DETAILED ACTION
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/07/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
1. Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 9, and 10, “An information processing apparatus”, “A response method”, and “A computer-readable non-transitory storage medium” are recited, which are directed to one of the four statutory categories of invention (machine; process; article of manufacture; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea.
The following limitations of claim 1, and similar limitations of claims 9 and 10, under their broadest reasonable interpretation, recite mental processes:
a reception process of receiving an utterance of the user: a person reads/listens to user utterance
a generation control process of causing …to generate a response to the utterance that has been received: a person writes down a response to the utterance
and a presentation process of presenting, to the user, the response generated: a person reads the response to the person
,in a case where the at least one processor receives a second utterance in a period from a time when a first utterance is received to a time when presentation of a first response to the first utterance is completed, …starting generation of a second response based on the second utterance and presenting, to the user, the second response generated: if a person gets interrupted by a user, the person starts writing down and reading out a second response
Claims 1, 9, and 10 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional limitations are “causing the language model to generate a reasponse…” (claims, 1, 9, 10), “An information processing apparatus that makes it possible to respond in natural language to a user with use of a language model trained by machine learning, said information processing apparatus comprising at least one processor,the at least one processor carrying out:” (claim 1), “A response method that makes it possible to respond in natural language to a user with use of a language model trained by machine learning, said response method comprising:at least one processor carrying out…” (claim 9), and “A computer-readable non-transitory storage medium storing a response program that makes it possible to respond in natural language to a user with use of a language model trained by machine learning, said response program causing a computer to carry out” (claim 10). These limitations are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claims 1, 9, and 10 are directed to abstract ideas.
Claims 1, 9, and 10 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 1, 9, and 10 are not patent eligible.
Regarding claims 2-8, “The information processing apparatus” is recited, which is directed to one of the four statutory categories of invention (machine; Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes or mathematical concepts which fall into the category of abstract idea.
The following limitations, under their broadest reasonable interpretation, recite mental processes or mathematical concepts:
Claim 2:
wherein the at least one processor presents, to the user, progress information that indicates a state of progress of generation of the first response, in the period from the time when the first utterance is received to the time when presentation of the first response to the first utterance is completed: a person writes down progress information related to how close they are to finishing writing down the response
Claim 3:
in the generation control process, the at least one processor causes the language model to generate the second response, on a condition that at a time when the second utterance is received, a progress percentage of generation of the first response is less than a predetermined threshold; andthe at least one processor presents, to the user, the predetermined threshold and the progress information that indicates the progress percentage of generation of the first response, during generation of the first response: a person writes down a new response in response to a percentage of them being done with the first response less than a threshold (i.e. 50% hallway done), the the person shows the progress and threshold to the user using pen and paper
Claim 4:
carries out an interruption determination process of determining whether or not to start generation of the second response, on the basis of at least one selected from the group consisting of content of the second utterance, voice of the second utterance, an image obtained by capturing an image of the user in at least part of a period from a time when the first utterance ends to a time when the second utterance ends, and biological information of the user that is measured in at least part of the period from the time when the first utterance ends to the time when the second utterance ends: a person decides to carry out a second response based on either the words in the second content, the voice of the second content, an image of the user, and biological information of the user
Claim 5:
wherein in the interruption determination process, the at least one processor determines whether or not to start generation of the second response, by applying an interruption determination method in accordance with the user among a plurality of interruption determination methods for determining whether or not to start generation of the second response: a person determines whether or not to start generation of the second response by choosing a method/set of rules for determination
Claim 6:
carries out a determination method decision process of deciding an interruption determination method to be applied in next and subsequent interruption determination processes, on the basis of a result of evaluation on the interruption determination method that was applied in the interruption determination process: a person carries out deciding based on a result of evaluating the method applied to the interruption determination process
Claim 7:
an interruption determination method that is applied in the interruption determination process has been determined …for deciding the interruption determination method: a person decides an interruption determination method
and the at least one processor carries out an optimization process of optimizing the method decision model by carrying out, for each interruption determination method that has been decided with use of the method decision model, a process of updating the method decision model on the basis of a result of evaluation on the interruption determination method that has been decided: optimizing a method decision model amounts to mathematical concept
Claim 7 contains the additional limitation of “a method decision model”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claim 8:
an emotion estimation process of estimating an emotion of the user in at least part of a period from a time when the first utterance ends to a time when the second utterance ends; and an interruption determination process of determining, on the basis of a result of estimation in the emotion estimation process, whether or not to start generation of the second response: a person detects an emotion of a user (i.e. frustration) and decides whether or not to write down a second response based on their determined emotion
Claims 2-8 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract ideas. Therefore, claims 2-8 are directed to abstract ideas.
