CTFR 19/001,656 CTFR 81378 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim(s) 1-3 and 5-10 are pending for examination. Claim(s) 1, 9, and 10 have been amended. Claim(s) 4 is cancelled. This action is Final. Response to Arguments 07-37 AIA Applicant's arguments filed 3/19/2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive. Applicant Argues: Applicant respectfully submits that claim 1 is patent eligible under Prong Two of the revised Step 2A of the Alice test. In Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. If the recited exception is integrated into a practical application of the exception, then the claim is eligible at Prong Two of revised Step 2A. Even if it is assumed claim 1 recites a judicial exception, which Applicant does not concede, Applicant respectfully submits that the claim is patent eligible under prong two of the revised Step 2A of the Alice test because the claim integrates the alleged judicial exception into a practical application. MPEP 2106.04(II)(A)(2) provides that in "Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception." Without any admissions and solely in an effort to expedite prosecution of the present application, amended claim 1 recites a processor configured to "acquire information of a first target of the user by automatically collecting job offering information of the first target from Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or a database that provides job offering information." Thus, the present application specifies a concrete configuration in which information is automatically collected via a network from Internet job listing sites or company pages (e.g., by crawling), or obtained from APIs/databases of job information services. Accordingly, Applicant submits claim 1 (and claims 9 and 10) integrates any possible judicial exception into a practical application of any alleged exception, and accordingly is patent eligible under Prong Two of the revised Step 2A of the Alice test. Examiner’s Response: The examiner respectfully disagrees. The examiner notes that the claim feature of “ acquire information of a first target of the user by automatically collecting job offering information of the first target from [sic] Internet via a network , wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database that provides job offering information .” (i.e., features not struck out) are limitations that fall under the abstract idea. The examiner respectfully notes that the additional elements of “automatically “collecting” from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database and use of a large language model” are additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Therefore, the examiner finds this argument not persuasive. Applicant Argues: Moreover, Applicant respectfully submits that even if it is assumed the claim is directed to an abstract idea, which is not conceded, independent claim 1 recites significantly more than any allegedly abstract idea. In particular, MPEP 2106.05(I)(A)(v) indicates that in evaluating Step 2B, an additional element or combination of elements "[adds] a specific limitation other than what is well- understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application," has been found to qualify as "significantly more" when recited in a claim with a judicial exception. Applicant submits that claim 1, as amended, provides an "inventive concept," and does not simply append well-understood, routine or conventional activities. For at least these reasons discussed above, Applicant respectfully submits that claim 1 and similarly claims 9 and 10 are directed to patent eligible subject matter. Claims 2-8 are patentable at least by virtue of dependencies. Examiner’s Response: The examiner respectfully disagrees. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for “automatically “collecting” from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database and use of a large language model ” amounts to mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in the claim(s) that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the examiner finds this argument not persuasive. Applicant's arguments filed 3/19/2026 with respect to the 35 U.S.C. 103 rejection have been considered but are moot in view of new grounds of rejection; however, the examiner noes the following: Applicant Argues: Bazigos does not disclose or suggest a processor configured to "propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the first target of the user changes before and after attendance of at least one educational content for the first target," as recited in claim 1. Further, Wheeler does not disclose or suggest at least the above features of claim 1. Accordingly, independent claim 1 is patentable over the cited references because each and every feature of the claim is not disclosed by the cited references. To the extent independent claims 9 and 10 recite features similar to those discussed above with respect to claim 1, Applicant respectfully submits claims 9 and 10 are patentable over the cited references for similar reasons. Claims 2-8 are patentable at least by virtue of dependencies. Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes that Bazigos does in fact disclose “propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the first target of the user changes before and after attendance of at least one educational content for the first target,” see [0044]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set). The examiner notes a development road map depicts feedback information how the attribute of the user and the request attribute of the first target changes after attendance of educational content as it “suggests a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets.” Therefore, the examiner finds this argument not persuasive . