DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the application filed 14 May 2025. Claims 1 and 2 are 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 and 2 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite methods of collecting department information data, comparing that data against personal competency information input by a user, and generating recommended department and admission prediction information which, under their broadest reasonable interpretation, constitute certain methods of organizing human activity (specifically, managing personal behavior or relationships or interactions between people) and mental processes. This judicial exception is not integrated into a practical application because the additional elements a platform server, a university server, and a user terminal are generic computer components that perform generic computing functions (collecting data, comparing data, generating outputs, and displaying results) that do not impose any meaningful limit on the abstract idea. The claims do not include additional elements sufficient to amount to significantly more than the judicial exception because the recited computer components and functions are well-understood, routine, and conventional.
Step 1
Claims 1–2 are directed to a method (process). Therefore, the claims fall within at least one statutory category of invention and require further eligibility analysis.
Step 2A, Prong One
Claims 1–2 recite an abstract idea. Specifically, independent claim 1 recites the following limitations that constitute the judicial exception:
• A first operation of collecting department information data comprising department information and department course information from a plurality of university institutions, and generating and storing department information analysis data through machine learning;
• A second operation of receiving personal competency information comprising certification information and extracurricular activity information input by the user through the user terminal;
• A third operation of generating at least one piece of university recommended department information by comparing the department information analysis data with the personal competency information;
• A fourth operation of illustrating and providing a diagram of a correlation between a plurality of recommended departments based on the recommended department information;
• A fifth operation of generating comparative analysis data corresponding to a resultant value of comparing the department information analysis data corresponding to a selected department with the personal competency information of the user; and
• A sixth operation of generating admission prediction data by comparing the comparative analysis data with admission data of university admitted students stored in the platform server and providing the generated admission prediction data to the user terminal.
Abstract Idea Grouping Analysis – Certain Methods of Organizing Human Activity and Mental Processes
Certain Methods of Organizing Human Activity (Primary Grouping): The core limitations of claims 1–2, taken together, fall within the “managing personal behavior or relationships or interactions between people” sub-grouping of certain methods of organizing human activity. See MPEP 2106.04(a)(2), subsection II. Specifically, the claims are fundamentally directed to the practice of counseling a prospective student (a user) on what academic department to pursue a longstanding human activity. The collection of institutional information (first operation), solicitation of a user’s personal competency data (second operation), comparison of that data against institutional requirements to generate department recommendations (third operation), visualization of correlations among recommended departments (fourth operation), comparative analysis of the user’s competency against a selected department’s requirements (fifth operation), and generation of admission prediction guidance based on historical admitted-student data (sixth operation) collectively describe a computerized version of a traditional academic advising or career counseling process. Academic advising and career counseling wherein a counselor gathers information about a student’s interests, aptitudes, and qualifications; compares them against institutional or programmatic requirements; recommends suitable programs; and provides admission likelihood assessments based on historical data are activities that humans have performed for decades, if not longer, without computers. The claims therefore recite the abstract idea of managing personal relationships or interactions between people, specifically the relationship between a prospective student and educational institutions in the context of career and admission planning. See, e.g., Voter Verified, Inc. v. Election Systems & Software, LLC, (human activity of voting found to be abstract idea because it was a fundamental social activity performed by humans for a long period of time).
Additionally, the claims can also be viewed as a Mental Processes (Alternative/Additional Grouping): Additionally, and alternatively, several of the core limitations of claims 1-2 fall within the mental processes grouping, as they can practically be performed in the human mind (including with pen and paper). See MPEP 2106.04(a)(2), subsection III. Specifically:
• The third operation of “generating at least one piece of university recommended department information by comparing the department information analysis data… with the personal competency information” encompasses a mental evaluation an academic counselor can mentally (or with pen and paper) review department requirements and a student’s qualifications and form a recommendation. This is an observation, evaluation, judgment, and/or opinion.
• The fifth operation of “generating comparative analysis data corresponding to a resultant value of comparing the department information analysis data corresponding to the selected department with the personal competency information of the user” encompasses a human mental comparison of a student’s competencies against department requirements, resulting in a judgment about the degree of match a classic mental process.
• The sixth operation of “generating admission prediction data by comparing the comparative analysis data… with admission data of university admitted students” at its core involves a human evaluator reviewing the user’s competency profile against profiles of previously admitted students to assess likelihood of admission a judgment or opinion that can be performed mentally or with pen and paper.
