DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 1/21/2026.
Claims 1-20 are pending and have been examined.
All previous objections / rejections not mentioned in this Office Action have been withdrawn by the examiner.
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 .
Response to Amendments
Applicant’s arguments filed on 1/21/2026 have been fully considered but they are not persuasive. Applicant has amended independent claim 1, 8, and 15. The amended claim language includes “wherein leveraging the conversation flowchart comprises integrating the chatbot into the large language model to interpret an intent associated with the user input”, “wherein the output is recorded to a database and utilized by the conversation flowchart to determine a next step”, and “wherein the large language model rephrases responses from the conversation flowchart and injects into the generated prompt based on the context and the intent”.
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 101, applicant asserts that independent claims are not directed to an abstract idea because the claims disclose an improvement to the technical problem of lack of naturalness and/or genuineness associated with chatbot interactions in the field of computing by “allowing for augmentation of traditional flowchart-based chatbots through the use of generative pre-trained transformer networks to allow prompts and responses generated by the chatbot to sound more natural and human-like”. Examiner respectfully disagrees. During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111. Also, claims should not be interpreted by reading limitations of the specification into the claim, to narrow the scope of the claim, by implicitly adding disclosed limitations that have no express basis in the claim language. In re Prater, 415 F.2d 1393. Here, the steps in the claim language are broad and examiner interprets the claim broadly. A chatbot that generates a response from a prompt is analogous to a human thinking of a response to a question in the mind. The integration of a chatbot to a large language model, an additional element, does not integrate the judicial exception into a practical application or describe a technical improvement. The output being recorded to a database is analogous to a human writing a response on paper using a pen or pencil. The use of a database is generic computer component that cannot provide an inventive concept. First, the claim limitation does not include the use of a generative pre-trained transformer network to allow prompts and responses. The claim language utilizes “large language model”, but the specification does not specifically define “large language model” to be a generative pre-trained transformer network. Specification, P0040. Therefore, examiner interprets “large language model” broadly as a model that generates a response from a prompt. Second, MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the claim recites desired outcomes without providing details of how the claim covers a particular solution to a problem or a particular way to achieve the desired outcomes. The claim invokes the steps of “leveraging” the conversational flowchart to extract a context, “generating a natural response to the received data”, and “output is recorded to a database” fails to provide technical details of how the steps are utilized to improve naturalness of chatbot interaction. Therefore, the claims as currently recited does not overcome the 35 U.S.C. § 101 abstract idea rejection.
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 103, applicant has amended independent claims 1, 8, and 15. Hence, the Applicant’s arguments are moot in view of new grounds of rejection. The added limitations raise new grounds for rejection. During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” Here, “chatbot” is interpreted to include at least a chatbot front-end, a large language model, a prompt module, and a conversation flowchart. (Spec. P0038) The word “database” is not specifically defined in the specification and is interpreted as a storage of data.
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.
Claims 1, 8, and 15 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. Specifically, the as filed disclosure does not disclose the claim limitation “wherein the large language model rephrases responses from the conversation flowchart and injects into the generated prompt based on the context and the intent”. The specification
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, 8, and 15 the limitations of “receiving data corresponding to a user input to a chatbot”, “generating a prompt for a large language model based on the received data and a conversation flowchart associated with the chatbot”, “inputting the generated prompt to the large language model”, “leveraging, via the large language model, the conversation flowchart to extract a context associated with the user input”, “wherein leveraging the conversation flowchart comprises integrating the chatbot into the large language model to interpret an intent associated with the user input”, “generating a natural language response to the received data based on an output from the large language model, wherein the generated natural language response corresponds to a response associated with the conversation flowchart”, “wherein the output is recorded to a database and utilized by the conversation flowchart to determine a next step”, and “wherein the large language model rephrases responses from the conversation flowchart and injects into the generated prompt based on the context and the intent”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human reading text, think of the latest text rephrased based on context of the text, and think of a response in consideration of previous text context. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of a computer system in claims 8 and computer readable storage device in claim 15, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using P0055-P0056 in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to read text, think of the latest text rephrased based on context of the text, and think of a response in consideration of previous text context amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, 9, and 16, the claim recites “further comprising prompting the user for additional information based on an answer to the user input not existing within the conversation flowchart”, which reads on a human writing on paper using a pen or pencil asking for additional information. No additional limitations are present.
