CTFR 18/359,930 CTFR 92146 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. Notice to Applicant The following is a Final Office Action for Application Serial Number: 18/359,930, filed on July 27, 2023. In response to Examiner’s Non-Final Rejection dated December 09, 2025, Applicant on February 27, 2026, amended claims 1-20 and added new claims 21. Claims 1-21 are pending in this application and have been rejected below. Response to Amendment Applicant's amendments are acknowledged. Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection. The 35 U.S.C. § 112 rejections of claims 1 and 9, are hereby withdrawn pursuant to Applicant’s amendments to claims 1 and 9. The 35 U.S.C. § 102 rejection of claims 1, 5, 9, 13 and 17 are withdrawn in light of Applicant’s amendments. The 35 U.S.C. § 103 rejections of claims 2-4, 6-8, 10-12, 14-16 and 18-20 are hereby withdrawn pursuant to Applicants amendments. Response to Arguments Applicants Remarks regarding the 35 U.S.C. 101 rejection is acknowledged. Examiner respectfully finds Applicants arguments to be moot because they are directed to amended claim language and the claims have been heavily amended. Specifically, the independent claims have been changed from claims 1, 9, 17 to now claims 8, 16 and 17, making the previous §101 analysis invalid. Please see the 35 U.S.C. 101 rejection for an update analysis and rationale. 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. Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter. Claims 1-8 and 21 are directed towards a non-transitory computer readable medium, claims 9-16 are directed towards a method and claims 17-20 are directed towards a system, which are among the statutory categories of invention. Step 2A – Prong One: The claims recite an abstract idea. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite filter tasks using machine learning to analyze user qualifications. Claim 8 recites limitations directed to an abstract idea based on certain methods of organizing human activity. Specifically, identifying a first set of task parameters for a first task, wherein the first set of task parameters includes a first set of user qualifications required to perform the first task; comparing the first set of user qualifications to a plurality of sets of user qualifications associated with a plurality of users; and modifying the first set of task parameters for the first task to include the one or more alternative user qualifications to generate a second set of task parameters for the first task; receiving first user identification information associated with a first user; responsive to receiving the first user identification information, obtaining a second set of user qualifications associated with the first user; and based on the second set of user qualifications, filtering a set of tasks to generate a first filtered set of tasks that match the second set of user qualifications, at least by: comparing the second set of user qualifications to a plurality of sets of task parameters corresponding to a plurality of tasks, including the second set of task parameters for the first task; and including the first task in the first filtered set of tasks based at least on determining the second set of user qualifications meets the threshold similarity level with the one or more alternative user qualifications of the first task constitutes methods based on managing personal behavior and commercial interactions The recitation of a non-transitory computer readable medium comprising instructions executable by hardware processors, work environment management platform and terminal, does not take the claim out of the certain methods of organizing human activity grouping. Thus the claim recites an abstract idea. Claims 16 and 17 recite certain method of organizing human activity for similar reasons as claim 8. Step 2A – Prong Two: The judicial exception is not integrated into a practical application. The judicial exception is not integrated into a practical application. In particular, claim 8 recites presenting, by the terminal, the first filtered set of tasks responsive to receiving the first user identification information constitutes methods based on business relations, which is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 8 recites a non-transitory computer readable medium comprising instructions executable by hardware processors, work environment management platform and terminal at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 8 recites responsive to determining that the first set of user qualifications does not meet a threshold similarity level with any of the plurality of sets of user qualifications, applying a first machine learning model to the first set of task parameters associated with the first task, wherein the first machine learning model is trained by iteratively adjusting model parameters based on comparing model-generated output labels against labels of training data, wherein the training data comprises historical task assignment records, historical user qualification data of users who performed historical tasks, and success rate data and recidivism rate data associated with historical task completions; responsive to applying the first machine learning model to the first set of task parameters, generating, by the first machine learning model, a first recommendation corresponding to one or more alternative user qualifications for performing the first task. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 8 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method recited in claim 16 and system comprising processors and memory storing instructions executable by the processors in claim 17 also amount to no more than mere instructions to apply the exception using generic computer components; see MPEP 2106.05(f). Thus, the additional elements recited in claims 16 and 17 do not integrate the abstract idea into practical application for similar reasons as claim 8. Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a non-transitory computer readable medium comprising instructions executable by hardware processors, work environment management platform, terminal and system comprising processors and memory storing instructions executable by the processors amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec , 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc. , 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial , 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs. , 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0030]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. § 101 Analysis of the dependent claims. Regarding the dependent claims, dependent claims 3, 11 and 19 recite updating the second set of user qualification information in a database, which is considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claims 1, 9 and 21 recite limitations that are not technological in nature and merely confines the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Claims 3, 11 and 19 recite a database at a high level of generality such that it amounts to no more than generic computer component used as a tool to apply the instructions of the abstract idea; MPEP 2106.05(f). Additionally, claims 7 and 15 recites applying a second machine learning model to a third set of task parameters associated with a particular task and responsive to applying the second machine learning model to the third set of task parameters, generating, by second the machine learning model, second a recommendation corresponding to one or more users for performing the particular task. The general use of a machine learning techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the machine learning models disclosed in aforementioned claims are