It is acknowledged that the claims recite “machine learning” in the first operation to generate department information analysis data. However, the mere recitation of a generic machine learning process for collecting and classifying department information does not remove the claims from the abstract idea groupings identified above where, as here, the machine learning is applied generically to collect and classify data and the outputs of that machine learning are then used in the same manner a human counselor would use compiled reference information as a basis for comparison against a user’s personal profile and for making advisory recommendations. Per the 2024 AI SME Update and the guidance in the Federal Register Notice of July 17, 2024, the analysis must proceed based on what the claim as a whole is “directed to,” not merely the presence of AI/machine learning terminology. The claims as a whole are directed to the abstract idea of personalized academic department recommendation and admission counseling, for which machine learning is invoked generically as a tool. See also the 2024 AI SME Update reminder that “there is no fixed list of terms that render a claim eligible or ineligible” and that every claim must be evaluated on a case-by-case basis.
Because the limitations of claims 1-2 fall within at least one (and in fact two) groupings of abstract ideas, it is reasonable to conclude that the claims recite an abstract idea in Step 2A, Prong One. The claims therefore require further analysis in Step 2A, Prong Two.
Step 2A, Prong Two
Identification of Additional Elements
The claims recite the following additional elements beyond the identified abstract idea:
• A platform server configured to collect department information and perform machine learning to generate department information analysis data;
• A university server configured to provide department information data to the platform server;
• A user terminal configured to receive personal competency information from the user and to display the diagram of correlation between departments and admission prediction data.
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP 2106.05(a)): The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. The specification characterizes the disclosed system as providing a “department recommendation service” that “analyzes customized information such as competency, interests, and desired career paths of the prospective students and recommending department information aligned with career or major suitability” (Spec. par. [0018]). The specification further describes the platform server’s machine learning as performing language-based collection, classification, and storage of department information through “main keywords, similarity keywords” (Spec. par. [0043]), and describes the system’s overall function as collecting, comparing, and providing information to prospective students (Spec. par. [0039], [0044]–[0057]). Nowhere in the specification does the Applicant describe an improvement to computer functionality itself, nor an improvement to how machine learning systems are trained or how they operate. The machine learning is used as a generic tool to collect and classify existing department data, and the overall system merely uses computers to implement the abstract idea of academic advising and department recommendation more efficiently. This is precisely the type of claim that uses technology “merely as a tool” to automate a pre-existing human process rather than to improve the technology itself. See Trading Technologies Int’l v. IBG, (claimed user interface that provided more information to traders improved the business process of market trading but did not improve computers or technology). Compare Ex Parte Desjardins, Appeal No. 2024-000567 (claims eligible where specification described improvements to how the machine learning model itself operates, including overcoming “catastrophic forgetting” in continual learning systems). No such technological improvement is disclosed or claimed here. See MPEP 2106.05(a).
Particular Machine (MPEP 2106.05(b)): The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited “platform server,” “university server,” and “user terminal” are generic computing components described at a high level of generality. The specification confirms this, as it describes the “terminal” as any “computer or a portable terminal capable of being connected to a server” including generic notebook computers, desktops, laptops, and VR headsets (Spec. par. [0035]), and describes a “network” as any available network technology including LAN, WAN, the Internet, and various wireless standards (Spec. par. [0036]). The platform server is described only in functional terms corresponding to the abstract idea itself, not in terms of any particular machine architecture or unconventional configuration. The recitation of these generic components does not impose any meaningful limit on the scope of the abstract idea.
Mere Instructions to Apply the Exception (MPEP 2106.05(f)): The additional elements amount to no more than mere instructions to implement the abstract idea on generic computer components. The “platform server” is merely instructed to collect data, run machine learning, compare data, and generate outputs all generic computing functions that correspond directly to the steps of the abstract idea itself. The “user terminal” is merely instructed to receive user input and display results. This is the functional equivalent of simply appending “apply it on a computer” to the abstract idea. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l. The 2024 AI SME Update expressly distinguishes between claims that reflect an improvement to technology and claims “in which the additional elements amount to no more than (1) a recitation of the words ‘apply it’ (or an equivalent) or are no more than instructions to implement a judicial exception on a computer, or (2) a general linking of the use of a judicial exception to a particular technological environment or field of use.” The present claims fall within the latter category. The specification itself does not describe the claimed machine learning as providing any specific technical improvement, but rather confirms that the machine learning performs the generic function of collecting and classifying department information by language-based analysis of “main keywords, similarity keywords” (Spec. par. [0043]) a generic use of a known technique as a tool.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)): Several of the claimed operations constitute insignificant extra-solution activity. The first operation of “collecting department information data… from the plurality of the university institutions” is mere data gathering that is preparatory to the abstract idea. The second operation of “receiving personal competency information… input by the user through the user terminal” is likewise mere data gathering it is the collection of the input on which the abstract advisory process is performed. The fourth operation of “illustrating and providing, to the user terminal, a diagram of a correlation between a plurality of recommended departments” is a data display step the mere presentation of results of the abstract comparison and recommendation process. The sixth operation’s step of “providing the generated admission prediction data to the user terminal” is similarly a mere output/display step. These data gathering and data outputting steps are incidental to the primary abstract idea and constitute insignificant extra-solution activity that does not integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Considering the additional elements individually and in combination, the claims as a whole do not integrate the judicial exception into a practical application. The additional elements generic server and terminal components do not impose any meaningful limits on practicing the abstract idea of personalized academic department recommendation and admission counseling. The claims merely implement this pre-existing human advisory process on generic computing equipment, with the machine learning component used generically as a tool to collect and organize data. No improvement to computer functionality or other technology is disclosed in the specification or reflected in the claims. Accordingly, the claims are directed to an abstract idea.