With respect to claim 3, 10, and 17, the claim recites “further comprising rejecting the user input based on detecting a regulatory compliance issue associated with the user input”, which reads on a human not considering text read that is not in regulatory compliance in the mind. No additional limitations are present.
With respect to claim 4, 11, and 18, the claim recites “further comprising packaging the generated natural language response to fit a pre-determined output prompt template”, which reads on a human rephrasing response into a specific format in the mind. No additional limitations are present.
With respect to claim 5, 12, and 19, the claim recites “further comprising packaging the generated prompt to fit a pre-determined input prompt template corresponding to the large language model”, which reads on a human rephrasing read text into a specific format in the mind. No additional limitations are present.
With respect to claim 6, 13, and 20, the claim recites “wherein the large language model is a generative pre-trained transformer network”, which reads on a human thinking of a response from the text read. The use of a large language model of generative pre-trained transformer network are general commonplace method and does not show improvement in functionality that demonstrates patent eligibility. MPEP 2106.05(a). No additional limitations are present.
With respect to claim 7 and 14, the claim recites “wherein the large language model rephrases responses from the conversation flowchart based on maximizing a naturalness score associated with the responses”, which reads on a human thinking of a response to text read that is natural. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
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.
Claims 1, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Heller et al. (U.S. PG Pub No. 20240242037), hereinafter Heller, in view of Guo et al. (U.S. PG Pub No. 20180082184), hereinafter Guo.
Regarding claim 1, 8, and 15 Heller teaches:
(Claim 1) A method of enhancing chatbot responses, executable by a processor, comprising: (P0031, The method may be performed in order to generate new text based on input text provided by a client machine.; P0052, The chat interface may facilitate text-based communication with the client machines.; P0108, A system suitable for implementing embodiments described herein includes a processor.)
(Claim 8) A computer system for enhancing chatbot responses, the computer system comprising: (P0017, A text generation interface system serves as an interface between one or more client machines and a text generation system configured to implement a large language model.; Fig. 8, Example chat session with text generation modeling system.)
(Claim 8) one or more computer-readable storage media configured to store computer program code; and (P0108, A system suitable for implementing embodiments described herein includes a processor, a memory module, a storage device.; P0281, Techniques disclosed herein may be implemented, at least in part, by computer-readable media that include program instructions.)
(Claim 8) one or more computer processors configured to access said computer program code and operate as instructed by said computer program code, said computer program code including: (P0108, The processor may perform operations such as those described herein. Instructions for performing such operations may be embodied in the memory, on one or more non-transitory computer readable media, or on some other storage device.)
(Claim 15) A computer program product for enhancing chatbot responses, comprising: (P0004, Apparatus, methods and computer program products for novel text generation.; P0017, A text generation interface system serves as an interface between one or more client machines and a text generation system configured to implement a large language model.)
(Claim 15) one or more computer-readable storage devices; and program instructions stored on at least one of the one or more computer-readable storage devices, the program instructions configured to cause one or more computer processors to: (P0108, The processor may perform operations such as those described herein. Instructions for performing such operations may be embodied in the memory, on one or more non-transitory computer readable media, or on some other storage device.)
receiving data corresponding to a user input to a chatbot; (P0032, At 102, original input text and a text generation flow for generating novel text is determined based on a request received from a client machine.)
generating a prompt for a large language model based on the received data and a conversation flowchart associated with the chatbot; (P0032, At 102, original input text and a text generation flow for generating novel text is determined based on a request received from a client machine.; P0033, The text generation flow may define a procedure for interacting with a large language model to generate output text based on the original input text. For instance, the text generation flow may define one or more prompts or instructions to provide to the large language model.)
