DETAILED ACTION
Response received on April 20, 2026 has been acknowledged. Claims 1, 3, 6-7, 10, 15-16, and 19 have been amended and Claims 4-5 and 13-14 are cancelled. Therefore, Claims 1-3, 6-12, and 15-19 are pending.
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 Final Office action is in response to the application filed on 6/03/2024 and in response to Applicant’s Arguments/Remarks filed on 04/20/2026. Claims 1-3, 6-12, and 15-19 are pending.
Priority
Application 18732238 was filed on 06/03/2024 and claims the priority benefit of U.S. provisional patent application 63/470,330 filed 06/01/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/20/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Applicant’s Reply
Applicant's response of April 20, 2026 has been entered. The examiner will address applicant’s remarks at the end of this office action. The examiner acknowledges the amendments made to Claim 1, 3, 6-7, 10, 15-16, and 19.
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, 6-12, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1‐3 and 6-9 are directed to a method (process), Claims 10-12 and 15-18 are directed to a system (machine), and Claim 19 is directed to a non-transitory computer readable storage medium storing (machine/apparatus). Thus, these claims fall within one of the four statutory categories of invention. (Step 1: YES).
For step 2A, the Examiner has identified independent method Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent claim 10 and 19. Claim 1, as exemplary is recited below, isolating the abstract idea from the additional elements, wherein the abstract idea is set in bold:
A method for predicting student coursework status, the method comprising; storing coursework progression data in a memory-based course database, wherein the coursework progression data includes metadata associated with coursework performance data of different students enrolled in one or more online courses; training a machine-learning model in accordance with data correlating a student status type to coursework performance data of a student from amongst the different students to generate a knowledge graph, wherein the knowledge graph links the coursework performance data to a status of the student; predicting a status for the student and in accordance with a received request based on analysis of a filtered set of coursework progression data, wherein the coursework progression data is filtered based on the metadata associated with the student, and wherein the knowledge graph and the predicted status are updated based on new data regarding the one of the students; and generating one or more customized learning activities accessible by a student device of the student based on the predicted status of the student as derived from the knowledge graph.
The above bolded limitations recite the abstract idea of assessing learning progress of different students within online courses and providing a student status engine to track the learning progress of each student. These limitations under its broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people) but for the recitation of generic computer components. That is, other than reciting a system implemented by a data processor (computer) the claimed invention amounts to the abstract idea stated above. For example, for the related computer components, this claim encompasses educational management activities that could conventionally be performed by teachers, academic advisors, or administrators manually as part of monitoring student coursework performance and progression. This evaluation process can be done manually using paper gradebooks, spreadsheets, or through direct observation of student’s performance over time. Additionally, predicting student coursework status is considered a method of organizing human activity related to educational performance management, because it involves analyzing and categorizing student administrative activity aimed at instructional decisions and student outcomes. If a claim limitation, under its broadest reasonable interpretation, covers management of interactions between parties, but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. The mere nominal recitation of a “a memory-based course database”, “online courses”,” training a machine-learning model”, and “student device” do not take the claim out of the methods of organizing human interactions grouping. Thus, claims 1, 10, and 19 recites an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A). In particular, Claim 1 recites additional elements of “a memory-based course database”, “online courses”,” training a machine-learning model”, and “student device”. Claim 10 recites the same additional elements of claim 1 with the additions of “one or more processors” and “a non-transitory computer readable storage medium”. Claim 19 recites the same additional elements of claim 1 with the additions of “non-transitory machine-readable medium”. These additional elements are all considered nothing more than generic computing devices to perform generic communicating functions such as storing data and instructions, transmitting and receiving data between computers. These elements are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of communicating data between users) such that they amount no more than mere instructions to apply the exception using a generic computer component in technological environment. See MPEP 2106.05(f) and (h). The claims recite the additional element “machine learning model” and “training data” which is considered nothing more than a general link machine learning because there is no recitation of specifics of how this additional element is being used. Accordingly, these additional elements (combination of computer and the use of machine learning) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are recited at a high level of generality when considered both individually and as a whole. Thus, Claims 1, 10, and 19 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
For step 2B, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they do not amount to more than simply instructing one to practice the abstract idea by using generic computer components to carry out the steps that define the abstract idea, as discussed above. This does not render the claims as being eligible. See MPEP 2106.05(f). The additional elements of using computer, a processor, machine learning model, and a memory when considered both individually and as an ordered combination did not add significantly more to the abstract idea because they were simply applying the abstract idea using generic computer components. In addition, the claims recite the additional element which is considered nothing more than a general link machine learning because there is no recitation of specifics of how this additional element is being used. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (See MPEP 2106.05(f)). Accordingly, these additional elements, do not change the outcome of the analysis, and claims 1, 10, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Claims 2, 6, 9, 11, 15, and 18 recite limitations that further define the same abstract idea of independent claims to include wherein predicting the student status includes identifying a similarity between the filtered set coursework progression data and historical coursework progression data based on one or more rules weighted, a knowledge graph that includes graph-structured data correlating one or more of historical assignment scores, project scores, and exam grades to a student status level, labeling the filtered set of coursework progression data based on feedback regarding the predicted status. In addition, the claims recite the additional element “machine learning model” which is considered nothing more than a general link machine learning because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore, the claims are patent ineligible.
