DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is a FINAL office action in response to the Applicant’s response filed 6 November 2025.
Claims 1, 7-10 ,13, 16, 17, and 19 have been amended.
Claims 4-6, 14, 15, and 20 have been cancelled.
Claims 1-3, 7-13, and 16-19 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 6 November 2025 have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on page 12 of their response, “On similar grounds, the Applicant submits that the human mind is not equipped at least, for example, to perform the features of ‘determining, using a trained machine learning module, a college signature for each of the plurality of colleges based on the respective college data, wherein the college signature comprises one or more admission criteria and a weight associated with each of the one or more admission criteria; generating, using the trained machine learning module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges,’ as recited in amended independent claim 1.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, the Examiner notes that the Applicant has merely asserted that the claim elements, “determining, using a trained machine learning module, a college signature for each of the plurality of colleges based on the respective college data, wherein the college signature comprises one or more admission criteria and a weight associated with each of the one or more admission criteria; generating, using the trained machine learning module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges,” do not recite a “mental process,” however they have provided no reasoning or evidence as to why this is the case. Further, the Examiner notes that, the previous rejection stated in paragraph 6, “In addition, the claims recite receiving student profile data of a student, obtaining college data associated with each of a plurality of colleges, determining a college signature for each of the plurality of colleges based on the respective college data and that comprises admission criteria and a weight associated with each admission criteria, generating an admission score corresponding to each of the plurality of colleges for the student based on a comparison between the student profile data and the college signature for each of the plurality of colleges, and generating a list of recommended colleges for the student based on the admission score; which are elements that can be performed in the human mind (observation, evaluation, judgement, opinion), as the series of elements merely encompass collecting student and college information, using evaluation/judgement to create profiles for the students and colleges, comparing the profiles, and determining recommended matches based on the comparison, which can purely be performed in the human mind.” As shown here, the Examiner specifically identified the elements of the claim which recite elements that can be performed in the human mind, and has also explained why this is the case. As the Applicant has merely made a conclusory argument that the claims do not recite a “mental process,’ without providing any evidence or reasoning, and because the Applicant has failed to rebut the specific rejection made, the Examiner is not persuaded of error. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on pages 13 and 14 of their response, “The Examiner states that the claims are ‘directed to managing commercial activity, marketing, or human relationships’ and ‘can be performed in the human mind.’ This interpretation overgeneralizes the claim language and ignores critical machine-learning and data-structuring limitations that are technical in nature. Independent claim 1, for example, recites: determining, using a trained machine learning module, a college signature for each of a plurality of colleges based on the respective college data, wherein the college signature comprises one or more admission criteria and a weight associated with each…; generating, using the trained machine learning module, an admission score… and generating a list of recommended colleges…’ This recitation is not a mere abstract ‘recommendation’ process. It specifies: (i) Use of a trained ML module - a specialized computational model trained on historical acceptance/rejection data, impossible to perform mentally. (ii) Derivation of a structured data representation (college signature) - a computer-generated data artifact containing weighted criteria. (iii) Generation of predictive scores and threshold categorizations - machine-based computation not performed by a human mind. These operations require non-trivial machine learning computations, high-dimensional data processing, and threshold-based categorization, all inherently technological.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that the argued elements specifies, “Use of a trained ML module - a specialized computational model trained on historical acceptance/rejection data, impossible to perform mentally,” the Examiner is not persuaded. In particular, it is noted that the Applicant’s claim 1 (and similarly 13 and 19) do not recite any elements regarding training a machine learning model, nor do they specify what the model is trained on. As such, any argument regarding actually training the model is beyond the scope of the claims and thus is not relevant to determining whether the claims are directed to an abstract idea. Additionally, it is noted that the use of the machine learning model to determine a college signature and to generate an admission score, was identified as merely invoking the use of a computer as a tool to carry out the abstract idea of determining a college signature and generating an admission score. Notably, MPEP 2106.04(a)(2)(III)(C) states, “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” In this case, the mere use of a computer to perform the elements that otherwise could be performed mentally, does not prevent the claim from reciting a mental process. Second, with respect to the Applicant’s argument that the claims recite, “Derivation of a structured data representation (college signature) - a computer-generated data artifact containing weighted criteria,” and that this is not a mental process, the Examiner is not persuaded. In particular, it is noted that “a college signature” is not defined as any type of “data artifact,” merely that it comprises, “one or more admission criteria and a weight associated with each of the one or more admission criteria.” The Applicant has provided no reasoning as to why one could not mentally assign a single criteria and a weight associated with the criteria, to a college (e.g. a higher required GPA is important), as the Examiner maintains that a human could easily do this element mentally. Further, merely being a “structured data representation” has no bearing on whether an element claimed is a mental process, or that a claim in general recites a mental process. Thus, the Examiner is not persuaded by the Applicant’s argument that deriving college signatures are not a mental process. Third, with respect to the Applicant’s argument that the claims specify, “Generation of predictive scores and threshold categorizations - machine-based computation not performed by a human mind,” the Examiner is not persuaded. Initially, the Examiner notes, the claims do not recite generating any “predictive scores” or “threshold categorizations;” and instead recite calculating weight admission scores, calculating threshold scores, calculating an admission score for a college for a student, and assigning a category to the college. Thus, is it noted that any argument for “predictive scores” and “threshold categorizations” is beyond the scope of the claim, and is not relevant to determining if the claim recites an abstract idea. Next, the Examiner notes that claim 1 states, “calculating a weighted admission score for each profile of the accepted student profile data and the rejected student profile data based on the assigned weight for each of the one or more of admission criteria, the accepted student profile data, and the rejected student profile data; and calculating a threshold score for the college, based on the weighted admission score of each profile from the accepted student profile data and the rejected student profile data; generating, using the trained machine learning module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges; and comparing the admission score of the student for the college with the threshold score for the college; assigning a category from a plurality of categories to the college for the student based on the comparison.” (Emphasis added). As shown and emphasized here, the Applicant’s claims recite calculating a weighted admission score for student profiles, ccalculating a threshold score for a college based on the weighted admission score of each profile, and generating an admission scores for a student to different colleges based on a the student’s profile and the college signatures, and assigning a category to the college for the student; which, as discussed in the previous rejection, recite a “Mental Process.” Notably, as discussed above, the use of the machine learning model to calculate a score is merely invoking the computer as a tool to perform the abstract idea. Further, as noted above, the mere use of a computer to perform an abstract idea, does not restrict a claimed element from reciting a “mental process,” unless the element could not be performed in the human mind. In this case, the Applicant has failed to identify any reasoning as to why a human mind can not compute admission scores or threshold scores. Further, it is noted that assigning a category for a college for the student, could encompass merely a “likely, unlikely, and no chance” categories, which a human mind could easily determine. Thus, the Applicant’s lack of explanation as to their conclusory argument regarding a mental process is deemed not persuasive. It is further noted that regarding the Applicant’s argument that “these operations require non-trivial machine learning computations, high-dimensional processing, and threshold-based categorization, all inherently technological,” the is not persuaded. Notably, the Applicant has failed to identify in their arguments or claims any specific computations being performed, and instead merely referenced general aspects, which as discussed above, do recite a mental process. Regarding the use of “machine learning,” it is noted that MPEP 2106.05(f) states, “Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.” In this case, the Applicant’s use of machine learning the calculate these various scores, is merely invoked as an idea of a solution, without reciting how the trained model actually accomplishes the solution. As such, the Applicant has not claimed some specific machine-learning computations, but instead, merely mental process computations that are performed by a machine learning model. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 14 of their response, “A human being cannot manually: (i) Train a model on thousands of historic student-college data points; (ii) Compute multi-weighted signatures and thresholds; or (iii) Generate per-student scores across a dataset of hundreds of colleges. Further, amended claim 1 also recites analyzing accepted/rejected profiles, assigning weights to admission criteria from that analysis, computing weighted scores for those historical profiles, and deriving a threshold score, thus describing a concrete model-training and calibration sequence that produces computer-created artifacts (college-specific learned weights and thresholds) used by the system for subsequent inference. Thus, the claims cannot reasonably be performed mentally. The 2019 PEG (Section III.A.2) and October 2019 Update both emphasize that a claim requiring computer-based processing of large datasets or ML computation is not a ‘mental process.’” