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 AIA .
Status of Claims
This communication is a Non-Final office action in response to RCE filed on 10/23/2025. Claims 1, 5, 9, and 13 have been amended. Therefore, claims 1-16 are currently pending and have been addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/23/2025 has been entered.
Response to Amendment
Applicant has amended claims 1 and 9. Examiner withdraws the 112(a) rejections for the claims.
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 1-16 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-8 are directed to a system (i.e. a machine). Claims 9-16 are directed to a method (i.e., a process). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Representative independent claim 1, analogous to independent claim 9 recites: A system for recommending potential careers or career paths, comprising: store information for the system, the information including at least one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; determine a role and/or an opportunity for a user based at least on one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; recommend individuals for the determined role based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies; generates the recommendation and a confidence score corresponding to a strength of a correlation; and provide the recommendation, improve over time by determining the strength of the positive correlation identified from the information comprising the organization data, the user data and the historical information.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). As the claims discuss recommending potential careers or career paths, including to determine a role and/or an opportunity for a user based at least on one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; recommend individuals for the determined role based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies; and provide the recommendation, and is one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (claim 1 recites for example, “recommending potential careers or career paths”, “determine a role and/or an opportunity for a user based at least on one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies”, “recommend individuals for the determined role based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies”, “generates the recommendation and a confidence score corresponding to a strength of positive correlation of assigned weights between the user data and the historical information”, “provide the recommendation”.) Concepts performed in the human mind as mental processes because the steps of recommending, determining, analyzing, and providing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
Dependent claims add additional limitations, for example: (claims 2 and 10) determine the role and/or opportunity for the user and to recommend individuals for roles; (claims 4 and 12) recommend individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies; (claims 5 and 13) personal profile of an individual, including role, interests, background education, competencies, competency gaps; information from third parties including universities, the information indicating what programs lead into certain skills; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; and available job opportunities; (claims 6 and 14) determining an opportunity and/or career role for an individual based on a statistical analysis of an extent of overlap between competencies and/or interests of the individuals; (claims 7 and 15) matching roles and individuals based on a competency gap that is less than a pre-determined threshold; (claims 8 and 16) recommending a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of certain methods of organizing human activity and mental processes but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) one or more computing devices, a network with the system, a graphical user interface, server, analytics engine, parsed internet sources using semantic analysis, a trained artificial intelligence model (claims 2 and 10) a trained model (claims 3 and 11) probabilistic model, regression model, and a stochastic model. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds 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, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or artificial intelligence, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-16 have been fully analyzed to determine whether there are additional limitations recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claims add significantly more to an abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Habichler et al. (US 2007/0203710 A1), hereinafter “Habichler”, over Varga et al. (US 2020/0302564 A1), hereinafter “Varga”.
Regarding Claim 1, Habichler teaches A system for recommending potential careers or career paths, (Habichler, Abstract, competency-related information is used to assist members of an organization in managing future career paths);
comprising: one or more computing devices that communicate over a network with the system, at least one computing device comprising a graphical user interface for providing data to the system and outputting data to a user; and a server configured to: communicate with the one or more computing devices; (Habichler, Figure 11, teaches a computing system, including a server, one or more computing devices, display, input/output devices; para 0106, teaches the I/O devices include a display 1111, a network connection 1112, a computer-readable media drive, and various other I/O devices);
store information for the system, the information including at least one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; implement at least an …, wherein the at least one … is configurable to: (Habichler, para 0106, teaches the competency management server includes storage; See at least para 0024, 0073, 0083, 0091, teaches history of changes that occur in individuals' competencies in an organization is tracked; para 0111, Career path management information for employees is stored in the employee career path information database on storage.);
determine a role and/or an opportunity for a user based at least on one of organization data, user data ... and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; (Habichler, para 0027, the organization will have one or more defined networks of related position types for that organization, with an appropriate defined network indicating the future position types to which a current position type can lead);
recommend individuals for the determined role based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies; and provide the recommendation to the at least one computing device (Habichler, Figures 8a-8c; para 0131, identifying available work positions for the employee for consideration (e.g., for future work position types along the career path if the employee is expected to be qualified for those work positions by the time that the work positions are to be filled); providing various details about one or more work position types along the career path (e.g., a job description, a salary range, a comparison to actual or example employees that are currently in that work position type, etc. Habichler throughout teaches recommendations for an individual, see at least para 0088, personalized learning recommendations; Further, para 0095, recommends that Employee ZZ begin applying for internal work positions of type “Senior Software Engineer—ABC Division”);
Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches analytics engine wherein the analytics engine comprises a trained artificial intelligence model (Varga, para 0057, engines and programs include an analysis engine; see at least Varga, para 0089, teaches training of the model; Varga, para 0105, the analysis engine 113 may apply one or more machine learning algorithms, analytics, artificial intelligence or deep learning techniques) Internet sources parsed using semantic analysis (See at least Varga, para 0060, Examples of tools or data collection solutions that may be implemented by the data collection module include webpage crawling tools, natural language processing of text or videos, data mining, text pattern matching, HTML parsing, document object model parsing, scraping metadata or semantic markups and annotation, etc.) wherein the analytics engine generates the recommendation and a confidence score corresponding to a strength of a correlationdetermining the strength of the positive correlation identified from the information comprising the organization data, the user data, Internet sources parsed using semantic analysis, and the historical information provided to the analytics engine (see at least Varga, para 0060, data collection solutions include webpage crawling tools, natural language processing of text or videos, data mining, text pattern matching, HTML parsing, document object model parsing, scraping metadata, semantic markups; Varga, para 0113, teaches in the algorithm, embodiments of the knowledge base may input the data… each of the options may be scored and have a probability assigned; para 0117-0118, teaches updating the records; para 0076, weighted more heavily than other in some embodiments to accommodate for the quality of data used to construct the node and/or user feedback from historically presented career and educational options to historical users of the vocational application. For example, in some embodiments, nodes can be rated using a data quality index with a quality score assigned to each node (i.e. 0-100; 0-1, etc.), wherein nodes having a higher quality score are more heavily weighted (Examiner notes higher confidence); Varga, para 0025, allow for refinements to the predictive analysis of the vocational actions presented to the user, expand the knowledge base used to make the recommendations of each vocational option and improve predictions. Further, Examiner notes a score is only briefly mentioned once in Applicant’s specification.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with analytics engine wherein the analytics engine comprises a trained artificial intelligence model ... Internet sources parsed using semantic analysis...wherein the analytics engine generates the recommendation and a confidence score corresponding to a strength of a correlation as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract). The Habichler invention now incorporating the Varga invention, has all the limitations of claim 1.
Regarding Claim 2, Habichler, now incorporating Varga, teaches The system of claim 1.
Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches wherein the analytics engine comprises a trained model that is trained over time with historical data to determine the role and/or opportunity for the user and to recommend individuals for roles (Varga, para 0057, engines and programs include an analysis engine; Varga, para 0089, During the training phase, the knowledge base 117 may learn the correct outputs by analyzing and describing well known historical data and information that may be stored by the knowledge base 117. Examples of data modeling include classification, regression, prediction and gradient boosting. Under a supervised learning technique, the knowledge base 117 may be trained using historical data describing previous users having a defined profile based on user data 130 and user-defined parameters, vocational data describing one or more career, vocation or education options and feedback from the historical users describing the success of the knowledge base to predict which proposed career, vocation and education options presented resonated with the interests of the user). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with wherein the analytics engine comprises a trained model that is trained over time with historical data to determine the role and/or opportunity for the user and to recommend individuals for roles as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract).
Regarding Claim 3, Habichler, now incorporating Varga, teaches The system of claim 2.
Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches wherein the trained model comprises at least one of: a probabilistic model, a regression model, and a stochastic model (Varga, para 0089, teaches training models; Varga, para 0003, Predictive modeling may be referred to as a process through which a future outcome or behavior can be predicted based on known results. A predictive model is able to learn how different data points connect with and/or influence one another in order to evaluate future trends. The two most widely used predictive models are regression and neural networks. Predictive modeling works by collecting and processing historical data, creating a statistical model comprising a set of predictors or known features and applying one or more probabilistic techniques.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with wherein the trained model comprises at least one of: a probabilistic model, a regression model, and a stochastic model as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract).
Regarding Claim 4, Habichler, now incorporating Varga, teaches The system of claim 3, wherein … to recommend individuals for roles based on characteristics pertaining to the individual, and historical information pertaining to others that followed similar paths or developed similar competencies (Habichler, Figures 8a-8c; para 0131, identifying available work positions (Examiner notes roles) for the employee for consideration (e.g., for future work position types along the career path). For example, Habichler, para 0095, recommends that Employee ZZ begin applying for internal work positions of type “Senior Software Engineer—ABC Division”).
Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches the probabilistic model is adapted (Varga, para 0003, Predictive modeling works by collecting and processing historical data, creating a statistical model comprising a set of predictors or known features and applying one or more probabilistic techniques.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with the probabilistic model is adapted as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract).