solely used as a tool to perform the instructions of the abstract idea. Additionally, claims 1-7, 9-15 and 18-21 recite steps that further narrow the abstract idea. Therefore claims 1-7, 9-15 and 18-21 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Distinguishable over the Prior Art of Record The prior art rejections of the amended claims are removed in light of Applicant’s Amendments and Remarks filed February 27, 2026, in particular pg. 34-36 regarding the prior art of record. Examiner analyzed amended claim 8 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine references with a reasonable expectation of success. Reshef et al. (US 20210272045 A1) teaches automatically selecting expert sub-contractors and estimating cost for executing contracted tasks using Machine Learning (ML) models evolving to scale according to dynamic availability of the expert sub-contractors (see par. 0002). Specifically, Reshef discloses applying the ML model(s) for the contracted task's assignment and optimize the performance of the constantly evolving ML model(s). The various contracted task's assignment, contraction and reward phases may be interdependent and interlinked such that optimizing the ML model(s) according to these interdependencies and correlations may therefore significantly enhance the ML model(s)' performance. The improved ML model(s)' performance may be reflected in automatically selecting the most suitable expert sub-contractors, ensuring their commitment in return to the automatically estimated fair reward and further according to the competition model and automatically rewarding them according to their achievement measured by satisfaction of the contractor and optionally adjusted based on system and/or project considerations (see par. 0142), the evolving selection algorithm is further configured to select to the subset one or more expert sub-contractors from one or more other knowledge domains different from the knowledge domain of the contracted task. The selection is based on at least partial match between one or more professional attributes identified for one or more of the expert sub-contractors and the plurality of requirements (see par. 0072) and the evolving selection algorithm may evaluate such expert sub-contractor(s) of the different knowledge domain in case of little or no availability of expert sub-contractors expert in the knowledge domain of the contracted task. The evolving selection algorithm may select the expert sub-contractor(s) of the different knowledge domain for evaluation according to at least a partial match between one or more of their professional attributes with respect to requirements of the contracted task. Based on their at least partial match, the evolving selection algorithm may estimate that the selected expert sub-contractors 104 may successfully execute the contracted task even though it is not in their specific knowledge domain (see par. 0228). However, Reshef, individually or in combination with the prior art of record, does not explicitly teach the combination of claim limitations as recited in independent claim 8. Thus, claim 8 is found to be distinguishable over the prior art. Claims 16 and 17 are distinguishable over the prior art for similar reasons as cited for claim 8. Dependent claims 1-7, 9-15 and 18-21 are distinguishable because they depend on claims 8, 16 and 17 respectively. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dechu et al. (US 20240161025 A1) – Mechanisms are provided for generating, executing, orchestrating, and monitoring an information technology (IT) incident remediation task workflow. An IT incident notification is received and a knowledge data structure associated with an IT resource corresponding to the IT incident is retrieved. IT remediation task(s) are extracted from the knowledge data structure and correlated with skills in a plurality of predetermined skills. Automated tools are correlated with corresponding skills in the plurality of predetermined skills. An IT incident remediation task workflow is generated based on a matching of skills associated with the IT remediation tasks and automation tools. The generated IT incident remediation task workflow is automatically executed on the at least one IT resource. Grover et al. (US 20180173803 A1) - Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method accesses, by a social networking server, a plurality of job applications, with each job application being submitted by a member for a job in a company, the member and the job having a respective industry from a plurality of industries. Semantic analysis of the job applications is performed by a machine-learning program to identify similarity coefficients among the plurality of industries. A job search query is received from a first member, the job search query including a query industry, and the job search query is expanded with industries that are similar to the query industry. The social networking server executes the expanded job search query to generate a plurality of job results. Presentation is provided on a display of one or more of the top job results. Yang et al. (A Hierarchical Tracking Framework With Adaptive Skill Utilization and Experience Sharing) – Active tracking technology holds critical applications in dynamic scenarios ranging from autonomous driving to service robotics. Therefore, developing efficient and robust tracking strategies is crucial. While deep reinforcement learning has emerged as a promising approach for complex tracking demands, most existing methods face significant challenges when handling intricate tasks: manually designed dense reward functions and complex network architectures not only induce overfitting risks but also entail substantial labor and temporal costs. To address this, we propose a hierarchical reinforcement learning based tracking framework, whose core innovation lies in decoupling low-level task skills from high-level decision-making logic through task decomposition. The selection agent adaptively selects task skills, then activates corresponding modules of the capability agent to output actions. This achieves model simplification while maintaining performance and enhancing interpretability. To improve sample utilization efficiency, we propose a cross task experience sharing mechanism that enables skill synergy through shared training samples, further boosting model performance. Experimental results demonstrate that this mechanism effectively enhances the performance of all task skills in the capability agent while ensuring stability. Simulation experiments reveal that compared to the baseline models, our framework exhibits superior task performance and interpretability through adaptive skill switching. 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 Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm. 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, Patty Munson can be reached at (571)270-5396. 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. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CRYSTOL STEWART/Primary Examiner, Art Unit 3624 Application/Control Number: 18/359,930 Page 2 Art Unit: 3624 Application/Control Number: 18/359,930 Page 4 Art Unit: 3624 Application/Control Number: 18/359,930 Page 5 Art Unit: 3624 Application/Control Number: 18/359,930 Page 6 Art Unit: 3624 Application/Control Number: 18/359,930 Page 7 Art Unit: 3624 Application/Control Number: 18/359,930 Page 8 Art Unit: 3624 Application/Control Number: 18/359,930 Page 9 Art Unit: 3624 Application/Control Number: 18/359,930 Page 10 Art Unit: 3624 Application/Control Number: 18/359,930 Page 11 Art Unit: 3624 Application/Control Number: 18/359,930 Page 12 Art Unit: 3624
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