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 .
Claim Interpretation
Regarding claim 5, the claim recites, “requirements of university courses, demands of
the university courses, ratings given to the university courses,” [0040], [0008], with no additional information. Specification, [0040], [0008], repeats the above statement without additional information, any criteria, or identification of an authoritative source for the type of information to be collected. The Examiner will interpret each of these as being nonce terms.
Claim Rejections – 35 U.S.C. § 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 non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of mentoring and career counseling to assist users in their career progression.
Claim 1 discloses a method, comprising: A method comprising:
identifying a position a user wishes to achieve in a request for a personalized career recommendation; (following rules or instructions, observation, evaluation, judgement, opinion),
comparing a user profile of the user to a marketplace profile for the position to determine whether there are one or more gaps in the user profile that would prevent the user from achieving the position; (following rules or instructions, observation, evaluation, judgement, opinion),
responsive to determining there is a gap in the user profile, assigning the user a task to address the gap in the user profile; (following rules or instructions, observation, evaluation, judgement, opinion),
generating a performance score using a model, wherein the performance score is based on an evaluation of the user completing the task; (following rules or instructions, observation, evaluation, judgement, opinion), and
responsive to updating the user profile with the performance score, generating
a recommendation wherein the recommendation includes a series of actions the user may take to achieve the position identified and an appropriate timeline, (following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method on gathering, a set of information about the user; and creating the user profile with the set of information about the user, (following rules or instructions, observation, evaluation, judgement, opinion – claim 2), wherein the information is a current state of the user, includes employment history of the, user, relevant job skills, an educational and training history, one or more preferences, one or more passions and one or more purposes of the user, (following rules or instructions, observation, evaluation, judgement, opinion – claim 3), subsequent to identifying the position the user wishes to achieve, gathering information about educational requirements for the position; information about the workplace; available positions; and creating the marketplace profile for the position with the set of information gathered, (following rules or instructions, observation, evaluation, judgement, opinion – claim 4), where the educational requirements include, requirements for university courses, the information on the workplace includes the job market, and the information about the position includes requirements, and candidate profile for the position, ( (following rules or instructions, observation, evaluation, judgement, opinion – claim 5), where the assignment of the task to fill the gap is based on criterion from Akaike or Schwarz information criterion, (following rules or instructions, observation, evaluation, judgement, opinion – claim 6), where the task includes an additional qualification, course, or skill the user needs to develop, a soft skill, industry experience, or greater external eminence, (organizing human activity, managing personal behavior or interactions between people, following rules or instructions, observation, evaluation, judgement, opinion – claim 7), and subsequent to generating the performance score, updating the user profile to include the task completed and the performance score generated, (following rules or instructions, observation, evaluation, judgement, opinion – claim 8).
Each of these claimed limitations involve organizing human activity, managing personal behavior or interactions between people, following rules or instructions, or employ mental processes to include observation, evaluation, judgement, and opinion.
Claims 9-20 recite similar abstract ideas as those identified with respect to claims 1-8.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a computer, one or more processors, a reinforcement learning model, machine learning, online courses, a computer program product, and computer readable storage media, Akaike information criteria, and Schwarz information criteria, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-20 are directed to abstract ideas.
Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-20 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(a)(1) that
form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on
sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 7-10, 13-16, 19-20 are rejected under 35 U.S.C. § 102(a)1 as being taught by
Sabatini, (US 20220138881 A1), hereafter Sabatini, “Systems and Methods for Skill Development Monitoring and Feedback.”
Regarding Claim 1, A computer-implemented method comprising:
identifying, by one or more processors, a position a user wishes to achieve in a request for a personalized career recommendation; Sabatini teaches, (The processor determines, based on the set of activities, a skill history for the user. The skill history identifies a first plurality of skills associated with the user. The processor determines a preferred career associated with the user, [Abstract]).
comparing, by the one or more processors, a user profile of the user to a marketplace
profile for the position (46) to determine whether there are one or more gaps in the user profile that would prevent the user from achieving the position; (Having selected a desired career or profession (or multiples of the same) the system can compare the skills associated with the selected career profession to the skills (and skill levels) that have been achieved by the user. In this manner, the system may identify skill gaps (e.g., skills associated with the career profession that are not present in the individual's skill history) and so can make recommendations to the user as to skills to develop for the user's desired career or profession and/or specific activities that the user may undertake to develop skills that require development, [0074]),
responsive to determining there is a gap in the user profile, assigning, by the one or more processors, the user a task to address the gap in the user profile; (the system may identify skill gaps (e.g., skills associated with the career profession that are not present in the individual's skill history) and so can make recommendations to the user as to skills to develop for the user's desired career or profession and/or specific activities that the user may undertake to develop skills that require development, [0074]),
generating, by the one or more processors, a performance score using a reinforcement
learning model, wherein the performance score is based on an evaluation of the user completing the task; (For example, the skill processing engine 318 may perform an analysis of a description associated with a particular activity undertaken by the user to determine skills that may have been developed or improved by participation in the activity to map activities to
skills. This may involve natural language processing (NLP) of the text associated with the
description of the activity, or more advanced approaches using machine learning (ML) to analyze data (e.g., textual descriptions, images and/or multimedia) associated with a particular activity to determine the skills that were honed or improved by participation in the activity, [0093]), a skill history reports that may be generated by the system in accordance with step 402 of the method of FIG. 4. The report includes a summary of activities 502 undertaken by the user. Under each activity 502, the report summarizes skills 504 that were user developed as part of the activity. Each skill is associated with a virtual indicator (e.g., a bar, gauge, color code, score, etc.) that reflects the user's level with each particular skill, [0108]), and
responsive to updating the user profile with the performance score, generating, by the one or more processors, a recommendation using machine learning, wherein the recommendation includes a series of actions the user may take to achieve the position identified and an appropriate timeline, (The processor may further be programed to identify activities to improve the first plurality of skills associated with the user and output the identified skills. The processor may also be programmed to determine a skill history by outputting a user interface that includes a text entry window configured to receive a description of an activity or experience; receive a text entry; and apply at least one of natural language processing or machine learning to map the text entry to the second plurality of skills, [0015], the system processor may further be programmed to generate a display correlating a skill with a graphical depiction of the user's mastery of the skill and/or to display a progress timeline of the second plurality of skills, [0016]).
