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 .
Status of Claims
This action is in reply to the claims filed on 1 October 2025.
Claims 1, 14, 17, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
Claim Objections
Claims 1-20 are objected to because of the following informalities:
Claim 1, line 5; Claim 14, line 3; and Claim 20, line 6 recites “historical student-specific financial data, historical student-specific outcomes”. This appears to be a typographical error of “historical student-specific financial data, and historical student-specific outcomes”.
Claims 2-13 and 15-19 inherit the deficiencies of claims 1 and 14, respectively.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Claims 1-13 is/are drawn to an apparatus (i.e., a machine), claims 14-19 is/are drawn to a method (i.e., a process), and claim 20 is/are drawn to a non-transitory machine-readable storage medium (i.e., a manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.
Representative Claim 1 recites:
accessing a model trained with at least historical student-specific academic data, historical student-specific financial data, and historical student-specific outcomes associated with a plurality of institutions;
extract features from column names of a data file associated with at least one institution of the plurality of institutions, using lexical and compositional semantic analysis;
display an initial institution-specific column mapping based on the extracted features;
receive an indication of a user input to edit the institution-specific column mapping;
edit the institution-specific column mapping based on received indication;
further train the model based on the edited institution-specific column mapping and historical institutional policy data; and
apply to the model at least one set of subject student-related academic data and subject student-related financial data to generate at least one of: (a) one or more advisor-facing metrics relating to student progress, or (b) one or more student-facing metrics relating to student progress.
As noted by the claim limitations above, the claimed invention describes generating student metrics related to a student’s progress at an academic institution. This is considered to be an abstract idea because it is a concept of managing a personal behavior which falls within the category of “certain methods of organizing human activity.”
Furthermore, the limitations of “accessing a model trained with at least historical student-specific academic data, historical student-specific financial data, historical institutional policy data, and historical student-specific outcomes;”, “further train the model based on the edited institution-specific column mapping and historical institutional policy data;”, and “apply to the model at least one set of subject student-related academic data and subject student-related financial data” are considered to be a mathematical concepts. The broadest reasonable interpretation of the model in light of the specification is a mathematical equation and the act of training the model is a mathematical calculation, which falls within the category of “mathematical concepts.”
See MPEP 2106.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Step 2A - Prong Two: This judicial exception is not integrated into a practical application. In particular, claim 1 recites the following additional element(s): at least one processor and at least one memory including computer program code; natural language processing (NLP); an administrator-facing user interface associated with the at least one institution of the plurality of institutions and the administrator-facing user interface. This/these additional elements individually or in combination do not integrate the exception into a practical application because they merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Accordingly, these additional element(s) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claim 1 is directed to an abstract idea.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) merely use a computer as a tool to perform an abstract idea, which does not render a claim as being significantly more than the judicial exception. Accordingly, claim 1 is ineligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claim 1 is not eligible subject matter under 35 USC 101.
Dependent claim(s) 2-4, 6-7, 9-10, and 12 further recite(s) the additional element(s): a student-facing user interface, (claims 2-3 and 6), an advisor-facing interface (claims 4 and 6), an administrator-facing user interface (claims 7 and 10), systems (claim 9), institutional systems (claim 10), a large language model (claim 12), natural language feedback strings (claim 12). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 2-4, 6-7, 9-10, and 12 is/are ineligible.
Dependent claim(s) 5, 8, 11 and 13 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 1. Therefore claim(s) 5, 8, 11 and 13 are ineligible.
Claim 14 is parallel in nature to claim 1. Claim 14 recites an abstract idea similar in nature to claim 1. Furthermore, claim 14 recites the following additional elements: natural language processing (NLP); an administrator-facing user interface associated with the at least one institution of the plurality of institutions and the administrator-facing user interface. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Dependent claim(s) 15-17 and 19 further recite(s) the additional element(s): a student-facing user interface (claims 15-16 and 19), an advisor-facing interface (claims 17 and 19). This/these additional element(s) alone or in ordered combination does no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim(s) into a practical application nor does it render a claim as being significantly more than the abstract idea. Accordingly, claim(s) 15-17 and 19 is/are ineligible.
Dependent claim(s) 18 merely further limit the abstract idea and do not recite any additional elements beyond those already recited in claim 14. Therefore, claim(s) 18 is ineligible.
