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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 9, 2026, has been entered.
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicant submits that claim 1 improves the functioning of the computers and networks by processing data in a manner that reduces data that needs to be processed, reduces data complexity and reduces processing time. The processing steps that reduce the data are part of the abstract idea. Even when considered in combination with the additional elements, using a computer as a tool to implement algorithms to process the data does not integrate the abstract idea into a practical application. Further, Applicant asserts the tagged records and creation of SKUs as machine-readable deployment units based on a rule-based mapping action are not abstract idea. Examiner notes that tagging records and creating the SKUs based on mapping of supply of skills and demand is all part of the abstract idea. The machine readable deployments units, as described in the specification, is the process of displaying the SKUs on an interface, which is not a technological improvement. With respect to the “closed-loop” learning mechanism, training/updating a machine learning model amounts to using a computer to implement a learning algorithm and does not integrate the abstract idea into a practical application. There is no improvement to the learning models or to the computer or technology.
Applicant also contends that the specially programmed computer to execute the AI engine and processing steps of the AI engine demonstrates an enhanced computational ability that goes beyond a generic computer performing abstract mental processes and asserts that the claimed processor qualifies as a special purpose computer. From MPEP2106 I., ‘The programmed computer or "special purpose computer" test of In re Alappat, 33 F.3d 1526, 31 USPQ2d 1545 (Fed. Cir. 1994) (i.e., the rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim for the "special purpose" of executing the algorithm or software) was also superseded by the Supreme Court’s Bilski and Alice Corp. decisions. Eon Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 623, 114 USPQ2d 1711, 1715 (Fed. Cir. 2015) ("[W]e note that Alappat has been superseded by Bilski, 561 U.S. at 605–06, and Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 110 USPQ2d 1976 (2014)"); Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer")’. Examiner points out that the AI engine, as claimed, amounts to a computer processor used to implement the abstract idea. Even if programmed with an algorithm to process the skill information, there is no improvement to any computer or technology; only a general link of the claimed abstract idea to a technological environment. The claims recite steps to map skill supply to skill demand and generate a list of resources mapped to the skill demand which is certain method of organizing human activity as it relates to commercial interactions and managing personal behavior or relationships between people.
Examiner notes, if applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”. As indicated in the rejection below, the added elements do not integrate the abstract idea into a practical application or provide significantly more than the abstract idea – there is no improvement to any computer or technology, only mere instructions to implement the abstract idea on a computer or using a computer as a tool to perform the abstract idea.
Applicant’s arguments with respect to claim(s) as currently amended, have been considered but are not persuasive. Applicant argues the cited prior art does not explicitly teach the newly added receiving limitations. Examiner notes that Borrajo et al describes client devices communicating with the matching module to initiate the matching process as described in the rejection below. Applicant contends that Borrajo fails to disclose deployable SKUs instantiated from rule-based mapping of data comprising generated bill of materials, skill supply constructs and location preferences and deployable SKUs comprising lists of resources and using to automatically update the machine learning model. Examiner disagrees. While Borrajo et al does not use the same terminology, the disclosure recites processing (mapping) resume data (bill of materials) and available jobs to generate a match (SKUs). This process evaluates resumes and available jobs based on a plurality of applicant and job attributes and sends messages to notify.
On pages 15-17 Applicant argues the references individually and makes arguments regarding the references failure to disclose claim limitations that were taught in the primary reference. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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.
Claim(s) 1-5, 7-11, 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-5, 7-11, 13 is/are directed to a method, system, and computer program product. Thus, all the claims are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes and certain methods of organizing human activity.
More specifically, independent claim recite:
receiving,
parsing,
generating,
mapping,
generating,
But for the AI engine in claim 1, the system comprising at least one processor and an AI engine hosted on a cloud computing environment in claim 7, and the non-transitory computer readable storage medium comprising machine executable code to perform the abstract idea in claim 13, the claims recite data analysis steps to map skill supply to skill demand and generate a list of resources mapped to the skill demand. Further, storing the skill information in a repository with tags amounts to using a computer as a tool to perform an abstract idea of labeling the skill information. The concept of data analysis is a fundamental business practice long prevalent in our system of commerce and also relates to managing personal relationships or interactions between people and considered Certain Methods of Organizing Human Activity. The use of data analysis is also a building block of ingenuity in corporate planning. Thus, data analysis, like hedging, is an "abstract idea" beyond the scope of §101. See Alice Corp. Pty. Ltd. at 2356. In addition, the claims recite mental processes as indicated in the reproduced claim above. The nominal recitation of computer elements described above does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. The AI engine in claim 1, the system comprising at least one processor and an AI engine hosted on a cloud computing environment in claim 7, and the non-transitory computer readable storage medium comprising machine executable code to perform the abstract idea in claim 13 amount to using a computer as a tool to perform the abstract idea. These additional limitations do not provide an integration into a practical application. The receiving step being triggered by an action on a user interface amounts to using a computer as a tool to implement the abstract idea and does not integrate the abstract idea into a practical application. Using the SKUs as historic data to update the rules for a machine learning model amounts to implementing a learning model using a computer and does not integrate the abstract idea into a practical application.