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim(s) 1-3 and 5-10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Step 1: claim(s) 1-10 are directed to a machine, process, and/or manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03 . Prong 1, Step 2A: claim 1, and similar claim(s) 9 and 10, taken as representative, recites at least the following limitations that recite an abstract idea: An education support apparatus comprising: at least one memory storing instructions, and at least one processor configured to execute the instructions to: acquire information of a user; acquire information of a first target of the user by automatically collecting job offering information of the first target from [sic] Internet via a network , wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database that provides job offering information. analyze an attribute of the user and a request attribute of the first target of the user by using a language model , from the information of the user and the information of the first target of the user, wherein analyzing the difference comprises: representing a text of at least one of the attribute and the request attribute as a vector, and calculating the difference as at least one of: (i) a cosine similarity degree between vectors, (ii) a Euclidean distance between vectors, or (iii) a similarity degree between vectors embedded by a pre-trained language model ; analyze a difference in an attribute between the attribute of the user and the request attribute of the first target of the user; and generate at least one educational content for the first target for causing the user to add an attribute, based on the difference in the attribute, and propose the generated educational content to the user; and propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the first target of the user changes before and after attendance of at least one educational content for the first target. The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II) , in that they recite managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The broadest reasonable interpretation of these limitations for claim 1, and for similar claim(s) 9 and 10 includes acquire information of a user, acquire information of a first target of the user....; analyze an attribute of the user and a request attribute of the first target of the user from the information of the user and the information of the first target of the user; analyze a difference in an attribute between the attribute of the user and the request attribute of the first target of the user, wherein analyzing the difference comprises: representing a text of at least one of the attribute and the request attribute as a vector, and calculating the difference as at least one of: (i) a cosine similarity degree between vectors, (ii) a Euclidean distance between vectors, or (iii) a similarity degree between vectors embedded; generate at least one educational content for the first target for causing the user to add an attribute, based on the difference in the attribute, and propose the generated educational content to the user, and propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the first target of the user changes before and after attendance of at least one educational content for the first target, thus, claim 1, and similar claim(s) 9 and 10 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite managing personal behavior or relationships or interactions between people. Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 9 and 10, recite i.e., apparatus w/ memory/medium processor automatically “collecting” from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database and use of a large language model. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, ⁋[0023], ⁋[0031], and ⁋[0069]) . These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 9 and 10 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d). Since claim 1, and similar claim(s) 9 and 10 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 9 and 10 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d). Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 9 and 10, i.e., apparatus w/ memory/medium processor automatically “collecting” from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database and use of a large language model. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 9 and 10 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05 . Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 9 and 10 is ineligible. Regarding Claims 2-3, and 5-8, claims 2-3, and 5-8 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite further concepts of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bazigos et al. (US 2009/0276231 A1) in view of Ellis et al. (US 2011/0196802 A1) and Wheeler et al. (US 2024/0412313 A1) and Ghandi et al. (US 2023/0394391 A1) . Regarding Claim 1; Bazigos discloses an education support apparatus (Abstract and FIG. 10 and [0031]) comprising: at least one memory storing instructions, and at least one processor configured to execute the instructions to (FIG. 10 and [0033]): acquire information of a user ([0033]-[0035] - When a user logs onto the system, information regarding their past jobs and current job is made available. This information may be entered or updated when the user logs on and [0036]); acquire information of a first target of the user ... that provides job offering information ([0036] - The database or other computer readable medium also includes a plurality of job roles