DETAILED ACTION
This final Office action is responsive to amendments filed November 7th, 2025. Claims 1, 8, 10, and 15 have been amended. Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/24/25, 8/13/25, and 12/26/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 11/7/25 have been fully considered but they are not persuasive.
On page 11 of the provided remarks, Applicant argues that the amended claims represent statutory subject matter. Specifically, Applicant argues “the claims recite a practical application of the alleged abstract idea by improving the technical field of task facilitation.” Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Examiner further asserts, addressing the amended claim limitations, that the claimed “technical field of task facilitation” is itself an abstract idea in the form of Certain Methods of Organizing Human Activity in the form of managing personal behavior as well as Mental Processes. The amended, “generating one or more labels associated with characteristics of the proposal based on the feedback, wherein the one or more labels are associated with the first user, and wherein the one or more labels indicate a contribution of the characteristics on the polarity; determining that a percentage of the training data corresponding to the first user is greater than a threshold, wherein determining that the percentage of the training data corresponding to the first user is greater than the threshold is based in part on the one or more labels;” are mental evaluations of the human mind directed to the abstract idea of Mental Process. Further, the amended, “modifying the training data by removing a portion of the training data associated with the second user” and “retraining the trained machine-learning model using the modified training data to customize the trained machine-learning model relative to the first user” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019). Additionally, the amended, “generating a new proposal using the trained machine-learning model and the modified training data associated with the first user, wherein the new proposal is a discrete implementation of a second task that is particularly tailored to the first user, and wherein the new proposal is configured to distribute instructions to computing devices associated with a different third-party service provider” further describes the abstract idea as the new proposal distributes instructions to a different service provider which is Certain Methods of Organizing Human Activity. Therefore, the 35 U.S.C. 101 rejection is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 11-12, filed 11/07/25, with respect to claims 1-20 have been fully considered and are persuasive. The 35 USC 103 rejection of 06/06/25 has been withdrawn.
Claim Objections
Claims 2, 9, and 16 are objected to because of the following informalities: the limitation beginning "identifying a first address" recites "the first member" which lacks antecedent basis and should recite "the first user". Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Step 1: Independent claims 1 (method), 8 (system), and 15 (non-transitory computer-readable storage medium) and dependent claims 2-7, 9-14, and 16-20, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a method (i.e. process), claim 8 is directed to a system (i.e. machine), and claim 15 is directed to a non-transitory computer-readable storage medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite generating a proposal using a trained machine- learning model and training data associated with a first user and a second user, wherein the proposal is a discrete implementation of a task for the first user, and wherein the proposal is configured to distribute instructions to a computing device associated with a third-party service provider that are for executing the task; generating a first interface configured to translate commands of a task- facilitation service into commands native to the third-party service provider based on an identification of the computing device, wherein the commands native to the third-party service provider are configured to cause the third-party service provider to execute at least a portion of the task; transmitting a first communication including the commands native to the third-party service provider; receiving feedback associated with the first interface and the first communication, wherein the feedback includes a polarity associated with execution of the task and execution information, wherein the polarity is a representation of an opinion of the first user with respect to the third-party service provider; generating one or more labels associated with characteristics of the proposal based on the feedback, wherein the one or more labels are associated with the first user, and wherein the one or more labels indicate a contribution of the characteristics on the polarity; determining that a percentage of the training data corresponding to the first user is greater than a threshold, wherein determining that the percentage of the training data corresponding to the first user is greater than the threshold is based in part on the one or more labels; modifying the training data by removing a portion of the training data associated with the second user, wherein the training data is modified in response to determining that the percentage of the training data corresponding to the first user is greater than the threshold; retraining the trained machine-learning model using the modified training data, to customize the trained machine-learning model relative to the first user; and generating a new proposal using the trained machine-learning model and the modified training data associated with the first user, wherein the new proposal is a discrete implementation of a second task that is particularly tailored to the first user, and wherein the new proposal is configured to distribute instructions to computing devices associated with a different third-party service provider. (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite to the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating a proposal of tasks customized for a user; generating an interface to translate commands; generating one or more labels associated with characteristics of the proposal based on the feedback; causing the execution of tasks; and generating a new proposal of tasks for service providers, which is managing personal behavior. The Applicant’s claimed limitations are generating proposals causing the execution of tasks by service providers, which recite the abstract idea of Certain Methods of Organizing Human Activity.
The steps/function disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are generating a proposal of tasks customized for a user; generating an interface to translate commands; generating one or more labels associated with characteristics of the proposal based on the feedback; determining a percentage of training data corresponding to the first user is greater than a threshold; causing third-party resources to execute tasks; and generating a new proposal of tasks customized to service providers, which can be performed as functions of the human mind including observation, judgment, and evaluation. The Applicant’s claimed limitations are generating proposals and causing the execution of tasks, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-4, 6-7, 9-11, 13-14, 16-18, and 20 further narrow the abstract idea and recite the definition of resource allocation mapping; identifying a first interface configured to translate commands of a task-facilitation service into commands native to the one or more third-party service providers; by defining the interface at runtime and identifying a set of interfaces configured to translate commands; defining a sequence of communications configured to facilitate execution of the proposal, the sequence of communications including the first communication; and identifying one or more third interfaces configured to present the data indicating of the status of the task to the task-facilitation service. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include managing personal behavior as well as mental process. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas. Dependent claims 5, 12, and 19 will be addressed in Prong 2 analysis below.