The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application. Dependent claims 2 and 8 recite updating the skill cluster repository and skill supply information based on deployment of the stock keeping unit. This is mental process and certain methods of organizing human activity for the same reasons specified in the independent claims. Any computer implementation amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Claims 3 and 9 recite details of the stock keeping unit which is an abstract idea as identified in the independent claims. Any computer implementation amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Claims 4 and 10 recite details of the skill demand information which is an abstract idea as identified in the independent claims. Any computer implementation amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Claims 5 and 11 recite details of the skill supply information which is an abstract idea as identified in the independent claims. Any computer implementation amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements 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, and when considered individually and in combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
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.
Claim(s) 1, 3-5, 7, 9-11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Borrajo et al, US 2022/0180322, in view of Agapitov, US 2021/0319406, and Ashkenazi et al, US 2017/0357945.
As per independent claim 1, Borrajo et al discloses a computer implemented method for automated skill forecast and fulfilment (abstract, [0006] – extracting information from resumes of applicants and matching the applicants with suitable positions within an organization), comprising:
receiving, by an Artificial Intelligence (AI) engine ([0009] – AI algorithm), a skill demand information from a skill cluster repository and a skill supply information from a skill inventory ([0011] – information that relates to at least one requirement of the first available job (demand) and [0006] – receiving a first resume that relates to an applicant (supply)), wherein the receiving step is triggered via an action on a user interface or the receiving step is initiated by the AI engine by continuously monitoring the data stored in the skill cluster repository and the skill inventory and detecting a new dataset of a modified dataset in the skill cluster repository and skill inventory responsive to an external trigger from a client network; ([0070-0071 – process is executed by client device communicating with the matching module and upon being started the module executes the process for extracting and matching resumes of applicants with suitable positions);
parsing, by the Al engine, the received skill demand information and the skill supply information ([0072 – extracts applicant attributes from the received resumes; [0073] – receives information that relates to job requirements for available jobs within the organization – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
generating, by the Al engine, a bill of materials using the parsed skill demand information ([0073] – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
mapping, by the Al engine, the generated bill of materials with the skill supply information based on pre-defined rules ([0075] - applies an algorithm in the applicant attributes, the job requirements and team goals in order to calculate a respective score for each resume – the algorithm may be an artificial intelligence algorithm that implements natural language processing); and
generating, by the Al engine, one or more stock keeping units based on the mapping, further causing deployment of the generated one or more stock keeping units at client network, wherein the one or more stock keeping units comprise list of resources mapped to the skill demand information ([0015] – generating a plurality of scores for a plurality of resumes and a plurality of available jobs; and determining an assignment of at least one resume from among the plurality of resumes; [0079] – when one or more particular resumes are assigned to a particular job, the matching module forwards a message to the team with the applicants that have a high suitability for the job) and wherein the generated stock keeping units are used as historic data for automatically updating the pre-defined rules for a machine learning model to predict and map effectively in subsequent iteration ([0075] - the algorithm may implement a machine learning algorithm, and may thus be configured so that historical information that relates to resumes, job requirements, and team goals may be used to “train” the algorithm for improved score accuracy).
Borrajo et al fails to disclose, while Agapitov discloses the skill demand and skill supply information are stored with tags associated with each skill for identifying premium skills ([0011, 0039-0041] – tags are added to skills to indicate required skills). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to tag skill information as taught by Agapitov since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Borrajo et al and Agapitov does not teach wherein the skill supply information is grouped into clusters based on research for reducing complexity, processing data and processing time which provides effective skill mapping, forecasting and analysis. However, these limitations merely recite intended results of the invention. A recitation of the intended results claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended results, then it meets the claim. The claimed recitations of intended result neither result in a structural difference between the claimed invention and the prior art nor in a manipulative difference as compared to the prior art; therefore, the claimed invention is not deemed to be patentably distinct over the prior art.
