Prosecution Insights
Last updated: April 19, 2026
Application No. 17/400,797

DATA-ADAPTIVE INSIGHT AND ACTION PLATFORM FOR HIGHER EDUCATION

Non-Final OA §101
Filed
Aug 12, 2021
Examiner
TRAN, AMY NMN
Art Unit
2126
Tech Center
2100 — Computer Architecture & Software
Assignee
Civitas Learning Inc.
OA Round
4 (Non-Final)
36%
Grant Probability
At Risk
4-5
OA Rounds
5y 2m
To Grant
84%
With Interview

Examiner Intelligence

Grants only 36% of cases
36%
Career Allow Rate
10 granted / 28 resolved
-19.3% vs TC avg
Strong +48% interview lift
Without
With
+47.9%
Interview Lift
resolved cases with interview
Typical timeline
5y 2m
Avg Prosecution
24 currently pending
Career history
52
Total Applications
across all art units

Statute-Specific Performance

§101
32.5%
-7.5% vs TC avg
§103
44.2%
+4.2% vs TC avg
§102
6.0%
-34.0% vs TC avg
§112
15.6%
-24.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 28 resolved cases

Office Action

§101
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/18/2025 has been entered. Priority This application repeats a substantial portion of prior Application No. 14592821, filed 10/29/2020, and adds disclosure not presented in the prior application. Because this application names the inventor or at least one joint inventor named in the prior application, it may constitute a continuation-in-part of the prior application. Should applicant desire to claim the benefit of the filing date of the prior application, attention is directed to 35 U.S.C. 120, 37 CFR 1.78, and MPEP § 211 et seq. Status of Claims Applicant’s submission filed 09/18/2025 has been entered. The status of the claims is as follows: Claims 1-17 remain pending in the application. Claims 18-20 are canceled. Claims 1, 6 and 11 are amended. Response to Arguments In reference to rejections under 35 U.S.C. § 103: Applicant’s arguments, see Remarks pg. 1-4, filed 09/18/2025, with respect to the rejections under 35 U.S.C 103 have been fully considered and are persuasive. The Claim Rejections under 35 U.S.C of Claims 1-17 have been withdrawn. In reference to rejections under 35 U.S.C. § 101: Step 2A Prong One: Abstract Ideas: The Office Action asserts the independent claims are directed to abstract ideas because they allegedly recite both a mental process and a mathematical concept. The Applicant does not concede that characterization, but for this response, they’re focusing on Step 2A Prong Two and Step 2B, while reserving the right to further dispute the Step 2A Prong One “abstract idea” labeling. Response: The Examiner respectfully notes that the independent claims, as amended, remain directed to an abstract idea because they recite only a mathematical concept and a mental process. In particular, the claims broadly recite collecting or extracting student features, segmenting and clustering students data using a “similarity threshold”, selecting feature subsets based on “model performance”, and then using predictive models, propensity score matching, and statistical hypothesis testing, all of which are mathematical relationships and calculations used to organize and evaluate information. The recited “machine learning process” and “creating analytical models” are stated only at a high level and do not require any specific technological improvement or particular machine implementation beyond applying these calculations to student data. Accordingly, the claims amount to analyzing information using mathematical techniques and mental judgements (grouping, comparing, selecting), which falls within the judicial exception (mathematical concepts and mental processes). Step 2A Prong Two: Integration of a Judicial Exception into a Practical Application: Applicant argues in Remarks pg. 6-7 that, even if any portions of the claims could be characterized as a judicial exception, the claims are not directed to that exception because, as a whole, they integrate it into a practical application under Step 2A Prong Two. Specifically, Applicant contends the claims recite a particular, ordered method for building analytical/ predictive models that addresses data-availability issues by (i) segmenting students based on feature availability using similarity-based clustering (with a threshold < 100%), (ii) selecting feature subsets based on model performance for each segment, and (iii) clustering within each segment using those selected features. Applicant asserts that this ordered combination yields a technical improvement over prior systems that allegedly lack sufficiently predictive similarity-based clusters, and therefore amounts to significantly more than merely claiming the abstract idea. Response: Applicant’s “improvement” argument is not persuasive because Step 2A, Prong Two asks whether the claim recites additional elements that integrate the judicial exception into a practical application, not whether the abstract idea is useful or produces a better result. Here, the claim’s purported improvement comes directly from the recited mathematical/ mental process steps themselves, and the remaining limitations, e.g., “extracting features”, “creating analytical models using a machine learning process” are recited at a high level of generality without any specific technological implementation, particular computing architecture, or improvement to the computer functionality. As such, the additional elements amount to no more than generic data processing and applying conventional analytic techniques to student data, and therefore do not impose a meaningful limit on the exception or integrate it into a practical application under Step 2A, Prong Two. Step 2B: Whether a Claim Amounts to Significantly More Applicant argues in Remarks pg. 7-9 that, under Step 2B, claims 1, 6 and 11 recite at least one nonconventional limitation, which is segmenting student into data-availability segments based on feature availability using similarity-based clustering of unique valid feature combinations with a similarity threshold less than 100%, and asserts that the Office Action has not shown these limitations are well-understood or conventional (WURC). Applicant further argues the Office Action improperly focused on the claims’ mental/ mathematical aspects and therefore overlooked the meaningful contribution of these limitations. Response: Applicant’s Step 2B argument is not persuasive because the relied-upon “similarity threshold being less than 100%” limitation is itself part of the recited mathematical concept used to compare and cluster feature combinations. As such, it is not an additional element beyond the judicial exception that can supply “significantly more” in Step 2B. Applicant’s arguments filed on 09/18/2025 have been fully considered but they are not persuasive. 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 1-17 are rejected under U.S.C 101 for containing an abstract idea without significantly more. Regarding claim 1: Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is a process. Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the claim recites an abstract idea. segmenting the students into data-availability segments based on availability of the extracted features in the raw student data, wherein segmenting is based on similarity-based clustering of unique valid feature combinations, wherein the similarity-based clustering is set at a similarity threshold less than 100% - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each data-availability segment, determining a subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) clustering the students within each data-availability segment into segment clusters using one or more features in the subset of features - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each segment cluster, determining another subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) wherein the creating the analytical models includes combining predictive models with propensity-score matching, including identifying key success features from a predictive model building process, constructing propensity-score models using one or more of the key success features to enable matching in predictive propensity-score domain - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) performing statistical hypothesis testing to explain what interventions work for which segments of the students under what context. This limitation is directed to mathematical calculation (see MPEP 2106.04(a)(2) l. C.) Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements: extracting features from raw student data; This limitation is directed to insignificant extra-solution activity (see MPEP 2106.05(g)). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements are: extracting features from raw student data; – This limitation is directed to receiving or transmitting data over a network. The courts have recognized receiving or transmitting data over a network as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II.). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Regarding claim 2, Claim 2 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations: predicting initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) identifying similar courses similar students have taken, and dynamic feature-based prediction. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 3, Claim 3 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations: estimating inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 4, Claim 4 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations: producing a heat map of a particular student that includes faculty-student interactions, student-student interactions, student performance and predicted scores to provide an understanding of how these variables interact with one another. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 5, Claim 5 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations: producing a table of effective faculty-student and faculty features as a function of student segments/clusters using student success measures and changes in student behavior post faculty engagement. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 6: Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is a process. Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the claim recites an abstract idea. segmenting the students into data-availability segments based on availability of the extracted features in the raw student data, wherein segmenting is based on similarity-based clustering of unique valid feature combinations wherein the similarity-based clustering is set at a similarity threshold less than 100%; - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each data-availability segment, determining a subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) clustering the students within each data-availability segment into segment clusters using one or more features in the subset of features - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each segment cluster, determining another subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) wherein the creating the analytical models includes combining predictive models with propensity-score matching, including identifying key success features from a predictive model building process, constructing propensity-score models using one or more of the key success features to enable matching in predictive propensity-score domain - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) performing statistical hypothesis testing with Bonferroni correction as a function of time and various segments to explain what interventions work for which segments of the students under what context. This limitation is directed to mathematical calculation as it is performing testing using Bonferroni correction as a function of time (see MPEP 2106.04(a)(2) l. C.) Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements: A non-transitory computer-readable storage medium containing program instructions for a method for building analytical models for an education application, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform steps comprising – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). extracting features from raw student data; This limitation is directed to insignificant extra-solution activity (see MPEP 2106.05(g)). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements are: A non-transitory computer-readable storage medium containing program instructions for a method for building analytical models for an education application, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to perform steps comprising – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). extracting features from raw student data; – This limitation is directed to receiving or transmitting data over a network. The courts have recognized receiving or transmitting data over a network as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II.). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Regarding claim 7, Claim 7 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 6 which includes an abstract idea (see rejection for claim 6). The additional limitations: predicting initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and identifying similar courses similar students have taken, and dynamic feature-based prediction. This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 8, Claim 8 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 6 which includes an abstract idea (see rejection for claim 6). The additional limitations: estimating inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success. This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 9, Claim 9 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 6 which includes an abstract idea (see rejection for claim 6). The additional limitations: producing a heat map of a particular student that includes faculty-student interactions, student-student interactions, student performance and predicted scores to provide an understanding of how these variables interact with one another. This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 10, Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 6 which includes an abstract idea (see rejection for claim 6). The additional limitations: producing a table of effective faculty-student and faculty features as a function of student segments/clusters using student success measures and changes in student behavior post faculty engagement. This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 11: Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is a process. Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the claim recites an abstract idea. segmenting the students into data-availability segments based on availability of the extracted features in the raw student data, wherein segmenting is based on similarity-based clustering of unique valid feature combinations, wherein the similarity-based clustering is set at a similarity threshold less than 100%; - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each data-availability segment, determining a subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) clustering the students within each data-availability segment into segment clusters using one or more features in the subset of features - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) for each segment cluster, determining another subset of features based on model performance - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) wherein the creating the analytical models includes combining predictive models with propensity-score matching, including identifying key success features from a predictive model building process, constructing propensity-score models using one or more of the key success features to enable matching in predictive propensity-score domain - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) performing statistical hypothesis testing with Bonferroni correction as a function of time and various segments to explain what interventions work for which segments of the students under what context. This limitation is directed to mathematical calculation as it is performing testing using Bonferroni correction as a function of time (see MPEP 2106.04(a)(2) l. C.) Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements: memory; and at least one processor configured to: – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). extracting features from raw student data; This limitation is directed to insignificant extra-solution activity (see MPEP 2106.05(g)). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements are: memory; and at least one processor configured to: – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). extracting features from raw student data; – This limitation is directed to receiving or transmitting data over a network. The courts have recognized receiving or transmitting data over a network as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II.). creating the analytical models for the segment clusters using a machine learning process, the analytical models providing at least actionable insights, - 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. Regarding claim 12, Claim 12 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 11 which includes an abstract idea (see rejection for claim 11). The additional limitations: wherein the at least one processor is configured to – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). predict initial course success for guidance using at least one of course/student similarity analyses, collaborative filtering, clustering of the students based on a predictive feature subset for course success and identifying similar courses similar students have taken, and dynamic feature-based prediction. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 13, Claim 13 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 11 which includes an abstract idea (see rejection for claim 11). The additional limitations: wherein the at least one processor is configured to – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)). estimate inherent course difficulties adjusted for student skills to identify gatekeeper courses, and toxic or synergistic course combinations using representations of concurrent-course combinations and their grades along with key student attributes for success. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 14, Claim 14 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 11 which includes an abstract idea (see rejection for claim 11). The additional limitations: wherein extracting features from raw student data comprises transforming raw student data into usable data and 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. extracting features from the usable data. Under Step 2A-Prong Two, this limitation is directed to insignificant extra-solution activity (see MPEP 2106.05(g)). Under Step 2B, this limitation is directed to receiving or transmitting data over a network. The courts have recognized receiving or transmitting data over a network as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II.). Regarding claim 15, Claim 15 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 14 which includes an abstract idea (see rejection for claim 14). The additional limitations: wherein transforming comprises transforming the raw student data into enrollment, session, and or term levels. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Regarding claim 16, Claim 16 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations: wherein extracting features from raw student data comprises transforming raw student data into usable data and 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 [see MPEP 2106.05(f)] and therefore fails to integrate the exception into a practical application. extracting features from the usable data. Under Step 2A-Prong Two, this limitation is directed to insignificant extra-solution activity (see MPEP 2106.05(g)). Under Step 2B, this limitation is directed to receiving or transmitting data over a network. The courts have recognized receiving or transmitting data over a network as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II.). Regarding claim 17, Claim 17 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 16 which includes an abstract idea (see rejection for claim 16). The additional limitations: wherein transforming comprises transforming the raw student data into enrollment, session, and or term levels. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.) Allowable Subject Matter Claims 1-17 are allowed over prior art. None of the prior art, either alone on in combination, fairly discloses or suggests the limitation of claims 1, 6 and 11 in particular: “wherein the similarity-based clustering is set at a similarity threshold less than 100%” Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY TRAN whose telephone number is (571)270-0693. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /AMY TRAN/Examiner, Art Unit 2126 /DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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Prosecution Timeline

Aug 12, 2021
Application Filed
Dec 14, 2023
Final Rejection — §101
May 20, 2024
Request for Continued Examination
Jun 04, 2024
Response after Non-Final Action
Aug 05, 2024
Non-Final Rejection — §101
Jan 09, 2025
Response Filed
Apr 14, 2025
Final Rejection — §101
Sep 18, 2025
Request for Continued Examination
Sep 25, 2025
Response after Non-Final Action
Mar 03, 2026
Non-Final Rejection — §101 (current)

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2y 5m to grant Granted Sep 23, 2025
Patent 12288074
GENERATING AND PROVIDING PROPOSED DIGITAL ACTIONS IN HIGH-DIMENSIONAL ACTION SPACES USING REINFORCEMENT LEARNING MODELS
2y 5m to grant Granted Apr 29, 2025
Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

4-5
Expected OA Rounds
36%
Grant Probability
84%
With Interview (+47.9%)
5y 2m
Median Time to Grant
High
PTA Risk
Based on 28 resolved cases by this examiner. Grant probability derived from career allow rate.

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