Claim 20 is parallel in nature to claim 1. Claim 20 recites an abstract idea similar in nature to claim 1. Furthermore, claim 20 recites the following additional elements: at least one non-transitory computer readable storage medium having computer-executable program code instructions stored therein. These additional elements do no more than merely use a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), which does not integrate the claim into a practical application nor does it render a claim as being significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 4, 8-9, 11, 13-15, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Refila (US 20200134759 A1) in view of Morris (US 20210390871 A1).
Regarding claim 1, Refila teaches an apparatus comprising at least one processor (Paragraph [0131] “processor 604”; el. 604 of Fig. 6) and at least one memory (Paragraph [0132] “memory 606 […] for storing information and instructions to be executed by processor 604”; el. 606 of Fig. 6) including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
access a model trained with at least historical student-specific academic data, historical student-specific financial data, and historical student-specific outcomes associated with a plurality of institutions; (Paragraph [0069] “machine learning analytic 130 is configured to train a set of machine learning models using the persisted set of inputs and outputs from automated packaging engine 112.”; Paragraph [0035] “academic data 102 includes information about a student's prior […] courses”; Paragraph [0038] “financial data 108 includes information about the […] previous financial status of a student”; Paragraph [0037] “institutional data 106 includes information about institutions associated with a student. For example, […] institutional data may include information related to in-person or online interactions of a student with an institution, such as surveys, student service requests, student card system data, and/or parking system data.”; Paragraph [0042] “Automated packaging engine 112 may be configured to receive a set of inputs from student information repository 110 […] and output a set of student outcomes.”; Examiner notes Fig. 1 shows that academic data 102, financial data 108, and institutional data 106 are part of the student information repository.)
via an (Paragraph [0096] “FIG. 5B illustrates screenshot 508, which displays an interface for reviewing a current financial aid summary and adjusting funding. Interface element 510 displays a summary of an estimated cost of attendance, which is based on the estimated tuition, fees, living expenses, and other expenses for attending an educational institution”; Examiner notes Fig. 5B shows a display for a specific institution (i.e., vision college) and a column mapping based on values input into a model (see Paragraph [0042])0)
via the (Paragraph [0096] "Interface element 512 may include links such as link 514, which allows a user to update the acceptance on eligible financial aid."; el. 514 of Fig. 5b; Examiner notes Fig. 5b shows a specific institution (i.e. vision college).)
edit the institution-specific column mapping based on received indication; (Paragraph [0096] "Interface element 512 may include links such as link 514, which allows a user to update the acceptance on eligible financial aid."; el. 514 of Fig. 5b; Examiner notes Fig. 5b shows a specific institution (i.e. vision college).)
further train the model based on the edited institution-specific column mapping and historical institutional policy data; (Paragraph [0027] " The set of training data in this context may generally comprise a set of inputs (e.g., student profile information, student actions, and/or other student data) and a set of corresponding outputs (e.g., financial, academic, and/or career outcomes)."; Paragraph [0045] “a predictive model may predict […] financial outcomes (e.g., eligible financial aid, predicted debt levels, etc.)”; Paragraph [0038] "financial data 108 includes information about the current and previous financial status of a student. Financial data 108 may include, for example, information identifying scholarships, loans, grants, and/or other sources of student funding."; Examiner notes the acceptance of financial aid is represented by the students’ current and previous scholarship, loans, etc. This data is used to train the model to predict other outcomes.) and
apply to the model at least one set of subject student-related academic data and subject student-related financial data to generate at least one of: (a) one or more advisor-facing metrics relating to student progress, or (b) one or more student-facing metrics relating to student progress. (Paragraph [0088] “interface manager 140 presents actionable items to a student and/or adviser based on outputs received from machine learning analytic 130.”; Paragraph [0090] “The set of operations include generating a student feature vector for a student for which the evaluation process is being performed (operation 402). […] The values in a feature vector may include […] academic information, and/or financial information.”; Paragraph [0096] “Interface element 510 displays a summary of an estimated cost of attendance, which is based on the estimated tuition, fees, living expenses, and other expenses for attending an educational institution. […] Interface element 516 presents a comparison of estimated cost of attendance versus the current funding. […] Interface element 518 may further provide recommendations on additional funding sources based on students with similar profiles.”; Fig. 1; el. 402 of Fig. 4; Fig. 5B)
Refila does not teach:
extract features from column names of a data file associated with at least one institution of the plurality of institutions, using lexical and compositional semantic analysis and natural language processing (NLP);
via an administrator-facing user interface associated with the at least one institution of the plurality of institutions, display an initial institution-specific column mapping based on the extracted features;
via the administrator-facing user interface, receive an indication of a user input to edit the institution-specific column mapping.
