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 .
Status of the Application
This final office action is in response to the arguments filed 4/16/2026. Claims 1, 3, 4, 6, 7, 8,
12, 13, 14, 15, 16, 17, and 20 have been amended. Claims 18-19 have been cancelled. Claims 21 and 22 have been added. Claims 1-17 and 20-22 are currently pending and have been examined below.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 22 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 22 recites the limitations: “reject output generated by the language model that does not correspond to the predetermined action schema; and generate a revised prompt that constrains inferences by the language model to the executable subset, thereby modifying operation of the language model to reduce invalid outputs during action execution.” While paragraph [0073] of the published specification recites that the prompt constructor can obtain a query and metadata and generate a prompt and paragraph [0056] recites “create logical inferences between various complex structures in the data set to generate coherent outputs for prompts input into the models,” Examiner thoroughly reviewed Applicant’s specification but was unable to find written description support for rejecting output generated by the language model that does not correspond to a predetermined action scheme and generating a revised prompt that constrains inferences.
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 1-17 and 20-22 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, 12, and 20 recite the limitations “wherein the list of compatible actions correspond to the first account identifier and include a payroll function to execute an electronic transaction, a personnel record management function to modify an object data structure, and a time management module to modify electronic timekeeping data.” It is unclear how the list of compatible actions can include a time management module to modify electronic timekeeping data, as a module cannot be an action. Examiner notes that Applicant’s specification does not recite the term “module” or “timekeeping” but does recite that compatible actions include “approving timesheets” and “request time off.” For purposes of applying prior art Examiner will interpret the “time management module to modify electronic timekeeping data” as reciting the compatible action of modifying electronic timekeeping data such as approving timesheets. Claims, 2-11, 13-17, and 21-22 are rejected by virtue of their dependency on claims 1, 12, and 20.
Claim Rejections – 35 U.S.C. 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 1-17 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, Claim 12 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
identifying, a request to execute an action associated with a first account identifier of a client;
selecting, a prompt that corresponds to the action, the prompt structured as text including one or more fields, the prompt identifying a list of compatible actions corresponding to actions that the client is capable of executing wherein the list of compatible actions correspond to the first account identifier, and include actions include a payroll function to execute a transaction, personnel record management function to modify an object data structure, and modify timekeeping data;
embedding content of the first account identifier into one or more of the fields of the prompt the content including at least a portion of the text or a least a portion of a metadata;
obtaining a response to the prompt that indicates a recommended action and a second account identifier associated with the recommended action;
validating that the recommended action corresponds to at least one of the compatible actions, and the second account identifier corresponds to the first account identifier; and
executing the recommended action for the first account identifier in response to the validation of the recommended action and the second account identifier.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations recite recommending actions compatible with users accounts (i.e., payroll and timekeeping functions) based on user requests. For additional context, paragraph [0065] of Applicant’s specification recites a number of human resource related actions where “[t]he list of compatible actions can include promoting, terminating, hiring, searching, updating, approving payroll, adjusting salary, compensation, activity log, teams, delegated approval, career profile, additional information, documents, accommodations, issuing bonus, approving timesheets, request time off, schedule shifts, conduct performance review, assign training, set goals, enroll in benefits, update benefits, review benefits usage, viewing organization information or a combination thereof.” Recommending actions compatible with user accounts (e.g., executing payroll transactions or modifying timekeeping data as claimed) qualifies as both business relations and managing personal behavior and therefore falls within the certain methods of organizing human activities group of abstract ideas. Additionally, the limitations also fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Specifically, a human being can mentally (or with pen and paper) identify a request to execute an action associated with a first account identifier of a client; selecting a prompt that corresponds to the action; embedding content of the first account identifier into the prompt; obtaining a response to the prompt that indicates a recommended action and a second account identifier associated with the recommended action; validating that the recommended action corresponds to at least one of the compatible actions, and the second account identifier corresponds to the first account identifier; and executing the recommended action for the first account identifier in response to the validation of the recommended action and the second account identifier. Accordingly claim 12 recites an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 12 recites the additional limitations of:
one or more processors [to perform the steps of the method];
a client system;
[the actions, transactions, and timekeeping data are] electronic;
a time management module [to modify electronic timekeeping data];
provide, to language a model trained with machine learning, the prompt embedded with the content; and
[obtain] from the model [a response to the prompt].
