DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
[2] A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7 July 2026 has been entered.
Notice to Applicant
[3] This communication is in response to the Amendment and the Request for Continued Examination (RCE) filed 7 July 2026. It is noted that this application benefits from Foreign Priority to Application Serial No. 2024-100361 (Japan) filed 21 June 2024. Claims 9-14 have been cancelled. Claims 1 and 8 have been amended. Claims 15-26 have been added. Claims 1-8 and 15-26 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
[4] The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 1 as presented by amendment recites the following limitations which are interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
[i] “…means for grasping the business instruction…” which performs recited functions of “…grasps the business instruction by the dialogue…stores a content of the dialogue with the user as a dialogue log in a short-term objective storage unit, and stores text data of the dialogue log in a long-term objective storage unit…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of dialog unit is an AI mechanism that captures and stores dialog). The recited means is reasonably understood to be limited to using the noted structures and programming to gather and store text data.
[ii] “…means for creating the task list…” which performs recited functions of “…creates the task list by decomposing the business instruction into tasks…passes an execution instruction of each task to the means for executing the work, and presents an execution result…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of solution unit is an AI mechanism that decomposes instructions into tasks). The recited means is reasonably understood to be limited to using the noted structures and programming to enable a human user to observe instructions and create tasks to accomplish the functions.
[iii] “…means for executing the work…” which performs recited functions of “…executes the work by using a corresponding data source and passes the execution result to the means for creating the task list…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of execution unit is an aggregate of AI mechanisms that executes the decomposed work items/tasks). The recited means is reasonably understood to be limited to using the noted structures and programming that facilitates a user or mechanism to access stored information to perform tasks.
[iv] “…means for referring to the dialogue log…” which performs recited functions of “…extracts information regarding a predetermined matter set in advance, grasps a context of the dialogue, predicts a content to be dealt with next, stores the content as a summary in a short-term subjective storage unit, stores text data of the content of the summary in a long-term subjective storage unit…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), memory, and server(s) operating in a network environment and see of disclosure of monitoring unit refers to the text information exchanged and refers to the information in real-time). The recited means is reasonably understood to be limited to using the noted structures and programming to enable the retrieval of stored information to perform specified tasks.
Claim Rejections - 35 USC § 112
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.
[5] Claims 1-7 and 15-26 are 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 1 recites the following structural elements performing the associated functions:
[i] “…means for grasping the business instruction…” which performs recited functions of “…grasps the business instruction by the dialogue…stores a content of the dialogue with the user as a dialogue log in a short-term objective storage unit, and stores text data of the dialogue log in a long-term objective storage unit…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of dialog unit is an AI mechanism that captures and stores dialog). The recited means is reasonably understood to be limited to using the noted structures and programming to gather and store text data.
[ii] “…means for creating the task list…” which performs recited functions of “…creates the task list by decomposing the business instruction into tasks…passes an execution instruction of each task to the means for executing the work, and presents an execution result…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of solution unit is an AI mechanism that decomposes instructions into tasks). The recited means is reasonably understood to be limited to using the noted structures and programming to enable a human user to observe instructions and create tasks to accomplish the functions.
[iii] “…means for executing the work…” which performs recited functions of “…executes the work by using a corresponding data source and passes the execution result to the means for creating the task list…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), and server(s) operating in a network environment and see of disclosure of execution unit is an aggregate of AI mechanisms that executes the decomposed work items/tasks). The recited means is reasonably understood to be limited to using the noted structures and programming that facilitates a user or mechanism to access stored information to perform tasks.
[iv] “…means for referring to the dialogue log…” which performs recited functions of “…extracts information regarding a predetermined matter set in advance, grasps a context of the dialogue, predicts a content to be dealt with next, stores the content as a summary in a short-term subjective storage unit, stores text data of the content of the summary in a long-term subjective storage unit…” and is interpreted as involving the structures and functions disclosed as follows: (Specification.; paragraph(s) [0032]-[0039]; See at least processors, computer(s), memory, and server(s) operating in a network environment and see of disclosure of monitoring unit refers to the text information exchanged and refers to the information in real-time). The recited means is reasonably understood to be limited to using the noted structures and programming to enable the retrieval of stored information to perform specified tasks.