Step 2B
As discussed with respect to Step 2A, Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the abstract idea using generic computer components. The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f); Alice Corp.
Well-Understood, Routine, Conventional Activity Analysis
The additional elements, when considered individually and in combination, represent well-understood, routine, and conventional activities in the field of computer-implemented systems. Specifically:
• Receiving or transmitting data over a network (first and second operations, collecting data from university servers and receiving input from user terminals): The courts have recognized receiving or transmitting data over a network as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Symantec Corp., (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC; OIP Techs., Inc., v. Amazon.com, Inc., (sending messages over a network).
• Storing and retrieving information in a database (first operation, storing department information analysis data in the platform server database): The courts have recognized storing and retrieving information in memory as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II), citing Versata Dev. Group, Inc. v. SAP Am., Inc; OIP Techs. The specification confirms this by describing the platform server as including a generic “database 120 configured to store the department information analysis data” (Spec. par. [0063]).
• Applying machine learning to classify and compare data (first and third operations): The specification describes the analysis machine learning component at a high level of generality, stating only that it “collects and analyzes, based on language, relevant information” and uses “main keywords, similarity keywords” (Spec. par. [0043]–[0064]). No specific, novel machine learning architecture, training process, or technique is disclosed. This is a generic invocation of machine learning to automate a data classification task, which does not constitute an inventive concept. Compare Ex Parte Desjardins, Appeal No. (claims eligible where the machine learning method itself was improved by addressing catastrophic forgetting; claims here do not improve how the machine learning model operates but merely use it generically).
• Displaying results on a graphical user interface / user terminal (fourth and sixth operations, providing a diagram of department correlations and admission prediction data to the user terminal): The courts have recognized displaying results or outputting information on a display as well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). The specification confirms this by describing the user terminal as receiving and displaying information with no specific technical improvement to the display technology or process (Spec. par. [0035], [0051]–[0057]).
Considering the additional elements individually and in combination, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. All additional elements generic servers, a user terminal, and their associated functions of data collection, data storage, data comparison, machine learning, and data display represent well-understood, routine, and conventional activities and components that were known in the art at the time of the invention. These elements, individually and in combination, do not provide an inventive concept that transforms the abstract idea into patent-eligible subject matter. The claims are not patent eligible.
Dependent Claim Analysis
Claim 2 depends from claim 1 and further specifies that the admission prediction data includes “personal competency information corresponding to existing university admission, and information about desired departments, accepted departments, and selected departments.” This limitation merely further defines the content and granularity of the admission prediction data output generated in the sixth operation of claim 1. It specifies additional categories of informational content within the output types of data to be gathered and displayed but does not add any additional element that would integrate the abstract idea into a practical application or provide an inventive concept. The additional specification of types of data to be included in the output (desired departments, accepted departments, selected departments) constitutes additional insignificant extra-solution activity (MPEP 2106.05(g)) and further defines the field of use of the abstract idea (MPEP 2106.05(h)), neither of which is indicative of integration into a practical application. The rejection of claim 1 therefore applies with equal force to claim 2.
For the foregoing reasons, claims 1–2 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. The claims are directed to the abstract idea of personalized academic department recommendation and admission prediction counseling, which constitutes both certain methods of organizing human activity (managing personal behavior or relationships or interactions between people) and, alternatively, mental processes. The additional elements a generic platform server, university server, and user terminal, including a generically recited machine learning component do not integrate the judicial exception into a practical application, nor do they provide an inventive concept sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 2 is/are rejected under pre-AIA 35 U.S.C. 102(a)(1) as being anticipated by Hoon et al. (KR 2022-0121325 A) (hereafter Hoon).