Step 2A Prong 2: In this application, even if not directed toward the abstract idea, the above “transmitting a first communication including the commands native to the one or more third-party service providers; receiving feedback associated with the first interface and the first communication, wherein the feedback includes a polarity associated with the execution of the task and execution information, wherein the polarity is a representation of an opinion of the first user with respect to the third-party service provider” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “a computing device; a third-party service provider; a computing device associated with a third-party service provider; a first interface; a second interface; a different third-party service provider; a device; a set of interfaces; A system comprising: one or more processors; and a non-transitory computer-readable storage medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations; A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
In addition, dependent claims 2-4, 6-7, 9-11, 13-14, 16-18, and 20 further narrow the abstract idea and dependent claims 5, 7, 12, 14, and 19 additionally recite “receiving, from the third-party service provider, the first interface” and “receiving, from the third-party service provider, data indicative of a status of task, the data being received via a second interface” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and the claimed “third-party service provider” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 1, 8, and 15 recite the following limitation, “using a trained machine-learning model and training data associated with a user”; “modifying the training data by removing a portion of the training data associated with the second user”; “retraining the trained machine-learning model using the modified training data to customize the trained machine-learning model relative to the first user”; and “using the trained machine-learning model and the modified training data associated with the user”. The “trained machine learning model”; “training data”; “retraining the trained machine learning model”; and “modified training data” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “a computing device; a third-party service provider; a computing device associated with a third-party service provider; a first interface; a second interface; a different third-party service provider; a device; a set of interfaces; A system comprising: one or more processors; and a non-transitory computer-readable storage medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations; A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 1-7; system claims 8-14; and non-transitory computer-readable storage medium claims 15-20 recite “a computing device; a third-party service provider; a computing device associated with a third-party service provider; a first interface; a second interface; a different third-party service provider; a device; a set of interfaces; A system comprising: one or more processors; and a non-transitory computer-readable storage medium that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform operations; A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0294-295, 0302, and Figures 1-7 & 9-10. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “transmitting a first communication including the commands native to the one or more third-party service providers; receiving feedback associated with the first interface and the first communication, wherein the feedback includes a polarity associated with the execution of the task and execution information, wherein the polarity is a representation of an opinion of the first user with respect to the third-party service provider” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” of independent claims 1, 8, and 15 is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim.
In addition, claims 2-4, 6-7, 9-11, 13-14, 16-18, and 20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 5, 7, 12, 14, and 19 additionally recite “receiving, from the third-party service provider, the first interface” and “receiving, from the third-party service provider, data indicative of a status of task, the data being received via a second interface” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “third-party service provider” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
Claims 1-20 are allowable over the prior art of record. Although Brown et al. and Catalfano et al. and Blassin et al. collectively teach generating a proposal using a trained machine-learning model and training data associated with a user, wherein the proposal is a discrete implementation of a task customized for the user, and wherein the proposal is configured to distribute instructions to a computing device associated with amachine-learning model will generate proposals associated with the third-party service provider according to the polarity; and generating a new proposal using the trained machine-learning model and the modified training data associated with the user, wherein the new proposal is a discrete implementation of a second task, and wherein the new proposal is configured to distribute instructions to computing devices associated with a different third-party service provider [See Office Action mailed 06/06/25 for prior art citations pertinent to the above-noted subject matter], the prior art of record does not teach generating one or more labels associated with characteristics of the proposal based on the feedback, wherein the one or more labels are associated with the first user, and wherein the one or more labels indicate a contribution of the characteristics on the polarity; determining that a percentage of the training data corresponding to the first user is greater than a threshold, wherein determining that the percentage of the training data corresponding to the first user is greater than the threshold is based in part on the one or more labels; modifying the training data by removing a portion of the training data associated with the second user associated with the user based on execution of the portion of the task and the feedback;, wherein the training data is modified in response to determining that the percentage of the training data corresponding to the first user is greater than the threshold; retraining the trained machine-learning model using the modified training data to customize the trained machine-learning model relative to the first user; and generating a new proposal using the trained machine-learning model and the modified training data associated with the first user, wherein the new proposal is a discrete implementation of a second task that is particularly tailored to the first user, and wherein the new proposal is configured to distribute instructions to computing devices associated with a different third-party service provider, as recited in amended independent claim 1, thus rendering claims 1-20 as allowable over the prior art.
These claims are not allowed, however, because claims 1-20 stand rejected under 35 USC 101 and claims 2, 9, and 16 stand objected, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kolodner (U.S 2020/0210939 A1) discloses systems, methods, and devices for providing services such as services provided by professionals or experts in the field;
Liu et al. (CN 110728455 A) discloses a service processing method, a service processing device, storage medium and electronic device, belonging to the information processing technical field;
Kanj, Sawsan, et al. "Editing training data for multi-label classification with the k-nearest neighbor rule." Pattern Analysis and Applications 19.1 (2016): 145-161.
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 KRISTIN ELIZABETH GAVIN whose telephone number is (571)270-7019. The examiner can normally be reached M-F 7:30-4:30 PM EST.
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624