The supportive disclosure indicates that each of the above noted “means” is “an AI mechanism” or “an aggregate of AI mechanisms”. However, beyond the general indication that the means are AI mechanisms, the disclosure fails to provide written description of AI mechanisms that conveys how the recited means perform the associated/recited functions.
As amended, claim 1 recites “…wherein the agent system is configured to perform a dynamic feedback loop control to resolve execution-time inconsistencies, the feedback loop control being triggered by the means for executing the work detecting that a target task is inexecutable using the corresponding data source…”. Claims 24 and 26 also reference functions performed by the “dynamic feedback loop control”.
The supportive disclosure does not appear to reference or otherwise describe a “dynamic feedback loop control” as presented by amendment.
Accordingly, claim 1 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.
Claims 2-7 and 15-26 depend directly or indirectly from claim 1 and inherit and fail to remedy the deficiencies of claim 1 through dependency. Accordingly, claims 2-7 and 15-26are also 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.
Fo purposes of further examination, Examiner assumes the inventive system employs general mechanisms for determining feasibility or validity of tasks performance and includes function to enable modification or insertion of new tasks based on observe feedback data.
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.
[6] Previous rejection(s) of claims 1-14 (now claims 1-8 and 15-26 as presented by amendment) under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-8 and 15-26 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1 and 8 are directed to systems and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1 and 8 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 8, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction, which is reasonably considered to be method comprising processes/functions which are performable by Human Mental Processing and/or or by a human using pen and paper (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011).
The courts have previously identified subject matter limited to steps/processes performable by Human Mental Processing and/or by a human using pen and paper to be ineligible abstract ideas (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). Lastly, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2)).
With respect to functions/steps limited to processes performable by Human Mental Processing and/or by a human using pen and paper, representative claim 8 as amended recites:
“…grasps a business instruction from a dialogue with a user and executes a task related to the business instruction…creates the task list by decomposing the business instruction into tasks…and presents an execution result to the user, executes a work by using a corresponding data source…corresponding to the task related to the execution instruction, and refers to the dialogue log at any time, extracts information regarding a predetermined matter set in advance, grasps a context of the dialogue…predicts a content to be dealt with next…determining that a task is inexecutable based on a status of the corresponding data source…; and dynamically modifying the task list based on the correction instruction to resolve the inconsistency”
Respectfully, absent further clarification of the processing steps executed by the recited processor, one of ordinary skill in the art would readily be relied upon to determine that a task is not executable based on an observation that required data is not available, determine an instruction, create task list, and modify and perform tasks while referring to the dialogue employing by the human mental processing (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“a method that can be performed by human thought alone is merely an abstract idea and is not patent eligible under 35 U.S.C 101).
Claim 8 recites technical elements which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
As presented by amendment, additional technical elements of claim 8 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject are limited to: “central processing unit”, “storage units”, “specific computing process”, and “generative AI”. With respect to these potential additional elements:
(1) The “central processing unit” is identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “storage” is identified as storing subjective and objective data in short term units and long-term units.
(3) The “generative AI” is identified as receiving information and providing results to the system. Claim 1 further specifies that the recited “means” are “an AI mechanism” or “an aggregate of AI mechanisms”
(4) The “specific computing process” is identified as being used by the CPU to “…resolve execution-time inconsistencies by: determining that a task is inexecutable based on a status of the corresponding data source; extracting, from the short-term subjective storage unit, context information regarding a predicted next content to generate a correction instruction; and dynamically modifying the task list based on the correction instruction to resolve the inconsistency…”.
With respect to the recited functions associated with the general “software process”, as presented the claimed functions are limited to mental processes of determining data availability/executability of a task, estimating a next content required for a task, and modifying the task to resolve inconsistencies. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2))
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); (3) Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g); and/or (4) Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to the identification of the using generative AI, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “by the AI” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training and/or particular machine learning models. Accordingly, the recited machine-learning processes and associated training are reasonably understood to be generic machine learning processes.