Claims 3 and 12 recite limitations that further define the same abstract idea of independent claims to include wherein the training data includes known historical coursework progression data inputs and known status outputs and identify probability-weighted associations between the inputs and the outputs. In addition, the claims recite the additional element “machine learning model” and “neural network”, which is considered nothing more than a general link machine learning because there is no recitation of specifics of how this additional element is being used. See MPEP 2106.05(f) and (h) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore, the claims are patent ineligible.
Claims 7-8, and 16-17, recite limitations that further define the same abstract idea of independent claims to include wherein the learning activities are customized based on the predicted status of the student, adjusting one or more weights associated with one or more input features that include one or more of student attendance, quiz scores, assignment scores, course grades, and grade categories, and wherein the predicted status includes one or more likelihoods of failure of one of the online courses. The dependent claims do not include any new additional elements and therefore are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 6-11, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lynch et al. (US 20240394817) in view of Issac et al. (US20240394817), further in view of Danson et al. (US 20150100303).
With regards to Claim 1, Lynch et al. teaches a method for predicting student coursework status, the method comprising: (See Abstract & FIG 1)
storing coursework progression data in a memory-based course database, (See [0101]-One or more servers may extract courseware-level data 600 derived from the tracked online activities of the plurality of students taking the plurality of online courses. The courseware-level data 600 is organized based on the structure of each course in a plurality of courses and stored in a database.), wherein the coursework progression data includes metadata associated with coursework performance data of different students enrolled in one or more online courses; (See [0101]- As examples, the courseware-level data 600 derived from the online activities of the plurality of students taking the plurality of online may comprise exam scores, homework scores and time spent on homework. Also See [0090]- The metadata, or categories of information that may be collected for the courseware-level data 600, student-level data 601, institution-level data 602 and/or teacher-level data 603, may be determined in any desired manner. Also See [0093]- Data may be continuously and automatically collected and analyzed to generate course-level data for one or more online digital courses and student-level data 601 for one or more students taking the courses.)
generate a knowledge graph, wherein the knowledge graph links the coursework performance data to a status of the student (See [0017]- FIG. 9 illustrates a hierarchical graph illustrating a possible arrangement of the different levels that may be used with the present invention. Also See [0022]-FIG. 14 illustrates a graph showing a predicted of which course objectives are on average the most difficult for students based on a plurality of posterior distributions from a Bayesian multi-level model. Also See [0136]- FIG. 14 illustrates a graph showing a prediction 802 of which course objectives are on average the most difficult for students based on a plurality of posterior distributions 801 from a Bayesian multi-level model 800. As indicated by the graph, the plurality of posterior distributions 801 predicts (as the numbers are higher for those course objectives) four different course objectives that are particularly difficult. Specifically, the plurality of posterior distributions 801 used to generate FIG. 14 predicts that course objectives 1400 are going to be the most difficult based on the past experiences of other students taking the course. Also See [0078]- graphical representations are used, preferably automatically, to generate and administer remediations 803 that may be performed on the students and/or teacher.)
wherein the knowledge graph and the predicted status are updated based on new data regarding the one of the students; (See [0078]- The plurality of posterior distributions 801 may be used to generate one or more predictions 802 which may be used to generate one or more graphical illustrations 1102 to inform the teacher of potential problem areas, even though the teacher has never taught the class before. After the completion of the class, performance data for the students 1104 may be used to update 1105 the Bayesian multi-level model 800.)
