DETAILED ACTION
The following is a Non-Final Office Action in response to communications filed April 9, 2026. Claims 1 and 11–12 are amended. Currently, claims 1–5, 8–15, and 18–20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 9, 2026 has been entered.
Response to Amendment/Argument
Applicant’s Response is sufficient to overcome the previous objection to claims 1 and 11 for informalities. Accordingly, the previous objection to claims 1 and 11 is withdrawn.
Applicant’s Response is sufficient to overcome the previous rejection of claim 12 under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Accordingly, the previous rejection of claim 12 under 35 U.S.C. 112(b) is withdrawn.
However, Applicant’s Response necessitates new grounds of rejection under 35 U.S.C. 112(b), and Examiner directs Applicant to the relevant explanation below.
With respect to the previous rejection under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive.
Under Step 2A Prong One, Applicant first asserts that, in view of Intellectual Ventures I LLC v. Capital One Bank, “claims do not recite a method for organizing human activity involving managing personal behavior or relationships where there is no tracking of financial transactions.” Examiner disagrees. As noted in MPEP 2106.04(a)(2)(II)(C), the claims of Intellectual Ventures are exemplary and do not constitute a rule requiring financial elements within a claim involving managing personal behavior or relationships.
As indicated in Applicant’s Specification, a commitment datum describes “a pattern that is representative of user activity progressing to match a target” (See, Spec. ¶ 3), a novelty datum describes “any type of datum or data describing conception of a novel method, idea, product, and/or the like, as well as identification, incorporation and/or usage of tools, activities, educational workshops and other items or services that assisted the user achieve performance improvements related to attaining the enumerated target within the defined duration” (See, Spec. ¶ 11), and a skill factor datum describes “the ability to use one’s knowledge effectively and readily in execution or performance, dexterity, or coordination especially in the execution of learned physical tasks, or a learned power of doing something competently, such as a developed aptitude or ability” (See, Spec. ¶15). Further, a “directed process” is described as “a set of steps required to achieve a user’s goal or target” (See, Spec. ¶ 66).
Here, the claims recite a process for generating a directed process based on analyzing targets in the context of commitment datum, novelty datum, skill datum, which in view of Applicant’s Specification, is a process for managing a user’s progression toward a goal by managing a user’s steps toward the goal based on a user’s activities, opportunities, and knowledge. As a result, Applicant’s remarks are not persuasive because the recited elements encompass managing personal behavior by managing a user’s progress toward achieving a goal.
To the extent that Applicant asserts that the claims cannot recite certain methods of organizing human activity because the claims include machine learning elements, Examiner disagrees. As an initial matter, Examiner notes that the technical elements of claim 1 are not identified as reciting an abstract idea under Step 2A Prong One. Further, MPEP 2106.07(a) sets forth the process for formulating a rejection under 35 U.S.C. 101 and indicates that a proper rejection (1) identifies claim elements reciting an abstract idea under Step 2A Prong One and (2) evaluates the additional elements under Step 2A Prong Two and Step 2B. Examiner submits that the inclusion of additional elements within a claim does not prevent a finding that a claim recites an abstract idea under Step 2A Prong One because the Step 2A Prong One analysis occurs prior to any additional element considerations in the eligibility determination process. As a result, Applicant’s remarks are not persuasive.
Applicant further asserts that the claims cannot recite mental processes because “the claim requires a several-step, computer-implemented manipulation of interrelated data structures and machine-learning outputs that cannot practically be performed in the human mind or with pen and paper.” Examiner disagrees. As noted above, the technical elements of claim 1 are not identified as reciting an abstract idea under Step 2A Prong One, and the inclusion of additional elements does not preclude a finding that a claim recites an abstract idea. Further, as noted above, the identified steps, under a broadest reasonable interpretation consistent with Applicant’s Specification, encompass a process for managing a user’s progression toward a goal by managing a user’s steps towards the goal based on a user’s activities, opportunities, and knowledge. As a result, the recited functions to, for example, “determine a target datum as a function of the commitment datum” or “identify a novelty datum as a function of the commitment datum and the target datum,” describe career progression observations or evaluations that can be practically performed in the human mind by reviewing a resume or career opportunity documentation. Therefore, Applicant’s remarks are not persuasive.
With respect to Step 2A Prong Two, Applicant asserts that the machine learning and technical elements improve the functioning of the computing system. Examiner disagrees. As currently presented, the machine learning elements are claimed generically because the claims generally recite training and using models without claiming any substantive details defining the machine learning implementation. Further, Applicant’s Specification does not disclose any improvements in machine learning technology or improvements in computer functionality derived from the recited machine learning models. As a result, Applicant’s remarks are not persuasive.