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that a human mind cannot, “Train a model on thousands of historic student-college data points,” the Examiner notes that this is not recited or discussed in the claims, and thus is beyond the scope of the claims and is irrelevant at evaluating whether the claims recite an abstract idea. Second, with regards to the Applicant’s argument that a human mind cannot, “Compute multi-weighted signatures and thresholds,” the Examiner is not persuaded. As discussed above, the claimed “college signature” merely comprises a criteria and a weight of the criteria; and as discussed above, the threshold is based on the weighted admission score of accepted and rejected students. Notably, a human mind could easily make a determination of a college criteria and its weight, and can further make some threshold score based on student scores (i.e. a threshold is set to the average score). Thus, the Applicant’s argument that these elements cannot be performed in the human mind is not persuasive. Third, with respect to the Applicant’s argument that the human mind cannot, “generate per-student scores across a dataset of hundreds of colleges,” the Examiner notes that the claims do not require “hundreds of colleges,” and instead merely require “a plurality,” which under the broadest reasonable interpretation is two colleges. Further, no restriction is placed on the number of student records considered, which could also be two students. As such, the Applicant’s argument regarding some large dataset is not reflective of the claims, and thus, is not relevant to determining whether the claims recite an abstract idea. Fourth, with respect to the Applicant’s argument that the claims recite, “a concrete model-training and calibration sequence that produces computer-created artifacts (college-specific learned weights and thresholds) used by the system for subsequent inference,” the Examiner is not persuaded. In particular, as discussed above, the Applicant’s claims do not recite any elements regarding actually training a machine learning model, and thus the Applicant’s argument is not reflective of the claimed invention. Further, no “calibration sequence” is found in any of the Applicant’s claims, and thus the Applicant’s argument is not reflective of the claimed invention. Further, as discussed above, the claims do not recite creating any “artifacts,” and thus the Applicant’s argument is not reflective of the claimed invention. Further, regarding the determination of college-specific learned weights and thresholds and using these for subsequent inference, the Examiner notes that merely using collected data for further analysis and judgements, is a mental process. The Applicant has provided no reasoning as to why a human mind could not collect historic information, and use historic information to perform additional analysis or make predictions for future similar tests (i.e. use former student data to predict future student results). As such, the Applicant’s argument is deemed conclusory and thus is not persuasive. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 15 of their response, “The claims are analogous to those found eligible in controlling case law (i) Enfish, LLC V. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016): The Federal Circuit held that a claim directed to a specific data structure for improving database functionality was not abstract. In claim 1, the "college signature" comprising weighted admission criteria is a machine- generated data structure that improves how the computer retrieves and matches data for recommendations. (ii) McRO, Inc. V. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016): Claims automating a manual process through specific rules were found eligible. Similarly, claim 1 automates subjective human evaluations with defined computational rules and learned weighting schemes, producing consistent, objective outcomes. (iii) Finjan, Inc. V. Blue Coat Sys., 879 F.3d 1299 (Fed. Cir. 2018): The court found eligibility for claims generating a "security profile" i.e. a computer-specific data structure used in subsequent processing. In claim 1, the "college signature" serves a parallel role i.e. a digital artifact enabling machine-level comparison and ranking. At least for the above-mentioned reasons, the Applicant respectfully submits that claim 1 meets standard for patent eligibility under Prong One of Step 2A of 2019 Revised Patent Subject Matter Eligibility Guidance.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument regarding Enfish, the Examiner is not persuaded. Notably, MPEP 2106.05(a)(I) states, “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691.” (Emphasis added). As shown and emphasized here, the Court in Enfish found the claims not directed to an abstract idea because it recited an improvement to computer functionality. Specifically, the court relied on the “the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility,” and noted it did “not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts.” Unlike this finding, the Applicant has failed to provide any support in their specification that the generation of a college signature with a weighted admission criteria improves any form of computer functionality. Notably, the Applicant’s argument merely asserts, without evidence that the college signature improves how the computer retrieves and matches data for recommendations, which is deemed merely conclusory, and thus, not persuasive. Second, with regards to McRO, the Examiner is not persuaded. In particular, the Applicant has argued, “Claims automating a manual process through specific rules were found eligible. Similarly, claim 1 automates subjective human evaluations with defined computational rules and learned weighting schemes, producing consistent, objective outcomes;” the Examiner respectfully disagrees. With regards to McRO, it is noted that MPEP 2106.05(a) states, “An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01.” (Emphasis added). As shown and emphasized here, the Court found McRO to be directed towards an improvement in computer technology based on the “specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans.” Contrary to this finding, the Applicant’s specification fails to provide any explanation or evidence that automating subjective human evaluation with “defined computational rules and learned weighting schemes,” improves any computer functionality or technology. As such, the Applicant’s conclusory argument is deemed not persuasive. Third, with respect to the Applicant’s argument regarding Finjan, the Examiner is not persuaded. In this case, the Examiner notes that MPEP 2106.05(a)(I) describes the improvement in computer functionality found by the Court in Finjan, “A method that generates a security profile that identifies both hostile and potentially hostile operations, and can protect the user against both previously unknown viruses and "obfuscated code," which is an improvement over traditional virus scanning. Finjan Inc. v. Blue Coat Systems, 879 F.3d 1299, 1304, 125 USPQ2d 1282, 1286 (Fed. Cir. 2018).” (Emphasis added). Ass shown and emphasized here, the Court identified the computer functionality improved upon by the claims, specifically traditional virus scanning. Unlike this finding, the Applicant’s argument that “the ‘college signature’ serves a parallel role i.e. a digital artifact enabling machine-level comparison and ranking,” the Examiner is not persuaded. In particular, comparing and ranking data is not “computer functionality.” Further, the Applicant has failed to provide any evidence or explanation as to how a “college signature” that comprises admission criteria and a weight, improves any computer functionality similar to Finjan’s improved virus scanning; thus, their conclusory statement of similarity is deemed not persuasive. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 16 of their response, “Similar to claim 2 of Example 37 as recited above, amended independent claim 1 requires the use of a server or a processor to determine the college signature and generate admission score using the trained machine learning model.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. With regards to Example 37, the Examiner notes that the USPTO stated for the reasons of eligibility, “The claim does not recite any of the judicial exceptions enumerated in the 2019 PEG. For instance, the claim does not recite a mental process because the claim, under its broadest reasonable interpretation, does not cover performance in the mind but for the recitation of generic computer components. For example, the “determining step” now requires action by a processor that cannot be practically applied in the mind. . In particular, the claimed step of determining the amount of use of each icon by tracking how much memory has been allocated to each application associated with each icon over a predetermined period of time is not practically performed in the human mind, at least because it requires a processor accessing computer memory indicative of application usage. Further, the claim does not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claim does not recite a mathematical relationship, formula, or calculation. Thus, the claim is eligible because it does not recite a judicial exception.” (Emphasis added). As shown and emphasized here, the Example identified the claim as patent eligible because it did not recite any judicial exception that fell into one of the categories of abstract idea; particularly, it did not recite a mental process, a certain method of organizing human activity, or mathematical concepts. Specifically regarding “mental processes,” which claim 1 was identified as reciting, the Example stated, “determining the amount of use of each icon by tracking how much memory has been allocated to each application associated with each icon over a predetermined period of time is not practically performed in the human mind, at least because it requires a processor accessing computer memory indicative of application usage.” That is, the Example identified specific claimed processes that could not be practically be performed in the mind, because a human brain cannot access computer memory to determine application use. Notably, this Example did not state that using a server and processor to perform any actions on a computer rendered a claim not a mental process, and thus the Applicant’s argument of similarity to the Example is found not persuasive. Further, it is noted that while the Applicant’s argument was solely based on the claimed invention not reciting a “Mental Process,” it is noted that the Examiner specifically stated in paragraph 6 of the previous Non-Final rejection that the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. That is, even if for arguendo the claims did not recite a “Mental Process,” the claims still recite “Certain Methods of Organizing Human Activity,” and thus the findings of claim 2 of Example 37 would still not show that the Applicant’s claims did not recite an abstract idea. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 17 of their response:
“The claims improve the functioning of the computer itself
The invention improves computational efficiency in large-scale admissions matching by:
(i) Learning weighted admission criteria that reduce redundant computation;
(ii) Generating threshold-based categories to streamline comparison; and
(iii) Producing computer-readable outputs optimized for subsequent recommendation display.
The 2019 PEG (and the October 2019 and 2024 AI updates) provide that claims integrate a judicial exception when they apply it in a manner that improves computer functionality or another technology.
Currently amended claim 1 describes calibration/thresholding to compute weighted scores for accepted and rejected populations and derive a threshold score from those empirical distributions-a calibration step that yields deterministic, machine-applicable decision boundaries consumed later for inference.
This is not a generic "apply it on a computer" invocation. It is a particular machine- learning arrangement that changes the computer's processing of admissions data (objective, calibrated, scalable) and yields structured artifacts that drive subsequent computerized categorization and top-K selection.”