Lynch et al. teaches a received request and a knowledge graph but does not teach training a machine-learning model in accordance with data correlating a student status type to coursework performance data of a student from amongst the different students and predicting a status for the student and in accordance with a received request based on analysis of a filtered set of coursework progression data, wherein the coursework progression data is filtered based on the metadata associated with the student.
However, Issac et al. teaches:
training a machine-learning model in accordance with data correlating a student status type to coursework performance data of a student from amongst the different students to [generate a knowledge graph, wherein the knowledge graph links the coursework performance data to a status of the student]; (See [0005]- train a predictive model that predicts a score in a course, wherein the predictive model comprises a decision tree model. Also See [0044]- the predictive model is trained using supervised learning using a training dataset that includes student data labeled with an actual course grade for plural students that previously enrolled in the course…the prediction module 235 is configured to use the predictive model to predict student performance in a course at different points in time and to detect a negative (e.g., downward) trend in the predicted performance for a student.)
predicting a status for the student and in accordance with a received request based on analysis of a filtered set of coursework progression data, wherein the coursework progression data is filtered based on the metadata associated with the student, (See [0003]- monitoring, by the processor set, performance of the student in a course using a predictive machine learning model that predicts a score in the course based on the student data. Also See [0004]- in response to receiving opt-in consent from a student, obtain student data associated with the student; train a predictive machine learning model that predicts a score in a course; monitor performance of the student in the course using the student data with the predictive machine learning model.),
Lynch et al. and Issac et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch et al. reference to further include training a machine-learning model in accordance with data correlating a student status type to coursework performance data of a student from amongst the different students and predicting a status for the student and in accordance with a received request based on analysis of a filtered set of coursework progression data, wherein the coursework progression data is filtered based on the metadata associated with the student as taught by Issac et al. This is desirable such that it offers better co-learning for K-12 students and higher education experienced candidates by blending prediction and allocation modeling approaches and making offerings compatible with the current sustainability development goals (SDGs). (See Issac, [0015]).
The Lynch-Issac combination teaches a predicted status and a knowledge graph but does not teach generating a display that presents the predicted status for each of the students associated with the received request. However, Danson et al. teaches:
generating one or more customized learning activities accessible by a student device of the student based on the predicted status of the student as derived from [the knowledge graph] (See [0021]- more processing devices such as servers (not shown), each having one or more processors. The servers can be configured to send information (e.g., electronic files such as web pages) to be displayed on one or more devices (e.g., instructor device 102 and student device 104). Also See [0053]- After generation of the scores, the scores may be provided to the display module 140. The display module 140 is configured to generate one or more visual displays to convey the scores and/or student performance predictions to the instructor device 101 and/or student device 103. Also See [0071]- output a score, display the score, predict a likelihood of a student outcome, etc.).
Lynch et al., Issac et al., and Danson et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac combination to further include generating one or more customized learning activities accessible by a student device of the student based on the predicted status of the student as derived from [the knowledge graph] as taught by Danson et al. This is desirable such that it advantageously analyzes online classroom communications or posts by students and makes predictions on the students' likely outcomes in the class. (See Danson, [0015]).
In regards to Claim 10 the Lynch-Issac-Danson combination teaches the claimed invention similar to Claim 1 with the addition of:
Lynch et al. teaches:
A system for predicting student coursework status, the system comprising: (See Abstract & FIG 1)
one or more processors that execute instructions stored by a non-transitory computer readable storage medium to: (See Abstract & FIG 1, Also See [0039]- One or more processing units 204 may be implemented as one or more integrated circuits. Also See [0035]- one or more data stores 110 may reside on a non-transitory storage medium within the server 102. Also See [0049]- Computer system 200 may comprise one or more storage subsystems 210, comprising hardware and software components used for storing data and program instructions, such as system memory 218 and computer-readable storage media 216.)
In regards to Claim 19 the Lynch-Issac-Danson combination teaches the claimed invention similar to Claim 1 with the addition of:
Lynch et al. teaches:
A non-transitory computer-readable storage medium comprising instructions executable by a computing system to perform a method for predicting student coursework status, the method comprising: (See Abstract & FIG 1, Also See [0035]- one or more data stores 110 may reside on a non-transitory storage medium within the server 102. Also See [0049]- Computer system 200 may comprise one or more storage subsystems 210, comprising hardware and software components used for storing data and program instructions, such as system memory 218 and computer-readable storage media 216.)