With respect to Step 2B, Applicant asserts that the claimed elements amount to a non-conventional arrangement of a computer device and functions. However, Applicant neither identifies any specific element or combination of elements nor includes any argument or rationale underpinning that would support Applicant’s assertions.
Further, although Applicant lists a series of claim elements, Examiner notes that many of the elements identified by Applicant are abstract claim elements identified under Step 2A Prong one. However, MPEP 2106.05 indicates that “[a]n inventive concept ‘cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.’ Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, “we then ask, ‘[w]hat else is there in the claims before us?”) (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”).” As a result, Applicant’s remarks are not persuasive because abstract claim elements cannot be furnished by abstract claim elements.
Accordingly, Applicant’s remarks are not persuasive, and the previous rejection is maintained.
With respect to the previous rejection under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are not moot in view of the updated grounds of rejection asserted below.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–5, 8–15, and 18–20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 recite “the first factor datum” in the element for “refining the first skill factor datum”. Although the claims previously recite a “first skill factor datum”, there is insufficient antecedent basis for “the first factor datum” in the claims.
For purposes of examination, claims 1 and 11 are interpreted as reciting “the first skill factor datum” in the element for “refining the first skill factor datum”.
Claims 1 and 11 further recite “the classification” in the element for “refining the first skill factor datum”. There is insufficient antecedent basis for “the classification” in the claims. Additionally, claims 1 and 11 previously recite both “classifying the novelty datum into one or more resource categories” and “classifying the novelty datum to the first factor datum”. As a result, the scope of the claims is further indefinite because it is unclear whether Applicant intends for “the classification” to reference either “classifying the novelty datum into one or more resource categories” or “classifying the novelty datum to the first factor datum”.
Finally, claims 1 and 11 recite “the aggregated first skill factor datum and at least the second skill factor datum” in the element reciting “classifying the aggregated first skill factor datum and at least the second skill factor datum”. Although the claims previously recite “aggregating the first skill factor datum with at least a second skill factor datum,” there is insufficient antecedent basis for “the aggregated first skill factor datum and at least the second skill factor datum” in the claims.
For purposes of examination, the claims are interpreted as reciting “refining the first skill factor datum by classifying the novelty datum to the first skill factor datum and aggregating the first skill factor datum with at least a second skill factor datum based on classifying the novelty datum to the first skill factor datum to produce an aggregated skill factor datum” and “classifying the aggregated
Accordingly, claims 1 and 11 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2–5, 8–10, 12–15, and 18–20, which depend from claims 1 and 11, inherit the deficiencies described above. As a result, claims 2–5, 8–10, 12–15, and 18–20 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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 1–5, 8–15, and 18–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–5, 8–15, and 18–20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements to “receive a commitment datum describing a pattern that is representative of user activity progressing to match a target”; “determine a target datum as a function of the commitment datum”; “identify a novelty datum as a function of the commitment datum and the target datum, wherein the novelty datum comprises data related to management of resources, and wherein identifying the novelty datum comprises: classifying the novelty datum into one or more resource categories”; “generating efficiency data as a function of the one or more resource categories”; “generate a skill factor datum as a function of the novelty datum by: identifying a first skill factor datum as a function of the novelty datum; refining the first skill factor datum by classifying the novelty datum to the first factor datum and aggregating the first skill factor datum with at least a second skill factor datum based on the classification; and classifying the aggregated first skill factor datum and at least the second skill factor datum to data describing a frequency of implementation of the first skill factor datum and the second skill factor datum to the target”; “determine at least an obstacle datum as a function of the target datum and the skill factor datum, which comprises: generating obstacle training data, wherein the obstacle training data comprises correlations between exemplary target datums and exemplary skill factor datums”; “determining the at least an obstacle datum”; “generate a directed process as a function of the at least an obstacle datum, wherein the directed process comprises a set of instructions to improve the efficiency data, and wherein generating the directed process comprises: generating process training data, wherein the process training data comprises correlations between exemplary obstacle datums, exemplary efficiency data and exemplary directed processes”; “generating the directed process”.
