DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
[2] This communication is in response to the patent application filed 12 June 2025 and the Preliminary Amendment filed 12 June 2025. It is noted that this application is a National Stage Entry of PCT/JP2023/042154 having an International Filing Date of 24 November 2023, which benefits from Foreign Patent Application Serial No. 2022-205626 (Japan) filed 22 December 2022. The Information Disclosure Statement (IDS) filed 12 June 2025 has been entered and considered. As per the Preliminary Amendment filed 12 June 2025: Claims 10 and 11 are cancelled; Claims 1-9 and 12-20 are pending.
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.
[3] Claims 1-9 and 12-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-9 and 12-20 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 12, and 19 are directed to a method, a system, and non-transitory computer-readable storage medium, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 12, and 19 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 12, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace, which is reasonably considered to be an inventive concept directed to Organizing Human Activity.
In particular, the general subject matter to which the claims are directed serves to estimate a market value for skills of employment seekers in a marketplace in consideration of market supply and demand assessments for markable skills, which is an ineligible concept of Organizing Human Activity, namely: fundamental economic principles or practices, e.g., determinations of relative supply and demand for a product or service.
With respect to functions/steps limited to abstract Organizing Human Activity, Examiner respectfully directs Applicant’s attention to the claim limitations of representative claim 12. In particular, claim 12 includes:
“…receiving attribute information of an individual; estimating, based on the received attribute information of the individual, a value of a work performance ability included in the attribute information…by using demand information on a demand for the work performance ability, supply information on a supply of the work performance ability, relevant information that may affect at least one of the demand and the supply, and value information indicating a value of the work performance ability as teacher data; and outputting estimated value information indicating the estimated value…”
Considered as an ordered combination, the steps/functions of claim 12 are reasonably considered to be representative of the inventive concept and are further reasonably understood to be series of actions or activities directed to a general process of assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace, which is an ineligible concept of Organizing Human Activity, namely: fundamental economic principles or practices, e.g., determinations of relative supply and demand for a product or service (See MPEP 2106.04(a)(2)).
Claims 1, 12, and 19 recite technical elements which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Additional technical elements of claim 12 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject are limited to: “a trained model”. Claims 1 and 19, directed to an apparatus and non-transitory computer-readable medium introduce a “processor”, “memory”, and processor-executable “instructions”. Dependent claims 4 and 15 introduce “natural language processing”. With respect to these potential additional elements:
(1) The “processor”, “memory”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “trained model” is identified as being trained to estimate a value of work performance ability using demand, supply, and relevant information.
(3) The “natural language processing” is identified as being employed to gather/extract input information for the model.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g); and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to the identification of the trained model, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “using a model” and “training a model” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training and/or particular machine learning models. Accordingly, the recited machine-learning processes and associated training are reasonably understood to be generic machine learning processes.
Each of the above noted limitations states a result (e.g., attributes are received, demand, supply, and relevant information is gathered/extracted, a value is estimated and output etc.) as associated with a respective “processor” or “model”. Beyond the general statement that processor, instructions, and model are applied, generally, to accomplish the recited steps/functions, the limitations provide no further clarification with respect to the functions performed by the “processor/instructions” and “model” in producing the claimed result. A recitation of “by a processor” or “by a model”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., attributes outputs); (2) storing and retrieving information and data from a generic computer memory (e.g., demand/supply information); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., assigning weights to skills and estimating a value) (See MPEP 2106.05(f)).
Accordingly, claim 12 is reasonably understood to be conducting standard, and formally manually performed process of assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment. The claimed assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace benefits from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0071]-[0081]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include:
(1) The “processor”, “memory”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “trained model” is identified as being trained to estimate a value of work performance ability using demand, supply, and relevant information.
(3) The “natural language processing” is identified as being employed to gather/extract input information for the model.
