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 AIA .
Status of Claims
This communication is a Non-Final office action in response to RCE filed on 02/12/2026. Claims 1, 3, 4, 8, 9, 10, 11, 13 and 16 have been amended. Claims 2 and 12 have been canceled. Therefore, claims 1, 3-11 and 13-20 are currently pending and have been addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/12/2026 has been entered.
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, 3-11 and 13-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1, 3-10 are directed to a method (i.e. a process). Claims 11, 13-15 are directed to a computer program product (i.e. an article of manufacture). Claims 16-20 are directed to a system (i.e. a machine). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea. Examiner note: With respect to claims 11 and 13-15, which are directed to a computer program product comprising one or more computer readable storage media, Applicants specification, para 0024, recites: “A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”
Step 2A: Prong One: Abstract Ideas
Claims 1, 3-11 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claims 11 and 16 recite: A method comprising: in response to receiving opt-in consent from a student, obtaining student data associated with the student; monitoring performance of the student in a course; predicts a score in the course based on the student data; receives the student data, the student data comprising: quiz grades; hours met with any mentor for the course; life events for the student during the course; and mentor evaluation scores; detecting a negative trend in the performance of the student based on the monitoring; and in response to detecting the negative trend, matching the student with a mentor for the course using a matching model.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under at least the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including 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)). As independent claims discuss a method to obtain student data associated with the student, monitor performance of the student in a course, detecting a negative trend in the performance of the student based on the monitoring, matching the student with a mentor for the course, which is managing personal behavior or relationships or interactions between people including teaching and following rules or instructions, which is one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (claim 1, analogous to claims 11 and 16 recite for example: “receiving consent from a student”; “obtaining student data associated with the student”; “monitoring performance of the student in a course”; “predicts a score in the course based on the student data”; “receives the student data, the student data comprising: quiz grades; hours met with any mentor for the course; life events for the student during the course; and mentor evaluation scores”; “detecting a negative trend in the performance of the student based on the monitoring”; “in response to detecting the negative trend, matching the student with a mentor for the course”. Concepts performed in the human mind as mental processes because the steps of receiving, obtaining, monitoring, predicting, detecting, responding, matching and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)). Dependent claims add additional limitations, for example: (claim 4) a predicted score for the student based on the student data and the decision tree model determines the predicted score by aggregating results, wherein the hours met with any mentor for the course and the life events for the student during the course were submitted by the student at the time of review; (claim 5) the matching comprises identifying the mentor and a mentoring date; (claims 6, 10, 14 and 19) wherein the matching model comprises a cost function that includes parameters, costs, and constraints; cost of lost productivity of the student if not allocated before a scheduled quiz; cost of student hours; cost of mentoring; cost of lost productivity due to quiz; cost of lost productivity of mentor hours due to non-allocation of the student; the cost function includes constraints based on one or more selected from a group consisting of: a number of students that can be scheduled to the mentor in a single day; maintaining a schedule with a planning horizon; time zones; holidays; availability of mentors; availability of students; and time zone tolerance within a number of hours; (claim 8) the cost function includes parameters comprising: availability of the student; availability of the mentor; capacity of student; cost of lost productivity; probability of failure of mentor matching; cost of student hours; cost of mentor hours; cost of early replacement of the mentor; productive value of the mentor per session; expected span or duration of mentoring; favorable time zones; and number of mentoring sessions, wherein the cost function is solved using a parameter estimation algorithm; (claim 9) the cost function includes costs comprising: cost of lost productivity of the student if not allocated before a scheduled quiz; cost of student hours; cost of mentoring; cost of lost productivity due to quiz; cost of lost productivity of mentor hours due to non-allocation of the student, wherein the cost function is an exemplary expression for cost optimization defined for a mentee’s scheduling optimization; (claim 10) the cost function includes constraints based on: a number of students that can be scheduled to the mentor in a single day; maintaining a schedule with a planning horizon; time zones; holidays; availability of mentors; availability of students; and time zone tolerance within a number of hours, wherein a constraint is used to enforce a three hours tolerance window to incorporate the time zone difference of at least one mentor and at least one mentee; (claims 13 and 18) receives the student data as input; outputs a predicted score for the student based on the student data; and determines the predicted score by aggregating results; (claims 7, 15 and 20) solving the cost function; minimizing the cost function, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations of certain methods of organizing human activity and mental processes but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements (claim 1) processor set, predictive machine learning model; decision tree model (claims 3 and 17) gradient boosted decision tree model (claims 4 and 13) L1 regularizer (claims 7 and 14) multiple integer linear programming (claim 11) computer program product comprising; computer readable storage media, train predictive machine learning model (claim 16) system, processor set, computer readable storage media, train predictive model, decision tree model. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds 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, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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 generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1, 3-11 and 13-20 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Burrell et al. US 2023/0325953 - A method and system for training and applying at least two neural networks for matching prospective students to institutions is disclosed This method and system enhances access to high-quality, affordable postsecondary education by predicting the likelihood of success that a given student will have at an institution. This is accomplished by predicting the similarity between a student and institution alumni on both objective and subjective criteria.