Regarding Claim 5, Habichler, now incorporating Varga, teaches The system of claim 1, wherein the … using at least one of: personal profile of an individual, including role, interests, (Habichler, para 0085, personal interests or goals of Employee) background education, competencies, competency gaps; (Habichler, throughout teaches employee competencies, see for example, Figures 5A-5C and 8D) … the information indicating what programs lead into certain skills; (Habichler, para 0088, a competency gap between Employee ZZ's current skill level of “Intermediate” for the “C++ Skills” competency and a target skill level of “Expert” for that competency, with the entries providing different options that can each be used by Employee ZZ to eliminate that competency gap. The learning activities can be of various types (e.g., courses internal to Organization XX, courses external to the organization, exams, self-study, experiential activities that provide learning by doing, etc.), crowd sourcing tagging of skills and competencies; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; (Habichler, para 0026, target competencies for members of an organization are identified at least in part based on information specified by appropriate other members of the organization (e.g., supervisors or group managers). For example, a manager of a group in an organization can specify aggregate target competencies for the group, and can view information about resulting competency gaps for the group) and available job opportunities (Habichler, para 0095, applying for internal work positions of type “Senior Software Engineer—ABC Division”).
Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches trained model is trained (Varga, para 0088-0089, teaches training models; trained using historical data describing previous users having a defined profile based on user data 130 and user-defined parameters, vocational data describing one or more career, vocation or education options and feedback from the historical users describing the success of the knowledge base to predict which proposed career, vocation and education options presented resonated with the interests of the user.) information from third parties including universities (Varga, Abstract, educational and career information may be collected and presented to the user, including enrollment information from one or more colleges and universities, specific educational programs aligned with the user's defined profile, and descriptions of courses and curriculum). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with trained model is trained … information from third parties including universities as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract).
Regarding Claim 6, Habichler, now incorporating Varga, teaches The system of claim 1, wherein the server is further configured to determine an opportunity and/or career role for an individual based on a statistical analysis of an extent of overlap between competencies and/or interests of the individuals (Habichler, Figure 17 and para 0144, employees that match the search criteria …perform a search to identify employees having any competencies, skill levels, and skill level history information that was indicated to be required.)
Regarding Claim 7, Habichler, now incorporating Varga, teaches The system of claim 1, wherein the server is further configured to match roles and individuals based on a competency gap that is less than a pre-determined threshold (Habichler, 0101, As shown in FIG. 9B, in this example a gap exists for group members that possess an “Expert” skill level for the competency, with the group being short two such members (Examiner notes less than a pre-determined threshold). In order to eliminate this competency gap, the manager reviews competency-related information for the group members, and specifies the “Expert” skill level as a target competency for two group members that do not currently possess that skill level (in this example, Employees ZZ and KK). In this manner, the manager distributes (or “rolls down”) the required competencies to selected group members in such a manner that it becomes the responsibility of those group members to each satisfy the portion of the required competency that was given to them).
Regarding Claim 8, Habichler, now incorporating Varga, teaches The system of claim 1, wherein the server is further configured to recommend a promotion for an individual to a role in connection with a personalized learning pathway to assist the individual in attaining competencies associated with the role (Habichler, para 0080, employees with a current work position of the type “Entry-Level Product Manager” are eligible to be promoted to the engineering-track work position of the type “Intermediate Software Engineer”; Further, para 0092, Employee ZZ performing career path management activities that use various competency-related information. In particular, with respect to FIG. 8A, Employee ZZ is presented with a network of work position types that begins with the current work position type of Employee ZZ (in this example, “Intermediate Software Engineer”), such as in response to a request for such information. In some embodiments, a variety of interactive controls (not shown) will be provided, such as to select a starting work position type for career planning purposes that is different from the current work position type of Employee ZZ.)
Regarding claim 9, the claim is an obvious variant to claim 1 above, and is therefore rejected on the same premise. Habichler further teaches a method (Habichler, Abstract, a method for using competency-related information for individuals to provide a variety of benefits.)
Regarding claim 10, the claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding claim 11, the claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise.
