DETAILED ACTION
The following is a Final Office Action in response to communications filed December 31, 2025. Claims 1–2, 4, 9, 11–12, 14, and 19 are amended, and claims 6–7 and 16–17 are canceled. Currently, claims 1–5, 8–15, and 18–20 are pending.
Response to Amendment/Argument
Applicant’s Response is not sufficient to overcome each previous rejection of claims 2, 4, 9–10, 12, 14, and 19–20 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, 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 asserts that the newly amended elements of claims 1 and 11 do not recite mental processes or certain methods of organizing human activity. Although Examiner agrees that the amended elements for “iteratively training” and “using the trained obstacle machine-learning model” do not recite an abstract idea under Step 2A Prong One, Examiner maintains that “generating obstacle training data” and “determining the obstacle datum” recite mental processes and certain methods of organizing human activity for the same reasons as previously stated. Further, although Applicant argues that the “training” element does not recite an abstract idea, Examiner notes that Applicant has not provided any arguments indicating why “generating obstacle training data” or “determining the obstacle datum” do not recite an abstract idea. As a result, Applicant’s remarks are not persuasive.
With respect to Step 2A Prong Two, Applicant asserts that the elements reciting “the obstacle machine-learning model” and “the process machine-learning model” integrate the abstract idea into a practical application because the learning models are stacked, such that “the output of the first machine learning model is an input to the second machine learning model.” Examiner disagrees. In exemplary claim 1, the obstacle machine-learning model is used to determine obstacle datum. However, obstacle datum is not used as either an input to the process machine-learning model. Instead, the process machine-learning model generates a directed process without any specified inputs and is trained on “correlations between exemplary obstacle datums, exemplary efficiency data and exemplary directed processes,” such that even the training data is not directly obtained from the obstacle machine-learning model. As a result, the machine learning models are standalone rather than stacked, and Applicant’s remarks are not commensurate with the scope of the claims.
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. As a result, Applicant’s remarks are not persuasive.
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 persuasive. More particularly, Applicant asserts that the references of record do not disclose the amended elements of claims 1 and 11. However, Applicant’s assertions do not include any argument or rationale underpinning that would support Applicant’s assertions. As a result, Applicant’s remarks amount to no more than a general allegation of patentability, such that the remarks do not comply with the requirements set forth under 37 CFR 1.111(b).
Accordingly, Applicant’s remarks are not persuasive, and the rejection of record is maintained.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claims 1 and 11 recite an element to “determine at least at obstacle datum … which comprises … determining the obstacle datum”. Claims 1 and 11 subsequently recite an element to “generate a directed process as a function of the at least an obstacle datum”. In view of the above, Examiner recommends amending the claims to recite an element to “determine at least at obstacle datum … which comprises … determining the at least an obstacle datum” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Appropriate correction is required.
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.
Claim 12 is 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.
Claim 12 recites “the classification” in the element for “identifying”. There is insufficient antecedent basis for “the classification” in the claim.
For purposes of examination, claim 12 is interpreted as reciting “identifying, using the at least a processor the novelty datum as a function of
Accordingly, claim 12 is 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”; “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 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).
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);
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 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.
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 Pasqualis et al. (U.S. 2013/0260351).
Claims 8 and 18: As disclosed above, Chetlur and Yin 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 and Yin 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, and Yin discloses a system directed to planning a career progression using a machine learning model. 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 and Yin 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 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 progression planning into similar systems. Further, applying a skill hierarchy 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.
Conclusion
THIS ACTION IS MADE FINAL. 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 WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623