In regards to Claim 2 and 11, the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. further teaches:
wherein predicting the student status includes identifying a similarity between the filtered set coursework progression data and historical coursework progression data (See [0106]- based on how students from the institution perform in the courses in the courseware-level data 600 compared to students from other institutions performing the same courses, may be stored in the database. In addition, or alternatively, another institutional ranking based on how students perform from the institution as compared to students from other institutions on any desired metric, such as standardized tests, national rankings, etc. may be collected and stored as institutional-level data. (Step 303)… the institutional-level data may comprise public/private data, admission requirement data and historical institution performance data. Also See [0131]- the data from the past is stored and used in future predictions as long as the data is relevant to predicting future course results (once data is no longer relevant to making future predictions or producing future posterior distributions 801, the data may be deleted).) based on one or more rules weighted (See [0006]- any part or parts of the data may be weighted to customize the analysis of the Bayesian multi-level model. In preferred embodiments, course data, in the courseware-level data, for the course being analyzed is heavily weighted. In some embodiments, the data for the student or students, in the student-level data, taking the course may be weighted.)
Lynch et al. does not teach by the status predicting machine-learning model. However, Issac et al. teaches:
by the status predicting machine-learning model (See [0044]- the predictive model is trained using supervised learning using a training dataset that includes student data labeled with an actual course grade for plural students that previously enrolled in the course. Also See [0005]- train a predictive model that predicts a score in a course, wherein the predictive model comprises a decision tree model. Also See [0044]- the predictive model is trained using supervised learning using a training dataset that includes student data labeled with an actual course grade for plural students that previously enrolled in the course…the prediction module 235 is configured to use the predictive model to predict student performance in a course at different points in time and to detect a negative (e.g., downward) trend in the predicted performance for a student.)).
Lynch et al., Issac et al., and Danson et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further include by the status predicting machine-learning model as taught by Issac et al. This is desirable such that it offers better co-learning for K-12 students and higher education experienced candidates by blending prediction and allocation modeling approaches and making offerings compatible with the current sustainability development goals (SDGs). (See Issac, [0015]).
In regards to Claim 6 and 15, the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. further teaches:
a knowledge graph that includes graph-structured data correlating one or more of historical assignment scores, project scores, and exam grades to a student status level (See [0017]- FIG. 9 illustrates a hierarchical graph illustrating a possible arrangement of the different levels that may be used with the present invention. Also See [0022]-FIG. 14 illustrates a graph showing a predicted of which course objectives are on average the most difficult for students based on a plurality of posterior distributions from a Bayesian multi-level model. Also See [0136]- FIG. 14 illustrates a graph showing a prediction 802 of which course objectives are on average the most difficult for students based on a plurality of posterior distributions 801 from a Bayesian multi-level model 800. As indicated by the graph, the plurality of posterior distributions 801 predicts (as the numbers are higher for those course objectives) four different course objectives that are particularly difficult. Specifically, the plurality of posterior distributions 801 used to generate FIG. 14 predicts that course objectives 1400 are going to be the most difficult based on the past experiences of other students taking the course.).
In regards to Claim 7 and 16, the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. further teaches:
further comprising adjusting one or more weights associated with one or more input features that include one or more of student attendance, quiz scores, assignment scores, course grades, and grade categories (See [0006]- any part or parts of the data may be weighted to customize the analysis of the Bayesian multi-level model. In preferred embodiments, course data, in the courseware-level data, for the course being analyzed is heavily weighted. In some embodiments, the data for the student or students, in the student-level data, taking the course may be weighted. Also See [0118]-In this case, the data for the students in the course may be weighted so that the analysis is more specific towards the students that are actually in the course. Also See [0101]- As examples, the courseware-level data 600 derived from the online activities of the plurality of students taking the plurality of online may comprise exam scores, homework scores and time spent on homework.).
In regards to Claim 8 and 17, the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. does not teach wherein the predicted status includes one or more likelihoods of failure of one of the online courses. However, Danson et al. further teaches:
wherein the predicted status includes one or more likelihoods of failure of one of the online courses (See [0015]- analyzes online classroom communications or posts by students and makes predictions on the students' likely outcomes in the class. Also See [0047]-The performance prediction module 130 is adapted to use the score(s) for the different student metrics to predict the likelihood of a student outcome using a predictive model. The predictive model predicts the likelihood of a student achieving certain goals, such as failing or passing a course, or staying in or dropping out of a course. The predictive model is built using sample sets of previous scores of previous students in previous classes. Also See [0069]- At step 206, the performance prediction module 130 takes the scores and inputs them into a predictive model. At step 208, the predictive model outputs the likelihood of a student outcome based on the scores. For example, the predictive model indicates the probability that a student will pass or fail a course.).