The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that, “The invention improves computational efficiency in large-scale admissions matching by: (i) Learning weighted admission criteria that reduce redundant computation; (ii) Generating threshold-based categories to streamline comparison; and (iii) Producing computer-readable outputs optimized for subsequent recommendation display,” the Examiner is not persuaded. In particular, the Examiner notes that the Applicant has failed to provide any evidence as to improvements in computer functionality. It is noted that MPEP 2106.05(a) states, “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).” (Emphasis added). As shown and emphasized here, the Applicant’s specification must set forth or describe how the Applicant’s invention improves computer functionality, another technology, or technical field. If the Applicant’s specification lacks this disclosure, then the Examiner is to conclude the aspects do not improve computer functionality, another technology, or technical field. In this case, the Applicant’s specification fails to describe any changes to computational efficiency, and instead, the Applicant has merely made this assertion in the arguments with no evidence to support it. As such, the Applicant’s argument regarding improvements to computational efficiency is not persuasive. Second, with regards to the Applicant’s argument regarding the claims reciting, “calibration/thresholding to compute weighted scores for accepted and rejected populations and derive a threshold score from those empirical distributions-a calibration step that yields deterministic, machine-applicable decision boundaries consumed later for inference,” and that this improved computer functionality, the Examiner is not persuaded. In particular, the Examiner notes that the claims do not recite any elements regarding “calibration,” and with regards to “thresholding,” the claim merely states, “calculating a threshold score for the college, based on the weighted admission score of each profile from the accepted student profile data and the rejected student profile data… comparing the admission score of the student for the college with the threshold score for the college,” which merely describes calculating a threshold score, and comparing a student’s score with the threshold in order to assign a category to a respective college, which is computer functionality, but instead a part of the abstract idea itself. Further, it is noted that yielding, “deterministic, machine-applicable decision boundaries consumed later for inference,” is merely setting boundaries on decisions, which is not computer functionality, but would be a part of the abstract idea. As such, the Applicant’s arguments regarding computer functionality are deemed not persuasive, as the Applicant has failed to identify any actual computer functionality which is improved upon by the claimed invention. Third, with respect to the Applicant’s argument that, “This is not a generic ‘apply it on a computer’ invocation. It is a particular machine-learning arrangement that changes the computer's processing of admissions data (objective, calibrated, scalable) and yields structured artifacts that drive subsequent computerized categorization and top-K selection,” the Examiner is not persuaded. In this case, nothing in the claims recite or involve, “structured artifacts that drive subsequent computerized categorization and top-K selection,” and thus the Applicant’s argument is deemed outside the scope of the claimed invention, and thus not relevant to determining whether the claims are directed to an abstract idea. Further, with regards to the Applicant’s argument that this is not “apply it on a computer,” the Examiner notes that MPEP 2106.05(f) states, “Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” As shown here, merely invoking the use of a computer as a tool in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. With respect to the Applicant’s claims, the Applicant’s claimed use of “machine learning” is merely being invoked to as a tool to carry out the abstract idea, and thus, would not integrate the abstract idea into a practical application. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on pages 17 and 18 of their response, “With respect to Prong Two of Step 2A, the Applicant respectfully refers to AI Example 47 of 2024 Update: Machine-learning-based model training and thresholding for image classification was held eligible as a technological improvement. Likewise, Applicant's invention uses ML to classify and recommend based on learned data structures, improving the technological process of data-driven prediction. Therefore, the claimed invention represents a concrete technological application, not a disembodied idea.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to Example 47, the Examiner notes that Example 47 did not concern “Machine-learning-based model training and thresholding for image classification,” but instead was directed towards, “Anomaly Detection.” In addition, said example included claims that were directed towards an abstract idea, and other claims that were not; however, none of these claims were related to image classification. As the Applicant’s argument incorrectly references an Example, and is merely a conclusory statement, the Examiner is not persuaded. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 18 of their response, “Per Berkheimer V. HP, Inc., 881 F.3d 1360 (Fed. Cir. 2018), whether elements are "well- understood, routine, and conventional" is a factual determination requiring evidentiary support. The Office Action offers none and it merely asserts genericness. This fails to satisfy Berkheimer V. HP, Inc.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that the Office Action offers no evidence for genericness, and likeness this to the Office Action stating elements are well-understood, routine, and conventional activity, the Examiner is not persuaded. In particular, the Examiner notes, as stated in paragraph 8 of the previous Non-Final Rejection, did not recite any elements of the independent claims as reciting well- understood, routine, and conventional activity. With respect to “genericness,” it is noted that the Non-Final Rejection identified the claimed generic computer elements in paragraph 7, that is, “machine learning model, memory, processors, non-transitory computer readable medium,” which is a separate analysis than well-understood, routine, and conventional activity (the former being MPEP 2106.05(f), and the later being MPEP 2106.05(d)). Second, with respect to evidence, it is noted that MPEP 2106.07(a)(III) states, “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” As shown here, the Examiner is required to provide evidence only for when limitations are indicated as well-understood, routine, conventional activities, which as noted above, was not recited in the previous office action. As such, the Applicant’s argument is deemed not persuasive as it is not relevant to the rejection previously made. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 18 of their response, “Following BASCOM Global Internet Servs. V. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016), even known components arranged in a non-conventional, non-generic manner can confer eligibility. Here, the ordered combination of: (i) Learning criterion-level weights from historical data; (ii) Generating structured signatures for each institution; (iii) Computing personalized student scores; and (iv) Applying calibrated threshold categorization constitutes an inventive ML architecture that yields improved computational performance and accuracy. No cited reference or prior art of record demonstrates that this specific ML-driven pipeline was well-understood or routine.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that the combination of (i)-(iv), “yields improved computational performance and accuracy,” the Examiner is not persuaded. In this case, as discussed above, the Applicant has failed to provide any evidence or disclosure within their specification that the elements improve computational performance and accuracy, and instead, they have relied solely on the conclusory argument recited here. As such, this argument is deemed not persuasive, as it is not in accorandce with MPEP 2106.05(a) as discussed above with respect to improvements in computer functionality or another technology. Second, with respect to the Applicant’s argument that, “No cited reference or prior art of record demonstrates that this specific ML-driven pipeline was well-understood or routine,” the Examiner is not persuaded. As noted above, MPEP 2106.07(a)(III) states, “At Step 2A Prong Two or Step 2B, there is no requirement for evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more than the exception unless the examiner asserts that additional limitations are well-understood, routine, conventional activities in Step 2B.” In this case, the Applicant’s argument is with regards to a rejection that that was not made by the Examiner, and thus the Examiner is not required to provide evidence to support a finding that the exception is not integrated into a practical application or that the additional elements do not amount to significantly more. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 19 of their response, “The claimed system provides measurable benefits including: (i) Improved model accuracy and consistency over manual methods; (ii) Reduced computational load in multi-college matching; and (iii) Scalable and objective prediction using high-dimensional data. Such technical benefits are akin to those recognized as inventive in DDR Holdings, Finjan, and McRO.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. In this case, the Applicant has listed a series of “benefits” reflected in the claim; however as discussed above, the Applicant has failed to provide any evidence of such improvement in their original written description. As such, the Applicant’s conclusory argument of improvement is deemed not persuasive. Further, it is noted that the Applicant has provided no discussion as to why DDR Holdings, Finjan, and McRO are relevant to the Applicant’s claims, any instead the Applicant has relied on a generical statement that “Such technical benefits are akin to those recognized as inventive in DDR Holdings, Finjan, and McRO,” which is not persuasive. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 20 of their response:
“Claim 12 requires obtaining a training corpus, processing it with the ML module to determine training criteria/weights/scores, and generating training recommendation lists. Thus, reciting a training-phase pipeline culminating in tangible model outputs consumed later for operational inference.
As the Specification explains, the invention employs a college analysis engine and categorization/recommendation engines implementing supervised and/or unsupervised training over historic accepted/rejected records to compute per-criterion learned weights, weighted scores, and thresholds, and then uses those artifacts for probabilistic matching and top-K selection. The Specification further teaches vector encodings, distance-based comparison, calibration/thresholding, and retraining triggers to maintain accuracy. See Specification at 11 [0031] and [0040].
These are technical ML operations that cannot be practically performed in the human mind at the claimed scale (plurality of colleges, historical corpora) and are not mere "marketing" judgments.
The Examiner's characterization of the claims as "mental processes" and "organizing human activity" overlooks that the focus of claim 12 is how the computer trains a model to compute weights and thresholds from accepted/rejected distributions and how it operationalizes those artifacts-not the business-level notion of "recommending colleges."
