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 .
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1 The claim recites a method comprising identifying, extracting, generating, selecting, and generating responses via an artificial intelligence circuit. Therefore, the claim is a process, which is a statutory category of invention under 35 U.S.C. § 101.
Step 2a prong 1
Limitation “extracting one or more instructions having a natural language structure, the one or more instructions associated with at least one action to resolve the situation”, classification: Mental Process. The underlying operational logic is a judicial exception because extracting and identifying instructions from an interaction requires analyzing communication data and pattern recognition in understanding linguistic structures-processes that can be performed mentally by observing, evaluating, and reasoning about information.
Limitation “generating a sentiment metric that indicates a characteristic of the interaction between the device and the computing system”, classification: Mental Process. The underlying operational logic is a judicial exception because determining characteristics of an interaction requires analyzing communication patterns and represents evaluation and assessment based on comparative judgment-mental activities that can be performed by human observation and analysis.
Limitation “selecting a mode from among a plurality of modes of operation of an artificial intelligence circuit of the computing system that uses a generative artificial intelligence model, wherein the artificial intelligence circuit is configured to operate in a first mode to generate responses according to a first natural language structure and to operate in a second mode to generate responses according to a second natural language structure”, classification: Mental Process. The underlying operational logic is a judicial exception because choosing among multiple options based on criteria involves decision-making and selection, represents classification and categorization by assigning operations to different modes based on linguistic input rulesets, and applies predetermined behavioral configurations-fundamental mental activities of decision-making that can be performed mentally.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Limitations mentioned are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2a prong 2
The claim recites the additional elements/limitations
identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation
by the computing system and from the interaction
by the computing system and based on the one or more instructions and the natural language structure
using a machine learning control system; based on the communication metric and the sentiment metric
generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions
MPEP § 2106.05(a) Improvements to Functioning of Computer or Technical Field
An eligibility evaluation of “identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions” confirms that the claim lacks the specific, structural technical parameters or algorithmic mechanics required to limit the invention to those improvements. The claim language defines broad operational goals at a high level of functional generality rather than an explicit technical mechanism that improves underlying computer processing speeds, memory limits, or network architecture hardware throughput. As detailed in specification paragraphs, [0025]- [0029], and, these operations rely on conventional data structures and routine computer functions, leading to the conclusion that these elements do not integrate the exception into a practical application via an improvement to a computer or technical field.
MPEP § 2106.05(b) Particular Machine
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms that the recited elements do not constitute a specialized or non-generic structural machine configuration. As detailed in specification paragraphs, [0035], [0036], [0089]-[0091], the operating environment utilizes ordinary processing units and basic storage structures, leading to the conclusion that these elements do not constitute a particular machine.
MPEP § 2106.05(c) Particular Transformation
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms these elements do not describe a permanent change of state for tangible items because they involve purely electronic manipulation of digital records rather than structural modifications to physical matter. As detailed in specification paragraphs, [0038]- [0041], [0084] and, the system manipulates digital bits rather than tangible items, leading to the conclusion that these elements do not constitute a particular transformation.
MPEP § 2106.05(e) Other Meaningful Limitations
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms these elements describe a processing framework at a high level of functional generality because they define broad operational goals-such as basic data ingestion, storage, or interface delivery-without imposing specific technical constraints. Furthermore, they fail to limit the absolute scope of the claim beyond generic or routine operational practices since they cover standard data handling features common across generic processing platforms. As detailed in specification paragraphs, [0025-[0034], the boundaries are left functionally wide open, leading to the conclusion that these elements do not introduce meaningful limitations because they define the processing environment at a broad level of functional generality, failing to introduce specific technical constraints that narrow the preemptive scope of the exception.
MPEP § 2106.05(f) Mere Instructions to Apply Exception
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms these elements recite a desired operational outcome at a high level of functional generality because they state what broad results are accomplished without specifying concrete technical mechanisms or specialized physical parameters to achieve them. They represent basic automation constraints because they apply to any computing environment performing routine input/output or database steps. As detailed in specification paragraphs, [0025]-[0029], the descriptions focus entirely on high-level goals rather than specific architectural boundaries, leading to the conclusion that these elements represent a mere instruction to apply the exception.
MPEP § 2106.05(g) Insignificant Extra-Solution Activity
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms these elements represent routine preprocessing or post-processing because they describe basic data acquisition or display operations that occur prior to or following the primary algorithmic exception logic. They are merely incidental to the primary process since they serve as conventional input/output mechanisms that facilitate standard interaction without adding an inventive or technical step to the actual core solution. As detailed in specification paragraphs, [0069]-[0071] and [0084], these activities are standard administrative or formatting tasks, leading to the conclusion that these elements constitute insignificant extra-solution activity.