Referring to Claim 1, Hoon teaches a method of recommending a department through personal competency analysis using a department recommendation system, said method comprising: (see; Abstract and pg. 3, par 2 of Hoon teaches the recommending a department through personal competency analysis).
a platform server configured to generate department information analysis data through machine learning after collecting department information of a plurality of university institutions (see; pg. 3, par. 3 of Hoon teaches servers that are used to generate an informational analysis including using machine learning to collect department information for the university).
analyze personal competency information of a user by comparing the department information analysis data with the personal competency information input by the user (see; Abstract and page 10, par. 4 of Hoon teaches analyzing a personal competency of a user by comparing that information to a department information of the university department).
generate and provide recommended department information based on the personal competency information (see; Abstract of Hoon teaches generate and provide a recommendation based on a person’s competency information).
generate desired competency information by comparing the personal competency information of the user and the department information analysis data (see; Abstract and page 3, par. 3 of Hoon teaches generating competency information by comparing personal competency information and department information).
a university server configured to provide department information data comprising department information and department course information of a corresponding university to allow the platform server to collect the department information (see; Abstract of Hoon teaches a university server utilized to provide department information as well as department class (i.e. course) information at a university using a server).
a user terminal configured to receive, from the user, an input of the personal competency information of the user to provide the personal competency information to the platform server, the method comprising (see; Abstract of Hoon teaches a terminal a user can use to input personal competency information to provide the information to the system).
a first operation of, by the platform server, collecting department information data comprising department information and department course information from the plurality of the university institutions (see; Abstract of Hoon teaches a platform server collecting department information and class information utilizing a server regarding a university institution
generating and storing department information analysis data through machine learning (see; Abstract of Hoon teaches generating and storing department information regarding the analysis using machine learning).
a second operation of receiving personal competency information comprising certification information and extracurricular activity information, the personal competency information being input by the user through the user terminal (see; Abstract and pg. 7, par. 5 of Hoon teaches receiving personal competency information including a certification and information regarding extra-curricular activity, this personal competency is input into a terminal by the user).
a third operation of generating at least one piece of university recommended department information by comparing the department information analysis data stored in the first operation with the personal competency information input by the user in the second operation (see; Abstract of Hoon teaches generating a recommended department information comparing the department information with personal competency information provided by the user).
a fourth operation of illustrating and providing, to the user terminal, a diagram of a correlation between a plurality of recommended departments based on the at least one piece of university recommended department information generated for the user in the third operation (see; Abstract of Hoon teaches providing using a terminal a diagram of correlation between recommended departments based on university recommended department information for the user).
a fifth operation of, when one department among the plurality of recommended departments in the fourth operation is selected, generating comparative analysis data corresponding to a resultant value of comparing the department information analysis data corresponding to the selected department with the personal competency information of the user (see; Abstract of Hoon teaches a department is recommended and a comparison analysis is completed and generate an analysis of department information and personal competency information).
a sixth operation of generating admission prediction data by comparing the comparative analysis data analyzed in the fifth operation with admission data of university admitted students, the admission data being stored in the platform server (see; Abstract and page 7, par. 7 of Hoon teaches predict utilizing the comparison data admission possibility utilizing the department information and personal competency information).
then, providing the generated admission prediction data to the user terminal (see; page. 7, par. 7 of Hoon teaches providing the possibility admission to the university and department).
Referring to Claim 2, see discussion of claim 1 above, while Hoon teaches the method above, Hoon further discloses a method having the limitations of:
with respect to the admission prediction data, personal competency information corresponding to existing university admission, and information about desired departments, accepted departments, and selected departments are generated and provided to the user terminal (see; Abstract and page 7, par. 7 of Hoon teaches an admission prediction based on personal competency and comparing it to university admission process, departments which can be viewed and terminal).
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Hyun et al. (KR 20250134854 A) discloses a system for providing college preparatory information platform.
Won et al. (KR 102610634 B1) discloses a system for comprehensive student management providing student management providing individualized major tracks based on competency evaluation and method for providing comprehensive student management using same).
Jo et al. (KR 20200071878 A) discloses a method and server for providing guide to pass entrance examination).
Kil et al. (U.S. Patent Publication 20170068895 A1) discloses a flexible personalized student success modeling for institutions with complex term structures and competency-based education.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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