However, Morris teaches:
extract features from column names of a data file associated with at least one institution of the plurality of institutions, using lexical and compositional semantic analysis and natural language processing (NLP); (Paragraph [0083] “The algorithm 100 aggregates 304 the collected data from the crawling 302 and extracts 306 the relevant content from the data. In some embodiments, the algorithm 110 extracts 306 using natural language processing (NLP).”; Paragraph [0141] “FIG. 22 is a flow diagram of a process 2200 for generating a map 102 of FIG. 2 […] The algorithm 110 creates 2206 an ontology or graph using word vectors, word semantics and probabilistic graphical models (for example by looking at the probability of co-occurrence of words or by finding similarities or semantic distance between different learning elements).”; Paragraph [0080] “The first element is titles that define the areas to be populated.”; Paragraph [0083] “the learner 402 can choose a title 410 (e.g., […] an academic title such as “Biology”). Based on the title 410 chosen, the algorithm 110 populates the appropriate areas 202.”; Fig. 4 of Morris; Examiner notes the titles in Morris teach the column names of a datafile.)
an administrator-facing user interface. (Paragraph [0093] “The type of map created depends on […] internal inputs by a platform administrator.”)
This operation of Morris is applicable to the system of Refila as they both share characteristics and capabilities, namely, they are directed to training educational machine learning models. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified system of Refila to incorporate extracting features using NLP and the administrator-facing interface as taught by Morris. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order automatically create a personalized curriculum (see paragraph [0002] of Morris).
Regarding claim 2, Refila in view of Morris teaches the apparatus according to claim 1, Refila further teaches:
wherein the one or more student-facing metrics indicate a financial estimate pertaining to completion of a degree and are provided via a student-facing user interface, wherein the student-facing user interface further enables a student-user to configure a student-specific academic plan. (Paragraph [0088] “interface manager 140 presents actionable items to a student and/or adviser based on outputs received from machine learning analytic 130.”; Paragraph [0096] “Interface element 510 displays a summary of an estimated cost of attendance, which is based on the estimated tuition, fees, living expenses, and other expenses for attending an educational institution. […] Interface element 516 presents a comparison of estimated cost of attendance versus the current funding. […] Interface element 518 allows a user to adjust funding to cover the shortfall. Interface element 518 may further provide recommendations on additional funding sources based on students with similar profiles.”; Fig. 5B)
Regarding claim 4, Refila in view of Morris teaches the apparatus according to claim 1.
wherein the one or more advisor-facing metrics indicate at least one of a student-specific academic plan progress status or a predicted student success indicator, and are provided via an advisor-facing interface. (Paragraph [0088] “interface manager 140 presents actionable items to a student and/or adviser based on outputs received from machine learning analytic 130.”; Paragraph [0096] “Interface element 510 displays a summary of an estimated cost of attendance, which is based on the estimated tuition, fees, living expenses, and other expenses for attending an educational institution. […] Interface element 516 presents a comparison of estimated cost of attendance versus the current funding. […] Interface element 518 may further provide recommendations on additional funding sources based on students with similar profiles.”; Fig. 5B)
Regarding claim 8, Refila in view of Morris teaches the apparatus according to claim 1. Refila further teaches:
wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
(Paragraph [0057] “A snapshot of student data may include up-to-date academic data 102, personal data 104, institutional data 106, financial data 108, or some combination thereof.”; Paragraph [0058] “Once the snapshot of student data has been received, the set of operations includes determining whether there was a change in student data from a previous snapshot (operation 204).”)
However, Refila does not teach:
routinely update and train the model.
However, Morris teaches:
routinely update and train the model. (Paragraph [0073] “As it learns, the algorithm 110 adds its initial output data into the training data, thereby training itself and autonomously producing increasingly accurate results.” Of Morris)
The motivation for making this modification to the teachings of Refila is the same as that set forth above, in the rejection of claim 1.