The additional limitations when viewed individually and when viewed as an ordered combination with the abstract limitations, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically:
The one or more processors [to perform the steps of the method] are recited at a high level of generality and merely generally link the abstract idea to a particular technological environment or merely utilize a generic computer as a tool to perform the abstract idea. Further, the client system is recited at a high level of generality and is not positively recited. The broadest reasonable interpretation of the claim merely requires that the account identifier is of a client system. At most, the client system only generally links the abstract idea to a particular technological environment (i.e. a generic computer of the client).
With respect to [the actions, transactions, and timekeeping data are] electronic and a time management module [to modify electronic timekeeping data], Examiner notes that these limitations are recited at a high level of generality. Reciting that the compatible actions relate to electronic actions including electronic transactions and electronic timekeeping data merely generally links the abstract idea to a particular technological environment (i.e., a computer to execute a transaction or modify timekeeping data). Further with respect to the time management module, Examiner notes that the specification does not recite any modules as noted in the 35 U.S.C. 112 rejection above. Under the broadest reasonable interpretation, Examiner interprets the time management module as generic software and/or hardware to adjust timekeeping data. At this level of generally the time management module merely generally links the abstract idea to a particular technological environment.
With respect to the limitations provide, to a language model trained with machine learning, the prompt embedded with the content and [obtain] from the language model [a response to the prompt], Examiner notes that these limitations are recited at a high level of generality. Applicant’s specification paragraph [0005] recites “a model trained with machine learning (e.g., a large language model).” Further, paragraph [0054] recites “The model can include one or more of: neural networks, decision-making models, linear regression models, natural language models, random forests, classification models, generative artificial intelligence models, reinforcement learning models, clustering models, neighbor models, decision trees, probabilistic models, classifier models, any other type and form of models, or a combination thereof.” Claim 12 does not specify what type of model is used (other than that it is a generic language model) or how a particular model is trained beyond specifying at a high level that the model is “trained with machine learning.” The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). At this level of generality, the recitation of claim limitations that attempt to cover any solution to an identified problem (i.e. training a model to indicate a recommended action in response to a prompt) merely generally links the abstract idea to a technical field/environment, namely a generic computing environment applying generic machine learning. Further, Examiner notes that Recentive Analytics, Inc. v. Fox Corp. et al., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) recently held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Here, Examiner takes the position that utilizing a generic machine learning to train a language model to indicate a recommended action in response to a prompt is the mere application of generic machine learning to a new data environment. Because no improvement to the underlying machine learning models is disclosed, this limitation does not integrate the abstract idea into a practical application.
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and only generally link the use of the judicial exception to a particular technological environment. Thus, the same analysis applies here in 2B, i.e., mere instructions to apply an exception is a particular technological environment cannot provide an inventive concept.
Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, independent system claim 1 and independent non-transitory computer system claim 20 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 12. The additional limitations in claim 1 (i.e., one or more processors coupled with memory) and the additional limitations of claim 20 (i.e., a non-transitory computer readable medium and processors) add nothing of substance to the underlying abstract idea. The components are merely providing a particular technological environment to implement the abstract idea.
Dependent claims 2-11, 13-17 and 21-22 are rejected on a similar rational to the claims upon which they depend. Specifically, each of the dependent claims merely further narrows the abstract idea or generally links the abstract idea to a particular technological environment.
Response to Arguments/Amendments
35 U.S.C. 103
Applicant's arguments, see pages 14-17, filed 4/16/2026, with respect to the rejection(s) of claims 1-17 and 20-22 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn.