inputting the generated prompt to the large language model; (P0037, One or more prompts are determined based on the parsed input text at 106. In some embodiments, a prompt may include a portion of parsed input text combined with one or more instructions to a large language model.)
wherein leveraging the conversation flowchart comprises integrating the chatbot into the large language model to interpret an intent associated with the user input; (P0034, The text generation flow may be determined based on user input. Alternatively, or additionally, a request from a client machine may be analyzed to aid in selecting a text generation flow. For example, a request from a client machine may include a natural language query that includes a request to “write an email” or to “summarize a topic”. Such natural language may be analyzed via a large language model or other machine learning tool to determine that a text generation flow for drafting correspondence or summarizing documents should be selected.; P0039, The text generation system includes client machines through in communication with a text generation interface system, which in turn is in communication with a text generation modeling system.)
generating a natural language response to the received data based on an output from the large language model, wherein the generated natural language response corresponds to a response associated with the conversation flowchart. (P0074, One or more text response messages are received from the remote computing system. According to various embodiments, the one or more text response messages include one or more novel text portions generated by a text generation model implemented at the remote computing system. The novel text portions may be generated based at least in part on the prompt received at the text generation modeling system, including the instructions and the input text.; P0078, The client response message may be determined based in part on the text generation flow determined and in part based on the one or more text response messages received and parsed.)
wherein the output is recorded to a database and utilized by the conversation flowchart to determine a next step; and (P0032, Original input text and a text generation flow for generating novel text is determined based on a request received.; P0033, The text generation flow may define a procedure for interacting with a large language model to generate output text based on the original input text.; P0116, The chat interaction may continue with successive iterations of the operations and elements shown at 802-824 in FIG. 8. In order to maintain semantic and logical continuity, all or a portion of previous interactions may be included in successive chat prompts sent to the text generation modeling system.)
Heller does not specifically teach:
leveraging, via the large language model, the conversation flowchart to extract a context associated with the user input
wherein the output is recorded to a database and utilized by the conversation flowchart to determine a next step; and
wherein the large language model rephrases responses from the conversation flowchart and injects into the generated prompt based on the context and the intent.
Guo, however, teaches:
leveraging, via the large language model, the conversation flowchart to extract a context associated with the user input (P0024, Present disclosure provides a context-aware chatbot method based on a neural conversational model, which may take contextual features into consideration.; P0046, When a question is received by the context-aware neural conversation model, the context-aware neural conversation model may recognize the contextual information even the context is not appeared. For example, the context-aware neural conversation model may add time, and event, etc., as input into the context-aware neural conversational model.)
wherein the output is recorded to a database and utilized by the conversation flowchart to determine a next step; and (P0032, The database may include one or more databases for storing certain data and for performing certain operations on the stored data, such as database searching.)
wherein the large language model rephrases responses from the conversation flowchart and injects into the generated prompt based on the context and the intent. (P0041, Present disclosure also provides a context-aware chatbot method. To take the contextual information into consideration, the context-aware chatbot method may model the response with context.; P0057, By analyzing the context in the questions, the user's question may be paired with a better answer. That is, the chatbot may provide more relevant responses to the users, and the users may find services and products they need in different contexts, significantly improving the user experience.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to extract context from user input and large language model providing responses based on the context. It would have been obvious to combine the references because in a real dialogue between a user and a chatbot, user's context could be substantially complex and continuously changed and context-aware model are highly desired to be incorporated into a chatbot system. (Guo P0005, P0022)
Regarding claim 4, 11, and 18 Heller in view of Guo teach claim 1, 8, and 15.
Heller further teaches:
further comprising packaging the generated natural language response to fit a pre-determined output prompt template. (P0075, The one or more responses are parsed at 416 to produce a parsed response. … A response message received from the large language model may include the instructions and/or the input text. Accordingly, the response message may be parsed to remove the instructions and/or the input text.)
Regarding claim 5, 12, and 19 Heller in view of Guo teach claim 1, 8, and 15.