Lynch et al., Issac et al., and Danson et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further wherein the predicted status includes one or more likelihoods of failure of one of the online courses as taught by Danson et al. This is desirable such that it advantageously analyzes online classroom communications or posts by students and makes predictions on the students' likely outcomes in the class. (See Danson, [0015]).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lynch et al. (US 20240394817) in view of Issac et al. (US20240394817), in view of Danson et al. (US 20150100303) , further in view of Chang et al. (US 20190102678).
In regards to Claim 3 and 12 the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. further teaches:
coursework progression data (See [0101]- As examples, the courseware-level data 600 derived from the online activities of the plurality of students taking the plurality of online may comprise exam scores, homework scores and time spent on homework.)
Lynch et al. does not teach wherein the training data includes known historical [coursework progression] data inputs and known status outputs, and further comprising generating the status predicting machine-learning model by. However, Issac et al. teaches:
wherein the training data includes known historical [coursework progression] data inputs and known status outputs, and further comprising generating the status predicting machine-learning model by (See [0117]- At step 710, the a performance tracking app of the system permits the user to track their performance. Step 710 may comprise the prediction module 235 predicting course scores for the student using the predictive model for each course and the student data… At step 750, the system collects data and makes a training dataset based on past performance of each student. At step 755, the system saves the collected data in a database.).
Lynch et al., Issac et al., and Danson et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further include wherein the training data includes known historical [coursework progression] data inputs and known status outputs, and further comprising generating the status predicting machine-learning model by as taught by Issac et al. This is desirable such that it offers better co-learning for K-12 students and higher education experienced candidates by blending prediction and allocation modeling approaches and making offerings compatible with the current sustainability development goals (SDGs). (See Issac, [0015]).
The Lynch-Issac-Danson combination does not teach using a neural network to identify probability-weighted associations between the inputs and the outputs. However, Chang et al. teaches:
using a neural network to identify probability-weighted associations between the inputs and the outputs (See [0005]- determining respective mapping functions corresponding to a multiclass output of the neural network in association with the input data. Also See [0031]- a neural network apparatus may include a processor configured to input input data to a neural network, determine respective mapping functions corresponding to a multiclass output of the neural network in association with the input data. Also See [0063]- [0063] Each of the nodes of the hidden layers 110 may produce or generate an output based on the each of the nodes implementing a corresponding activation function, e.g., associated with one or more connection weighted inputs from outputs of nodes of a previous layer. Here, though the connection weighted inputs will be discussed herein as being weighted inputs provided by a connection from a node of a previous layer,)
Lynch et al., Issac et al., Danson et al., and Chang et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further include using a neural network to identify probability-weighted associations between the inputs and the outputs as taught by Chang et al. This is desirable such that it provides a method for reliable recording and monitoring a user's academic progress to a central resource that is advantageous when considering persons and professionals already in the work force looking to improve their skills and knowledge. (See Chang, [0007])
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lynch et al. (US 20240394817) in view of Issac et al. (US20240394817), in view of Danson et al. (US 20150100303), further in view of Lin (US 20230320642).
In regards to Claim 9 and 18 the Lynch-Issac-Danson combination teaches the claimed invention as recited in the independent claim above.
Lynch et al. further teaches:
labeling the filtered set of coursework progression data (See [0101]-One or more servers may extract courseware-level data 600 derived from the tracked online activities of the plurality of students taking the plurality of online courses. The courseware-level data 600 is organized based on the structure of each course in a plurality of courses and stored in a database.)
Lynch et al. teaches coursework progression data but does not teach retraining the status predicting machine-learning model based on the labeled set of coursework progression.
Issac et al. further teaches:
retraining the status predicting machine-learning model based on the labeled set of coursework progression data (See [0044]- the predictive model is trained using supervised learning using a training dataset that includes student data labeled with an actual course grade for plural students that previously enrolled in the course.)
Lynch et al., Issac et al., and Danson et al. are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further include retraining the status predicting machine-learning model based on the labeled set of coursework progression data as taught by Issac et al. This is desirable such that it offers better co-learning for K-12 students and higher education experienced candidates by blending prediction and allocation modeling approaches and making offerings compatible with the current sustainability development goals (SDGs). (See Issac, [0015]).