Claims 2-8 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer. Even when viewed in combination with the claim as a whole, mere instructions to implement the judicial exception using a generic computer do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 2-8 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
2. Claims 1, 4-7, and 9-10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rossi & Peretti (US 2024/0347058 A1, hereinafter Rossi).
Regarding claim 1, Rossi discloses An information processing apparatus (Fig. 10) that makes it possible to respond in natural language to a user with use of a language model trained by machine learning (Fig. 6B, LLM system 120, para. 0056), said information processing apparatus comprising at least one processor (Fig. 10, 1002), the at least one processor carrying out (para. 0106): a reception process of receiving an utterance of the user (Fig. 9A, 905; para. 0088 “The conversation system 110 receives 905 a first speech input from a client device 130 of a user.”); a generation control process of causing the language model to generate a response to the utterance that has been received (Fig. 9A, 910-930: para. 0088 “Upon receiving the first speech input, the conversation system 110 converts 910 the first speech input into first text input.”; para. 0089 “The conversation system 110 generates 915 a first prompt for an LLM (e.g., LLM system 120) based on the first text.”; para. 0090 “The conversation system 110 receives 925 the first text response from the LLM and converts 930 the first text response into a first speech response.”); and a presentation process of presenting, to the user, the response generated (Fig. 9A, 935, para. 0090 “The conversation system 110 transmits 935 the first speech response back to the client device, causing the client device to play the first speech response to the user.”), in a case where the at least one processor receives a second utterance in a period from a time when a first utterance is received to a time when presentation of a first response to the first utterance is completed (Fig. 9A, 940 , para. 0091 “At the same time, the conversation system 110 causes the client device 130 to monitor ambient audio and detect vocal interruptions during the conversation. In some embodiments, the conversation system 110 detects 940 a vocal interruption comprising a second speech input from the user during the play of the first speech response. Responsive to detecting the vocal interruption, the conversation system 110 causes 945 the client device to stop playing the first speech response.”), the at least one processor starting generation of a second response based on the second utterance (Fig. 9A, 950-965, para. 0091 “The client device 130 captures and sends the second speech input to the conversation system 110. Upon receiving the second speech input, the conversation system 110 generates 950 a second prompt to the LLM based on the vocal interruption, including the second speech input.”; para. 0094 “The conversation system 110 transmits 955 the second prompt to the LLM. Again, upon receiving the second prompt, the LLM generates and sends a second text response to the conversation system 110. The conversation system 110 receives 960 the second text response from the LLM and converts 965 the second text response into a second speech response.”) and presenting, to the user, the second response generated (para. 0094 “The conversation system 110 transmits 970 the second text response back to the client device 130, causing the client device 130 to play the second speech response to the user.”).
Regarding claim 4, Rossi discloses wherein the at least one processor carries out an interruption determination process of determining whether or not to start generation of the second response, on the basis of at least one selected from the group consisting of content of the second utterance, voice of the second utterance, an image obtained by capturing an image of the user in at least part of a period from a time when the first utterance ends to a time when the second utterance ends, and biological information of the user that is measured in at least part of the period from the time when the first utterance ends to the time when the second utterance ends (2nd option disclosed: para. 0091 “At the same time, the conversation system 110 causes the client device 130 to monitor ambient audio and detect vocal interruptions during the conversation. In some embodiments, the conversation system 110 detects 940 a vocal interruption comprising a second speech input from the user during the play of the first speech response. Responsive to detecting the vocal interruption, the conversation system 110 causes 945 the client device to stop playing the first speech response.”).
Regarding claim 5, Rossi discloses wherein in the interruption determination process, the at least one processor determines whether or not to start generation of the second response, by applying an interruption determination method in accordance with the user among a plurality of interruption determination methods for determining whether or not to start generation of the second response (multiple methods of interruption determination disclosed: para. 0091-0093 “Upon receiving the second speech input, the conversation system 110 generates 950 a second prompt to the LLM based on the vocal interruption, including the second speech input. Note that this second text prompt doesn't just include the second text input; it also integrates metadata related to the vocal interruption. This includes the conversation's state at the moment of interruption, a signal indicating that the user has interrupted the previous response, and/or a time interval between the start of playing the first speech response and the detection of the vocal interruption.[0092] For example, if the vocal interruption occurs within a first threshold time after the start of the first speech response, the timing might suggest that the user has not finished speaking….As another example, if the vocal interruption occurs beyond a second threshold time since the first speech response began, this timing might suggest that the user has gathered sufficient information from the response and seeks to steer the conversation toward a new topic. …”).