Regarding claim 9, Refila in view of Morris teaches the apparatus according to claim 1. Refila further teaches:
wherein the historical student-specific academic data, historical student-specific financial data, historical institutional policy data, and historical student-specific outcomes are provided from disparate systems. (Paragraph [0034] “student information repository 110 is populated with student information from a variety of sources and/or systems. Student information repository 110 may include a variety of data related to students, such as academic data 102, demographic data 104, institutional data 106, and financial data 108.”)
Regarding claim 11, Refila in view of Morris teaches the apparatus according to claim 1. Refila further teaches:
wherein the apparatus wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
generate an insight regarding an impact of one of more student-specific academic data, student-specific financial data, or institutional policy data in predicting student-specific outcomes. (Paragraph [0067] “a trigger may be a newly received set of student data that was not previously received.”; Paragraph [0068] “Persisting this data over a student lifecycle results in an audit trail that tracks when changes in student outcomes occur and what caused the changes to occur. This information may be used to provide insight to students and advisers to track a student's academic career and the events that triggered changes a student's trajectory.”; Examiner notes that paragraph [0057] explains student data may include academic and financial data.)
Regarding claim 13, Refila in view of Morris teaches the apparatus according to claim 1. Refila further teaches:
wherein the apparatus wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
update the model with at least one of newly received academic data, newly received student-specific financial data, newly received institutional policy data, and newly received student-specific outcomes; (Paragraph [0057] “A snapshot of student data may include up-to-date academic data 102, personal data 104, institutional data 106, financial data 108, or some combination thereof.”; Paragraph [0058] “Once the snapshot of student data has been received, the set of operations includes determining whether there was a change in student data from a previous snapshot (operation 204).”)
in response to the update of the model, determine a change in the at least one of the one or more advisor-facing metrics relating to student progress, or the one or more student-facing metrics relating to student progress, such that at least one of: (a) the change, or (b) the changed one or more advisor-facing metrics or student-facing metrics, satisfies an alert criterion; and in response to determining the change, alert at least one of an advisor-user or a student-user of the change. (Paragraph [0065] “the set of operations further includes determining whether there was a change in the output (operation 208). Automated packaging engine 112 may compare one or more of the example outputs previously described to one or more corresponding outputs generated by automated packaging engine 112 based on an input data set from a previous student snapshot.”; Paragraph [0068] “The set of operations further includes persisting the set of inputs, outputs, and an indication of the triggers that caused the outputs to change (operation 212). Persisting this data over a student lifecycle results in an audit trail that tracks when changes in student outcomes occur and what caused the changes to occur. This information may be used to provide insight to students and advisers to track a student's academic career and the events that triggered changes a student's trajectory. This information may further be used to […] provide alerts”)
Claims 14, 15, and 17
Claims 14, 15, and 17, are directed to a computer-implemented method. Claims 14, 15, and 17 recite limitations parallel in nature as those addressed above for claims 1, 2 and 4 which are directed towards a system (i.e. apparatus). Claims 14, 15, and 17 are therefore rejected for the same reasons as set above for claims 1, 2, and 4, respectively.
Claim 20
Claim 20 is directed to a non-transitory computer readable storage medium. Claim 20 recites limitations parallel in nature as those addressed above for claim 1 which is directed towards a system (i.e. apparatus). Claim 20 is therefore rejected for the same reasons as set above for claim 1. Claim 20 further recites “at least one transitory computer readable medium having computer-executable program code instructions stored therein” (see Paragraph [0132] of Refila).
Claim(s) 3, 6, 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Refila (US 20200134759 A1) in view of Morris (US 20210390871 A1) in view of Cucchiara (US 20160358259 A1).
Regarding claim 3, Refila in view of Morris teaches the apparatus according to claim 2. Refila further teaches:
wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
(Paragraph [0043] “student plan manager 120 includes verification engine 116. Verification engine 116 may manage restrictions on which accounts are able to access student plan information. […] If the keys do not match or the user otherwise fails to be properly authenticated, verification engine 116 may prevent access to the student data.” Examiner notes that paragraph [0088] explains an account may be an advisor account and paragraph [0057] explains student data may include financial data.)
Refila in view of Morris does not teach:
via the student facing user interface, enable a student user to authorize an advisor-user to access student-related financial data.