The Lynch et al. teaches labeling the filtered set of coursework progression data but does not teach labeling the filtered set of coursework progression data based on feedback regarding the predicted status. However, Lin teaches
based on feedback regarding the predicted status; (See [0181]- Training the model: The cleaned and preprocessed data is then used to train the foundation model, such as GPT-3, using a process known as supervised learning. During this process, the model is exposed to examples of input and output pairs, such as a patient's statement and a corresponding therapeutic response. The model learns to recognize patterns in the data and generate appropriate responses based on those patterns. They can also be trained using self-supervised learning (SSL) methods, or reinforcement learning with human feedbacks (RLHF) if human annotators are available to score the quality of the outputs)
Lynch et al., Issac et al., Danson et al., and Lin are all considered to be analogous to the claimed invention because they are in the same field of predicting student coursework status. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Lynch-Issac-Danson combination to further include based on feedback regarding the predicted status as taught by Lin. This is desirable such that it teach the model to recognize patterns in the data and generate appropriate responses to support the therapeutic. (See Lin, [0184])
Response to Arguments
Applicant's arguments filed 4/20/2026 have been fully considered but they are not persuasive.
The comments regarding the 35 USC 101 rejection are noted. On page 8 of Applicant’s response, applicant disagrees with the office action’s rejection of claims 1-19 under 35 U.S.C. § 101 because the claimed invention is purportedly directed to judicial exception without significantly more, specifically in grouped within the method of organizing human activity of abstract ideas. Examiner respectfully disagrees. Examiner notes that the claims recite predicting a student’s coursework status and generating customized learning activities based on that status, which constitutes managing interactions between students and educational content and thus falls within certain methods of organizing human activity. Applicant further argues that regarding Step 2A Prong 1 of the Alice analysis, the present claims are not directed to an abstract idea. MPEP 2106.04(a) specifies that the "abstract ideas" category is limited to the following sub- categories: mathematical concepts; certain methods of organizing human activity; and mental processes. Examiner respectfully disagrees, as the Office has not expanded the abstract-idea category beyond the numerated groupings. Examiner notes that rather, the claimed concept organizes and tailors the provision of educational activities according to an evaluated student status, which is a human activity ordinarily performed in the educational context. Applicant further argues that rather than relating to organizing human activity, the claims recite 'training a machine learning model ... to generate a knowledge graph,' and 'generating one or more customized learning activities accessible by a student device based on the ... knowledge graph.' Such elements relate to examining specific "features, measurable properties, and parameters of a data set," by the machine-learning model to interlink various "objects, events, situations." Moreover, training a machine learning model and generating a knowledge graph cannot be performed by a human mind. Therefore, the claims limitations are neither a mathematical concept nor are they human activity, or a mental process, thereby qualifying as patent-eligible under Step 2A, Prong One. Examiner respectfully disagrees because the use of machine learning and a knowledge graph does not alter the underlying focus of the claim, which remains evaluating student performance to determine student status and tailor educational activities accordingly, and merely automating that educational decision-making process does not remove the claimed concept from the certain methods of organizing human activity grouping.
Applicant further argues that regarding Step 2A Prong 2 of the Alice analysis, the present claims implement a practical application. With respect to the practical application inquiry, the Applicant respectfully disagrees with the Office Action which argues that the "judicial exception is not integrated into a practical application" because the elements of the claims "do not impose any meaningful limits on practicing the abstract idea and are recited at a high level of generality." Examiner respectfully disagrees because the recited machine learning model, and other additional elements are recited at a high-level of generality and merely facilitate the abstract educational decision-making process and do not reflect an improvement to computer functionality or another technology, or otherwise impose a meaningful limit beyond applying the exception in a computerized environment. Applicant further argues that rather than merely using a computer as a tool to perform an abstract idea, the claims recite technical improvements inclusive of using an adaptive model in which artificial intelligence algorithms are used to deliver customized resources and learning activities, which may in turn be used in evaluating learning progress of a student. Examiner respectfully disagrees because using AI algorithms to adaptively select and deliver customized learning activities based on evaluated student progress improves the performance of the educational process itself, rather than the operation of the computer, machine learning technology, or another technical field. Applicant further argues that the claims affect a transformation or reduction by transforming discrete data points relating to objects, events, situations, or abstract concepts into a machine learning model and a knowledge graph. That the model and graph may be used to aid in predicting performance of the future status of a student is secondary to the patentable transformation of data. The claims are therefore patent-eligible under Step 2A, Prong 2. Examiner respectfully disagrees because converting student-related data into a machine learning model or knowledge graph constitutes manipulation and reorganization of data, not a transformation of a particular article into a different state or thing, and therefore does not integrate the judicial exception into a practical application.