Regarding claim 6, Rossi discloses wherein the at least one processor carries out a determination method decision process of deciding an interruption determination method to be applied in next and subsequent interruption determination processes, on the basis of a result of evaluation on the interruption determination method that was applied in the interruption determination process (interruption determination method applied (i.e. way of process interruption) applied to next and subsequent interruption determination processes based on result of evaluation of the method applied in the interruption determination process (i.e. machine learning model to handle and response to interruptions is evaluated, such as through user feedback, to learn best way to respond to interruption): para. 0056 “The machine-learning model may be trained to generate responses based on a conversation state when an interruption is detected. The training dataset may include examples of interruptions and their corresponding metadata about their states and timing. In some embodiments, the examples are also labeled with reasons for the corresponding interruptions, such as misunderstanding, impatience, and any change in the user's tone.”; para. 0057 “In some embodiments, the training module 292 is configured to use sequence modeling to handle the temporal nature of conversations. The machine learning model 296 may be trained to understand the flow of a conversation, recognize when an interruption occurs, and determine the state of the conversation at that moment. …”; para. 0058 “In some embodiments, the training module 292 further uses user feedback received by the feedback module 280 to retrain or fine-tune the machine learning model 296. In some embodiments, the training module 292 employs continuous learning techniques to refine the machine-learning model 296 as it encounters more diverse conversation patterns and interruption types. The training module 292 regularly updates the machine learning model 296 with new data and feedback to improve its accuracy and responsiveness.”).
Regarding claim 7, Rossi discloses wherein: an interruption determination method that is applied in the interruption determination process has been determined with use of a method decision model for deciding the interruption determination method (para. 0056 “The machine-learning model may be trained to generate responses based on a conversation state when an interruption is detected. The training dataset may include examples of interruptions and their corresponding metadata about their states and timing. In some embodiments, the examples are also labeled with reasons for the corresponding interruptions, such as misunderstanding, impatience, and any change in the user's tone.”; para. 0057 “In some embodiments, the training module 292 is configured to use sequence modeling to handle the temporal nature of conversations. The machine learning model 296 may be trained to understand the flow of a conversation, recognize when an interruption occurs, and determine the state of the conversation at that moment. …”); and the at least one processor carries out an optimization process of optimizing the method decision model by carrying out, for each interruption determination method that has been decided with use of the method decision model, a process of updating the method decision model on the basis of a result of evaluation on the interruption determination method that has been decided (para. 0058 “In some embodiments, the training module 292 further uses user feedback received by the feedback module 280 to retrain or fine-tune the machine learning model 296. In some embodiments, the training module 292 employs continuous learning techniques to refine the machine-learning model 296 as it encounters more diverse conversation patterns and interruption types. The training module 292 regularly updates the machine learning model 296 with new data and feedback to improve its accuracy and responsiveness.”).
Regarding claim 9, claim 9 is a method claim with limitations similar to those recited in apparatus claim 1, and thus is rejected under similar rationale.
Regarding claim 10, claim 10 is a method claim with limitations similar to those recited in apparatus claim 1, and thus is rejected under similar rationale.
Additionally, Rossi discloses A computer-readable non-transitory storage medium storing a response program that makes it possible to respond in natural language to a user with use of a language model trained by machine learning, said response program causing a computer to carry out (para. 0106 “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.”; para. 0107 “Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus.”; see Fig. 6B, LLM system 120, para. 0056).
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.
3. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Rossi in view of Herman et al. (US 2025/0278175 A1, hereinafter Herman).
Regarding claim 2, Rossi does not specifically disclose wherein the at least one processor presents, to the user, progress information that indicates a state of progress of generation of the first response, in the period from the time when the first utterance is received to the time when presentation of the first response to the first utterance is completed.