However Cucchiara teaches:
via the client client authorize an advisor-user to access client-related financial data. (Paragraph [0063] “at step 403 […] financial accounts computing platform 310 may receive, via the communication interface, from the customer computing device, an authorization message from a customer of a first financial institution, the authorization message comprising authorization information indicating that the customer has authorized the first financial institution to access data related to a third-party financial account maintained for the customer by a second financial institution different than the first financial institution.”; Paragraph [0093] “Financial accounts computing platform 310 may then send a notification to the financial advisor assigned to the customer indicating that the financial data for one or more third-party financial account maintained for the customer by one or more financial institution is available for viewing by the financial advisor.”; Fig. 4B of Cucchiara)
This operation of Cucchiara is applicable to the system of Refila as they both share characteristics and capabilities, namely, they are directed to providing a data exchange between a user and an advisor. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the student facing interface of Refila to incorporate enabling a client (i.e. student) to authorize an advisor-user to access their financial data as taught by Cucchiara. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to aggregate data for viewing by a financial advisor (see Abstract of Cucchiara).
Regarding claim 6, Refila in view of Morris teaches the apparatus according to claim 1. Refila in view of Morris does not teach:
wherein the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
facilitate interaction, via an advisor-facing user interface and a student-facing user interface, and between at least one student-user and at least one advisor-user, relating to the at least one of the one or more advisor-facing metrics relating to student progress or the one or more student-facing metrics relating to student progress.
However, Cucchiara teaches:
facilitate interaction, via an advisor-facing user interface and a client client client client client (Paragraph [0115] “At step 415, organization device 308 may display and/or otherwise present the results of the financial analysis within module 510 in advisor portal user interface 500. […] The results of the financial device may be transmitted to one or more customer computing devices via public network 314.”; Fig. 3; el. 415 of Fig. 4C of Cucchiara)
This operation of Cucchiara is applicable to the system of Refila as they both share characteristics and capabilities, namely, they are directed to providing a data exchange between a user and an advisor. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the student-facing interface and advisor-facing interface of Refila to incorporate facilitating interaction between two users as taught by Cucchiara. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to aggregate data for viewing by a financial advisor (see Abstract of Cucchiara).
Claims 16 and 19
Claim(s) 16 and 19 is/are directed to a computer-implemented method. Claim(s) 16 and 19 recite limitations parallel in nature as those addressed above for claim(s) 3 and 6 which are directed towards a system (i.e. apparatus). Claim(s) 16 and 19 is/are therefore rejected for the same reasons as set above for claim(s) 3 and 6, respectively.
Claim(s) 5 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Refila (US 20200134759 A1) in view of Morris(US 20210390871 A1) in view of Zhu (US 20020120306 A1).
Regarding claim 5, Refila in view of Morris teaches the apparatus according to claim 4. Refila further teaches:
wherein the predicted student success indicator comprises (Paragraph [0045] “Predictive models may be trained to predict the likelihood of outcomes given the current state of a student. For example, a predictive model may predict graduation dates”)
Refila in view of Morris does not teach:
wherein the predicted student success indicator comprises a two-tier hierarchical predictor indicating whether or not a student is predicted to graduate, and if so, whether the student will graduate within a predetermined time period.
However, Zhu teaches:
wherein the predicted student success indicator comprises a two-tier hierarchical predictor indicating whether or not a student is predicted to graduate, and if so, whether the student will graduate within a predetermined time period. (Paragraph [0024] “3) computing an estimated arrhythmia probability based upon the detected occurrence of the conditioning event; and 4) predicting the occurrence of an arrhythmia within a specified prediction time period if the estimated arrhythmia probability exceeds a specified threshold value.”)
This operation of Zhu is applicable to the system of Refila as they both share characteristics and capabilities, namely, they are directed to analyzing data in order to make a prediction. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the prediction of the likelihood of a graduation time of Refila to incorporate the two-tier hierarchical predictor as taught by Zhu. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to perform analysis to predict the occurrence of an event (see paragraph [0003] of Zhu).
Claim 18
Claim(s) 18 is/are directed to a computer-implemented method. Claim(s) 18 recite limitations parallel in nature as those addressed above for claim(s) 5 which are directed towards a system (i.e. an apparatus). Claim(s) 18 is/are therefore rejected for the same reasons as set above for claim(s) 5.
Claim(s) 7 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Refila (US 20200134759 A1) in view of Morris (US 20210390871 A1) in view of Parikh (US 20200394361 A1).