Further, Borrajo et al fails to explicitly disclose, while Ashkenazi et al discloses matching based on pre-defined rules and location preferences [0036]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to map according to location preferences and pre-defined rules as taught by Ashkenazi et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per independent claim 7, Borrajo et al discloses the system comprising at least one processor; at least one memory unit operatively coupled to the at least one processor having stored instructions; and an AI engine hosted on a cloud computing environment, wherein execution of the instruction stored on the at least one memory unit by the at least one processor causes the AI engine to implemented the method as claimed in claim 1 [0009 – AI algorithm, 0036, 0037 and rejection of claim 1];
receive, a skill demand information from a skill cluster repository and a skill supply information from a skill inventory ([0011] – information that relates to at least one requirement of the first available job (demand) and [0006] – receiving a first resume that relates to an applicant (supply)) wherein the receiving step is triggered via an action on a user interface or the receiving step is initiated by the AI engine by continuously monitoring the data stored in the skill cluster repository and the skill inventory and detecting a new dataset of a modified dataset in the skill cluster repository and skill inventory responsive to an external trigger from a client network; ([0070-0071 – process is executed by client device communicating with the matching module and upon being started the module executes the process for extracting and matching resumes of applicants with suitable positions);
parse the received skill demand information and the skill supply information ([0072 – extracts applicant attributes from the received resumes; [0073] – receives information that relates to job requirements for available jobs within the organization – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
generate a bill of materials using the parsed skill demand information ([0073] – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
map the generated bill of materials with the skill supply information based on pre-defined rules ([0075] - applies an algorithm in the applicant attributes, the job requirements and team goals in order to calculate a respective score for each resume – the algorithm may be an artificial intelligence algorithm that implements natural language processing); and
generate one or more stock keeping units based on the mapping, further causing deployment of the generated one or more stock keeping units at client network, wherein the one or more stock keeping units comprise list of resources mapped to the skill demand information ([0015] – generating a plurality of scores for a plurality of resumes and a plurality of available jobs; and determining an assignment of at least one resume from among the plurality of resumes; [0079] – when one or more particular resumes are assigned to a particular job, the matching module forwards a message to the team with the applicants that have a high suitability for the job) and wherein the generated stock keeping units are used as historic data for automatically updating the pre-defined rules for a machine learning model to predict and map effectively in subsequent iteration ([0075] - the algorithm may implement a machine learning algorithm, and may thus be configured so that historical information that relates to resumes, job requirements, and team goals may be used to “train” the algorithm for improved score accuracy).
Borrajo et al fails to disclose, while Agapitov discloses the skill demand and skill supply information are stored with tags associated with each skill for identifying premium skills ([0011, 0039-0041] – tags are added to skills to indicate required skills). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to tag skill information as taught by Agapitov since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Borrajo et al and Agapitov does not teach wherein the skill supply information is grouped into clusters based on research for reducing complexity, processing data and processing time which provides effective skill mapping, forecasting and analysis. However, these limitations merely recite intended results of the invention. A recitation of the intended results claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended results, then it meets the claim. The claimed recitations of intended result neither result in a structural difference between the claimed invention and the prior art nor in a manipulative difference as compared to the prior art; therefore, the claimed invention is not deemed to be patentably distinct over the prior art.