Applicant further argues that regarding Step 2B of the Alice analysis, Applicant's claims include an inventive concept. The Office Action argues that the claims "do not include additional elements that are sufficient to amount to significantly more than the judicial exception" because "all serve to gather and process data and do not add anything more significantly to the judicial exception." Office Action, 4. These arguments in the Office Action are stated as conclusions, with no evidence provided. MPEP § 2106.07(a)(III) requires that in step 2B. Examiner respectfully disagrees because the office action has not made a finding that the additional elements are well-understood, routine, and conventional, rather, the Step 2B determination is that, considered individually and as an ordered combination, the additional elements do not amount to significantly more than the judicial exception. Applicant further argues that examiner should not assert that an additional element (or combination of elements) is well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing and that the Office Action fails to "expressly support" its "rejection in writing" with any of the types of evidence listed in MPEP 2106.07(a)(III). Thus, the Office Action fails to show conventionality of any of the elements in the Applicant's claims. Examiner respectfully disagrees because the evidentiary requirements of MPEP § 2106.07(a)(III) apply when the Examiner makes a finding that an additional element is well-understood, routine, and conventional, whereas no such finding is relied upon here. Rather, the rejection concludes that the additional elements, individually and as an ordered combination, do not provide an inventive concept amounting to significantly more than the judicial exception.
Applicant further argues that the additional elements of at least the 'one or more processors' and 'memory' train 'a machine-learning model' and 'generate a knowledge graph,' to predict a status for one of the students, when considered together, represent an inventive concept ("something more") at least because they represent "improvements to the functioning of a computer," "improvements to any other technology or technical field," and "application of a particular machine" under MPEP § 2106.05(I)(A)(i)-(iii) for at least the reasons discussed above with respect to the considerations for Step 2A (prong 2). Examiner respectfully disagrees because, when considered individually and as an ordered combination, the recited additional elements are recited at a high-level of generality and merely carry out the abstract idea and do not recite any specific technological mechanism that improves computer functionality, advances machine learning technology itself, or ties the exception to a particular machine integral to its performance. Applicant further argues that the Office Action fails to further consider whether each and every element outside of the purported abstract idea-individually or in every ordered combination-adds significantly more so as to qualify as an inventive concept under the second part of the Alice analysis, thereby failing to establish the lack thereof with clear and convincing evidence as required by Berkheimer. Berkeimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018). In particular, the office Action fails to consider the additional elements in combination in a way that deviates from what is routine or conventional. Based on the foregoing, the Office Action fails to establish lack of patent-eligible subject matter under Section 101. Examiner respectfully disagrees because the Office Action considered the additional elements both individual and as an ordered combination and determined that they merely implement the judicial exception without providing an inventive concept. Additionally, Berkheimer does not require clear convincing evidence absent a finding that the additional elements are well-understood, routine, and conventional, which the Examiner has not made here. Thus, the rejections of Claims 1-3, 6-12, and 15-19 under 35 USC 101 are maintained.
The comments regarding the 35 USC 103 rejection are noted. On page 12 of Applicant’s response applicant asserts that Lynch merely discloses a conventional visual graph of aggregated student performance data and therefore fails to teach or suggest the claimed knowledge graph linking coursework performance data to an individual student’s status. The Examiner respectfully disagrees because Applicant’s argument focuses narrowly on the visual form of Lynch FIG. 14 rather than the predictive relationships taught by Lynch as a whole. Actually, the rejection is based on the combined teachings of Lynch, Issac, and Danson, and the applicant has not persuasively addressed why the cited combination fails to teach or suggest using coursework performance data to establish relationships indicative of student status for predictive purposes. Thus, the rejections of Claims 1-3, 6-12, and 15-19 under 35 USC 103 is withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension
of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the
mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this
final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory
period, then the shortened statutory period will expire on the date the advisory action is mailed, and any
extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In
no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final
action.
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/A.W.H./ Examiner, Art Unit 3626
/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626
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