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people because the elements describe a process for generating a directed process based on user efficiencies, skills, and targets. Further, the elements recite mental processes because the element describe observations or evaluations that can be practically performed in the mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 11 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 11 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–5, 8–10, 12–15, and 18–20 further describe the process for generating a directed process based on user efficiencies, skills, and targets and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 2–5, 8–10, 12–15, and 18–20 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a memory, an obstacle machine-learning model, a process machine-learning model, and elements for “iteratively training the obstacle machine-learning model”, “using obstacle process machine-learning model”, “iteratively training a process machine-learning model”, “using the process machine-learning model”, and “generate an interface query data structure, wherein the interface query data structure configures a display device to display”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 11 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 11 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 5 and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a target machine learning model and elements to “train” and “using” the target machine learning model (claims 5 and 15). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 5 and 15 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–4, 8–10, 12–14, and 18–20 do not include any additional elements beyond those included with respect to the claims from which claims 2–4, 8–10, 12–14, and 18–20 depend. As a result, claims 2–4, 8–10, 12–14, and 18–20 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a memory, an obstacle machine-learning model, a process machine-learning model, and elements for “iteratively training the obstacle machine-learning model”, “using obstacle process machine-learning model”, “iteratively training a process machine-learning model”, “using the process machine-learning model”, and “generate an interface query data structure, wherein the interface query data structure configures a display device to display”. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claim 11 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 11 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 5 and 15 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a target machine learning model and elements to “train” and “using” the target machine learning model (claims 5 and 15). The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 5 and 15 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2–4, 8–10, 12–14, and 18–20 do not include any additional elements beyond those included with respect to the claims from which claims 2–4, 8–10, 12–14, and 18–20 depend. As a result, claims 2–4, 8–10, 12–14, and 18–20 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–5, 8–15, and 18–20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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.
Claims 1–5, 9–15, and 19–20 are rejected under 35 U.S.C. 103 as being unpatentable over Chetlur et al. (U.S. 2017/0109850) in view of Yin (U.S. 2023/0068203), and in further view of CAHALANE et al. (U.S. 2021/0256644).
Claims 1 and 11: Chetlur discloses an apparatus for providing a skill factor hierarchy to a user, the apparatus comprising:
at least a processor (See FIG. 7); and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor (See FIG. 7) to:
receive a commitment datum describing a pattern that is representative of user activity progressing to match a target (See paragraphs 33–34, wherein career development events are logged with respect to each user; see also paragraph 25);
determine a target datum as a function of the commitment datum (See paragraph 22, wherein extracted events are mapped to career paths and career path roles; see also paragraphs 35–36);
identify a novelty datum as a function of the commitment datum and the target datum, wherein the novelty datum comprises data related to management of resources (See paragraphs 33–34, in view of paragraph 25, wherein events are recommended to the user based on previously attended events, user goals, and the personalized plan, and wherein each event is associated with peer, time, and instruction resources), and wherein identifying the novelty datum comprises:
classifying the novelty datum into one or more resource categories (See paragraph 25, wherein each event is classified with respect to attributes of the event); and
generating efficiency data as a function of the one or more resource categories (See paragraphs 27–30, wherein each event is classified and scored based on peer and time resources);
generate a skill factor datum as a function of the novelty datum (See paragraph 25, wherein skill attributes are generated with respect to event data) by: identifying a first skill factor datum as a function of the novelty datum (See paragraphs 24–25, wherein skills are identified with respect to event opportunities);
refining the first skill factor datum by classifying the novelty datum to the first factor datum (See paragraphs 24–25, wherein skills are mapped to event opportunities);
determine at least an obstacle datum as a function of the target datum and the skill factor datum (See paragraph 34, in view of paragraph 29, wherein time constraint data is determined from both events and users, and wherein time constraints are considered with respect to each path and associated skills);
generate a directed process as a function of the at least an obstacle datum, wherein the directed process comprises a set of instructions to improve the efficiency data (See paragraph 34, in view of paragraphs 28–30, wherein an optimal career path is generated based on time constraints, and wherein the career path is optimized according to efficiency metrics); and
generate an interface query data structure, wherein the interface query data structure configures a display device to display the efficiency data and the directed process (See FIG. 5 and paragraphs 34–35, in view of paragraph 5, wherein the optimized career path and recommended events are displayed to the user). Chetlur does not expressly disclose the remaining claim elements.
