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, it does not improve computer functionality or improve another technology or technical field.
23. Claims 10 and 17 each recites “wherein analyzing the raw data to identify an aggregate number of binary point anomalies comprises computing a vector for each user, the vector having a dimensionality equal to the number of BPAs and the values within the vector comprising a count of how many times a given user is associated with a respective BPA” is drawn to mathematical concepts such as computing vectors which further defines the abstract idea.
24. Claims 11 and 18 each recites “wherein prior to weighting the aggregate number of BPAs, the method further comprises normalizing the aggregate number of BPAs for each user” which is drawn to mathematical concepts of normalization and, therefore, further define the abstract idea.
25. Claims 12 and 19 each recites “wherein prior to output the corresponding total scores, the method further comprises normalizing the corresponding total scores” which represents the mathematical concept of normalization and further defines the abstract idea.
26. Claims 13 and 20 each recites “for a given user in the set of users, combining a corresponding total score with a … score generated using a subset of the raw data and processed features associated with the given user, the … score …” is drawn to mathematical concepts and further define the abstract idea. The claims include “machine learning” and “generated using an unsupervised learning algorithm” as additional elements. However, the claim lacks technological details regarding what generating a score using “machine learning” and “unsupervised learning” comprises. Therefore, they are no more than “apply it” (MPEP 2106.05(f)(1)). And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field.
27. Claim 14 recites “wherein the ML score is generated by scoring a set of features generated based on the subset of the raw data and processed features and averaging the scores to generate the ML score”. However, generating a score is a mathematical concept. Therefore, the claim does no more than further describe the abstract idea.
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 KEVIN T POE whose telephone number is (571)272-9789. The examiner can normally be reached on Monday-Friday 9:30am through 6pm EST.
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/K.T.P/Examiner, Art Unit 3692 /KEVIN T POE/
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692