Regarding claim 12, the claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Regarding Claim 13, Habichler, now incorporating Varga, teaches The method of claim 10, wherein the … using at least one of: personal profile of an individual, including role, interests, (Habichler, para 0085, personal interests or goals of Employee) background education, competencies, competency gaps; (Habichler, throughout teaches employee competencies, see for example, Figures 5A-5C and 8D) … the information indicating what programs lead into certain skills; (Habichler, para 0088, a competency gap between Employee ZZ's current skill level of “Intermediate” for the “C++ Skills” competency and a target skill level of “Expert” for that competency, with the entries providing different options that can each be used by Employee ZZ to eliminate that competency gap. The learning activities can be of various types (e.g., courses internal to Organization XX, courses external to the organization, exams, self-study, experiential activities that provide learning by doing, etc.), crowd sourcing tagging of skills and competencies; information pertaining to skill gaps at industry level; organizational competencies and needs in relation to competencies of current personnel; (Habichler, para 0026, target competencies for members of an organization are identified at least in part based on information specified by appropriate other members of the organization (e.g., supervisors or group managers). For example, a manager of a group in an organization can specify aggregate target competencies for the group, and can view information about resulting competency gaps for the group) and available job opportunities (Habichler, para 0095, applying for internal work positions of type “Senior Software Engineer—ABC Division”). Yet, Habichler does not appear to explicitly teach and in the same field of endeavor Varga teaches trained computer model is trained (Varga, para 0088-0089, teaches training models; trained using historical data describing previous users having a defined profile based on user data 130 and user-defined parameters, vocational data describing one or more career, vocation or education options and feedback from the historical users describing the success of the knowledge base to predict which proposed career, vocation and education options presented resonated with the interests of the user.) information from third parties including universities (Varga, Abstract, educational and career information may be collected and presented to the user, including enrollment information from one or more colleges and universities, specific educational programs aligned with the user's defined profile, and descriptions of courses and curriculum). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Habichler with trained computer model is trained … information from third parties including universities as taught by Varga with the motivation for leveraging the use of data collection, analytics and predictive modeling to selectively provide customized career and education counseling based on each individual user's interests, personality, preferences, geographic location, habits and experiences (Varga, Abstract).
Regarding claim 14, the claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding claim 15, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 8 above, and is therefore rejected on the same premise.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Botea et al. US 2019/0057338 A1 – recommending candidates; The present invention may include selecting a candidate based on the plurality of candidate evidence data. The present invention may include assigning the selected candidate to fill the selected role. The present invention may include iteratively selecting at least one additional role and at least one additional candidate to fill the selected at least one additional role on the team until a stopping criterion is met.
Cama et al. US 2015/0206102 A1 - human resource (HR) analytics engine. A method in accordance with an embodiment includes: filtering data related to a job candidate from a plurality of internal and external data sources using a plurality of ingestion engines; generating a composite profile for the job candidate using the filtered data output from each of the plurality of ingestion engines; and mapping the composite profile for each job candidate against a personnel matrix to determine if the job candidate is a potential match for a job.
Clark US 2020/0349521 A1 - A system and method for managing employment data and matching job seekers with hiring managers is disclosed.
De Ghellinck et al. US 2017/0032298 A1 – methods and systems for visualizing individual and group skill profiles
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Response to Arguments
Applicants arguments filed on 10/23/2025 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejections in light of the most recent claim amendments. Applicant’s arguments have been fully considered but are found unpersuasive and Examiner maintains the 101 rejection.
With respect to Applicant remarks: “Claims 1 to 16 are not directed to a mental process ... Applicant submits that the Office Action improperly characterizes the alleged abstract idea too broadly, and not consistently with the above. The presently claimed combination of features is not simply directed to managing personal behaviour or relationships nor would the human mind be properly equipped to perform the claim limitations. Rather, the presently claimed combination of features recite building a probabilistic model based on a large amount of historical data and aspects such as parsing Internet sources using semantic analysis which would not be practically analyzed by a human mind. In other words, the presently claimed combination of features performs the technical function of analyzing individuals and external data to provide a recommendation of an individual for a role. This is a technical improvement of a human resources system and, given the complexity and sheer amount of data to be combined, cannot be practically performed in or by the human mind.
In particular, Applicant draws the Examiner's attention to paragraphs [0007] and [0008] of the application where it is clear there is a problem with technologies not taking into account sufficient information. Further, the improvement to the human resource system is detailed in paragraph [0044], which states "the analytics engine can include a trained computer (Al) model that is trained over time with historical data to determine the role and/or opportunity for the user and to recommend individuals for roles". Applicant submits that an improvement to a human resource system, is necessarily improving technology associated with organizational technology. Further, it would be clear to one of skill in the art that such an amount of data pertaining to personal profiles, organizational needs and Internet sources is not practical as a mental process but requires the specialized technology of an improved/modified human resources system rather than a general purpose computer.
Thus, it is believed that the claims are not directed to an abstract idea, as the claims do not recite matter that falls within the enumerated groupings of abstract ideas noted in the 2019 Revised Patent Subject Matter Eligibility Guidance. (Step 2A: NO).”