Each of the above noted limitations states a result (e.g., instructions are grasped from dialogue, tasks are performed as associated with a respective “processor” or “memory”. A recitation of “by a “processor” or “in memory”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., instructions and tasks); (2) storing and retrieving information and data from a generic computer memory (e.g., objective and subjective information); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., grasping instructions) (See MPEP 2106.05(f)).
Accordingly, claim 8 is reasonably understood to be conducting standard, and formally manually performed process of abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment. The claimed abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction benefits from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include:
(1) The “central processing unit” is identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “storage” is identified as storing subjective and objective data in short term units and long-term units.
(3) The “generative AI” is identified as receiving information and providing results to the system. Claim 1 further specifies that the recited “means” are “an AI mechanism” or “an aggregate of AI mechanisms”
(4) The “specific computing process” is identified as being used by the CPU to “…resolve execution-time inconsistencies by: determining that a task is inexecutable based on a status of the corresponding data source; extracting, from the short-term subjective storage unit, context information regarding a predicted next content to generate a correction instruction; and dynamically modifying the task list based on the correction instruction to resolve the inconsistency…”.
With respect to the recited functions associated with the general “software process”, as presented the claimed functions are limited to mental processes of determining data availability/executability of a task, estimating a next content required for a task, and modifying the task to resolve inconsistencies. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2))
While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., instructions and tasks); (2) storing and retrieving information and data from a generic computer memory (e.g., objective and subjective information); and (3) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., grasping instructions).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of abstract idea of grasping a business instruction based on dialog and executing tasks in associated with the instruction benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: 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; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information over a network 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); 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 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claim 1, directed to an apparatus/system is rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2-7 and 15-26, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
[7] Claim(s) 1-8 and 15-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (United States Patent Application Publication No. 2025/0190449 hereinafter ‘Zhang’) in view of Pathan et al. (United States Patent Application Publication No. 2025/0298962 hereinafter ‘Pathan’) and further in view of Thompson, III, et al. (United States Patent Application Publication No. 2025/0371317 hereinafter ‘Thompson’).
With respect to (currently amended) claim 1, Zhang discloses an agent system that grasps a business instruction from a dialogue with a user and executes a task related to the business instruction, the agent system comprising: means for grasping the business instruction ; means for creating a task list ; means for executing a work ;and means for referring to the dialogue log, each of which is capable of individually using generative AI (Zhang et al.; paragraphs [0038] [0041]-[0043] [0085]; See at least general question and answer dashboard, prompt and response, i.e., dialogue. See further agent orchestrator, task allocation and specialization module, agent selection module, agent interaction module), wherein the means for grasping the business instruction grasps the business instruction by the dialogue with the user by the generative AI, and stores a content of the dialogue with the user as a dialogue log in a short-term storage unit (Zhang et al.; paragraphs [0038]-[0039] [0049]-[0050]; See at least prompt optimization and storing and reusing prompts in cache memory), the means for creating the task list creates the task by decomposing the business instruction grasped by the means for grasping the business instruction into tasks by the generative AI (Zhang et al.; paragraphs [0034] [0048] [0079]-[0080]; See at least disambiguation of prompt, extraction of features, and generation of tasks), passes an execution instruction of each task to the means for executing the work, and presents an execution result by the means for executing the work to the user via the means for grasping the business instruction (Zhang et al.; paragraphs [0052] [0085] [0093]-[0094]; See at least agent selection for sequence of tasks, execution of tasks, and display of responses on user GUI/dashboard), the means for executing the work executes the work by using a corresponding data source by the generative AI corresponding to the task related to the execution instruction passed from the means for creating the task list and passes the execution result to the means for creating the task list (Zhang et al.; paragraphs [0079]-[0085] [0093]; See at least analytical tasks executed and results reported), and the means for referring to the dialogue log refers to the dialogue log at any time (Zhang et al.; paragraphs [0039] [0093]; See at least orchestrator accesses stored prompts and responses), extracts information regarding a predetermined matter set in advance, grasps a context of the dialogue by the generative AI (Zhang et al.; paragraphs [0041] [0048]] [0054]; See at least extracts contextual data from prompt disambiguation. predicts intent and AI agents for tasks), predicts a content to be dealt with next, stores the content as a summary in a short-term storage unit (Zhang et al.; paragraphs [0051] [0075] [0085]; See at least caching of prompts and selection/modification of reusable prompts in advance of execution), and allows the dialogue unit, the solution unit, and the execution unit to refer to the content at any time (Zhang et al.; paragraphs [0027] [0039] [0085]; See at least access to stored prompts and response, i.e., dialogue).
