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 non-final, first office action in response to the application filed 21 September 2023.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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; 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 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.
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, 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; 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. 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. 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. 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 2-12, 14-18, and 20, 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 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, or add significantly more to the abstract idea (claim 4). In addition, the claims further recite; 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, managing human behavior, and performing mental processes; as the elements merely further recite calculating weighted admission scores and weighted threshold scores using accepted and rejected student profile information, which is managing the commercial activity of the colleges, and are steps 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 5, 14, and 20). In addition, the claims further recite 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, 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 6, 8, and 15). 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 1-6, 8-15, and 17-20 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).
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 based on the admission score of the student corresponding to each of the plurality of colleges (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).
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. 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.
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 4, Billmyer/Allen discloses all of the limitations of claim 1 as stated above. In addition, Billmyer teaches:
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).
With respect to claims 5, 14, and 20, Billmyer/Allen discloses all of the limitations of claims 1, 4, 13, and 19 as stated above. In addition, Billmyer teaches:
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).
Billmyer discloses all of the limitations of claims 5, 14, and 20 as stated above. Billmyer does not explicitly disclose the following, however Allen teaches:
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).
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. 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.
With respect to claims 6 and 15, Billmyer/Allen discloses all of the limitations of claims 1, 4, 5, 13, and 14 as stated above. In addition, Allen teaches:
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 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 8, Billmyer/Allen discloses all of the limitations of claims 1, 4, 5, and 6 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 claims 1, 4, and 5 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, 4, 5, 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, 4, 5, 13, and 14 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, 4-6, and 13-15 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, 4-6, and 13-15 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
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.
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Michael Harrington
Primary Patent Examiner
25 June 2025
Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628