Regarding claim 7, Refila in view of Morris teaches the apparatus according to claim 1. Refila in view of Morris does not teach:
wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
via an administrator-facing user interface, provide configuration information relating to the training of the model; and
via the administrator-facing user interface, enable (a) configuration of data used by the model, and (b) finetuning of parameters used by the model.
However, Parikh teaches:
wherein the at least one memory (Paragraph [0214] “memory 1703”) and the computer program code are further configured to, with the processor (Paragraph [0214] “processor 1701”), cause the apparatus to at least:
via an administrator-facing user interface, provide configuration information relating to the training of the model; (Paragraph [0170] “one or more administrators can also access the analytics dashboard 2010 and manually adjust the policy model. For instance, an administrator can manually adjust the lexicons, static phrase templates, dynamic phrase templates, or other aspects of the policy model. They can also manually adjust the violation threshold or scores assigned to specific words, phrases, or types of speech.” of Parikh) and
via the administrator-facing user interface, enable (a) configuration of data used by the model, and (b) finetuning of parameters used by the model. (Paragraph [0170] “one or more administrators can also access the analytics dashboard 2010 and manually adjust the policy model. For instance, an administrator can manually adjust the lexicons, static phrase templates, dynamic phrase templates, or other aspects of the policy model. They can also manually adjust the violation threshold or scores assigned to specific words, phrases, or types of speech.” of Parikh)
This operation of Parikh is applicable to the system of Refila as they both share characteristics
and capabilities, namely, they are directed to training machine learning models. Therefore, it would
have been obvious to one of ordinary skill in the art before the effective filling date of the claimed
invention to have modified the system of Refila to incorporate the administrator-facing user interface as taught by Parikh. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to tailor a model to a particular organization (see paragraph [0167] of Parikh).
Regarding claim 10, Refila in view of Morris teaches the apparatus according to claim 1. Refila in view of Morris does not teach:
wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
configure various instances of the model for different institutional systems; and
enable further configuration of one or more instances of the model via an administrator-facing user interface.
However, Parikh teaches:
wherein the at least one memory (Paragraph [0214] “memory 1703” of Parikh) and the computer program code are further configured to, with the processor (Paragraph [0214] “processor 1701” of Parikh), cause the apparatus to at least:
configure various instances of the model for different institutional systems; (Paragraph [0177] “two or more entities or corporations each have a management system, each management system having its own policy model trained for that entity or company.” Of Parikh) and
enable further configuration of one or more instances of the model via an administrator-facing user interface. (Paragraph [0177] “even where external responses far outnumber internal responses, the internal responses may still have a greater influence on training a policy model—depending on this weighting (which may be controlled by an administrator).” Of Parikh)
This operation of Parikh is applicable to the system of Refila as they both share characteristics
and capabilities, namely, they are directed to training machine learning models. Therefore, it would
have been obvious to one of ordinary skill in the art before the effective filling date of the claimed
invention to have modified the system of Refila to incorporate the configuring the various instances of the model as taught by Parikh. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to tailor a model to a particular organization (see paragraph [0167] of Parikh).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Refila (US 20200134759 A1) in view of Morris (US 20210390871 A1) in view of O’Malia (US 20220036153 A1).
Regarding claim 12, Refila in view of Morris teaches the apparatus according to claim 1. Refila in view of Morris does not teach:
wherein the apparatus wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least:
apply a large language model to the model to generate one or more natural language feedback strings pertaining to a student-specific scenario.
However, O’Malia teaches:
apply a large language model to the model to generate one or more natural language feedback strings pertaining to a student-specific scenario. (Paragraph [0068] “the ULLM 114 may generate (206) output text from the text prompt. For example, the output text may be ‘avoid the skull and fire’ and ‘grab the key.’”)
This operation of O’Malia is applicable to the system of Refila as they both share characteristics and capabilities, namely, they are directed to using artificial intelligence to provide advice. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified the model of Refila to incorporate an LLM that generates strings as taught by O’Malia. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Refila in order to process data from an AI agent and convert outputs of the ULLM into a format usable by the AI (see paragraph [0016] of O’Malia).
Response to Arguments
Applicant’s arguments, see Page 9, filed 1 October 2025, with respect to 112(b) and 112(d) rejections of claims 17-19 have been fully considered and are persuasive due to the amendment to claim 17. The 112(b) and 112(d) rejections of claims 17-19 have been withdrawn.