With respect to the recitation of “dialogue”, Zhang discloses a natural language exchange between the user and the system to determine and refine prompts/commands to AI agents to perform specified data analytical tasks. While these functions reasonably constitute a dialogue, Zhang fails to expressly state that a dialogue is performed.
However, as evidenced by Pathan, it is well-known in the art to maintain an ongoing dialogue and dialogue log between a user and AI agents and to extract AI instructions from the maintained dialogue (Pathan et al.; paragraphs [0058] [0078]-[0080]; See at least dialogue manager and extraction of context and instruction from dialogue).
Claim 1 has been amended with respect to the functions of the short-term memory and further specifies “…wherein the agent system is configured to perform a dynamic feedback loop control to resolve execution-time inconsistencies, the feedback loop control being triggered by the means for executing the work detecting that a target task is inexecutable using the corresponding data source, and wherein the means for referring to the dialogue log refers to context information and predicted next content stored in the short-term subjective storage unit to generate a task correction request, and the means for creating the task list corrects the task list in response to receiving the task correction request. While both Zhang and Pathan disclose use of short-term and long-term memory for storage of interaction information/logs, neither discloses task correction requests or resolution of execution time inconsistencies where content is differentially stored in short-term and long-term memory.
However, Thomson discloses a system which utilizes a multi-layered memory including short-term and long-term memory designated for storing distinct element of conversations and interactive context information based on human communications with automated AI agents (Thompson et al.; paragraphs [0341]-[0343] [0367]-[0372]; See at least multi-layers memory storage of conversation log information)
However, Thompson further discloses the agent system is configured to perform a dynamic feedback loop control to resolve execution-time inconsistencies, the feedback loop control being triggered by the means for executing the work detecting that a target task is inexecutable using the corresponding data source (Thompson et al.; paragraphs [0064] [0075] [0148] [0309]-[0312] [0315]; See at least conversation and dialog maintained as a log by system. See specialized agents perform tasks. See prompts and micro prompts used to validate response to tasks. Tasks outputs, feedback, and predicted user response/adjustment to tasks including new tasks, i.e., “feedback loop”. See error handling including exceptions or failures).
Thompson further discloses the means for referring to the dialogue log refers to context information and predicted next content stored in the short-term subjective storage unit to generate a task correction request, and the means for creating the task list corrects the task list in response to receiving the task correction request (Thompson et al.; paragraphs [process revisions and task revisions based on feedback. See further prediction of user response an associated tasks generation based on feedback and predictions [0281]-[0282] [0295] [0310]-[0311] [0380]-[0382] [0548]; See at least error and task failure handling. See further adding or modifying tasks responsive to conversational flow of dialog and tasks feedback. See further system layered architecture).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the natural language interactions and extraction of command prompts of Zhang by further including the well-known practice of enabling an ongoing dialogue between user and generative AI functions as taught by Pathan. The instant invention is directed to a system and method of extracting task information and generating instructions usable to generative AI models from natural language communications. As Zhang disclose the use of natural language interactions and extraction of command prompts in the context of a system and method for extracting task information and generating instructions usable to generative AI models from natural language communications and Pathan similarly discloses the utility of enabling an ongoing dialogue between user and generative AI functions in the context of a system and method for extracting task information and generating instructions usable to generative AI models from natural language communications, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of enabling accurate and contextually appropriate interactions within a user-agent dialogue framework (Pathan et al.; Abstract).