Herman teaches wherein the at least one processor presents, to the user, progress information that indicates a state of progress of generation of the first response, in the period from the time when the first utterance is received to the time when presentation of the first response to the first utterance is completed (Fig. 12D, 1218(1)-(4), para. 0315 “As described in more detail with respect to FIGS. 12A-12Y, various audible, visible, and haptic feedback mechanisms may be employed in order inform the user of the progress in completing the ordered task list. In general, a representation of the ordered task list may be presented to the user once the ordered task list is determined. As various tasks are performed, the representation is updated to reflect tasks currently being performed and/or tasks which have been completed. Indicators may reflect one or more tasks currently being performed (e.g., a spinning wheel). In some examples, a progress bar or progress wheel may indicate a percentage or approximate amount of task completion for a particular task.”; para. 0382 “In FIG. 12G, in response to determining that each of suggested tasks 1214 is complete, device 1200 displays, in DA user interface 1204, respective response objects 1220 (i.e., response objects 1220(1), 1220(2), 1220(3), and 1220(4)).”).
Rossi and Herman are considered to be analogous to the claimed invention as
they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rossi to incorporate the teachings of Herman in order to present to the user progress information that indicates a state of progress of generation of the first response, in the period from the time when the first utterance is received to the time when presentation of the first response to the first utterance is completed. Doing so would be beneficial, as progress bars provide clear feedback to the user as to the completion of the response.
4. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rossi in view of Herman, and further in view of Kaleta & Decker (US 2013/0246962 A1, hereinafter Kaleta).
Regarding claim 3, Rossi in view of Herman discloses wherein: in the generation control process, the at least processor causes the language model to generate the second response, on a condition that at a time when the second utterance is received, a progress …of generation of the first response is less than a predetermined threshold (second response generated conditional on the time it is received is less than a predefined time threshold: para. 0068 “Due to the timing of the user's second speech 430B detected before a first threshold time of the speech playing 450, the conversation system 110 determines that the interruption is a second type of interruption, and causes the speech playing 450 to stop and generate 460B a continuer voice response, e.g., “Uhm, go on.””; Fig. 9A, 950-965, para. 0091 “The client device 130 captures and sends the second speech input to the conversation system 110. Upon receiving the second speech input, the conversation system 110 generates 950 a second prompt to the LLM based on the vocal interruption, including the second speech input.”; para. 0094 “The conversation system 110 transmits 955 the second prompt to the LLM. Again, upon receiving the second prompt, the LLM generates and sends a second text response to the conversation system 110. The conversation system 110 receives 960 the second text response from the LLM and converts 965 the second text response into a second speech response.”); and the at least one processor presents, to the user…the progress percentage of generation of the first response, during generation of the first response (Herman, Fig. 12D, 1218(1)-(4), para. 0315 “As described in more detail with respect to FIGS. 12A-12Y, various audible, visible, and haptic feedback mechanisms may be employed in order inform the user of the progress in completing the ordered task list. In general, a representation of the ordered task list may be presented to the user once the ordered task list is determined. As various tasks are performed, the representation is updated to reflect tasks currently being performed and/or tasks which have been completed. Indicators may reflect one or more tasks currently being performed (e.g., a spinning wheel). In some examples, a progress bar or progress wheel may indicate a percentage or approximate amount of task completion for a particular task.”; para. 0382 “In FIG. 12G, in response to determining that each of suggested tasks 1214 is complete, device 1200 displays, in DA user interface 1204, respective response objects 1220 (i.e., response objects 1220(1), 1220(2), 1220(3), and 1220(4)).”).
Rossi and Herman are considered to be analogous to the claimed invention as
they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rossi to incorporate the teachings of Herman in order to present to the user the progress percentage of generation of the first response, during generation of the first response. Doing so would be beneficial, as progress bars provide clear feedback to the user as to the completion of the response.
Rossi in view of Herman discloses the concept of generating a progress percentage of generation of a first response (see above claim mapping for Herman), but does not specifically disclose to [generate…on a condition that at a time when the second utterance is received, ] a progress percentage of generation of the first response is less than a predetermined threshold] and [the at least one processor presents, to the user, the predefined threshold];
Kaleta discloses a conditional state of determining at a particular time a progress percentage of generation of the response is less than a predetermined threshold (Kaleta teaches determination of whether a triviality percentage threshold is met/not met, with the threshold not being met reading on the percentage being less than the predetermined threshold: para. 0031 “In some embodiments, a visual cue can be provided to alert a user when a threshold indicating that a task is "substantially complete" (also referred to as a triviality threshold) is met. As such, progress bars can provide a visual cue to a user to indicate when subtasks of a task of interest to the user is complete.”; para. 0035 “The terms "status of task" or "progress of task" can be used herein to refer to the amount or percentage of the task or operation that is complete at a moment in time. The terms "progress status indicator," "completion status indicator," or "status indicator" can be used herein to refer to a representation of a status or progress of a task in a progress bar”; para. 0046 “The triviality threshold may be application and task-context specific. For example, if the task is loading of a web page, the triviality threshold can be set to a threshold percentage of the web page being loaded, for example, when the page is deemed "substantially loaded" or "visually complete." In some embodiments, rocket effect generator 120 can determine a triviality threshold using a set of criteria. For example, the triviality threshold can be met when 30% of a web page has been loaded.”) and presents, to the user, the predefined threshold (para. 0032 “For example, a GUI element may be visually enabled in response to the position or completion status indicated by the completion status indicator in a progress bar. In some instances, the GUI may include a button (e.g., a button corresponding to a reader function) that is visually enabled (i.e., its function enabled) upon the triviality threshold being met and the completion status indicator animated to show completed status.”; para. 0064 “In some embodiments, upon determining that a threshold is met, browser application can display a visual cue or a type of animation to indicate to the user that the task is "substantially complete" or that the web page is "visually complete to a user."”).