While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., attributes outputs); (2) storing and retrieving information and data from a generic computer memory (e.g., demand/supply information); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., assigning weights to skills and estimating a value).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of assessing user skills and attributes and estimating a value for the user skills based on modelled supply and demand for skills in an employment marketplace benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information over a network buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 1 and 19, directed to an apparatus/system and computer-executable instructions stored on computer-readable media for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2-9, 13-18, and 20, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
[4] Claim(s) 1-9 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shi et al. (United States Patent Application Publication No. 2020/0394592 hereinafter ‘Shi’) in view of Agrawal et al. (United States Patent Application Publication No. 2018/0357608 hereinafter ‘Agrawal’)
With respect to claim 1, Shi discloses an information processing apparatus comprising:
at least one memory storing instructions; and at least one processor configured to execute the instructions (Shi et al.; paragraphs [0018]-[0021]; See at least processor, memory, and executable instructions including machine-learning models) to: receive attribute information of an individual (Shi et al.; paragraphs [0015] [0027] [0058]; See at least received user profile information including user attributes/skills); estimate, based on the received attribute information of the individual, a value of a work performance ability included in the attribute information by using a trained model (Shi et al.; paragraphs [0023]-[0024] [0056]-[0059]; See at least machine learned model generates affinity score for user based on scoring of user profile skills and attributes and scoring of job posting skills and attributes. The affinity score is reasonably a form of “value of work performance ability” based on user skills and job posting skills) trained by using demand information on a demand for the work performance ability (Shi et al.; paragraphs [0013]-[0016] [0024]-[0025] [0051]; See at least job-side information including skills and attributes derived from job posting and poster feedback as measures of skill/attribute demand used to train and retrain machine learning model), supply information on a supply of the work performance ability (Shi et al.; paragraphs [0013]-[0016] [0024] [0027] [0051]; See at least user-side information including skills and attributes derived/extracted from candidate profiles as measures of skill/attribute supply used to train and retrain machine learning model), and value information indicating a value of the work performance ability as teacher data (Shi et al.; paragraphs [0013]-[0016] [0024]-[0025] [0027] [0051]; See at least job-side and user-side information including skills and attributes derived/extracted from job postings and candidate profiles as measures of skill/attribute demand and supply including generated skill-specific scores used to train and retrain machine learning model); and output estimated value information indicating the estimated value Shi et al.; paragraphs [0023]-[0024] [0056]-[0059]; See at least machine learned model generates and outputs affinity score for user based on scoring of user profile skills and attributes and scoring of job posting skills and attributes. The affinity score is reasonably a form of estimated “value of work performance ability” based on user skills and job posting skills).
With respect to the recitation of “relevant information that may affect at least one of the demand and the supply”, while Shi discloses assessment of supply and demand for defined skills based on extraction of skills and attributes from job posting information and candidate skill profiles, Shi fails to expressly state that additional relevant information that impacts supply or demand, such as industry trends and future desirable skill profiles, is acquired and used by the model.
However, as evidenced by Agrawal, it is well-known in the art to utilize relevant information including reported trend analysis of changing or evolving demand for particular skills and desirable skills relevant to an industry to adjust modelling of supply and demand with respect to relative demand for specific skills and alternative skills in a workforce (Agrawal et al.; paragraphs [0020]-[0022] [0056] [0066] [0070]; See at least evolving/changing skills by industry over time. See further job market profile engine extracts skills from job postings over time to track trends in changing and alternative skill sets by industry. See exemplary clusters of skills including changing skill sets, obsolete skill sets, and new or future skill sets to estimate skill marketability over time).
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the assessment of supply and demand for defined skills based on extraction of skills and attributes from job posting information and candidate skill profiles features of Shi by further including utilizing relevant information including reported trend analysis of changing or evolving demand for particular skills and desirable skills relevant to an industry to adjust modelling of supply and demand with respect to relative demand for specific skills and alternative skills in a workforce as taught by Agrawal. The instant invention is directed to a system and method of assessing a value of an individual’s skill set in an employment marketplace based on estimated supply and demand for defined skills. As Shi discloses the use of assessment of supply and demand for defined skills based on extraction of skills and attributes from job posting information and candidate skill profiles in the context of a system and method for assessing a value of an individual’s skill set in an employment marketplace based on estimated supply and demand for defined skills and Agrawal similarly discloses the utility of relevant information including reported trend analysis of changing or evolving demand for particular skills and desirable skills relevant to an industry to adjust modelling of supply and demand with respect to relative demand for specific skills and alternative skills in a workforce in the context of a system and method for assessing a value of an individual’s skill set in an employment marketplace based on estimated supply and demand for defined skills, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of more effectively and efficiently predicting skills relevant to employers and job seekers in current and evolving employment marketplace (Agrawal et al.; paragraph [0002]).
With respect to claim 2, Shi discloses an information processing apparatus wherein the at least one processor is further configured to execute the instructions to: set a weight for at least one of the demand information, the supply information, and the relevant information (Shi et al.; paragraphs [0015]-[0016] [0024] [0058]-[0059] [0064]; See at least weights and scores for skills extracted from postings and profiles set by posters selecting skills via posting interface), wherein the trained model performs training using the set weight (Shi et al.; paragraphs [0013]-[0016] [0024]-[0025] [0027] [0051]; See at least job-side and user-side information including skills and attributes derived/extracted from job postings and candidate profiles as measures of skill/attribute demand and supply including generated skill-specific scores/weights set as a function of poster selections/feedback of skills used to train and retrain machine learning model).
With respect to claim 3, Shi discloses an information processing apparatus wherein the at least one processor is further configured to execute the instructions to receive input from a setter and set the weight (Shi et al.; paragraphs [0015]-[0016] [0024] [0058]-[0059] [0064]; See at least weights and scores for skills extracted from postings and profiles set by posters selecting skills via posting interface).