The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that, “the invention employs a college analysis engine and categorization/recommendation engines implementing supervised and/or unsupervised training over historic accepted/rejected records to compute per-criterion learned weights, weighted scores, and thresholds, and then uses those artifacts for probabilistic matching and top-K selection,” the Examiner is not persuaded. In this case, the Examiner notes that the claims do not recite or encompass any elements regarding a college analysis engine, a categorization/recommendation engine, nothing implements a supervised and/or unsupervised training over historic accepted/rejected records to compute per-criterion learned weights, weighted scores, and thresholds, and then uses those artifacts for probabilistic matching and top-K selection. As such, the Applicant’s argument is beyond the scope of the specifically recited claim elements, and therefore is not relevant to determining if the Applicant’s claims are directed to patent eligible subject matter. Notably, claim 12 states, “wherein training of the machine learning module comprises: obtaining training admissions data, wherein the training admissions data comprises training college data and training student data; processing the training admissions data using the machine learning module, wherein the processing of the training admissions data comprises determining a plurality of training admission criteria, training weights and training student scores; and based on the processing, generating one or more training recommendation lists.” Thus, the Applicant’s arguments are deemed not persuasive. Second, with respect to the Applicant’s argument that, “The Specification further teaches vector encodings, distance-based comparison, calibration/thresholding, and retraining triggers to maintain accuracy,” the Examiner is not persuaded. As noted above, the Applicant’s argued elements are beyond the scope of the specifically recited claim elements, and therefore is not relevant to determining if the Applicant’s claims are directed to patent eligible subject matter; thus, the Examiner is not persuaded. Third, with respect to the Applicant’s argument that, “These are technical ML operations that cannot be practically performed in the human mind at the claimed scale (plurality of colleges, historical corpora) and are not mere "marketing" judgments,” the Examiner is not persuaded. In particular, as noted above, the machine learning module recited in the claim is deemed merely as the tool to carry out the abstract idea (as discussed with respect to claim 1 in the rejection); and the elements of, “obtaining training admissions data, wherein the training admissions data comprises training college data and training student data; processing the training admissions data using the machine learning module, wherein the processing of the training admissions data comprises determining a plurality of training admission criteria, training weights and training student scores; and based on the processing, generating one or more training recommendation lists,” are all elements that are deemed managing marketing, human relations, and mental processes (observation, evaluation, judgement). With respect to the Applicant’s argument regarding a human mind, cannot at scale, practically perform the elements due to the plurality of colleges and historical corpora, the Examiner notes that the claims do not place any restrictions on the number of colleges or historical information considered, and thus, merely two schools and two students analyzed would satisfy the claims, which a human could easily analyzed in their mind. Further, it is noted whether a claim recites managing marketing and human relations, thus falling into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, does not have a restriction on whether a claim can be practically performed in the human mind. As such, the Applicant’s conclusory argument against “Certain Methods of Organizing Human Activity” is deemed not persuasive. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on pages 20 and 21 of their response:
“B. The claim language mirrors Federal Circuit-approved "software improvements" rather than abstract business logic
(i) Enfish, LLC V. Microsoft (822 F.3d 1327): software claims "directed to a specific type of data structure designed to improve the way a computer stores and retrieves data" are not abstract. Here, college signatures with learned per-criterion weights and thresholds function as computer-specific data structures that improve retrieval/matching operations, thus shifting from heuristic rules to trained, calibrated model artifacts.
(ii) McRO V. Bandai (837 F.3d 1299): automating a manual process via specific rules was patent-eligible. Claim 12 replaces subjective human counseling with objective, learned rules (weights/thresholds derived from distributions) applied by a machine.
(iii) Finjan V. Blue Coat (879 F.3d 1299): a computer-specific profile used by a scanner was eligible. Analogously, the college signature + threshold score are model artifacts used by the system's later inference/categorization engines.
Given their machine-learning-centric focus, claims 5 and 12 are not directed to an abstract idea under Prong 1 of Step 2A.”
The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s regarding Enfish, the Examiner is not persuaded. In particular, the Applicant has argued, “college signatures with learned per-criterion weights and thresholds function as computer-specific data structures that improve retrieval/matching operations, thus shifting from heuristic rules to trained, calibrated model artifacts.” The Examiner notes, that similar to claim 1 as discussed above, the Applicant has failed to provide any evidence or disclosure in their specification as to improvements in computer functionality, such as improving retrieval of data from memory; and instead, the Applicant has merely made a conclusory statement of improvement, which is not persuasive of improvements. Second, with respect to the Applicant’s argument regarding McRO, the Examiner is not persuaded. In particular, with respect to the Applicant’s argument that, “Claim 12 replaces subjective human counseling with objective, learned rules (weights/thresholds derived from distributions) applied by a machine,” the Examiner notes that using a computer as a tool to perform an abstract idea does not mean that a claim does not recite an abstract idea. In particular, as noted above, using a computer as a tool to perform an otherwise “Mental Process,” does not restrict the claim from reciting a “Mental Process.” Further, the use of a computer as a tool to managing marketing and human relations, does not restrict a claim from reciting “Certain Methods of Organizing Human Activity.” It is further noted that with respect to step 2A prong 2, MPEP 2106.05(f) states, “Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "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. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016).” (Emphasis added). In this case, merely invoking the computer as a tool to perform the abstract idea does not integrate the abstract idea into a practical application; however, McRO found that if the claimed rules improve computer capabilities or an existing technology, the claims may be integrated into a practical application. In this case, the Applicant has failed to identify and provide evidence of computer capability improvements, and therefore, the Applicant’s conclusory argument is not persuasive. Third, with respect to the Applicant’s argument regarding Finjan, the Examiner is not persuaded. In particular, with respect to the Applicant’s argument that, “Analogously, the college signature + threshold score are model artifacts used by the system's later inference/categorization engines,” the Examiner is not persuaded. Notably, the Applicant has failed to identify any reasoning as to why a college signature and threshold score are relevant to virus scanning discussed in Finjan. Notably, nothing the Applicant has stated is computer functionality or technology, and thus the relevance of Finjan has failed to be identified. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 22 of their response, “This is not a generic ‘apply it on a computer’ invocation. It is a particular machine- learning arrangement that changes the computer's processing of admissions data (objective, calibrated, scalable) and yields structured artifacts that drive subsequent computerized categorization and top-K selection.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. First, with respect to the Applicant’s argument that the claim training and determining threshold scores is a “particular machine- learning arrangement that changes the computer's processing,” the Examiner is not persuaded. Notably, the Examiner notes that the Applicant has failed to provide any evidence in their original written description that training a model and calculating threshold scores improves any computer functionality (e.g. processing of data); and instead, the computer appears to be merely used as a tool to carry out the elements, which as discussed above, is insufficient to integrate an abstract idea into a practical application. Therefore, the Examiner maintains that this rejection is proper.
The Applicant continues on page 22 of their response, “Analogies to the USPTO's Examples support practical application: The 2019 PEG and the AI-related eligibility examples (e.g., Examples 47-49) explain that claims reciting specific ML training/inference steps, feature extraction, and thresholding/calibration to produce technical outputs (scores/categories used by program logic) typically qualify as integrated practical applications, not disembodied math or business methods. Claim 12 fits squarely within those patterns: they specify what the model is trained on, how weights/scores/thresholds are derived, and how those artifacts are used by the computing system.” The Examiner respectfully disagrees with the Applicant’s interpretation of the requirements under 35 USC 101, the bounds of the claimed invention, and the grounds of the previous and current rejection. In this case, the Examiner notes that the Applicant has generically referenced Examples 47-49, without conducting any actual analysis or citing to any portion of the actual Examples, and have generally concluded the Applicant’s claims are similar. In this case, the Examiner notes that without further analysis beyond the general statement, the Examiner is not persuaded that the Examples listed are relevant. It is further noted that with respect to Example 47, claim 2 states, “(a) receiving, at a computer, continuous training data; (b) discretizing, by the computer, the continuous training data to generate input data; (c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; (d) detecting one or more anomalies in a data set using the trained ANN; (e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and (f) outputting the anomaly data from the trained ANN.” In this case, this claim, while still reciting training a machine learning model, using the trained model to detect anomalies, analyzing the anomalies using the model, and outputting results of the analysis from the trained model; was also identified as reciting an abstract idea, and were not integrated into a practical application. As such, the Applicant’s general reference to this Example does not show that their claims are directed towards patent eligible subject matter. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 6 November 2025 with respect to the Applicant’s argument regarding the prior art teaching analyzing accepted student profile and rejected student profile data have been fully considered but they are not persuasive.