MPEP § 2106.05(h) Field of Use and Technological Environment
An eligibility evaluation of identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions confirms these elements do not integrate into a field of use that imposes meaningful limits because the referenced technical environment is broad and conventional, failing to impose hard physical or structural limits beyond standard commercial system constraints. As detailed in specification paragraphs, [0020]-[0024], the environment is described as generic backdrop, leading to the conclusion that these elements are not integrated into a meaningful field of use because they merely restrict the application of the abstract exceptions to a conventional technological field without providing any non-generic structural or functional improvements to that environment.
Step 2b The ordered combination of true additional elements identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation, by the computing system and from the interaction, by the computing system and based on the one or more instructions and the natural language structure, using a machine learning control system; based on the communication metric and the sentiment metric, and generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions fails to add an inventive concept because they utilize routine, well-understood, and conventional processing activities executed on generic hardware. As detailed in specification paragraphs, [0025]- [0029], [0068]- [0072], these elements describe a general software/system process running on conventional hardware rather than a specific, non-generic technological solution to a technical problem inherent to the machine itself. As detailed in specification paragraphs, [0035] - [0041], [0089]- [0091], because the ordered combination of additional elements only utilizes standard computer components and conventional steps to execute the abstract indexing and sorting concepts, the independent claim is patent ineligible under 35 U.S.C. § 101.
Claim 2 recites “wherein the mode of operation is a next best action” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraph [0021]. The broad claim language fails to capture the narrow technical structures or specific circuit rules for determining next-best actions that may be disclosed in that paragraph. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 3 recites “generating the sentiment metric based on customer complaint data”. This feature describes an outcome at a high level of functional generality. The broad claim language fails to capture the narrow technical structures or specific circuit rules for processing complaint data that may be disclosed in those paragraphs. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 4 recites “simplifying the one or more responses according to natural language” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraph [0024]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 5 recites “obtaining, by the computing system, one or more documents fitting a domain, the one or more documents having the natural language structure and describing one or more actions of a flow; and generating, by the computing system and via a first artificial intelligence engine receiving one or more of the characteristics as an input, a flow object having a structure according to the one or more actions of the flow” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraphs [0078], [0079], and [0102]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 6 recites “generating, by the computing system and via a second artificial intelligence engine receiving the flow object as an input, a summary object including a text description of the flow”. This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraphs [0081], [0082], and [0110]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 7 recites “wherein the first artificial intelligence engine is configured to execute a machine learning model, and wherein the second artificial intelligence engine is configured to execute a large language model” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraphs [0054] and [0086]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 8 recites “segmenting, by the computer system, the one or more documents into a subset of at least one of the one or more actions” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraphs [0079], [0080], and [0085]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claim 9 recites “wherein the flow object corresponds to the subset of at least one of the one or more actions” This feature describes an outcome at a high level of functional generality, as disclosed in specification paragraphs [0079], [0080], and [0085]. Because the broad text fails to incorporate specific technological structures from the specification, the limitation fails to provide a patent-eligible restriction and leaves the claim ineligible under 35 U.S.C. § 101.
Claims 11-20 are similar to claims 1-3 and 5-18. The claims are rejected based on the same reasons.
Response to Arguments
Section Rejections Under 35 U.S.C. 101
Pg. 8-9
Argument: Claims recite operations that cannot be practically performed in the human mind, therefore not mental processes under USPTO Memorandum dated August 4, 2025. The Examiner disagrees. A person can observe communication data, evaluate sentiment characteristics, and make judgments about interaction using pen and paper. These are basic mental operations that a person can perform by reading text, assessing emotional content, and forming opinions. The claim limitations describe observations, evaluations, judgements, and opinions-core mental process activities.
selecting, by the computing system using a machine learning control system and based on the communication metric and the sentiment metric, a mode from among a plurality of modes...” cannot be performed in human mind. This limitation recites selecting among options based on criteria. A person can perform this operation mentally by reading input data, comparing it against predetermined categories, and choosing an appropriate response strategy. This is a mental process.
generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions” cannot be performed in human mind. This limitation can be performed mentally by reading input, understanding the requested action, and formulating an appropriate response. This represents basic automation constraints that apply to any computing environment performing routine input/output steps.
Human mind is not equipped to employ a machine learning control system to dynamically select among operational modes of an AI circuit using a generative AI model. The Examiner notes the applicant's point that a human mind is not equipped to run a machine learning control system. However, looking at the claim as it is currently written, the underlying logic is simply collecting data and choosing an operational mode. A human can conceptually perform this same type of selection using basic mental steps and human judgment.