Novati et al. US 11/551,321 - A computing system for generating a dynamic path for a fellow, includes a processor and a memory storing instructions that when executed by the processor cause the computing system to receive the fellow's skill graph, receive a target skill, receive a calendar object and generate the dynamic path including a task and/or a session. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to receive the fellow's skill graph, receive a target skill, receive a calendar object and generate the dynamic path including a task and/or a session. A method for generating a dynamic path for a fellow includes receiving the fellow's skill graph, receiving a target skill, receiving a calendar object and generating the dynamic path including a task and/or a session.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Response to Arguments
Applicant’s arguments filed on 02/12/2026 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejections in light of the most recent claim amendments. Applicant’s arguments have been fully considered but are found unpersuasive and Examiner maintains the 101 rejection.
With respect to Applicant’s remarks (page 8), “...Applicant submits that when “the claim” is properly considered...” Examiner asserts the claims have been properly considered under the 101 analysis. The claims fall under at least abstract groupings of certain methods of organizing human activity and mental processes, as explained in the above 101 analysis.
Applicant (pages 9-10) again argues “predictive machine learning model” and the human mind. Examiner again respectfully notes additional elements are considered in Step 2A: Prong Two and Step 2B; not in Step2A: Prong One: Abstract Ideas. Applicant’s arguments of a gradient boosted decision tree (claim 3) and Multiple Integer Linear Programming (claim 7) have been properly considered in the 101 analysis. See complete 101 analysis above.
Applicant solely argues mental processes, Examiner respectfully notes the claims also fall under the abstract grouping of certain methods of organizing human activity.
With respect to integration of the abstract idea into a practical application, the computing elements are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. Examiner fails to see how the generic recitations of these most basic computer components and/or of a system so integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. Thus, Examiner finds that the claims recite the judicial exception of certain methods of organizing human activity and mental processes and are not integrated into a practical application.
Further, Examiner finds Applicant's arguments fail to comply with 37 CFR 1.111 because they amount to a general allegation that the claims define a patent eligible invention without specifically pointing out how the language of the claims reflect a practical application (e.g., how the claims reflect an improvement).
With respect to Applicant’s remarks (page 12): “claim 1 is directed towards an improvement in the technical field of educational guidance”, Examiner respectfully notes “educational guidance” is not a technical field.
With respect to Applicant’s remarks (page 13): “Applicant takes this opportunity to repeat and emphasize the fact that, according to MPEP 2106.04(d)(III), the Step 2A Prong Two analysis must consider the claim as a whole, and must not focus only on the alleged additional limitations.” Examiner again asserts that each limitation individually and in combination has been considered and when considered as a whole and individually the claims do not recite any additional elements that amount to a practical application or significantly more. Therefore, Applicants remarks are found unpersuasive and Examiner maintains the 101 rejection.
Regarding 35 U.S.C. § 103 rejections. Applicant amended representative independent claim 16 to recite: “A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: in response to receiving opt-in consent from a student, obtain student data associated with the student; receive opt-in consent from a mentor including permission to access the mentor's calendar information; train a predictive model that predicts a score in a course based on the student data, wherein the predictive model comprises a decision tree model, and wherein the decision tree model receives the student data as input, the student data comprising: quiz grades; hours met with any mentor for the course; life events for the student during the course; and mentor evaluation scores; monitor performance of the student in the course using the student data with the predictive model; detect a negative trend in the performance of the student based on the monitoring; and in response to detecting the negative trend, match the student with the mentor for the course using a matching model.”
The closest prior art Azarias et al. (US 2023/0093518 A1), teaches in para 0050, machine learning to predict risks for the students; Azarias, para 0026, teaches systems and method may be utilized to assess at risk behaviors …real-time monitoring of progress and outcomes. The illustrative embodiments provide a systematic data collection and analysis system and process. The described methodologies may provide any number of actions, and interventions to teach, encourage, monitor, and instruct children; Azarias, 0078, the system performs any necessary tests, assessments, and observations … the student may be tested utilizing standardized or customized tests (e.g., academic, personality, stress, interaction, etc.). The student may be observed over time to determine relevant information both positive and negative. Prior art Hickey et al. (US 2021/0304630 A1), teaches machine learning algorithms for identifying at-risk students in online degree programs; Hickey, Abstract teaches multiple machine learning models may be implemented to enable iterative, updateable predictions of a student's grade in a course.
Yet, the prior art does not teach “A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: in response to receiving opt-in consent from a student, obtain student data associated with the student; receive opt-in consent from a mentor including permission to access the mentor's calendar information; train a predictive model that predicts a score in a course based on the student data, wherein the predictive model comprises a decision tree model, and wherein the decision tree model receives the student data as input, the student data comprising: quiz grades; hours met with any mentor for the course; life events for the student during the course; and mentor evaluation scores; monitor performance of the student in the course using the student data with the predictive model; detect a negative trend in the performance of the student based on the monitoring; and in response to detecting the negative trend, match the student with the mentor for the course using a matching model”. After consideration of Applicants arguments and conducting an updated prior art and non-patent literature (NPL) search, the examiner has yet to find applicable references for these limitations. Thus, prior art rejections have been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm EST.
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/R.R.N./Examiner, Art Unit 3629 /LYNDA JASMIN/ Supervisory Patent Examiner, Art Unit 3629
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