Regarding claim 2, The computer-implemented method of claim 1, further comprising:
prior to identifying the position the user wishes to achieve in the request for the
personalized career recommendation, gathering, by the one or more processors, a set of
information about the user; Sabatini teaches, (user metadata defining a set of activities undertaken by a user are received by a processor, [Abstract]), and
creating, by the one or more processors, the user profile with the set of information about the user, (User metadata defining a set of activities undertaken by a user are received by a processor. The processor determines, based on the set of activities, a skill history for the user, [Abstract], and the processor is programmed to retrieve user metadata defining a set of activities undertaken by a user from the user profile database; determine, based on the set of activities, a skill history for the user, the skill history identifying a first plurality of skills associated with the user, [0011], and FIGs 7-9, [0039-0041]).
Regarding claim 3, The computer-implemented method of claim 2, wherein the set of information about the user is a set of information about a current state of the user, and wherein the set of information about the user includes at least one of a set of information about an employment history of the user, [0090] one or more relevant job skills of the user, an educational and training history of the user, one or more preferences of the user, one or more passions of the user, and one or more purposes of the user, Sabatini teaches, (The processor determines, based on the set of activities, a skill history for the user. The skill history identifies a first plurality of skills associated with the user, [Abstract]).
Regarding claim 7, The computer-implemented method of claim 1, wherein the task includes at least one of an additional educational qualification the user needs to earn, an additional massive open online course the user needs to complete, an additional technical skill the user needs to develop, an additional soft skill the user needs to develop, an additional industry experience the user needs to gain, and a greater external eminence the user needs to achieve, Sabatini teaches, (the skill analytics engine 318 is configured to analyze the skills of a particular user (e.g., retrieved from the skill history database 332 for that user) and compare the user's set of skills to the sets of skills associated with different careers or professions in the career skill repository database 316 to identify skill gaps. Using this analysis, the skill analytics engine 318 can make suggestions of both skills that a particular user may develop to meet the skills sets associated with a particular desired career or profession, [0097]).
Regarding claim 8, The computer-implemented method of claim 1, further comprising:
subsequent to generating the performance score using the reinforcement learning model,
updating, by the one or more processors, the user profile to include the task completed and the performance score generated. Sabatini teaches, (the skill history database 332 may store information describing the user's academic and/or educational history and activities undertaken by the user. For example, the information may identify one or several courses of study that the user has initiated, completed, and/or partially completed, as well as grades received in those courses of study. The dates on which the various courses of study were completed may also be stored in the skill history database 332. In some embodiments, the student's academic and/or educational history can further include information identifying student performance on one or several tests, quizzes, and/or assignments. [0087] , the skill processing engine 314 analyzes historical activities of the user to determine a set of skills that have been achieved or undertaken by the user and, FIG. 5A depicts a skill history reports that may be generated by the system in accordance with step 402 of the method of FIG. 4. The report includes a summary of activities 502 undertaken by the user. Under each activity 502, the report summarizes skills 504 that were user developed as part of the activity. Each skill is associated with a virtual indicator (e.g., a bar, gauge, color code, score, etc.) that reflects the user's level with each particular skill, [0108]).
Claims 9-10, 13-16, 19-20 are rejected for reasons corresponding to claims 1-3 and 7-8. The addition of computer readable storage media storing program instructions, (claims 9-10, 13-14) and one or more processors, and computer readable storage media, (claims 15-16, 19-20) does not change the rationale for the rejections cited. Sabatini teaches computer-readable storage media, [0057], and one or more processing units which may include single core or multicore processors and processor caches, [0052].
Claim Rejections 35 U.S.C. §103
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 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 nonobviousness.
Claims 4-6, 11-12 and 17-18 are rejected under 35 U.S.C. § 103 as being taught by
Sabatini, (US 20220138881 A1), hereafter Sabatini, “Systems and Methods for Skill Development Monitoring and Feedback,” in view of Jersin, (US 20190197487 A1), “Automated Message Generation for Hiring Searches.”