Applicant's arguments, see Page(s) 11-19, filed 1 October 2025, with respect to the 35 USC § 101 rejection(s) of claim(s) 1-20 have been fully considered but they are not persuasive. Applicant argues 1) The claims are not directed to an abstract idea, 2) the trained model provides a specific technical improvement that integrates the claim into a practical application, and 3) the claims provide significantly more than the abstract idea. The Examiner respectfully disagrees.
Regarding argument 1, the applicant argues the claims are not directed to an abstract idea because the application addresses technical challenges with existing ERP software used by academic institutions. The Examiner respectfully disagrees because the challenges addressed are not technical in nature. The Applicant cites to using a trained model to make a vast array of data available to institutions, identify meaningful data across varying student populations, and to provide optimal services to advisors and their students. As described in the above 101 rejection, using a trained model/training a model is considered to be abstract because it falls under the abstract idea of mathematics. The limitations of “accessing a model trained with at least historical student-specific academic data, historical student-specific financial data, historical institutional policy data, and historical student-specific outcomes;”, “further train the model based on the edited institution-specific column mapping and historical institutional policy data;”, and “apply to the model at least one set of subject student-related academic data and subject student-related financial data” are considered to be a mathematical concepts because the broadest reasonable interpretation of the model in light of the specification is a mathematical equation and the act of training the model is a mathematical calculation, which falls within the category of “mathematical concepts.” Furthermore, the improvements of making a vast array of data available to institutions, identifying meaningful data across varying student populations, and providing optimal services to advisors and their students is abstract.
MPEP 2106.04(a)(2)(II) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
Making a vast array of data available to institutions and identifying meaningful data across varying student populations both involve managing information about student which falls under the sub-category of managing personal behavior. Furthermore, providing optimal services to advisors and their students is managing personal interactions between people because it manages the interaction between advisors and students. Managing personal behavior and interactions between people are both methods of organizing human activity (see MPEP 2106.04(a)(2)(II)). Since the use of the model to manage the student information does not go beyond the identified judicial exception, the improvement is not considered to be technical in nature. Therefore the invention is an improvement to the abstract idea and not to a specific technical problem.
The Applicant further claims their invention is eligible because it is similar to EcoServices, LLC v. Certified Aviation Servs., LLC, 830 F. App’x 634, 634 (Fed. Cir. 2020). EcoServices was eligible because it recites claim language directed to a specific system that improves jet engine washing. Unlike EcoServices, the claimed invention is directed to using an abstract idea (i.e. mathematics) to improve another abstract idea (i.e., method of organizing human activity). The computer code used is a generic computing element being used in its ordinary capacity. Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Since the use of the model to manage the student information does not go beyond the identified judicial exception, the improvement is not considered to be technical in nature. Therefore the invention is an improvement to the abstract idea and not to a specific technical problem.
Regarding argument 2, the Applicant argues the claims are eligible because the claimed trained model provides an improvement to a technical field such that the claim as a whole is integrated into a practical application. The Examiner respectfully disagrees. As explained in argument 1, the limitations of “accessing a model trained with at least historical student-specific academic data, historical student-specific financial data, historical institutional policy data, and historical student-specific outcomes;”, “further train the model based on the edited institution-specific column mapping and historical institutional policy data;”, and “apply to the model at least one set of subject student-related academic data and subject student-related financial data” are considered to be a mathematical concepts because the broadest reasonable interpretation of the model in light of the specification is a mathematical equation and the act of training the model is a mathematical calculation, which falls within the category of “mathematical concepts.” USPTO guidance uses the term ‘‘additional elements’’ to refer to claim features, limitations, and/or steps that are recited in the claim beyond the identified judicial exception. Since the model and it’s training is part of the abstract idea, the model and it’s training can not be an additional element. An additional element is what integrates a judicial exception into a practical application (see MPEP 2106.04(d)(I)). A judicial exception can not integrate a judicial exception into a practical application, so the Examiner maintains the claims are ineligible.
Regarding argument 3, the Applicant argues the claims are eligible because the claims provide significantly more the abstract idea. The Applicant further argues that the use of a generic system has no bearing on the patent eligibility of the claims. The Examiner respectfully disagrees. MPEP 2106.05 recites:
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
As explained in the above 101 rejection, the additional elements of: at least one processor and at least one memory including computer program code; natural language processing (NLP); an administrator-facing user interface associated with the at least one institution of the plurality of institutions and the administrator-facing user interface individually or in combination do not integ