or job descriptions 522 and job categories . Each job category has at least one associated job role. Each job role 522, as well as the employee's current and past job descriptions, is associated with a group of pre-defined quantifiable individual skill sets 520 required by the particular job. Preferably, the skill sets are standardized and a quantifiable proficiency level is also associated with each skill that comprises the skill set ); analyze an attribute of the user and a request attribute of the first target of the user..., from the information of the user and the information of the first target of the user ([0041]-[0043] - The system retrieves data regarding the user's qualifications 102. Specifically, the system retrieves the user's electronic resume 524 containing the user's skill set data. This user skill set data or user qualification data provides a starting point for the strategic planning or recommendations that will follow. The system server then retrieves data relating to the qualifications necessary for at least one job description or job role in the particular job category specified by the user 104. This job qualification data includes data regarding the predefined skill sets 520 required for each of the specified jobs or job categories associated therewith. Preferably, the skill set data is standardized, and includes, tenure, and proficiency information associated with each skill, however, additional information may be considered. The system compares the retrieved job data and the user qualifications data 106, and determines the difference between the retrieved job data and the user qualification data 108 ); analyze a difference in an attribute between the attribute of the user and the request attribute of the first target of the user ([0043]-[0044] - The system compares the retrieved job data and the user qualifications data 106, and determines the difference between the retrieved job data and the user qualification data 108. Specifically, the system determines the skill set gaps, the individual skills missing from the user's current skill set in view of the skill sets required by the at least one specified job role or job category ); generate at least one educational content for the first target for causing the user to add an attribute, based on the difference in the attribute, and propose the generated educational content to the user ([0044]-[0046] - FIG. 6 illustrates a screenshot 800 of an exemplarily embodiment of the career guidance, planning and strategic workforce management tool with a graphical display showing a review of the user's skill set inventory in view of the required skill set inventory for the retrieved target job role. This personalized assessment shows the target job roles skill overlap within the user's current skills and any deficiencies in the user's current skill set 810. The system retrieves data relating to the training necessary to address the skills gap between the user's documented qualifications (skill set) and the qualifications (skill set) necessary for the at least one job role and provides user specific strategy and recommendations 112 on how to transition from the user's current job into the selected job description or target job role. The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category ), and propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the first target of the user changes before and after attendance of at least one educational content for the first target ([0044]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set). Bazigos fails to explicitly disclose acquire information of a first target of the user by automatically collecting job offering information of the first target from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database that provides job offering information ...by using a language model... wherein analyzing the difference comprises: representing a text of at least one of the attribute and the request attribute as a vector, and calculating the difference as at least one of: (i) a cosine similarity degree between vectors, (ii) a Euclidean distance between vectors, or (iii) a similarity degree between vectors embedded by a pre-trained language model; However, in an analogous art, Ellis teaches acquire information of a first target of the user by automatically collecting job offering information of the first target from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database that provides job offering information (FIG. 9 and [0089] - Referring to FIG. 9, at block 905 of method 900 processing logic 175 retrieves job postings from multiple job listing services. Job postings may be retrieved using provided APIs that give access to databases of job listing services, by subscribing to RSS feeds, Atom feeds, XML feeds or other subscription feeds, or by other techniques). Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Ellis to the acquiring of Bazigos to include acquire information of a first target of the user by automatically collecting job offering information of the first target from [sic] Internet via a network, wherein the collecting uses at least one of (i) a web crawling technique and (ii) an API or database that provides job offering information One would have been motivated to combine the teachings of Ellis to Bazigos to do so as it provides / allows efficient “way” to apply for a job (as gleaned from, Elliz, [0002]-[0003]) Further, in an analogous art, Wheeler teaches [analyze an attribute of the user and a request attribute of the first target of the user] by using a language model... ([0114] and [0128]-[0129] and [0132]). Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Wheeler to the analyzation of Bazigos and Ellis to include [analyze an attribute of the user and a request attribute of the first target of the user] by using a language model.... One would have been motivated to combine the teachings of Wheeler to Bazigos and Ellis to do so as it provides / allows employee development resulting in a training process that is more efficient and effective, and/or otherwise incentivize the employee to perform better (Wheeler, [0024]). Further, in an analogous art, Ghandi teaches wherein analyzing the difference comprises: representing a text of at least one of the attribute and the request attribute as a vector ([0013] - The present embodiment further improves approaches to reskilling by providing a system that utilizes multiple word embedding models to more accurately generate vectors and calculate similarity scores for extracted sets of skill keywords. The present embodiment further improves approaches to reskilling by providing a system that automatically provides explainability statements and reskilling recommendations to the user based on the calculated similarity scores and [0039] - If a skill keyword in the job description has a corresponding vector embedding that has a cosine similarity score (calculated at 208 ) to the candidate detail skill keyword vector that is higher than the skill satisfaction threshold, then this skill keyword would be identified as being associated with a skill overlap), and calculating the difference as at least one of : (i) a cosine similarity degree between vectors ([0013] - ...calcualte cosine similarity...and [0039] - If a skill keyword in the job description has a corresponding vector embedding that has a cosine similarity score (calculated at 208) to the candidate detail skill keyword vector that is higher than the skill satisfaction threshold, then this skill keyword would be identified as being associated with a skill overlap) (ii) a Euclidean distance between vectors, or (iii) a similarity degree between vectors embedded by a pre-trained language model; Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Ghandi to the analyzation of difference Bazigos and Ellis and Wheeler to include wherein analyzing the difference comprises: representing a text of at least one of the attribute and the request attribute as a vector, and calculating the difference as at least one of: (i) a cosine similarity degree between vectors, (ii) a Euclidean distance between vectors, or (iii) a similarity degree between vectors embedded by a pre-trained language model. One would have been motivated to combine the teachings of Ghandi to Bazigos and Ellis and Wheeler to do so as it provides / allows automated method to determine skill adjacencies and skill gaps for efficiently reskilling workers is therefore desirable (Ghandi, [0002]). Regarding Claim 2; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 1. Bazigos further discloses wherein the at least one processor is further configured to execute the instructions to generate at least one educational content for the first target for causing the user to add an attribute in such a way as to reduce a difference in an attribute between the attribute of the user and the request attribute of the first target of the user ([0044]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set .). Regarding Claim 3; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 1. Bazigos further discloses wherein the user is a job seeker ([0039]), and the first target of the user is a job offerer ([0036] and [0042]). Regarding Claim 5; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 1. Bazigos further discloses wherein the at least one processor is further configured to execute the instructions to propose, to the user, a second target being different from the first target of the user and satisfying a predetermined condition ([0043]-[0046] and [0091]-[0092] - FIG. 5 illustrates a screen shot of an example embodiment of the career guidance, planning and strategic workforce management tool showing results from a skill set based search 700. As shown in FIG. 5, the exemplarily system has returned results divided into "close fit" and "near fit" job roles 710), and the predetermined condition is that the second target has a request attribute in which a difference in an attribute between the attribute of the user and the request attribute in a process of attending at least one educational content for the first target is equal to or less than a predetermined value ([0043]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set and [0057] - However, it may not include a measure of the relative size and complexity of attaining the skill, or measure the value of one skill relative to other skills . Preferably the system maintains extended attributes for each skill which may include a plurality of learning factor extensions and skill value extensions ). Regarding Claim 6; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 5. Bazigos further discloses wherein the at least one processor is further configured to execute the instructions to generate at least one educational content for the second target for causing the user to add an attribute ([0043]-[0046] and [0091]-[0092] - FIG. 5 illustrates a screen shot of an example embodiment of the career guidance, planning and strategic workforce management tool showing results from a skill set based search 700. As shown in FIG. 5, the exemplarily system has returned results divided into "close fit" and "near fit" job roles 710), based on a difference in an attribute between the attribute of the user and a request attribute of the second target of the user in a process of attending at