35 U.S.C. 101
Applicant's arguments, see pages 9-14, filed 4/16/2026, with respect to the rejection(s) of claims 1-17 and 20-22 under 35 U.S.C. 101 have been fully considered but are not persuasive.
First, Applicant argues that:
None of claims 1-20 specify a managing personal behavior or relationships or interactions between people, as the features of claims 1-20 cannot simply be performance or interactions between people, contrary to the assertion on pages 3-4 of the Office Action (remarks page 11).
Examiner respectfully disagrees and replies that the limitations recite recommending actions compatible with user accounts based on user requests. For example, paragraph [0065] of Applicant’s specification recites a number of human resource related actions where “[t]he list of compatible actions can include promoting, terminating, hiring, searching, updating, approving payroll, adjusting salary, compensation, activity log, teams, delegated approval, career profile, additional information, documents, accommodations, issuing bonus, approving timesheets, request time off, schedule shifts, conduct performance review, assign training, set goals, enroll in benefits, update benefits, review benefits usage, viewing organization information or a combination thereof.” Recommending actions compatible with user accounts (e.g., executing payroll transactions or modifying timekeeping data as claimed) qualifies as both business relations and managing personal behavior and therefore falls within the certain methods of organizing human activities group of abstract ideas. Examiner adds that certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping.
Second, Applicant argues that:
the claimed technology cannot reasonably be performed by a person or between people. Applicant submits that a human could not practically perform these operations mentally or with pen and paper. In particular, the claims require selecting a structured text prompt whose fields identify compatible electronic actions for the first account identifier, including payroll, personnel record management, and time management functions; embedding content associated with the first account identifier into one or more of the fields of the prompt, including the text or the metadata; validating both that the recommended action is among the compatible actions and that the second account identifier corresponds to the first account identifier; and executing the recommended action for the first account identifier in response to the validation of the recommended action and the second account identifier. These are computer-implemented control operations involving machine-learning interaction and electronic system execution, not mental judgments or human activity (e.g., executing an electronic transaction and modifying electronic data structures/timekeeping data). Therefore, the claims do not recite a mental process (or other enumerated abstract idea) and are not directed to a judicial exception (remarks page 12).
Examiner respectfully disagrees and replies that a human being can execute many recommended actions with pen and paper (e.g., modifying timekeeping data by approving time off). Therefore the claim recites an abstract idea. The use of an electronic system and machine learning are additional elements recited at a high level of generality (e.g., a generic computer used to modify timekeeping data and a generic machine learning model to receive a prompt and provide a response) that do not integrate the abstract idea into a practical application.
Third, Applicant argues that:
The claims as amended provide a practical application and are patent eligible under Step 2A, Prong Two. As clarified in the USPTO's precedential Appeals Review Panel decision in Ex Parte Desjardins and the corresponding revisions to MPEP §2106.04(d), claims that involve mathematical or analytical operations remain patent-eligible where the claims, as a whole, are directed to improvements in the operation of a computer or technical system itself, rather than to the abstract operations in isolation. The claims recite a control architecture in which machine-learning output is constrained, validated against system capabilities and account-specific metadata, and used to govern actual system execution, including preventing incompatible actions and altering subsequent processing behavior. As confirmed in Desjardins, Enfish, McRO, and the USPTO's PEG Examples (e.g., Examples 39, 40, and 47), the claims wherein do not merely use a model to generate information, but instead control how the system itself operates during execution, thereby improving reliability, safety, and resource utilization. Accordingly, the claims are not "directed to" an abstract idea and are patent-eligible at Step 2A without resort to Step 2B (remarks page 13).