Heller further teaches:
further comprising packaging the generated prompt to fit a pre-determined input prompt template corresponding to the large language model. (P0072, At 410, one or more prompts based on the prompt templates are determined. In some embodiments, a prompt may be determined by supplementing and/or modifying a prompt template based on the input text. For instance, a portion of input text may be added to a prompt template at an appropriate location. As one example, a prompt template may include a set of instructions for causing a large language model to generate a correspondence document.)
Regarding claim 6, 13, and 20 Heller in view of Guo teach claim 1, 8, and 15.
Heller further teaches:
wherein the large language model is a generative pre-trained transformer network. (P0018, Chunks are inserted into prompt templates for processing by a large language model such as the GPT-3 or GPT-4 available from OpenAl. [A person of ordinary skill in the art will understand that GPT stands for generative pre-trained transformer.])
Regarding claim 7 and 14 Heller in view of Guo teach claim 1, 8, and 15.
Heller further teaches:
wherein the large language model rephrases responses from the conversation flowchart based on maximizing a naturalness score associated with the responses. (P0114, The chat response message is parsed at to produce a parsed chat response at. In some embodiments, the chat response message received may include ancillary information such as all or a portion of the chat prompt message sent. Accordingly, parsing the chat response message may involve performing operations such as separating the newly generated chat response from the ancillary information included in the chat response message. For example, the response generated by the model may include information such as the name of a chat bot, which may be removed during parsing by techniques such as pattern matching.; P0119, Parsing the chat message may involve searching the chat response message for the natural language text and/or the one or more skill codes. Skill codes identified in this way may be used to influence the generation of the chat output message sent. For example, the chat output message sent may include instructions for generating one or more user interface elements such as buttons or lists allowing a user to select the recommended skill or skills. As another example, the chat output message sent may include text generated by the text generation interface system that identifies the recommended skill or skills.; P0140, Rank each response from most-reliable to least-reliable, based on the adjusted relevancy scores and how well the references support the response. Draft a concise answer to the question based only on the references and responses provided, prioritizing responses that you determined to be more reliable.)
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Heller in view of Guo and further view of Hong et al. (U.S. PG Pug No. 20240354513), hereinafter Hong.
Regarding claim 2, 9, and 16 Heller in view of Guo teach claim 1, 8, and 15.
Heller in view of Guo does not specifically teach:
further comprising prompting the user for additional information based on an answer to the user input not existing within the conversation flowchart.
Hong, however, teaches:
further comprising prompting the user for additional information based on an answer to the user input not existing within the conversation flowchart. (P0053, The user may select the icon to expand the initial prompt. In accordance with selecting the icon, the prompt user interface may suggest one or more words and/or phrases to expand the initial prompt. The suggestions may be based on the context of the initial prompt. For example, as shown in the example of FIG. 4B, the suggested expansions are “toward the surface,” “in groups of ten,” and “swiftly.” These suggestions are associated with the swimming behavior of fish.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to prompt the user for additional information. It would have been obvious to combine the references because supplementary words and/or phrases may provide additional information to address any missing details. (Hong P0055)
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Heller in view Guo and further view of Mulliganet et al. (U.S. PG Pug No. 20240267344), hereinafter Mulliganet.
Regarding claim 3, 10, and 17 Heller in view of Guo teach claim 1, 8, and 15.
Heller in view of Guo does not specifically teach:
further comprising rejecting the user input based on detecting a regulatory compliance issue associated with the user input.
Mulliganet, however, teaches:
further comprising rejecting the user input based on detecting a regulatory compliance issue associated with the user input. (P0258, The sensitive content filter component generates a response warning the user about the consequences of such action and may proactively create a task based on certain keywords if the platform policies indicates this needs to be escalated to law enforcement.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to reject user input based on regulatory compliance. It would have been obvious to combine the references because illegal, dangerous, or violative activity should not be shared with the chatbot system. (Mulliganet P0258)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL WONSUK CHUNG whose telephone number is (571)272-1345. The examiner can normally be reached Monday - Friday (7am-4pm)[PT].
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/DANIEL W CHUNG/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659