DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-20 are currently pending and have been considered below.
Priority
The present application, filed on 01/17/2025, is a CIP to 16/242,956, which claims priority to Provisional Application 62/614,759, filed on 01/08/2018.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and 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 1-20 are directed to a system (i.e. a machine). 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1 recites: A career management system comprising: responsive to input defining a career objective for a user, generate recommendations regarding tasks to complete to achieve the career objective from data selected according to the career objective and whether an account of the user with the career management system is a sponsored account from a company or a non-sponsored account such that, for sponsored accounts, the data selected is restricted to data approved by the company and for non-sponsored accounts, the data selected is not restricted to data approved by the company. 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 generating recommendations regarding tasks to complete to achieve a career objective in a career management system, which is a clear business relations and 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, “defining a career objective for a user”, “generate recommendations regarding tasks to complete to achieve the career objective from data selected according to the career objective”). Concepts performed in the human mind as mental processes because the steps of receiving, defining, selecting, generating and analyzing 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)).
Further, dependent claims add additional limitations, for example: (claim 2) wherein the input includes education data, income data, career status data, household data, or dependent data; (claim 3) generate alerts for the user about the tasks; (claim 4) responsive to input indicating the user has completed the tasks, generate a notification; (claim 5) generate the recommendations from data selected according to a score associated with a source of the data; (claim 6) exclude data from a source having a score less than a predefined value; (claim 7) prompt the user to provide a response regarding whether the recommendations are helpful and to alter the score based on the response; (claim 8) wherein a value of the score is based on whether the source is associated; (claim 9) wherein a value of the score is based on a results page rank; (claim 10) wherein a value of the score is based on a number of times the data has been cited; (claim 11) based on input defining an account of the user, tailor prompts specific to provide additional data to expand the input defining the account; (claim 12) perform on responses of the user to the prompts to adjust prompt strategy; (claim 13) match the input defining the account to employment classifications, industry classifications, skill sets, or certifications; (claim 14) based on the input defining the account, generate recommendations via filtering that identifies commonalities among similar users; (claim 15) based on input defining a mobility goal for the user, generate recommendations on historical user data and industry data regarding locations related to the mobility goal; (claim 16) generate targeted interventions for the user that evaluate engagement of the user with the tasks; (claim 17) verify at least some of the input defining the account via recognition and verification algorithms; (claim 18) indicate an increase in likelihood that completing one or more of the tasks will have on achieving the career objective based on similar user data, public data, or employer data; (claim 19) identify constraints or preferences of the user of responses by the user to questions about situational data, that includes number and ages of dependents, caregiving responsibilities, willingness to relocate, or willingness to delegate responsibilities, and to generate alternative career objectives and associated recommendations tailored to the user based on the constraints or preferences; (claim 20) prioritize the tasks to align with the constraints or preferences, 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) processor(s); (claim 8) secure sockets layer certificate or a transport layer security certificate; (claim 9) search engine; (claim 11) natural language processing; (claim 12) sentiment analysis; (claim 13) semantic search models; (claim 15) trained model; (claim 16) predictive models; (claim 19) natural language processing or sentiment analysis. 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, 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 1-20 have been fully analyzed to determine whether there are additional elements 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 claim adds significantly more to the 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 conventional 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Harris (WO 2017/123942 A1), hereinafter “Harris”, over CHEVALIER et al. (WO 2012/178130 A2), hereinafter “Chevalier”.
Regarding Claim 1, Harris teaches A career management system comprising: one or more processors programmed to, responsive to input defining a career objective for a user, generate recommendations regarding tasks to complete to achieve the career objective from data selected according to the career objective and whether an account of the user with the career management system is a ... such that, for ... to data approved by the company and for ... to data approved by the company (See at least Harris, Abstract, para 0004, teaches a system for facilitating career, skill, and experience management such that a career recommendation can be automatically recommended to a user. The system may comprise one or more computer processors executing instructions... The one or more computer processors may further be configured to receive a statement of the user. The statement may comprise an objective that the user wants to achieve. The one or more computer processors may further be configured to determine, using a semantic analysis, that the statement comprises a goal of the user to improve a skill, an experience, or both. Further, see Harris, Figures 1-3 and 15; Harris, para 0137, teaches company data, e.g. data received by a company commonly housed in company systems. It includes company systems 1510, employee data 1520, jobs data 1530, resources data 1540, employee personal data 1521, salary history 1524, jobs data 1530, job framework data 1531, job detail data 1532, job competency models 1533, job maps 1534, learning content 1541, and other resources. Examiner notes whether a user account is with a company would be obvious to be included in company data.)