least one educational content for the first target, and propose the generated educational content to the user ([0044]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set and [0057] - However, it may not include a measure of the relative size and complexity of attaining the skill, or measure the value of one skill relative to other skills . Preferably the system maintains extended attributes for each skill which may include a plurality of learning factor extensions and skill value extensions ) . Regarding Claim 7; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 6. Bazigos further discloses wherein the at least one processor is further configured to execute the instructions to propose, to the user as feedback information, information indicating how a difference in an attribute between the attribute of the user and the request attribute of the second target of the user changes before and after attendance of at least one educational content for the second target ([0044]-[0046] - The strategy and recommendations comprise strategic planning, educational and development recommendations known as a "skill development roadmap." This "skill development roadmap" 576 includes recommended skill set acquisition and training 578 for each of the selected jobs or job categories. The "skill development road map" preferably identifies formal training available 584 to an employee and provides a timeline 582 projecting the time investment necessary for the employee to develop each of the missing or deficient skills to the level required by the selected job or job category. Also, the "skill development road map" may identify "milestone jobs roles," job roles that are a prerequisite or otherwise necessary or advisable for the user to occupy to develop the skill set required for the target job. The skill development may also suggest a strategic order of "milestone job roles" to develop the relevant skill set for the target job as quickly and efficiently as possible, or to afford the organization the maximum value from the employee's current and future skill sets. In addition to the training availability and timeline, the system may also provide the financial resource commitment 580 necessary for the user to develop the required skill set and [0057] - However, it may not include a measure of the relative size and complexity of attaining the skill, or measure the value of one skill relative to other skills . Preferably the system maintains extended attributes for each skill which may include a plurality of learning factor extensions and skill value extensions ).) . Regarding Claim 8; Bazigos in view of Ellis and Wheeler and Ghandi disclose the apparatus according to claim 6. Wheeler further teaches wherein the difference in the attribute is represented by at least one of a cosine similarity degree , a Euclidean distance , and a similarity degree according to a pre-trained language model including BERT ([0059]). Similar rationale and motivation is noted for the combination of Wheeler to Bazigos in view of Ellis and Wheeler and Ghandi, as per Claim 1, above. Ghandi further teaches wherein the difference in the attribute is represented by at least one of a cosine similarity degree , a Euclidean distance, and a similarity degree according to a pre-trained language model including BERT ([0013]). Similar rationale and motivation is noted for the combination of Ghandi to Bazigos in view of Ellis and Wheeler and Ghandi, as per Claim 1, above. Regarding Claim(s) 9; claim(s) 9 is/are directed to a/an method associated with the system claimed in claim(s) 1. Claim(s) 9 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale. Regarding Claim(s) 10; claim(s) 10 is/are directed to a/an medium associated with the system claimed in claim(s) 1. Claim(s) 10 is/are similar in scope to claim(s) 1, and is/are therefore rejected under similar rationale. Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT). 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, JESSICA LEMIEUX can be reached at (571)270-3445. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626 Application/Control Number: 19/001,656 Page 2 Art Unit: 3626 Application/Control Number: 19/001,656 Page 3 Art Unit: 3626 Application/Control Number: 19/001,656 Page 4 Art Unit: 3626 Application/Control Number: 19/001,656 Page 5 Art Unit: 3626 Application/Control Number: 19/001,656 Page 6 Art Unit: 3626 Application/Control Number: 19/001,656 Page 7 Art Unit: 3626 Application/Control Number: 19/001,656 Page 8 Art Unit: 3626 Application/Control Number: 19/001,656 Page 9 Art Unit: 3626 Application/Control Number: 19/001,656 Page 10 Art Unit: 3626 Application/Control Number: 19/001,656 Page 11 Art Unit: 3626 Application/Control Number: 19/001,656 Page 12 Art Unit: 3626 Application/Control Number: 19/001,656 Page 13 Art Unit: 3626 Application/Control Number: 19/001,656 Page 14 Art Unit: 3626 Application/Control Number: 19/001,656 Page 15 Art Unit: 3626 Application/Control Number: 19/001,656 Page 16 Art Unit: 3626 Application/Control Number: 19/001,656 Page 17 Art Unit: 3626 Application/Control Number: 19/001,656 Page 18 Art Unit: 3626 Application/Control Number: 19/001,656 Page 19 Art Unit: 3626 Application/Control Number: 19/001,656 Page 20 Art Unit: 3626 Application/Control Number: 19/001,656 Page 21 Art Unit: 3626 Application/Control Number: 19/001,656 Page 22 Art Unit: 3626 Application/Control Number: 19/001,656 Page 23 Art Unit: 3626 Application/Control Number: 19/001,656 Page 24 Art Unit: 3626 Application/Control Number: 19/001,656 Page 25 Art Unit: 3626 Application/Control Number: 19/001,656 Page 26 Art Unit: 3626
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