Further, Borrajo et al fails to explicitly disclose, while Ashkenazi et al discloses matching based on pre-defined rules and location preferences [0036]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to map according to location preferences and pre-defined rules as taught by Ashkenazi et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per independent claim 13, Borrajo et al discloses a non-transitory computer readable storage medium for automated skill forecast and fulfilment, the non-transitory computer readable storage medium comprising machine executable code which when executed by at least one processor, causes Al engine to perform steps as claimed in claim 1 [0039-0042 and rejection of claim 1];
receiving a skill demand information from a skill cluster repository and a skill supply information from a skill inventory ([0011] – information that relates to at least one requirement of the first available job (demand) and [0006] – receiving a first resume that relates to an applicant (supply)) wherein the receiving step is triggered via an action on a user interface or the receiving step is initiated by the AI engine by continuously monitoring the data stored in the skill cluster repository and the skill inventory and detecting a new dataset of a modified dataset in the skill cluster repository and skill inventory responsive to an external trigger from a client network; ([0070-0071 – process is executed by client device communicating with the matching module and upon being started the module executes the process for extracting and matching resumes of applicants with suitable positions);
parsing the received skill demand information and the skill supply information ([0072 – extracts applicant attributes from the received resumes; [0073] – receives information that relates to job requirements for available jobs within the organization – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
generating a bill of materials using the parsed skill demand information ([0073] – information may include required skills, required education level, required number of years of experience, geographic requirements and or any other job requirements);
mapping the generated bill of materials with the skill supply information based on pre-defined rules ([0075] - applies an algorithm in the applicant attributes, the job requirements and team goals in order to calculate a respective score for each resume – the algorithm may be an artificial intelligence algorithm that implements natural language processing); and
generating one or more stock keeping units based on the mapping, further causing deployment of the generated one or more stock keeping units at client network, wherein the one or more stock keeping units comprise list of resources mapped to the skill demand information ([0015] – generating a plurality of scores for a plurality of resumes and a plurality of available jobs; and determining an assignment of at least one resume from among the plurality of resumes; [0079] – when one or more particular resumes are assigned to a particular job, the matching module forwards a message to the team with the applicants that have a high suitability for the job) and wherein the generated stock keeping units are used as historic data for automatically updating the pre-defined rules for a machine learning model to predict and map effectively in subsequent iteration ([0075] - the algorithm may implement a machine learning algorithm, and may thus be configured so that historical information that relates to resumes, job requirements, and team goals may be used to “train” the algorithm for improved score accuracy).
Borrajo et al fails to disclose, while Agapitov discloses the skill demand and skill supply information are stored with tags associated with each skill for identifying premium skills ([0011, 0039-0041] – tags are added to skills to indicate required skills). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to tag skill information as taught by Agapitov since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Borrajo et al and Agapitov does not teach wherein the skill supply information is grouped into clusters based on research for reducing complexity, processing data and processing time which provides effective skill mapping, forecasting and analysis. However, these limitations merely recite intended results of the invention. A recitation of the intended results claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended results, then it meets the claim. The claimed recitations of intended result neither result in a structural difference between the claimed invention and the prior art nor in a manipulative difference as compared to the prior art; therefore, the claimed invention is not deemed to be patentably distinct over the prior art.
Further, Borrajo et al fails to explicitly disclose, while Ashkenazi et al discloses matching based on pre-defined rules and location preferences [0036]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to map according to location preferences and pre-defined rules as taught by Ashkenazi et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claims 3 and 9, Borrajo et al discloses wherein each of the one or more stock keeping units is a combination of technological skill, role definitions, domain skill and location information ([0072] - the applicant attributes may include any one or more of an applicant skill, such as a specialized professional skill; an applicant education level, such as a degree that the applicant has attained; a name of a school, college, or university from which the applicant has graduated and/or received a degree; a previous work experience of the applicant; and an applicant qualification, such as, for example, a non-work experience, an award, a publication, and/or an achievement).
As per claims 4 and 10, Borrajo et al discloses wherein the skill demand information comprises at least one or combination of technological services, skill clusters, role definitions, functional skills, demand unit definitions and domain skills ([0073-0074] - required skills, required education level, required number of years of experience in a particular field, geographic requirements, and/or any other suitable job requirements).
As per claims 5 and 11, Borrajo et al discloses wherein the skill supply information comprises at least one or combination of role definitions, technical skills, domain skills and proficiency levels ([0072] - the applicant attributes may include any one or more of an applicant skill, such as a specialized professional skill; an applicant education level, such as a degree that the applicant has attained; a name of a school, college, or university from which the applicant has graduated and/or received a degree; a previous work experience of the applicant; and an applicant qualification, such as, for example, a non-work experience, an award, a publication, and/or an achievement).
Claim(s) 2 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Borrajo et al, US 2022/0180322, Agapitov, US 2021/0319406, and Ashkenazi et al, US 2017/0357945, in view of Peran et al, US 2020/0034776.
As per claims 2 and 8, Borrajo et al discloses the method of claim 1 and system of claim 7, respectively, but does not explicitly disclose updating the skill demand information stored in the skill cluster repository and the skill supply information stored in the skill inventory, based on the deployment of the generated one or more stock keeping units. Peran et al discloses updating information in a system is a commonly known measure in data processing systems [0022, 0024]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Borrajo et al the ability to update information as taught by Peran et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625