With respect to claim 1, the Applicant argues on page 24 of their response, “Allen generically states that machine-learning models can be "trained" using historical data to "determine weights." However, Allen does not disclose any step of separately analysing accepted and rejected student profiles for each college or using those separate distributions to derive per-criterion weights.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In this case, the Examiner notes that the Applicant’s claim 1 (and similarly claims 13 and 19) states, “analyzing the accepted student profile data and the rejected student profile data of the college to assign a weight to each of the one or more admission criteria for the college.” As shown here, the Applicant’s claim states generally analyzing the accepted and rejected student profile data of a college to assign a weight to an admission criteria. This element does not, under broadest reasonable interpretation, encompass separately analyzing the accepted student profile data and analyzing the rejected student profile data. Further, this element does not reference any distributions used to derive any “per-criterion weight.” With respect to Allen, it is noted that Allen states in paragraph 26, “Admit.me Index Factors are the numerical factors used to calculate the weighting of the categories in the context of the Admit.me Index. The following Factors can change based on a few considerations of the school or the user. Certain school types value different types of factors. For instance, most undergraduate colleges place a low value on Work Experience, whereas an MBA program would place a high value on Work Experience. Further, the less information the user enters within a given category, the greater the potential for variability of the section, which can result in reduced weighting of that category.” (Emphasis added). Allen continues in paragraph 27, “The system can also vary the factor weighting as the system learns more about the historical accuracy of a profile scoring, views user inputs and choices, and compares everything with actual matriculation data. This learning and re-weighting is preferably done automatically via machine learning or AI, but it can also be adjusted from time to time, for example by adding new factors or based on actual admissions statistics.” (Emphasis added). Further, Allen continues in paragraph 35, “The AMI takes the AMI score and compares the score to the average score for schools in the applicant's target area of academic focus. The algorithm makes a match based on overall AMI score compared to score ranges at a particular school. School AMI ranges are calculated based on publicly available admissions profile data as well as data provided about past and current student admission information.” (Emphasis added). Further, Allen states in paragraph 41, “Further, the AMI leverages machine learning. The algorithms learn based on historical data. As more or verified acceptance information is received, the weighting variables of the various inputs are rebalanced to make the profile scores more accurate. For instance, if the data shows that enough credit is not given for a particular factor, the algorithm can self-correct within a desired range. The algorithm can further be manually updated as we learn additional information, for example adding new categories or subcategories. However, in preferred embodiments the algorithm is self-maintained and improved, and requires no manual intervention or maintenance.” (Emphasis added). Allen further continues in paragraph 48, “Further systems, methods, and tools for improving an admission potential of a user are contemplated. An input regarding the user is received and includes information related to at least two criteria selected from an academic criteria, an experience criteria, or a customized criteria. A value is calculated representative of each criteria and summed to a user score. A user interest is received and used to identify a potential institution. A delta or difference between the user score and a threshold score of the institution is then identified. A first subset of information from the two criteria is identified that the user can improve, such that improving the first subset of information reduces the delta. The first subset of information is provided to the user with a suggested step or action to improve the first subset, and thus the user score.” (Emphasis added). Allen continues in paragraph 49, “Typically the threshold score or score range is either set by the institution or is representative of a median score for admission to the institution, for example based on matriculant data. The score or score range can additionally or alternatively rely on publicly available class profile data or proprietary information provided by the institution. In some embodiments improving the subset of information reduces the delta to at least zero, and can even increase the user score to greater than the threshold score. The user interest can also include at least one of a location, a degree, a field of work, a job responsibility, personal preferences, academic interests, or a desired institution.” (Emphasis added). As shown and emphasized here, Allen has described analyzing the admission data, including admittance data concerning students accepted, and generating weight to admission criteria, such as work experience and academics. Notably the admission data, including actual admitted students and historic acceptance data, would encompass students that were accepted and rejected; thus, Allen has disclosed the claimed element of, “analyzing the accepted student profile data and the rejected student profile data of the college to assign a weight to each of the one or more admission criteria for the college.” It is noted that were the Applicant to specify determining weights by contrasting acceptance and rejection population, as argued, then this rejection would be overcome. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 6 November 2025 with respect to the Applicant’s argument regarding the art disclosing calculating a threshold score for the college have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on page 25 of their response, “Allen nowhere teaches or suggests calculating a threshold score derived from statistical distributions of accepted and rejected weighted scores. Allen merely discusses comparing a user's score to an admission threshold supplied by an institution, not computing that threshold from empirical data. By contrast, the present invention algorithmically derives a threshold weighted score (a statistical decision boundary) for each college, based on historical outcome distributions. This generates an adaptive, data-driven calibration value, not a static cutoff.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In this case, the Examiner notes that the Applicant’s claim 1 (and similarly claims 13 and 19) states, “calculating a threshold score for the college, based on the weighted admission score of each profile from the accepted student profile data and the rejected student profile data.” As shown here, the Applicant has claimed calculating a threshold score for a college based on the weighted admission score of each student profile. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., threshold scores derived from statistical distributions of accepted and rejected weighted scores; and deriving a threshold weighted score for each college based on historical outcome distributions) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). This claim, under the broadest reasonable interpretation, does not encompass deriving threshold scores from statistical distributions of accepted and rejected weighted scores; nor does it encompass deriving a threshold weighted score for each college based on historical outcome distributions. In this case, regarding the broadest reasonable interpretation of the claim, Allen discloses calculating a threshold score for a college based on the weighted admission score of each student profile. Notably, Allen states in paragraph 30, “The AMI report is a document that provides multiple points of client assessment, namely 1. An AMI Score; 2. Profile Assessment by Category; 3. Key Factor Assessment; 4. Action Items; and 5. School Suggestion List. A candidate is provided an overall AMI score along with a visual (red/yellow/green meter) and text representation (School range declaration) of where the score fits compared to the overall applicant pool. Each category (e.g., intellectual horsepower, professional experience, quantitative skills, demonstrated leadership, extracurricular involvement, and x-factor) is outlined and assigned a particular sub-score within the AMI, and provided in the Profile Assessment by Category. For each category listed above, the Key Factor Assessment provides textual context on each key section impacting the AMI. For each category listed above, the AMI Report provides Action Items with textual suggestions on how each specific user can optimize their specific user profile within that particular category, highlighting weaknesses and areas for improvement specific to each user. The School Suggestion List provides a summary of schools the user has identified along with suggested schools based on identified interests and competitive profiles commensurate with user AMI score. In addition, the school suggestion list shows the median AMI score range for matriculated candidates and a general competitive likelihood of admission.” (Emphasis added). Allen continues in paragraph 35, “The AMI takes the AMI score and compares the score to the average score for schools in the applicant's target area of academic focus. The algorithm makes a match based on overall AMI score compared to score ranges at a particular school. School AMI ranges are calculated based on publicly available admissions profile data as well as data provided about past and current student admission information.” (Emphasis added). Further, Allen states in paragraph 41, “Further, the AMI leverages machine learning. The algorithms learn based on historical data. As more or verified acceptance information is received, the weighting variables of the various inputs are rebalanced to make the profile scores more accurate. For instance, if the data shows that enough credit is not given for a particular factor, the algorithm can self-correct within a desired range. The algorithm can further be manually updated as we learn additional information, for example adding new categories or subcategories. However, in preferred embodiments the algorithm is self-maintained and improved, and requires no manual intervention or maintenance.” (Emphasis added). Allen further continues in paragraph 48, “Further systems, methods, and tools for improving an admission potential of a user are contemplated. An input regarding the user is received and includes information related to at least two criteria selected from an academic criteria, an experience criteria, or a customized criteria. A value is calculated representative of each criteria and summed to a user score. A user interest is received and used to identify a potential institution. A delta or difference between the user score and a threshold score of the institution is then identified. A first subset of information from the two criteria is identified that the user can improve, such that improving the first subset of information reduces the delta. The first subset of information is provided to the user with a suggested step or action to improve the first subset, and thus the user score.” (Emphasis added). Allen continues in paragraph 49, “Typically the threshold score or score range is either set by the institution or is representative of a median score for admission to the institution, for example based on matriculant data. The score or score range can additionally or alternatively rely on publicly available class profile data or proprietary information provided by the institution. In some embodiments improving the subset of information reduces the delta to at least zero, and can even increase the user score to greater than the threshold score. The user interest can also include at least one of a location, a degree, a field of work, a job responsibility, personal preferences, academic interests, or a desired institution.” (Emphasis added). As shown and emphasized here, Allen has disclosed calculating a threshold score for individual colleges based on the weighted admission scores of student profile data. Notably, the threshold score or score range is either set by the institution or is representative of a median score for admission to the institution. As such, Allen has disclosed the claimed element; however, it is noted that were the claims to be amended to actually disclose calculating a threshold score from statistical distribution of accepted and rejected weighted scores, then this rejection would be overcome. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 6 November 2025 with respect to the art disclosing comparing the admission score with the threshold score and assigning a category have been fully considered but they are not persuasive.