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches sponsored account from a company or a non-sponsored account ... sponsored accounts, the data selected is restricted ... non-sponsored accounts, the data selected is not restricted (Sponsored accounts is taught throughout Chevalier, see at least Chevalier, para 0246, teaches a sponsor to allow a user to utilize the system; Examiner notes if an account is sponsored, an account can be non-sponsored and if an account can be restricted, an account can be non-restricted; Chevalier, para 0544, teaches the Engine may be configured to restrict registration of web user responses and/or interactions for a given, unique web user (e.g., such as may be designated by a unique IP address)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with sponsored account from a company or a non-sponsored account ... sponsored accounts, the data selected is restricted ... non-sponsored accounts, the data selected is not restricted as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004). The Harris invention now incorporating the Chevalier invention, has all the limitations of claim 1.
Regarding Claim 2, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the input includes education data, income data, career status data, household data, or dependent data (Harris, Figure 3, teaches user input includes professional work experience (Examiner notes career status data)).
Regarding Claim 3, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to generate alerts for the user about the tasks (Harris, para 0189, Time triggers 2110 are triggers based off of time (e.g. daily, weekly). Once a trigger is initiated, it will grab a question or inquiry from the Time question database 2140 dependent upon user analysis 2130, recommendation systems).
Regarding Claim 4, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to, responsive to input indicating the user has completed the tasks, generate a notification (Harris, para 0063-0064, User actions 321 is all data collected from users as they complete an action on the user interface; para 0190, User triggers are triggers based off of user actions).
Regarding Claim 5, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to generate the recommendations from data selected according to ... (Harris, generating recommendations to a user, see at least Harris, Abstract and para 0004).
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches a score associated with a source of the data (Scores are taught throughout Chevalier, see at least Chevalier, para 0703, teaches scoring job listings for an applicant(user). For example, the more criteria a job listing met, the higher the relative score would be. As discussed above, the scoring could similarly incorporate different priority levels and/or weights for different criteria in calculating a score.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with a score associated with a source of the data as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 6, Harris, now incorporating Chevalier, teaches The career management system of claim 5, wherein the one or more processors are further programmed to ...
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches exclude data from a source having a score less than a predefined value (Scores are taught throughout Chevalier, see at least Chevalier, para 0703, teaches scoring job listings for an applicant(user); Chevalier, para 0684, jobs scoring over a certain threshold (e.g., jobs scoring 90 or more out of 100); Chevalier, para 0702, teaches the matching system might exclude job listings outside the applicant's interest area; Chevalier, para 0345, a threshold may be specified, such that the system will provide/present only the top paths over the threshold). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with exclude data from a source having a score less than a predefined value as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 7, Harris, now incorporating Chevalier, teaches The career management system of claim 5, wherein the one or more processors are further programmed to prompt the user to provide a response regarding whether the recommendations are helpful and to alter the score based on the response (Harris, para 0066, a list of all experiences with feedback and ratings the user has obtained through the platform. Chevalier teaches scores throughout; see at least para 00756, teaches modify the weights of the score).
Regarding Claim 8, Harris, now incorporating Chevalier, teaches The career management system of claim 5. Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches wherein a value of the score is based on whether the source is associated with a secure sockets layer certificate or a transport layer security certificate (Chevalier, teaches scores throughout; Chevalier para 0606, teaches information server may support secure communications protocols such as, but not limited to Secure Socket Layer (SSL), Examiner notes different variations of the scoring system are certainly within the ability of those having ordinary skill in the art).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with wherein a value of the score is based on whether the source is associated with a secure sockets layer certificate or a transport layer security certificate as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 9, Harris, now incorporating Chevalier, teaches The career management system of claim 5, wherein a value of the score is based on a search engine results page rank (Chevalier, teaches scores throughout; Chevalier, para 0513, teaches conduct an initial ranking in the distribution pool according to a number of factors. For example, the content provider distribution characteristics, the web user characteristics, or some combination of the two may be used to create a ranking based on their relevance to the content provider and/or the web user.)
Regarding Claim 10, Harris, now incorporating Chevalier, teaches The career management system of claim 5, wherein a value of the score is based on a number of times the data has been cited (Chevalier teaches scores throughout, Chevalier, para 00717, teaches the concept of citing text; Harris, para 0063 teaches User actions is all data collected from users as they complete an action on the user interface 140. User actions 321 includes follow 810, save 820, like 830, share 898, other clicks 899.)