Yin discloses functionality to determine at least an obstacle datum as a function of the target datum and the skill factor datum using an obstacle machine-learning model which comprises: generating obstacle training data, wherein the obstacle training data comprises correlations between exemplary target datums and exemplary skill factor datums (See paragraphs 34–35 and 59, in view of paragraph 66, wherein training data includes both employee and job data, and wherein the machine learning model is trained to evaluate tradeoff situations that consider employment histories and skills; see also paragraphs 30 and 41);
iteratively training the obstacle machine-learning model using the obstacle training data as a function of previous iterations (See paragraphs 34–35, in view of paragraph 25, wherein a machine learning engine is trained and updated based on feedback, and see paragraphs 24 and 39, wherein the machine learning engine may include more than one machine learning model); and
determining the at least an obstacle datum using the trained obstacle machine-learning model (See paragraph 37, in view of paragraph 66, wherein the machine learning engine generates a career progression pathway based on constraint tradeoffs);
wherein generating the directed process comprises: generating process training data, wherein the process training data comprises correlations between exemplary obstacle datums, exemplary efficiency data and exemplary directed processes (See paragraphs 34–35 and 59, in view of paragraph 66, wherein training data includes both employee and job data, and wherein the machine learning model is trained to evaluate tradeoff situations that consider constraints, efficiencies, and employment histories; see also paragraphs 30 and 41);
iteratively training a process machine-learning model using the process training data as a function of previous iterations (See paragraphs 34–35, in view of paragraph 25, wherein the machine learning engine is trained and updated based on feedback, and see paragraphs 24 and 39, wherein the machine learning engine may include more than one machine learning model); and
generating the directed process using the trained process machine-learning model (See paragraph 37, wherein the machine learning engine generates a career progression pathway).
Chetlur discloses a system directed to recommending opportunities for career progression. Yin discloses a system directed to planning a career progression using a machine learning model. Each reference discloses a system directed to evaluating and generating a career progression plan. The technique of training a machine learning model to generate a directed process is applicable to the system of Chetlur as they each share characteristics and capabilities; namely, they are directed to evaluating and generating a career progression plan.
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Yin to the teachings of Chetlur would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate career progression planning into similar systems. Further, applying the generation of a directed process using machine learning techniques to Chetlur would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results. Chetlur and Yin do not expressly disclose the remaining claim elements.
Cahalane discloses functionality to generate a skill factor datum as a function of the novelty datum (See paragraph 52, wherein skills are associated with career progression vectors and career path nodes) by: identifying a first skill factor datum as a function of the novelty datum (See paragraph 52, wherein skills and skill-related data are identified with respect to career progression vectors in a career path; see also paragraphs 15 and 55–56);
refining the first skill factor datum by classifying the novelty datum to the first factor datum (See paragraph 52, wherein skills and skill-related data are associated with career progression vectors along sequential or chronological nodes in a career path; see also paragraphs 15 and 55–56) and aggregating the first skill factor datum with at least a second skill factor datum based on the classification (See paragraph 52, wherein skills are associated with a skill group according to career progression vectors along sequential or chronological nodes in a career path; see also paragraphs 15 and 55–56); and
classifying the aggregated first skill factor datum and at least the second skill factor datum to data describing a frequency of implementation of the first skill factor datum and the second skill factor datum to the target (See paragraphs 55–56, in view of paragraphs 15 and 52, wherein vectors or skill groups are associated with a target path based on frequency of occurrence).
As disclosed above, Chetlur discloses a system directed to recommending opportunities for career progression, and Yin discloses a system directed to planning a career progression using a machine learning model. Cahalane discloses a system directed to managing skill acquisition with respect to a target career path. Each reference discloses a system directed to evaluating and generating a career progression plan. The technique of mapping skill aggregation and frequency is applicable to the systems of Chetlur and Yin as they each share characteristics and capabilities; namely, they are directed to evaluating and generating a career progression plan.
One of ordinary skill in the art would have recognized that applying the known technique of Cahalane would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Cahalane to the teachings of Chetlur and Yin would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate career progression planning into similar systems. Further, applying skill aggregation and frequency mapping to Chetlur and Yin would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claims 2 and 12: Chetlur does not expressly disclose the elements of claim 2.
Yin discloses the element to classify the commitment datum to match the target datum between a minimum value and a maximum value of the target datum (See paragraphs 56–57, in view of paragraphs 30–31, wherein commitment data is evaluated with respect to minimum productivity and performance metrics associated with a target career, and wherein the machine learning model is trained using feature vectors that classify progression pathway features); and
identify the novelty datum as a function of classifying the commitment datum (See paragraphs 56–57 and 66, wherein career progression pathway steps are generated with respect to the classified features; see also paragraph 66).
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 3 and 13: Chetlur does not expressly disclose the elements of claim 3.