With respect to the claims, the Applicant argues on page 25 of their response, “Allen's disclosure is limited to comparing a user's calculated score to a threshold and indicating whether the user is "likely" to be admitted. Allen's process produces a binary outcome-admit or reject-not a categorical assignment (e.g., "likely," "within-reach," "out-of- reach") as claimed. The Applicant's invention employs an explicit categorization engine (see Spec., Fig. 8, block 818) that classifies colleges into multiple tiers based on continuous probability values and threshold comparisons. This multi-class categorization is unique and non-obvious over Allen's simplistic decision logic.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In this case, the Examiner notes that the Applicant’s claim 1 (and similarly claims 13 and 19) states, “comparing the admission score of the student for the college with the threshold score for the college; assigning a category from a plurality of categories to the college for the student based on the comparison.” As shown here, the Applicant has broadly claimed comparing the student admission score for a college with the college’s threshold score, and assigning a category to the college based on the comparison. This claim element, as shown here, is much broader than the Applicant’s argued categorization engine that classifies colleges into multiple tiers based on continuous probability values and threshold comparisons. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., a categorization engine that classifies colleges into multiple tiers based on continuous probability values and threshold comparisons) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In this case, as shown above, the Applicant has failed to claim any element regarding classifying colleges into multiple tiers based on continuous probability values and threshold comparisons; and instead, the Applicant appears to argue elements in the specification, which is deemed not reflective of the claimed invention itself. With regards to the actual claim elements, it is noted that Allen has disclosed in paragraph 30, “The AMI report is a document that provides multiple points of client assessment, namely 1. An AMI Score; 2. Profile Assessment by Category; 3. Key Factor Assessment; 4. Action Items; and 5. School Suggestion List. A candidate is provided an overall AMI score along with a visual (red/yellow/green meter) and text representation (School range declaration) of where the score fits compared to the overall applicant pool. Each category (e.g., intellectual horsepower, professional experience, quantitative skills, demonstrated leadership, extracurricular involvement, and x-factor) is outlined and assigned a particular sub-score within the AMI, and provided in the Profile Assessment by Category. For each category listed above, the Key Factor Assessment provides textual context on each key section impacting the AMI. For each category listed above, the AMI Report provides Action Items with textual suggestions on how each specific user can optimize their specific user profile within that particular category, highlighting weaknesses and areas for improvement specific to each user. The School Suggestion List provides a summary of schools the user has identified along with suggested schools based on identified interests and competitive profiles commensurate with user AMI score. In addition, the school suggestion list shows the median AMI score range for matriculated candidates and a general competitive likelihood of admission.” (Emphasis added). Allen continues in paragraph 35, “The AMI takes the AMI score and compares the score to the average score for schools in the applicant's target area of academic focus. The algorithm makes a match based on overall AMI score compared to score ranges at a particular school. School AMI ranges are calculated based on publicly available admissions profile data as well as data provided about past and current student admission information.” (Emphasis added). As shown and emphasized here, Allen has described comparing a student’s admission score to the threshold score of the school, and assigning a category to the college for the student, wherein the category includes the likelihood of admission. As such, the Examiner maintains that Allen discloses the broadest reasonable interpretation of the claimed invention itself; however, it is noted that were the Applicant to actually claim the argued, “categorization engine that classifies colleges into multiple tiers based on continuous probability values and threshold comparisons,” then this rejection would be overcome. Therefore, the Examiner maintains that this rejection is proper.
Applicant's arguments filed 6 November 2025 with respect to the combination of art have been fully considered but they are not persuasive.
With respect to the art, the Applicant argues on page 26 of their response, “Incompatible architectures: Billmyer's rule-based system relies on manually defined weighting factors and deterministic scoring logic. Integrating Allen's learning-based model would require fundamental re-engineering-Billmyer lacks any training data architecture (no accepted/rejected profiles), whereas Allen's model relies on such data globally, not per institution. A skilled artisan would have no reasonable expectation of success in merging these disparate paradigms.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In response to applicant's argument that Allen’s learning-based model would require fundamental re-engineering of Billmyer’s rule based system, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). In this case, the Examiner has previously stated that, “It would have been obvious to one of ordinary skill in the art at the time of filing the invention to combine the system and method of using collected student information college information to generate a matching admission score between students and colleges based on the comparison of the information, wherein a recommendation list is generated and provided to a student of Billmyer, with the system and method of using machine learning models to generate data information pertaining to students and colleges, wherein the machine learning model generates admission match scores for the student by comparing the student and college profile data, and wherein the system analyzes accepted and rejected profile information to calculate a weighted score based on admission criteria and calculate a threshold score representing likelihood of being accepted of Allen.” As shown here, the Examiner has identified that it would have been obvious to combine Allen’s use of machine learning models to generate college information and admission scores, with Bllmyer’s method of using collected student information college information to generate a matching admission score between students and colleges based on the comparison of the information. As the Applicant has failed to rebut this showing of obviousness, the Examiner maintains that this rejection is proper.
The Applicant continues on page 26 of their response, “Missing data foundations: Billmyer provides no disclosure of the training corpus required for Allen's model; conversely, Allen presupposes access to rich historical datasets. The combination assumes data that do not exist in either reference, amounting to impermissible hindsight reconstruction.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In this case, the Examiner has relied on only the knowledge available to one of ordinary skill in the art at the time the claimed invention was made, particularly shown via the citations throughout the references and the reasons for obviousness that follow. As the Applicant has failed to rebut this showing of obviousness, the Examiner maintains that this rejection is proper.
The Applicant continues on page 26 of their response, “Different problem domains: Billmyer optimizes student choice through preference matching; Allen optimizes score feedback for applicants. The claimed invention, in contrast, addresses a technological problem-creating statistically calibrated, per-college admission signatures and thresholds. The cited art solves different problems through different mechanisms.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In response to applicant's argument that Billmyer and Allen are nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Billmyer and Allen are both directed towards system and methods of utilizing student information and college information in order to determine matching colleges for students, which is the same field of endeavor as the Applicant’s claimed invention. As the Applicant has failed to rebut this showing of obviousness, the Examiner maintains that this rejection is proper.
The Applicant continues on pages 26 and 27 of their response, “No teaching, suggestion, or motivation (TSM): The Examiner's "predictable improvement in efficiency" rationale is conclusory and lacks evidentiary support, contrary to In re Magnum Oil Tools Int'l, 829 F.3d 1364 (Fed. Cir. 2016), which requires specific findings that the prior art provides such motivation. There is none. Thus, the Applicant respectfully submits that neither Billmyer nor Allen, alone or in combination, teaches or suggests a college data set including accepted and rejected student profiles, comparative analysis of those datasets to assign criterion weights, derivation of per- college threshold scores from weighted distributions, or categorical assignment of colleges based on such thresholds.” The Examiner respectfully disagrees with the Applicant’s interpretation of the cited prior art and the broadest reasonable interpretation of the claimed invention. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the Examiner has relied on knowledge generally available to one of ordinary skill in the art in order to combine the cited references. As the Applicant has failed to rebut this showing of obviousness, the Examiner maintains that this rejection is proper.