Regarding the combination that includes Thompson, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the cache and hard drive storage of Zhang by further including the well-known practice of use of short-term storage for in context, i.e., subjective, learning and long-term for storing derivative embeddings derived from ongoing dialogs and further implementing a modular system using a well-known logic layer system structure as taught by Thompson. The instant invention is directed to a system and method of extracting task information and generating instructions usable to generative AI models from natural language communications. As Zhang disclose the use of cache and hard drive storage in the context of a system and method for extracting task information and generating instructions usable to generative AI models from natural language communications and Thompson similarly discloses the utility of short-term storage for in context, i.e., subjective, learning and long-term for storing derivative embeddings derived from ongoing dialogs and further implementing a modular system using a well-known logic layer system structure in the context of a system and method for extracting task information and generating instructions usable to generative AI models from natural language communications, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of enabling accurate and contextually appropriate interactions within a user-agent dialogue framework.
With respect to claim 2, Zhang discloses an agent system wherein the dialogue unit confirms whether the task list created by the solution unit needs to be corrected by the user through the dialogue with the user (Zhang et al.; paragraphs [0038]-[0040] [0075]; See at least disambiguation including clarifications to/by user), passes a correction content instructed by the user to the solution unit to instruct correction of the task list in a case where correction is required, and instructs the solution unit to execute the task list in a case where correction is not required (Zhang et al.; paragraphs [0037]-[0038] [0085]; See at least task allocation of both modified prompts/instructions and prompts determined to be correct).
With respect to claim 3, Zhang discloses an agent system wherein when a dialogue between the dialogue unit and the user ends, the monitoring unit extracts predetermined information regarding a business background and/or a business situation of the user by the generative AI on a basis of the dialogue log, stores the extracted information in a long-term storage unit (Zhang et al.; paragraphs [0051] [0075] [0085]; See at least caching of prompts and selection/modification of reusable prompts in advance of execution), and allows the dialogue unit and the solution unit to refer to the extracted information at any time (Zhang et al.; paragraphs [0027] [0039] [0085]; See at least access to stored prompts and response, i.e., dialogue).
With respect to claim 4, Zhang discloses an agent system wherein when creating the task list by decomposing the business instruction grasped by the dialogue unit into tasks by the generative AI, the solution unit uses, as input information to the generative AI, information including a summary of the dialogue with the user stored in the short-term storage unit (Zhang et al.; paragraphs [0039] [0051]; See at least prompt and response history accessed to generate task instructions), a past business instruction acquired from the long-term storage unit and similar to the business instruction, and a task design at that time (Zhang et al.; paragraphs [0038]-[0040] [0051]; See at least mapping of similar entities and associated stored prompts).
With respect to claim 5, Zhang discloses an agent system wherein the execution unit determines whether each task related to the execution instruction passed from the solution unit is executable by corresponding generative AI, and requests the solution unit to correct a target task in a case where the task is inexecutable (Zhang et al.; paragraphs [0038]-[0040] [0048]-[0059]; See at least prompt optimization and data assembly to ensure execution of function/tasks).
With respect to claim 6, Zhang discloses an agent system wherein in a case where the target task is executable, the execution unit determines whether information necessary for executing the work by using the corresponding data source is insufficient, and in a case where the information is insufficient, the execution unit refers to information in the short-term storage unit and supplements the information (Zhang et al.; paragraphs [0039] [0049] [0051]; See at least prompt disambiguation and analysis of structured data).
With respect to claim 7, Zhang discloses an agent system wherein in a case where the information necessary for executing the work using the data source corresponding to the target task is insufficient, the execution unit makes an inquiry to the user via the dialogue unit to supplement the information (Zhang et al.; paragraphs [0038]-[0040] [0051]; See at least mapping of similar entities and associated stored prompts).