With respect to claim 4, Shi discloses an information processing apparatus wherein the trained model acquires information extracted using natural language processing as the relevant information and performs training using the acquired relevant information (Shi et al.; paragraphs [0037]-[0041] [0048]; See at least extraction of skills in text form to generate training data from poster inputs).
With respect to claim 5, as noted above, Shi fails to expressly state that additional relevant information that impacts supply or demand, such as industry trends and future desirable skill profiles, is acquired and used by the model.
However, Agrawal discloses an apparatus wherein the trained model performs training using a future market value estimated based on the demand information, the supply information, and the relevant information at a plurality of different times (Agrawal et al.; paragraphs [0020]-[0022] [0056]; See at least evolving/changing skills by industry over time. See further job market profile engine extracts skills from job postings over time to track trends in changing and alternative skill sets by industry).
Regarding claim 5, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 5 and are hereby incorporated by reference.
With respect to claim 6, as noted above, Shi fails to expressly state that additional relevant information that impacts supply or demand, such as industry trends and future desirable skill profiles, is acquired and used by the model.
However, Agrawal discloses an apparatus wherein the relevant information includes information on alternative means of the work performance ability, and the trained model performs training so that the value becomes smaller as at least one of a quality and a quantity of the alternative means increases (Agrawal et al.; paragraphs [0020]-[0022] [0056] [0066] [0070]; See at least job market profile engine extracts skills from job postings over time to track trends in changing and alternative skill sets by industry. See exemplary clusters of skills including changing skill sets, obsolete skill sets, and new or future skill sets to estimate skill marketability over time. The emerging and alternative skill sets which impact future demand for current skills are reasonably forms of “alternative means”).
Regarding claim 6, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 6 and are hereby incorporated by reference.
With respect to claim 7, Shi discloses an information processing apparatus wherein the trained model acquires the demand information based on a plurality of job postings and acquires the supply information based on attribute information of a plurality of individuals (Shi et al.; paragraphs [0013]-[0016] [0024]-[0025] [0027] [0051]; See at least job-side and user-side information including skills and attributes derived/extracted from job postings and candidate profiles as measures of skill/attribute demand and supply including generated skill-specific scores used to train and retrain machine learning model), and the trained model performs training by using the demand information and the supply information that have been acquired as the teacher data (Shi et al.; paragraphs [0013] [0024]-[0025] [0027] [0051]; See at least job-side and user-side information including skills and attributes derived/extracted from job postings and candidate profiles as measures of skill/attribute demand and supply including generated skill-specific scores used to train and retrain machine learning model).
With respect to claim 8, as noted above, Shi fails to expressly state that additional relevant information that impacts supply or demand, such as industry trends and future desirable skill profiles, is acquired and used by the model.
However, Agrawal discloses an apparatus wherein the relevant information includes information on trends in an industry to which a job offeror belongs (Agrawal et al.; paragraphs [0020]-[0022] [0056]; See at least evolving/changing skills by industry over time. See further job market profile engine extracts skills from job postings over time to track trends in changing and alternative skill sets by industry).
Regarding claim 8, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 8 and are hereby incorporated by reference.
With respect to claim 9, as noted above, Shi fails to expressly state that additional relevant information that impacts supply or demand, such as industry trends and future desirable skill profiles, is acquired and used by the model.
However, Agrawal discloses an apparatus wherein the relevant information includes information extracted from a publication issued by the job offeror or the industry (Agrawal et al.; paragraphs [0020]-[0022] [0056]; See at least evolving/changing skills by industry over time. See further job market profile engine extracts skills from job postings over time to track trends in changing and alternative skill sets by industry including profiles derived from industry reports).
Regarding claim 9, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 9 and are hereby incorporated by reference.
Claims 12-18 and 19-20 substantially repeat the subject matter addressed above with respect to claims 1-9 as directed to performed method/process and computer-readable medium storing computer-executable instructions. With respect to these elements, Shi discloses performance of the method/process steps employing the enabling executable instructions (See at least Shi paragraphs [0019]-[0024][0071]-[0075]). Accordingly, claims 12-18 and 19-20 are rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claims 1-9.
Conclusion
[5] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Terhark et al., INTERNET SYSTEM AND METHOD WITH PREDICTIVE MODELING, United States Patent Application Publication No. 2018/0232751, paragraphs [0069]-[0073]: Relevant Teachings: Terhark discloses a system/method that provides for a timeline or marketability value of a job seeker’s skill sets based on supply and demand for specified skills in the marketplace.
Flynn et al., SYSTEM AND METHOD FOR PREDICTION OF JOB PERFORMANCE, United States Patent Application Publication No. 2021/0073737, paragraphs [0016]-[0019]: Relevant Teachings: Flynn discloses a system/method that provides scoring of job candidate skills and attributes with respect to a calculated market demand for specified skills.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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