Examiner respectfully disagrees.
As an initial matter, Applicant is arguing limitations that are additional elements (probabilistic model, parsing Internet sources, trained AI model). Additional elements are considered in Step 2A, Prong Two and Step 2B, not Step 2A, Prong One. See above 101 analysis.
Further, with respect to Applicants arguments on the abstract idea, Examiner notes that the claims fall under both certain methods of organizing human activity and mental processes. Examiner respectfully notes the Office Action explains the claimed invention recites business relations (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As the claims discuss recommending potential careers or career paths, including to determine a role and/or an opportunity for a user based at least on one of organization data, user data and historical information pertaining to individuals that followed pre-determined paths or developed pre-determined competencies; recommend individuals for the determined role based on characteristics pertaining to the individual. Examiner further notes the claims also fall under a mental process as the human mind can recommend a career path based on organization, user and historical information pertaining to individuals.
Applicant further remarks: “Under the two-part framework, no further analysis is necessary. If, however, the Examiner were to maintain that the claims are directed to an abstract idea, Applicants further submit that that the claims include additional elements that are significantly more than the judicial exception. Applicant submits that the claim, as a whole, provides an improvement to the technology of a human resource system and to the field of matching a user to an appropriate career.
The presently claimed combination of features demonstrates a technology rooted solution to a computer network-centric problem, specifically a networked human management system with analytics engine, and thus amounts to significantly more than mental processes. The problem associated with determining individualized user roles targeted to organizational needs with third party crowd sourcing input is inherently a problem having a solution that is "rooted" in technology. Further the presently claimed combination of features is extensive, automatic and integrated into a practical application such that it does not pre-empt any alleged abstract idea.
For example, the presently claimed combination of attributes recite features that provide the ability to determine historical data from various sources including the Internet. Further, the determination of similarity of user data, organizational need and parsing Internet sources at this scale is not practical and is likely to be infeasible by a human mind. Such features offer an ability of a human resource system to quickly and efficiently provide recommendations to careers, and are other than what is well-understood, routine, and conventional in the field. The analytics engine can also be updated for all users automatically, which would be impractical for a human mind. Applicant submits that these technological improvements allow the human resource system to be able to adapt to each user's knowledge in an automatic and real-time manner.”
Examiner respectfully disagrees.
As an initial matter, with respect to Applicants remarks (remarks page 9) “Further the presently claimed combination of features is extensive, automatic and integrated into a practical application such that it does not pre-empt any alleged abstract idea.”, Examiner respectfully notes that preemption is not the proper standard for determining whether a claimed invention is abstract. “While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test).” MPEP 2106.04(I).
Further, with respect to Applicant’s remarks: “Applicant submits that the claim, as a whole, provides an improvement to the technology of a human resource system and to the field of matching a user to an appropriate career.” Examiner respectfully does not find this assertion persuasive because Applicant does not explain how or why any limitations of the claims recite specific improvements, Applicant only makes the assertion. Applicant's arguments are a general allegation that the claims define a patent eligible invention without specifically pointing out how the language of the claims reflect an improvement.
With respect to integration of the abstract idea into a practical application, the computing elements are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. Examiner fails to see how the generic recitations of these most basic computer components and/or of a system so integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. Thus, Examiner finds that the claims recite the judicial exception of mental processes and certain methods of organizing human activity and is not integrated into a practical application.
Further, Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Each step does no more than require a generic
computer to perform generic computer functions. The claims do not, for example, purport to improve the functioning of the computer itself. In addition, the claims do not affect an improvement in any
other technology or technical field. The specification spells out different generic equipment and parameters that might be applied using the concept and the particular steps such conventional processing would entail based on the concept of information access (See for example, Applicants spec, para 0031, recites “computer programs executing on programmable computers, each comprising at least one processor, a data storage system, at least one input device, and at least one output device…the programmable computer may be a programmable logic unit, a mainframe computer, server, and personal computer, cloud based program or system, laptop, personal data assistance, cellular telephone, smartphone, or tablet device.”). Thus, the claims at issue amount to nothing significantly more than instructions to apply the abstract idea using some unspecified, generic computer(s). Therefore, Applicants remarks are found unpersuasive and Examiner maintains the 101 rejection.
Regarding 35 U.S.C. § 103 rejections. Examiner has updated the prior art rejections and maintains the prior art rejections. With respect to Applicant’s arguments, Applicants arguments have been considered but are moot as the Examiner has updated the rejections using the Varga reference.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached at (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.R.N./Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629