Regarding claim 4, The computer-implemented method of claim 1, further comprising:
subsequent to identifying the position the user wishes to achieve in the request for the personalized career recommendation,
gathering, by the one or more processors, a set of information about a state of educational requirements for the position; Sabatini does not teach, Jersin teaches, the attribute extractor 304 may be configured to extract attributes from a fully-fledged job posting with a textual description of the job requirements and fields specifying numerous attributes such as industry, full-time vs part-time, salary range, seniority level, and education requirements, [0100]),
gathering, by the one or more processors, a set of information about a state of a
workplace; (This one of the relevancy models 420 can contain information as basic as a standardized job title(s) and location(s), company trends, industry hiring trends and previous candidate rankings, [0154]),
gathering, by the one or more processors, a set of information about one or more
available positions; (In accordance with certain embodiments, a candidate stream includes suggested candidates based on comparing member profile attributes to features of a job opening. The comparison may compare attributes and features such as, but not limited to, geography, industry, job role, job description text, member profile text, hiring organization size (e.g., company size), required skills, desired skills, education (e.g., schools, degrees, and certifications), experience (e.g., previous and current employers), group memberships, a member's interactions with the hiring organization's website (e.g., company page interactions), past job searches by a member, past searches by the hiring organization, and interactions with feed items, [0073]), and
creating, by the one or more processors, the marketplace profile for the position with [0101]the set of information about the state of educational requirements for the position, (the attribute extractor 304 may be configured to extract attributes from a fully-fledged job posting with a textual description of the job requirements and fields specifying numerous attributes such as industry, full-time vs part-time, salary range, seniority level, and education requirements. In some embodiments the job posting is retrieved from one of the databases 318 (e.g., the jobs database shown in the example of FIG. 3), [0100]), the set of information about the state of the workplace, (for example, the intelligent matches system 400 infers criteria with information derived from the hiring manager's organization (e.g., company), job descriptions, other organizations and companies in similar industries, and feedback supplied by the hiring manager), [0123], and the set of information about the one or more available positions, (The intelligent matches system 400 can create the new candidate stream 404 for a new role (e.g., a new job opening or new position). The new candidate stream 404 can be created based on basic information, such as, for example, the new role's title and location, together with an optional job description, [0122]).
Sabatini and Jersin are both considered to be analogous to the claimed invention because they are both in the field of career development and hiring. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the skills development and improvement techniques of Sabatini with the job opening search tools of Jersin so that in a search for candidates (e.g., talent search) translating the criteria of a hiring position into a search query will lead to desired candidates, [0004].
Regarding claim 5, The computer-implemented method of claim 4, wherein:
the set of information about the state of educational requirements for the position
includes at least one of one or more requirements of university courses, one or more demands of the university courses, and one or more ratings given to the university courses;
the set of information about the state of the workplace includes a set of information about a job market; Sabatini does not teach, Jersin teaches, (This one of the relevancy models 420 can contain information as basic as a standardized job title(s) and location(s), company trends, industry hiring trends and previous candidate rankings, [0154]), and
the set of information about the one or more available positions includes at least one of one or more requirements of the one or more available positions, Jersin teaches, (when a recruiter submits query terms for a candidate search, a machine-learning program, trained with social network data, utilizes the member data and the job data, from the job the recruiter wishes to fill, to search for candidates that match the job's requirements (e.g., skills, seniority, and education, [0206]), and one or more profiles of one or more candidates for the one or more available positions, (In one example, a server receives, from a client device, a request for job candidates for an employment position, the request comprising search criteria. The server generates, based on the request, a set of job candidates for the employment position. The server provides, to the client device, a prompt for ordering the set of job candidates. The
server receives, from the client device, a response to the prompt. The server orders the set of job candidates based on the received response. The server provides, for display at the client device, an output based on the ordered set of job candidates, [0068]).
Sabatini and Jersin are both considered to be analogous to the claimed invention because they are both in the field of career development and hiring. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the skills development and improvement techniques of Sabatini with the job opening search tools of Jersin so that in a search for candidates (e.g., talent search) translating the criteria of a hiring position into a search query will lead to desired candidates, [0004].
Regarding claim 6, The computer-implemented method of claim 1, wherein the assignment of the task is based on an information criterion, and wherein the information criterion is at least one of an Akaike information criterion or a Schwarz information criterion.
The specification states, the assignment of the one or more tasks may be based on an information criterion, such as Akaike information criterion or Schwarz information criterion, [0009, [0046]. The Examiner is taking official notice of the well-understood, routine, conventional nature of these additional elements, as noted in Stone, “Comments on Model Selection Criteria of Akaike and Schwarz,” 1979, (MPEP § 2106.07(a)III).
Claims 11-12, 17-18 are rejected for reasons corresponding to claims 4-6. The addition of computer readable storage media storing program instructions, (claims 11-12) and one or more processors, and computer readable storage media, (claims 17-18) does not change the rationale for the rejections cited. Sabatini teaches computer-readable storage media, [0057], and one or more processing units which may include single core or multicore processors and processor caches, [0052].
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
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, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624