With respect to (new) claim 15, Zhang discloses a system wherein the means for referring to the dialogue log extracts information based on important information defined in setting information from the dialogue log at an end of a dialogue session, performs structuring as an embedding representation or a summary, and transfers the structured information to the long-term subjective storage unit to improve predictive accuracy for a future dialogue (Zhang et al.; paragraphs [0051] [0075] [0085]; See at least caching of prompts and selection/modification of reusable prompts in advance of execution, i.e., a dialog summary).
With respect to (new) claim 16, while Zhang discloses functions of grasping business instructions, creating task lists, and executing work/tasks using specialized AI agents. Zhang fails to specify that the system is comprising a layered architecture.
However, Thompson discloses a system wherein the agent system is divided into a task layer and a meta layer, the task layer including the means for grasping the business instruction, the means for creating the task list, and the means for executing the work, and the meta layer including the means for referring to the dialogue log, and wherein the short-term subjective storage unit is utilized as a shared bus for synchronized coordination between the task layer and the meta layer (Thompson et al.; paragraphs [0548]-[0553]; See at least layers system architecture).
Regarding claim 16, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 16 and are hereby incorporated by reference.
With respect to (new) claim 17, Zhang discloses a system wherein each of the means for grasping the business instruction, the means for creating the task list, the means for executing the work, and the means for referring to the dialogue log operates as an individual AI agent specializing in a specific role and individually uses a different prompt and a corresponding data source (Zhang et al.; paragraphs [0038] [0041]-[0043] [0085]; See at least general question and answer dashboard, prompt and response, i.e., dialogue. See further agent orchestrator, task allocation and specialization module, agent selection module, agent interaction module).
With respect to (new) claim 18, Zhang discloses a system wherein when the means for executing the work determines that information necessary for executing the work using the corresponding data source is insufficient, the means for executing the work refers to context information in the short-term subjective storage unit to autonomously supplement the insufficient information before requesting correction of the task list (Zhang et al.; paragraphs [0039] [0049] [0051]; See at least prompt disambiguation and analysis of structured data. The output from the disambiguation is a reasonably form of information supplement).
With respect to (new) claim 19, Zhang discloses a system wherein in a case where the information necessary for executing the work cannot be supplemented from the short-term subjective storage unit, the means for executing the work makes an inquiry to the user via the means for grasping the business instruction to supplement the information, and executes the task by using the supplemented information (Zhang et al.; paragraphs [0038]-[0040] [0051]; See at least mapping of similar entities and associated stored prompts).
With respect to (new) claim 20, Zhang discloses a system wherein the means for creating the task list creates the task list by using, as input information to the generative AI, a past business instruction acquired from the long-term subjective storage unit and a past task design associated with the past business instruction by performing a similarity search based on the current business instruction (Zhang et al.; paragraphs [0038]-[0040] [0051]; See at least mapping of similar entities and associated stored prompts).
With respect to (new) claim 21, Zhang discloses a system wherein the means for referring to the dialogue log summarizes user preference characteristics from the dialogue log and stores the characteristics in the long-term subjective storage unit, and the means for grasping the business instruction personalizes a response to the user by referring to the user preference characteristics to improve a success rate of the business instruction (Zhang et al.; paragraphs [0051] [0075] [0085]; See at least caching of prompts and selection/modification of reusable prompts in advance of execution, i.e., a dialog summary).
With respect to (new) claim 22, Zhang discloses a system wherein the means for referring to the dialogue log extracts information from the dialogue log based on important information defined in setting information, and stores the extracted information in the short-term subjective storage unit as process data that enables the agent system to determine its own state (Zhang et al.; paragraphs [0038]-[0040] [0048]-[0059]; See at least prompt optimization and data assembly to ensure execution of function/tasks, optimization process to ensure execution by a specialized agent is reasonably a form of determining agents own state).
With respect to (new) claim 23, Zhang fails to explicitly state that tasks lists are stored using Java Script notation.