Examiner respectfully disagrees and replies that the validation limitation is recited at a high level of generality. As noted above, the list of compatible actions includes actions such as modifying timekeeping data (e.g. approving time off). A human being can mentally validate a recommended action such as approving time off is a compatible action for a particular employee ID and execute the action of approving time off. The fact that a computer is used to automate the verification process at a high level of generality merely generally links the abstract idea to a particular technological environment or utilizes a computer as a tool to perform the abstract idea.
Finally, Applicant argues that:
Indeed The Examiner's characterization of the claims as "recommending actions compatible with user accounts" reflects an impermissible oversimplification that abstracts away the claimed technical features. The claims are not directed to human resource decisions or business rules, but to a machine-implemented action control framework that constrains, validates, and executes system actions based on machine-learning outputs and system capabilities. The claims describe how a computing system safely converts natural-language input into executable operations, and are not directed to organizing or managing human activity. Unlike the claims at issue in Recentive Analytics, the present claims do not merely apply generic machine-learning to new data, but instead expressly constrain, validate, and govern how machine-learning outputs are used to control executable system behavior, including rejecting invalid outputs and modifying subsequent processing (remarks page 14).
Examiner respectfully disagrees and replies that the validation limitation (which constrains the executed actions) is recited at a high level of generality. As noted above, the list of compatible actions includes actions such as modifying timekeeping data (e.g. approving time off). A human being can mentally validate a recommended action such as approving time off is a compatible action for a particular employee ID and execute the action of approving time off. The fact that a computer is used to automate the verification process at a high level of generality merely generally links the abstract idea to a particular technological environment or utilizes a computer as a tool to perform the abstract idea. Moreover, the use of machine learning is recited at a high level of generality. Applicant’s specification paragraph [0005] recites “a model trained with machine learning (e.g., a large language model).” Further, paragraph [0054] recites “The model can include one or more of: neural networks, decision-making models, linear regression models, natural language models, random forests, classification models, generative artificial intelligence models, reinforcement learning models, clustering models, neighbor models, decision trees, probabilistic models, classifier models, any other type and form of models, or a combination thereof.” Claim 12 does not specify what type of model is used (other than that it is a generic language model) or how a particular model is trained beyond specifying at a high level that the model is “trained with machine learning.” The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). At this level of generality, the recitation of claim limitations that attempt to cover any solution to an identified problem (i.e. training a model to indicate a recommended action in response to a prompt) merely generally links the abstract idea to a technical field/environment, namely a generic computing environment applying generic machine learning. Further, Examiner notes that Recentive Analytics, Inc. v. Fox Corp. et al., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) recently held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Here, Examiner takes the position that utilizing a generic machine learning to train a model to indicate a recommended action in response to a prompt is the mere application of generic machine learning to a new data environment. The fact that the prompt includes compatible actions and that a subsequent validation step is performed to ensure the recommendation is compatible with the list of compatible actions does not change how the underlying model operates. Because no improvement to the underlying machine learning models is disclosed, this limitation does not integrate the abstract idea into a practical application.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publication Number 20220229832 (“Li”) discloses receiving a user request from a user, identifying user query intent and generating an action for the query
US Patent Application Publication Number 20250217769 (“Kumar”) discloses a particular engineered prompt template can be selected based on a desired task for which output of a generative output engine may be useful
US Patent Application Publication Number 20250005260 (“Mansour”) discloses creating a prompt based on a user query where the prompt includes a list of permitted actions and one or more fields that can be populated with data
US Patent Application Publication Number 20240013217 (“Schoen”) discloses verifying consistency between actions by comparing a consistency score to a predetermined threshold
US Patent Application Publication Number 20240168928 (“Pfante”) discloses generating a vector for a first field and identifying a plurality of clusters based on score similarities
However, the prior art fails to teach each and every limitation as claimed, and would involve
hindsight reasoning to arrive at the claimed invention. Therefore, the claims are considered allowable
over the prior art.
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 ALLAN J WOODWORTH, II whose telephone number is (571)272-6904. The examiner can normally be reached Mon-Fri 9:00-5:30.
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, Ilana Spar can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
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