Ross, Herman, and Kaleta are considered to be analogous to the claimed invention as Ross and Herman are in the same field of natural language processing and Kaleta is in the same field of graphical user interfaces. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rossi in view of Herman to incorporate the teachings of Kaleta in order to detect a condition at a time that a progress percentage of generation of the first response is less than a predetermined threshold, and to present to the user the predetermined threshold. Doing so would be beneficial, as this would provide a clear indication to the user of when the task is substantially complete (para. 0031).
5. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rossi in view of Herman, and further in view of Madwed et al. (US 2021/0375272 A1, hereinafter Madwed).
Regarding claim 8, Rossi discloses an emotion estimation process of estimating an emotion of the user in at least part of a period from a time when the first utterance ends to a time when the second utterance ends (while second utterance is happening, frustration detection performed: para. 0071 “Furthermore, during the user's speech 416D, the conversation system 110 also evaluates a tone of the user and identifies 434D whether the user is frustrated based on the determined tone of the user. In some embodiments, the conversation system 110 applies a machine learning model 296 to data associated with the user speech 416D to determine the tone of the user.”).
Rossi further discloses an interruption determination process (Fig. 4A-C, interruption type detected based on timing information) but does not specifically disclose an interruption determination process of determining, on the basis of a result of estimation in the emotion estimation process, whether or not to start generation of the second response.
Madwed teaches determining, on the basis of a result of estimation in the emotion estimation process, whether or not to start generation of the second response (para. 0034 “In one example, the user input may be part of a multi-turn dialogue exchange/session between the user 5 and the system(s) 120.”; para. 0035 “The system(s) 120 determines (136) if the user is frustrated using, for example, sentiment data corresponding to the user input. The sentiment data may be determined by the system(s) 120 based on acoustic characteristics of the audio data representing the user input, where processing/evaluation of the acoustic characteristics indicates that the user is frustrated, agitated, angry, disappointed, disgust, or is exhibiting other similar sentiments.”; para. 0036 “If the system(s) 120 determines that the user is not frustrated, then the system(s) 120 performs (136) an action responsive to the user input.”; para. 0037 “If the system(s) 120 determines that the user is frustrated, then the system(s) 120 performs step 138…”; para. 0038 “If the user input is a repeat of a prior user input, then the system(s) 120 performs step 140…”; Fig. 1A, responsive to user frustration, a second response can be generated (142 or 144); for user inputs without frustration, a second response is not generated (136)).
Rossi, Herman, and Madwed are considered to be analogous to the claimed invention as they are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rossi in view of Herman to incorporate the teachings of Madwed in order to determine, on the basis of a result of estimation in the emotion estimation process, whether or not to start generation of a second response. Doing so would be beneficial, as such process would reduce user frustration and lead to a more desirable user experience (para. 0028).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jansson et al. (US 2026/0141895 A1): determination of whether audio input represents an intended interruption of the AI assistant speech, tracks portion of response that was actually spoken (Abstract)
Varshney et al. (US 12,002,471 B2): detecting interruption during generation of response, refrain from generating response while interruption is occurring, generate reply after interruption (Fig. 1A)
Acero et al. (US 2018/0330723 A1): detecting additional speech after initial speech utterance, forgoing presentation of results from a first performance, and initiating second performance of natural language processing to instead present second results (Fig. 10)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CODY DOUGLAS HUTCHESON whose telephone number is (703)756-1601. The examiner can normally be reached M-F 8:00AM-5:00PM EST.
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/CODY DOUGLAS HUTCHESON/ Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/ Supervisory Patent Examiner, Art Unit 2659
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