However, Thompson discloses a system wherein the means for creating the task list creates the task list as a list decomposable into subtasks in a format of JSON (JavaScript Object Notation), and the means for executing the work sequentially executes the subtasks by using a function (Tool) corresponding to each subtask (Thompson et al.; paragraphs [0078] [0163]; See at least JSON path expressions for tasks).
Regarding claim 23, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 23 and are hereby incorporated by reference.
With respect to (new) claim 24, Zhang discloses a system wherein the means for executing the work determines whether the task is within a range resolvable by the agent system by comparing the task with searchable items in the corresponding data source, and when the task is out of the range, the means for executing the work triggers the dynamic feedback loop to correct the task list (Zhang et al.; paragraphs [0038]-[0040] [0048]-[0059]; See at least prompt optimization and data assembly to ensure execution of function/tasks).
With respect to (new) claim 25, Zhang fails to explicitly state conversation or dialog is stored as embeddings.
However, Thompson discloses a system wherein the information stored in the long-term subjective storage unit is maintained as an embedding representation, and the means for creating the task list performs a similarity search based on the embedding representation to retrieve a past task design associated with a similar business context (Thompson et al.; paragraphs [0056] [0082] [0093] [0121]; See at least context and dialog stored as embeddings in multitiered memory structure).
Regarding claim 25, the conclusions of obviousness and rationale to modify as established for claim 1 above are applicable to claim 25 and are hereby incorporated by reference.
With respect to (new) claim 26, Zhang discloses a system wherein when the task list is corrected by the dynamic feedback loop control, the means for grasping the business instruction presents a revised task list to the user to seek interactive confirmation before the means for executing the work re-executes the work (Zhang et al.; paragraphs [0038]-[0040] [0048]-[0059]; See at least prompt optimization and data assembly to ensure execution of function/tasks, optimization process is reasonably a form of interactive confirmation).
Amended claim 8 substantially repeats the subject matter addressed above with respect to amended claim 1 as directed to an alternate illustration of the inventive system. Accordingly, claim 8 is rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claim 1.
Response to Remarks/Amendment
[8] Applicant's remarks filed 7 July 2026 have been fully considered and are addressed as follows:
[i] Applicant’s remarks in response to previous rejection(s) of claim(s) 1-14 (now claims 1-8 and 15-26 as presented by amendment) under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 8 April 2026 are reasonably considered to have been fully addressed in the context of the revised rejection of the claims presented above responsive to the amendments to the subject claims and in consideration of the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update), published in the Federal Register, 17 July 2024. Additionally, Applicant substantially rehashes arguments previously presented in the prior response. These arguments are addressed in accordance with Examiner’s response in the prior Office Action(s) mailed 8 April 2026 and 1 October 2026, incorporated in their entirety in response.
[ii] Applicant’s remarks directed to previous rejection(s) of claim(s) 1-14 (now claims 1-8 and 15-26 as presented by amendment) under 35 U.S.C. 103 as being unpatentable as set forth in the previous Office Action mailed 8 April 2026 have been fully considered and are moot in light of newly added grounds of rejection responsive to the amendments to the subject claims. See revised rejection under 35 U.S.C. 103 presented above.
Conclusion
[9] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cited PATENT Literature:
Lintz, DYNAMIC CONTEXT WINDOW MANAGEMENT FOR CONVERSATIONAL AI AGENTS, United States Patent Application Publication No. 20260161683, paragraphs [0014]-[0020]: Relevant Teachings: Lintz discloses a system/method that includes steps/functions for managing conversation agents which employ short and long term memory structures for differentially storing long term conversation summary information and recent complete dialog for referencing AI agents.
MYSTETSKYI, MODULAR AI AGENT SYSTEM WITH DYNAMIC SKILL REGISTRY AND RESOURCE MANAGEMENT FOR ENTERPRISE APPLICATIONS, United States Patent Application Publication No. 20260024037, paragraphs [0099]-[0103]: Relevant Teachings: MYSTETSKYI discloses a system/method that includes steps/functions of tasking AI agents to perform tasks using an interactive dialog and instructions/prompting interface.
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/ROBERT D RINES/ Primary Examiner, Art Unit 3625