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, 7-13, and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite receiving student profile data of a student; obtaining college data associated with each of a plurality of colleges; determining, using a trained machine learning module, a college signature for each of the plurality of colleges based on the respective college data, wherein the college signature comprises one or more admission criteria and a weight associated with each of the one or more admission criteria; wherein the college data for a college from the plurality of colleges comprises at least one of: college information relating to the college accepted student profile data, and rejected student profile data, and wherein determining the college signature for the college from the plurality of colleges comprises: determining one or more admission criteria for the college based on the college data; analyzing the accepted student profile data and the rejected student profile data of the college to assign a weight to each of the one or more admission criteria for the college; calculating a weighted admission score for each profile of the accepted student profile data and the rejected student profile data based on the assigned weight for each of the one or more of admission criteria, the accepted student profile data, and the rejected student profile data: and calculating a threshold score for the college, based on the weighted admission score of each profile from the accepted student profile data and the rejected student profile data; generating, using the trained machine learning module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges; comparing the admission score of the student for the college with the threshold score for the college; assigning a category from a plurality of categories to the college for the student based on the comparison; and generating a list of recommended colleges for the student based on the assigned category to the college for the student
The limitations of receiving student profile data of a student, obtaining college data associated with each of a plurality of colleges, determining a college signature for each of the plurality of colleges based on the respective college data and that comprises admission criteria and a weight associated with each admission criteria, determining admission criteria for the college based on the college data, analyzing accepted and rejected student profile data of the college to assign a weight to each of the criteria, calculating a weighted admission score for each profile based on the assigned weight and profile data, calculating a threshold score for the college based on the weighted admission score, generating an admission score corresponding to each of the plurality of colleges for the student based on a comparison between the student profile data and the college signature for each of the plurality of colleges, comparing the admission score of the student for the college with the threshold score for the college, assigning a category to the college for the student, and generating the list of recommended colleges for the student based on the assigned category; as drafted under the broadest reasonable interpretation, encompasses the management of commercial activity (marketing, business relations), managing human behavior and relationships, and elements that can be performed in the human mind. That is, other than reciting the use of generic computer elements (machine learning model, memory, processors, non-transitory computer readable medium), the claims recite an abstract idea. In particular, receiving student profile data of a student, obtaining college data associated with each of a plurality of colleges, determining a college signature for each of the plurality of colleges based on the respective college data and that comprises admission criteria and a weight associated with each admission criteria, generating an admission score corresponding to each of the plurality of colleges for the student based on a comparison between the student profile data and the college signature for each of the plurality of colleges, and generating a list of recommended colleges for the student based on the admission score of the student corresponding to each of the plurality of colleges; which encompasses receiving student (i.e. a customer) information, determining colleges’ data and colleges’ profiles (i.e. seller/service data and profile), comparing the student and college profiles, and making a recommended list of colleges for the student to apply for (i.e. marketing a list of service providers); thus the claims recite the management of commercial activity (marketing, business relations), managing human behavior and relationships. Further, determining admission criteria for the college based on the college data, analyzing accepted and rejected student profile data of the college to assign a weight to each of the criteria, calculating a weighted admission score for each profile based on the assigned weight and profile data, and calculating a threshold score for the college based on the weighted admission score; which further encompasses managing commercial activity (marketing, business relations), managing human behavior and relationships. Further, comparing the admission score of the student for the college with the threshold score for the college, assigning a category to the college for the student, and generating the list of recommended colleges for the student based on the assigned category; which further encompasses comparing the student data to college data, in order to determine the likelihood that a user would be accepted, which is the management of marketing and human relationships. Therefore, the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. In addition, the claims recite receiving student profile data of a student, obtaining college data associated with each of a plurality of colleges, determining a college signature for each of the plurality of colleges based on the respective college data and that comprises admission criteria and a weight associated with each admission criteria, generating an admission score corresponding to each of the plurality of colleges for the student based on a comparison between the student profile data and the college signature for each of the plurality of colleges, and generating a list of recommended colleges for the student based on the admission score; which are elements that can be performed in the human mind (observation, evaluation, judgement, opinion), as the series of elements merely encompass collecting student and college information, using evaluation/judgement to create profiles for the students and colleges, comparing the profiles, and determining recommended matches based on the comparison, which can purely be performed in the human mind. Further, determining admission criteria for the college based on the college data, analyzing accepted and rejected student profile data of the college to assign a weight to each of the criteria, calculating a weighted admission score for each profile based on the assigned weight and profile data, and calculating a threshold score for the college based on the weighted admission score; which further encompasses elements that can be performed in the human mind (observation, evaluation, judgement, opinion). Further, comparing the admission score of the student for the college with the threshold score for the college, assigning a category to the college for the student, and generating the list of recommended colleges for the student based on the assigned category; which further encompasses comparing the student data to college data, in order to determine the likelihood that a user would be accepted; which further encompasses elements that can be performed in the human mind (observation, evaluation, judgement, opinion). Therefore, the claims recite elements that fall into the “Mental Processes” grouping of abstract ideas. The claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite additional elements, when taken individually and in an ordered combination with the abstract idea, that improve the functioning of a computer, another technology, or technical field. The claims do not recite the use of, or apply the abstract idea with, a particular machine, the claims do not recite the transformation of an article from one state or thing into another. Finally, the claims do not recite additional elements, taken individually and in an ordered combination, that apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Instead, the claims recite the use of generic computer elements (machine learning model, memory, processors, non-transitory computer readable medium) as tools to carry out the abstract idea. Further, the claims recite the content of college data, which merely narrows the field of use, and thus does not recite additional elements that integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claim(s) does/do not include additional elements, when taken individually and in an ordered combination with the abstract idea, 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 of using generic computer elements and machines to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are directed to non-patent eligible subject matter.
The dependent claims 2, 3, 7-12, and 16-18, when taken individually and in an ordered combination with the abstract idea, do not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea. In particular, the claims recite content of the student profile data, which merely narrows the field of use, and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 2). In addition, the claims further recite that the student data is received via user interface, which merely invokes the use of generic computer elements (user interface) as a tool to carry out the abstract idea, and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 3). In addition, the claims further recite assigning a category of likelihood of admission; which is the management of marketing, human relationships, and processes that can be performed in the human mind (observation, evaluation, judgement); thus recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claim 8). In addition, the claims further recite generating a list of recommended colleges based on the category of the college and providing it to the user so that they can select a college to apply for; which encompasses making a list or recommendations to market to a customer, which is the management of marketing, human relationships, and processes that can be performed in the human mind (observation, evaluation, judgement); thus recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claims 7 and 16). In addition, the claims further recite determining the demographics of colleges and comparing them to the students’, which is deemed managing marketing, human relations, and mental processes (observation, evaluation, judgement); and thus recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claims 9 and 11). In addition, the claims further recite generating an admission score for the student basis on their profile and the weights of criteria, which is deemed managing marketing, human relations, and mental processes (observation, evaluation, judgement); and thus recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claims 10 and 17). In addition, the claims further recite obtaining training admissions data comprising training college data and training student data, processing the training admissions data comprising determining a plurality of training admission criteria, training weights and training student scores, and generating one or more training recommendation lists based on the processing; which encompass collecting historic information regarding admissions, college, and student data, and using this information to formulate a model including a model recommendation list; which is deemed managing marketing, human relations, and mental processes (observation, evaluation, judgement); and thus recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes” grouping of abstract ideas (claims 12 and 18).
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.
Claims 1-3, 8-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Billmyer (US 2009/0081629 A1) (hereinafter Billmyer), in view of Allen (US 2023/0274378 A1) (hereinafter Allen).
With respect to claims 1, 13, and 19, Billmyer teaches:
Receiving student profile data of a student (See at least paragraphs 6, 7, and 41-43 which describe receiving student profile information, including demographics, major, affordability, location, GPA, interests, and extracurricular activities).
Obtaining college data associated with each of a plurality of colleges (See at least paragraphs 6, 7, 39, 46, 53, 88, 93, and 94 which describe collecting college information for a plurality of colleges, including their academic standards, demographics, activities offered, location, and affordability).
Determining, using a module, a college signature for each of the plurality of colleges based on the respective college data, wherein the college signature comprises one or more admission criteria and a weight associated with each of the one or more admission criteria (See at least paragraphs 6, 7, 39, 46, 53, 57, 59, 61, 88, 93, and 94 which describe collecting college information for a plurality of colleges, and determining a college profile using the data, along with admission criteria and importance of different criteria).
Wherein the college data for a college from the plurality of colleges comprises at least one of: college information relating to the college accepted student profile data, and rejected student profile data (See at least paragraphs 6, 7, 39, 46, 53, 88, 93, and 94 which describe collecting college information for a plurality of colleges, including their academic standards, demographics, activities offered, location, and affordability).
Wherein determining the college signature for the college from the plurality of colleges comprises: determining one or more admission criteria for the college based on the college data (See at least paragraphs 6, 7, 39, 46, 53, 88, 93, and 94 which describe collecting college information for a plurality of colleges, including their academic standards and admission criteria).
Generating, using the module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges (See at least paragraphs 6, 7, 9, 53, 59, 61, 63, 78, 87, 94, 99, 100, and 101 which describe generating a match score between each college and student based on the comparison of the college and student profiles).
Generating a list of recommended colleges for the student (See at least paragraphs 6, 7, 39, and 100-103 which describe generating a list of recommended colleges for the students based on the match scores).