Human mind cannot cause such an artificial intelligence circuit to generate responses according to different natural language structures based on selected mode of operation. While the applicant states that a human cannot cause an artificial intelligence circuit to generate responses, the current claim text only describes this at a broad, functional level. The claim states what the system accomplishes, but fails to recite the actual technical mechanics required to lock in the latency reductions and resource optimizations described in paragraphs [0026]-[0027]. To turn this from an abstract description into a patent-eligible technological solution, the claim must explicitly incorporate those physical hardware and threshold constraints.
Pg. 9-10
Applicant argues that claim features recite a particular technical solution providing technical advantages described in paragraphs [0026]-[0027], including optimizing performance, resource utilization, improved communication effectiveness, and handling high volumes of interactions without compromising quality or speed.
Paragraphs [0026]- [0027] describe general goals such as “optimizing performance,” “reduced latency,” and “improved communication effectiveness.” These are broad functional outcomes. The claim text recites “selecting a mode” and “generating responses”- but fails to recite the specific algorithmic mechanics or computational rules required to achieve those improvements. The claim language defines broad operational goals at a high level of functional generality rather than an explicit technical mechanism that improves underlying computer processing speeds, memory limits, or network architecture hardware throughput.
Applicant argues that the claim features related to selecting modes of operation and generating responses directly reflect these technical improvements rather than reciting an alleged mental process. Even if the specification describes technical benefits in paragraphs [0026] - [0027], the claim text itself does not recite specific structural limitations to lock in those improvements. The claim recites broad functional goals -"selecting a mode" and "generating responses"- that could be implemented on any computing system performing routine input/output steps. Because the current language fails to lock in the actual text in the specification, the argument relies entirely on high-level functional concepts common across generic processing platforms.
Consideration
Claim 1 A method, comprising:
identifying, by a computing system, a communication metric corresponding to an identifier of a communication channel of an interaction between a device and the computing system regarding a situation;
extracting, by the computing system and from the interaction, one or more instructions having a natural language structure, the one or more instructions associated with at least one action to resolve the situation;
generating, by the computing system and based on the one or more instructions and the natural language structure, a sentiment metric that indicates a characteristic of the interaction between the device and the computing system;
selecting, by the computing system using a machine learning control system and based on the communication metric and the sentiment metric, a mode from among a plurality of modes of operation of an artificial intelligence circuit of the computing system that uses a generative artificial intelligence model, wherein selecting the mode comprises applying a value to determine an overall user sentiment, wherein if the value satisfies a threshold criterion relating to a "bad mood," the system can prevent performance of certain actions relating to responses, until sentiment meets a defined range of emotions where the value satisfies a different threshold criterion, wherein the artificial intelligence circuit is configured to operate in a first mode to generate responses according to a first natural language structure and to operate in a second mode to generate responses according to a second natural language structure, wherein operating in the different modes changes the allocated computational resources based on the sentiment of the communication between an end-user and the system to optimize the artificial intelligence circuit's operation; and
generating, by the computing system and via the artificial intelligence circuit operating according to the selected mode of operation, one or more responses having the natural language structure of the communication channel based on the one or more instructions.
The Support
Paragraph [0021]: ...if the speech pattern indicates that the customer is not in a good mood (e.g., the score satisfies a threshold criterion relating to a 'bad mood'), the AI engine can prevent performance of certain actions relating to responses, until sentiment meets a defined range of emotions (e.g., the score satisfies a different threshold criterion)." Paragraph [0027]: … by analyzing sentiment of the communication between an end-user and the AI engine, the systems and methods described herein may be configured to operate in different modes which changes the allocated computational resources, leading to improved performance and reduced latency.
By incorporate the threshold from paragraph [0021] and the hardware mechanism from paragraph [0027] that dynamically changes the allocated computational resources of the AI circuit, the additional elements create an computer improvement under MPEP § 2106.05(a).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
U.S. Pub 2025/0158942 – Rodgers discloses a set of instructions for executing a chatbot for engaging in a chat conversation with a user. Multiple outbound messages comprised in the chat conversation are generated using the chatbot, where the outbound messages request information corresponding to a service request. The outbound messages are transmitted to a client device associated with the user. Multiple inbound messages comprised in the chat conversation are received from the client device, where the inbound messages comprise the information corresponding to the service request. A trigger for transferring the chat conversation from the chatbot to a human agent is detected. The chat conversation is analyzed (210) to select a particular portion of the chat conversation for enhancement relative to one or multiple other portions of the chat conversation.
U.S. Pub 2025/0053743 – Rader discloses a method of generating a support summary includes extracting chat information including natural language data from a support chat with a user. Tokenized language is generated by performing feature extraction on this natural language data. This tokenized language is subjected to sentiment analysis to produce sentiment data reflecting sentiment of the support user during the support chat, and to semantic analysis to extract support-relevant features. A support summary made up of natural language text identifying a support issue and information germane to the support issue is then generated from the extracted chat information using a language model, at least in part from the extracted support-relevant features and the sentiment data.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
Prosecution Insights