Regarding Claim 11, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to, based on input defining an account of the user, tailor prompts specific to the user via natural language processing to provide additional data to expand the input defining the account (Harris, para 0181-0190, teaches a conversation system that details questions asked by users through the conversational UI.... Harris, para 0183, taking a user question and converting it using semantic and text analysis, to a normalized question that is recognized in the question database; Harris, para 0190, User triggers are triggers based off of user actions, characteristics, or other attributes. For example, a user is heavily searching for new careers. Once a trigger is initiated, it will grab a question or inquiry from the User question database dependent upon user analysis 2130, recommendation systems 1910, consolidated data 1920, and user factors 1930.)
Regarding Claim 12, Harris, now incorporating Chevalier, teaches The career management system of claim 11, wherein the one or more processors are further programmed to perform sentiment analysis on responses of the user to the prompts to adjust prompt strategy (Harris, para 0181-0190, teaches a conversation system that details questions asked by users through the conversational UI; Harris, para 0183, taking a user question and converting it using semantic and text analysis; Further, Chevalier, para 00540, teaches positive and negative user responses are registered).
Regarding Claim 13, Harris, now incorporating Chevalier, teaches The career management system of claim 11, wherein the one or more processors are further programmed to match the input defining the account to employment classifications, industry classifications, skill sets, or certifications via semantic search models (Harris, para 0201, using semantic analysis, the user's purpose 316 may be broken down into an array of key terms... a list of the user experiences 650, skill gaps 1130 and the user skills 630 may be compiled.)
Regarding Claim 14, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to, based on the input defining the account, generate recommendations via filtering that identifies commonalities among similar users (Harris, para 0152, FIG. 16 shows an exemplary people recommendation engine 170 that provides users a list of people to connect with based upon multiple variables and data... People recommendation engine 170 includes User data 210, people search 1610, and people result 1620; Further, Chevalier teaches filtering processes throughout, see at least Chevalier, para 00183).
Regarding Claim 15, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to, based on input defining a mobility goal for the user, generate recommendations via a ... regarding locations related to the mobility goal (Harris, para 0060, preference data entered by the user, this usually includes, but is not limited to desired salary, desired location, desired travel frequency, and the likes; Harris, para 0061, User purpose is a statement or series of multiple statements entered by the user that describes their career purpose and/or goals they are striving for.)
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches model trained on historical user data and industry data (Chevalier, para 0312, aggregation and/or analysis of user historical parameter(s);User historical parameters 2955 may, for example, comprise salary, location, state, benefits, other benchmarking and/or user generated content, and/or the like. The statistics associated with the state record may be summed 2960 and added to the is state statistical records in one or more state models...For each state data record ... may analyze the record using any of a variety of statistical analysis tools. Numerous methods of topic modeling may be employed as discussed in... Journal of Machine Learning Research", 4303. Markov models may also be employed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with model trained on historical user data and industry data as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 16, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to generate targeted interventions for the user ... that evaluate engagement of the user with the tasks (Harris, see at least para 0191-0194, teaching User analysis 2130 is an analysis of how engaged the user is on the platform including active, somewhat active and not active; Harris, para 0188, FIG. 21 shows an exemplary proactive system that details automatic inquiries sent to the user from the career management system with the purpose of reengaging the user).
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches via predictive models (See at least Chevalier, para 00256, teaching Statistical modeling 1220 involves aggregating information from (preferably) statistically significant number of users and extracting trends or correlations between variables therein. Data from user-created information and related-user information sources in a user profile 1210 may be aggregated, allowing for prediction of unknown information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with via predictive models as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 17, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to verify at least some of the input defining the account via ...
Yet, Harris does not appear to explicitly teach and in the same field of endeavor Chevalier teaches recognition and verification algorithms (See at least Chevalier, para 0140, teaches verifying the candidate's suitability and/or abilities).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Harris with recognition and verification algorithms as taught by Chevalier with the motivation for matching people, companies, organizations, and/or the like that may benefit from being connected (e.g., job candidates and recruiters, donors and charitable organizations, advertisers and target audiences, self-forming groups, and/or the like) (Chevalier, para 0004).
Regarding Claim 18, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to indicate an increase in likelihood that completing one or more of the tasks will have on achieving the career objective based on similar user data, public data, or employer data (Harris, Abstract, para 0004, teaches a system for facilitating career, skill, and experience management such that a career recommendation can be automatically recommended to a user... comprise an objective that the user wants to achieve; Chevalier, para 0318, teaches an implementation of a path-independent model with attributes data record ... may further include a plurality of attributes (3305, 3330). Each attribute, in turn, may include listings of next states 3310 and of previous states, states comprising states and associated probabilities.)