Billmyer discloses all of the limitations of claims 1, 13, and 19 as stated above. Billmyer does not explicitly disclose the following, however Allen teaches:
Determining, using a trained machine learning module, a college signature for each of the plurality of colleges; Generating, using the trained machine learning module, an admission score corresponding to each of the plurality of colleges for the student, based on a comparison between the student profile data and the college signature for each of the plurality of colleges (See at least paragraphs 14, 15, 26, 27, 30, 32, 33, 35, 37, 41, 48-50 which describe using machine learning models to generate data information pertaining to students and colleges, wherein the machine learning model generates admission match scores for the student by comparing the student and college profile data).
Analyzing the accepted student profile data and the rejected student profile data of the college to assign a weight to each of the one or more admission criteria for the college; Calculating a weighted admission score for each profile of the accepted student profile data and the rejected student profile data based on the assigned weight for each of the one or more of admission criteria, the accepted student profile data, and the rejected student profile data; and Calculating a threshold score for the college, based on the weighted admission score of each profile from the accepted student profile data and the rejected student profile data (See at least paragraphs 15, 26, 27, 30, 35, 41, 48, and 49 which describe analyzing historic acceptance and rejected student information to determine weights and criteria for admission to a college, wherein a weighed admission score is calculated using the determined information, and calculating a threshold score for the college that indicates the likelihood of a user being accepted or rejected).
Wherein generating the list of recommended colleges for the student comprises: comparing the admission score of the student for the college with the threshold score for the college; assigning a category from a plurality of categories to the college for the student based on the comparison; and generating the list of recommended colleges for the student based on the assigned category to the college for the student (See at least paragraphs 15, 26, 27, 30, 35, 41, and 48-50 calculating the admission score for the student, wherein it is compared to the threshold score and the user is determined to be likely or not to be admitted, and providing the results to the user).
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to combine the system and method of using collected student information college information to generate a matching admission score between students and colleges based on the comparison of the information, wherein a recommendation list is generated and provided to a student of Billmyer, with the system and method of using machine learning models to generate data information pertaining to students and colleges, wherein the machine learning model generates admission match scores for the student by comparing the student and college profile data, and wherein the system compares a user admission score to a threshold score to determine their likelihood of being accepted and providing results to the user of Allen. By utilizing machine learning models to generate college information and admission scores, a system will predictably be able to generate scores using a collection of information in an efficient manner. By analyzing historic acceptance records in order to generate weighted admission scores for the acceptance into a college and a threshold score, a system would predictably be able to identify the threshold admission score that colleges use to admit students, thus making a recommended list of schools for the student the most accurate recommendations. By using a threshold score analysis to determine recommended schools for a student, a system would predictably be able to quickly identify how much of a fit a student is to various schools, thus ensuring the recommendations are most accurate.
With respect to claim 2, the combination of Billmyer and Allen discloses all of the limitations of claim 1 as stated above. In addition, Billmyer teaches:
Wherein the student profile data comprises at least one of: an academic performance record of the student, an extracurricular activities record of the student, a list of preferred colleges, or demographic data of the student (See at least paragraphs 6, 7, and 41-43 which describe receiving student profile information, including demographics, major, affordability, location, GPA, interests, and extracurricular activities).
With respect to claim 3, Billmyer/Allen discloses all of the limitations of claim 1 as stated above. In addition, Billmyer teaches:
Receiving the student profile data of the student through a user interface (See at least paragraphs 6, 7, and 41-43 which describe the student inputting information through a user interface).
With respect to claim 8, Billmyer/Allen discloses all of the limitations of claim 1 as stated above. In addition, Billmyer teaches:
Wherein plurality of categories indicates a likelihood of admission, and wherein the plurality of categories comprises at least one of: a likely category, a within reach category, and an out of reach category (See at least paragraph 10 which describes using an match admission score to determine the likelihood a student would be accepted, wherein the school is assigned a category for the user including target school, a reach school, and a safety school).
With respect to claims 9, Billmyer/Allen discloses all of the limitations of claim 1 as stated above. In addition, Billmyer teaches:
Wherein determining the college signature for the college further comprises: determining a demographic pattern for the college based on the college data of the college (See at least paragraphs 6, 7, 39, 46, 53, 88, 93, and 94 which describe collecting college information for a plurality of colleges, including their academic standards, demographics, activities offered, location, and affordability).
With respect to claims 11, Billmyer/Allen discloses all of the limitations of claims 1 and 9 as stated above. In addition, Billmyer teaches:
Wherein generating the admission score of the student for the college comprises: comparing demographic data of the student with the demographic pattern for the college (See at least paragraphs 6, 7, 9, 53, 59, 61, 63, 78, 87, 94, 99, 100, and 101 which describe generating a match score between each college and student based on the comparison of the college and student profiles, including the student demographic data compared to the college demographic data).
With respect to claims 10 and 17, Billmyer/Allen discloses all of the limitations of claims 1 and 13 as stated above. In addition, Billmyer teaches:
Wherein generating the admission score of the student for the college comprises: calculating a student score of the student based on the student profile data and the weight of each of the one or more admission criteria for the college; generating the admission score of the student for the college based on an aggregation of the student score for each of the one or more admission criteria for the college (See at least paragraphs 6, 7, 9, 53, 59, 61, 63, 78, 87, 94, 99, 100, and 101 which describe generating a match score between each college and student based on the comparison of the college and student profiles, wherein the admission score considers the weight of admission criteria and the total score for each criteria).
With respect to claims 12 and 18, Billmyer/Allen discloses all of the limitations of claims 1 and 13 as stated above. In addition, Allen teaches:
Wherein training of the machine learning module comprises: obtaining training admissions data, wherein the training admissions data comprises training college data and training student data; processing the training admissions data using the machine learning module, wherein the processing of the training admissions data comprises determining a plurality of training admission criteria, training weights and training student scores; and based on the processing, generating one or more training recommendation lists (See at least paragraphs 14, 15, 27, 32, 33, 41, and 44 which describe using a machine learning model to determine admission scores for students to colleges, wherein the machine learning model is trained using historic acceptance and student information, wherein the criteria and weights are determined and used to generate a recommended list of schools).
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to combine the system and method of using collected student information college information to generate a matching admission score between students and colleges based on the comparison of the information, wherein a recommendation list is generated and provided to a student of Billmyer, with the system and method of using a machine learning model to determine admission scores for students to colleges, wherein the machine learning model is trained using historic acceptance and student information, wherein the criteria and weights are determined and used to generate a recommended list of schools of Allen. By utilizing machine learning models to generate college information and admission scores, a system will predictably be able to generate scores using a collection of information in an efficient manner. In addition, by utilizing historic information, a system will be able to make a more accurate model in order to determine recommended schools.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Billmyer and Allen as applied to claims 1 and 13 as stated above, and further in view of Guerra (US 2004/0138913 A1) (hereinafter Guerra).
With respect to claims 7 and 16, Billmyer/Allen discloses all of the limitations of claims 1 and 13 as stated above. In addition, Billmyer teaches:
Wherein generating the list of recommended colleges for the student comprises: selecting one or more colleges from the plurality of colleges based on an assigned category for each of the plurality of colleges for the student, wherein the category is assigned to each of the plurality of colleges based on a comparison between admission score of the student for the college with corresponding the threshold score for the college; and Generating the list of recommended colleges for the student based on the selected one or more colleges; and Rendering for display the list of recommended colleges for the student (See at least paragraphs 6, 7, 10, 39, 53, 94, and 99-103 which describe generating a list of recommended colleges for the students based on the match scores, colleges are assigned a rating based on the comparison between the admission score and a median score, such that the list includes schools that are more likely to be a fit for the user).
Billmyer discloses all of the limitations of claims 7 and 16 as stated above. Billmyer does not explicitly disclose the following, however Guerra teaches:
Rendering for display the list of recommended colleges for the student and one or more graphical user interface elements selectable by a user to apply for admission (See at least paragraph 53 which describes generating a list of schools for a user, wherein the list includes a selectable link to apply to the schools).
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to combine the system and method of using collected student information college information to generate a matching admission score between students and colleges based on the comparison of the information, wherein a recommendation list is generated and provided to a student of Billmyer, with the system and method of using machine learning models to generate data information pertaining to students and colleges, wherein the machine learning model generates admission match scores for the student by comparing the student and college profile data of Allen, with the system and method of generating a list of schools for a user, wherein the list includes a selectable link to apply to the schools of Guerra. By supplying the students with links to apply to recommended colleges, students will predictably be able to complete their desired transaction, that is applying for schools, thus completing commercial transactions.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P HARRINGTON whose telephone number is (571)270-1365. The examiner can normally be reached Monday-Friday 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman can be reached at (571)-272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Michael Harrington
Primary Patent Examiner
6 July 2026
Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
Prosecution Insights