Yin discloses the element to generate an extended target datum as a function of the target datum and the skill factor datum (See paragraphs 58 and 60, wherein interim objectives and positions are identified, and wherein the destination target is an extended target in view of interim target objectives).
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 4 and 14: Chetlur discloses the apparatus of claim 3, wherein the memory contains instructions further configuring the at least a processor to: determine the at least an obstacle datum as a function of the target datum (See paragraph 34, in view of paragraph 29, wherein time constraint data is determined from both events and users, and wherein time constraints are considered with respect to each path and associated skills); and
generate the directed process as a function of the at least an obstacle datum and the target datum (See paragraph 34, wherein an optimal career path is generated based on time constraints and the career target). Chetlur does not expressly disclose the remaining claim elements.
Yin discloses an extended target datum (See paragraphs 58 and 60, wherein interim objectives and positions are identified, and wherein the destination target is an extended target in view of interim target objectives).
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 5 and 15: Chetlur does not expressly disclose the elements of claim 5.
Yin discloses elements to generate target training data, wherein the target training data comprises correlations between exemplary commitment datums and exemplary target datums (See paragraphs 34–35 and 59, in view of paragraph 66, wherein training data includes both employee and job data, and wherein the machine learning model is trained to evaluate tradeoff situations that consider employment histories; see also paragraphs 30 and 41);
train a target machine-learning model using the target training data (See paragraphs 34–35, in view of paragraph 25, wherein the machine learning engine is trained and updated based on feedback, and see paragraphs 24 and 39, wherein the machine learning engine may include more than one machine learning model); and
determine the target datum using the target machine-learning model (See paragraph 37, wherein the machine learning engine generates a career progression pathway).
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 9 and 19: Chetlur discloses the apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to: determine a portion of the novelty datum as a function of a plurality of attributes; and generate a first skill factor as a function of the portion (See paragraph 34, in view of paragraph 25, wherein the career progression plan is generated based on skills, and wherein each event includes metadata defining the skill acquired and the skill level).
Claims 10 and 20: Chetlur discloses the apparatus of claim 9, wherein the memory contains instructions further configuring the at least a processor to receive at least one user-input datum, wherein the at least one user-input datum comprises a preferred attribute of the plurality of attributes (See paragraph 33, wherein the user inputs preferences, and wherein the preferences include event attribute preferences).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chetlur et al. (U.S. 2017/0109850) in view of Yin (U.S. 2023/0068203), and in further view of CAHALANE et al. (U.S. 2021/0256644) and Pasqualis et al. (U.S. 2013/0260351).
Claims 8 and 18: As disclosed above, Chetlur, Yin, and Cahalane disclose the elements of claim 1.
Chetlur discloses the apparatus of claim 1, wherein the memory contains instructions further configuring the at least a processor to: receive at least one user-input datum (See paragraphs 32–33, wherein the user inputs preferences). Although Chetlur discloses display functionality (See FIG. 5 and paragraph 5), Chetlur does not expressly disclose the remaining claim elements.
Yin discloses an element to classify the at least one user-input datum as a function of the target datum (See paragraphs 25 and 31, wherein features are classified for model training, and wherein features include preference features); see also paragraphs 41 and 66).
One of ordinary skill in the art would have recognized that applying the known technique of Yin would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Chetlur, Yin, and Cahalane do not expressly disclose the remaining claim elements.
Pasqualis discloses an element to display a first skill factor datum and a second skill factor datum of the skill factor datum hierarchically based at least a user-input datum (See FIG. 2 and paragraphs 30 and 35, in view of paragraph 17, wherein a skill hierarchy is displayed according to user inputs).
As disclosed above, Chetlur discloses a system directed to recommending opportunities for career progression, Yin discloses a system directed to planning a career progression using a machine learning model, and Cahalane discloses a system directed to managing skill acquisition with respect to a target career path. Pasqualis discloses a system directed to sequencing lessons to optimize skill progression. Each reference discloses a system directed to evaluating and generating a progression plan. The technique of utilizing a skill hierarchy is applicable to the systems of Chetlur, Yin, and Cahalane as they each share characteristics and capabilities; namely, they are directed to evaluating and generating a progression plan.
One of ordinary skill in the art would have recognized that applying the known technique of Pasqualis would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Pasqualis to the teachings of Chetlur, Yin, and Cahalane would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate progression planning into similar systems. Further, applying a skill hierarchy to Chetlur, Yin, and Cahalane would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Zhang et al. (U.S. 2018/0121880) discloses a system directed to inferring skills associated with a job using skill frequency scores.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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