Regarding Claim 19, Harris, now incorporating Chevalier, teaches The career management system of claim 1, wherein the one or more processors are further programmed to identify constraints or preferences of the user via natural language processing or sentiment analysis of responses by the user to questions about situational data, that includes number and ages of dependents, caregiving responsibilities, willingness to relocate, or willingness to delegate responsibilities, and to generate alternative career objectives and associated recommendations tailored to the user based on the constraints or preferences (Harris, para 0198, a career options recommendation engine that provides users a list of other career possibilities based upon multiple variables and data, according to some embodiments... For example, SQL Database Developer has 3 of the skills in the skill list 630 and 3 of the experiences in the user experiences 650 for a total of 6. The list is then reduced further by querying and matching other attributes 670, e.g. other direct user inputs 660, at step 2212. For example, does SQL Database Developer match the users preferences 315 (step 2214), personality 314 (step 2216), interests 313 (step 2218), and purpose 316 (step 2220); Harris, para 0200-0201, teaches using semantic analysis, the user's purpose 316 may be broken down into an array of key terms).
Regarding Claim 20, Harris, now incorporating Chevalier, teaches The career management system of claim 19, wherein the one or more processors are further programmed to prioritize the tasks to align with the constraints or preferences (Harris, para 0201, an exemplary recommendation engine that provides users a list of other experiences possibilities based upon multiple variables and data, according to some embodiments. At step 2402, the user data 210 may be obtained from the server. At step 2404, user's purpose 316 may be examined or reviewed if the purpose exists. At step 2406, by using semantic analysis, the user's purpose 316 may be broken down into an array of key terms. At step 2408, whether there is any indication of a desired career change, advancement, or destination may be determined. At step 2410, if there is an indication of a desired career change, advancement, or destination, a user's followed jobs 840 may be compiled. At step 2412, from the user's followed jobs 840, a list of the user experiences 650, skill gaps 1130 and 1140 and the user skills 630 may be compiled. Next, at step 2416, the available experiences 1821 (e.g., experience post 1810, work experience 311, and experience gained 324) may be queried and a list of the ones with the most possible combination of skills and experiences that are mapped to both may be compiled.)
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:
Bolte et al. US 2018/0039946 A1 – Career Data Analysis systems and methods
Omar US 2016/0012395 - The present invention provides a method and system for managing career data using a computing environment. The method and system includes receiving user career-related data from an input source and generating a rating of the user career-related data, wherein the rating is dynamically updated over time as contributing factors change. The method and system further includes generating a gap analysis of the user career data from the general career data, wherein the gap analysis includes at least one determination of delta factors between the user career data and the general career data.
JP 2017/515246A - A method and system for assessing a candidate's career profile is disclosed. Multiple parameters and multiple scores for each parameter, multiple scores for each score, an analysis system configured to analyze the carrier profile to identify at least one parameter in the carrier profile, and at least one identified in the carrier profile A system comprising a database configured to include an analysis engine configured to retrieve a score from a plurality of scores for a given parameter, wherein at least one category is identified in the carrier profile Is a system that provides feedback to candidate career profiles according to at least one category of calculated scores.
NPL - Gerard H. Gaynor, “Making People Decisions”; Part of “Decisions: An Engineering and Management Perspective”; Published in: Wiley-IEEE Press 2015 (Edition: 1, Pages: 320); Abstract - This chapter focuses on the people decisions related to building and maintaining the competence of the organization's talent base for the sustainability of the organization. All of the issues involve a decision about people. While some may appear to be trivial, on the surface, the consequences can damage an organization's reputation and destroy careers. It should not take years to determine that an employee has not met expectations; it does take courage to resolve situations related to inadequate performance. The decisions concerning employees begins and ends in the human resource (HR) department. Hiring policies and practices originate in the HR department and on occasion with input from managers in the functional departments and subsequently approved by some executive-level committee. Managing involves taking risks and making major decisions with minimal information. As the global economy expands, executives needs to come to terms with succession competence across all organizational units.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
It appears the inventor(s) filed the current application pro se (i.e., without the benefit of representation by a registered patent practitioner). While inventors named as applicants in a patent application may prosecute the application pro se, lack of familiarity with patent examination practice and procedure may result in missed opportunities in obtaining optimal protection for the invention disclosed. The inventor(s) may wish to secure the services of a registered patent practitioner to prosecute the application, because the value of a patent is largely dependent upon skilled preparation and prosecution. The Office cannot aid in selecting a patent practitioner.
A listing of registered patent practitioners is available at https://oedci.uspto.gov/OEDCI/. Applicants may also obtain a list of registered patent practitioners located in their area by writing to Mail Stop OED, Director of the U.S. Patent and Trademark Office, P.O. Box 1450, Alexandria, VA 22313-1450.
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.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.R.N./Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629