System

Abstract

An object of a system according to an embodiment is to receive complaints, complaints, and complaints of a user and to make the user feel positive.SOLUTION: A system includes an input unit, a generation unit, and a response unit. The input unit receives complaints, complaints, and complaints of a user. The generation unit analyzes the content received by the input unit. The response unit generates a nod based on the analysis result generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide a means for users to properly receive their complaints, dissatisfaction, and complaints and to turn them into positive people, so there is room for improvement.

[0005] The system according to the embodiment aims to listen to the user's complaints, dissatisfaction, and grumbling and to help them feel more positive. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and a response unit. The input unit receives complaints, dissatisfaction, and grumbling from users. The generation unit analyzes the content received by the input unit. The response unit generates a nod based on the analysis result generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accept the user's complaints, dissatisfaction, and grumbling and make them feel positive. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses generative AI to encourage people to confide their worries to Mr. Tanuki, providing hints on how to be more positive and helping them realize that the world isn't so bad. This system allows users to launch an app and write down their complaints, dissatisfaction, and grievances. Mr. Tanuki responds by nodding, allowing the user to organize their problems and find solutions. This allows the user to confide their worries to Mr. Tanuki and receive hints on how to be more positive. For example, by talking to Mr. Tanuki about work stress or interpersonal worries, the user can organize their problems and find solutions. Furthermore, by supporting anger management and a bird's-eye view of things, the system allows users to calmly deal with problems.

[0029] The system according to the embodiment includes an input unit, a generation unit, and a response unit. The input unit accepts user complaints, dissatisfaction, and groaning. For example, a user may enter content such as "My work isn't going well" or "My relationship with my friend isn't going well" into the app. The generation unit analyzes the content accepted by the input unit. For example, a generation AI performs text analysis to evaluate the emotional intensity of the input content. The generation unit can also refer to the user's past input history and suggest solutions from other users with similar concerns. The response unit generates a nod for the raccoon dog based on the analysis results generated by the generation unit. For example, the raccoon dog nods in a way that indicates "yes, yes" or "that's right." This allows the system to help the user organize their problems and find a solution. For example, the generation AI performs real-time emotional analysis of the complaints, dissatisfaction, and groaning entered by the user into the app and quantifies the emotional intensity. This allows the raccoon dog's response to be adjusted. It is also possible to develop an algorithm for analyzing user input and evaluating the emotional intensity. Furthermore, we can build a system that adjusts the intensity and frequency of Tanuki's nods based on the results of emotion analysis. This allows us to adjust the intensity and frequency of Tanuki's nods by evaluating the emotional intensity of the user's input.

[0030] The generation unit can refer to the input history and present solutions from other users with similar problems. For example, the generation unit stores the user's past input history in a database and builds a system that searches for solutions from other users with similar problems. For example, it presents how users with similar problems have solved them in the past. The generation unit also develops an algorithm that analyzes the user's input content and compares it with the past input history. For example, it uses text mining technology to extract similar problems. The generation unit also designs an interface for presenting solutions from other users with similar problems. For example, it displays the solutions in list format so that the user can refer to them. This allows the user to refer to the user's past input history and present solutions from other users with similar problems.

[0031] The input unit is equipped with a voice input function, allowing users to input complaints, dissatisfaction, and complaints by speaking. For example, the input unit adds a voice input function to an app, allowing users to input complaints, dissatisfaction, and complaints by speaking. For example, it uses a microphone to convert speech to text. The input unit also builds a system that uses voice recognition technology to convert what the user says into text in real time. For example, it automatically converts voice input content into text. The input unit also uses the voice input function to design an interface that allows users to input complaints, dissatisfaction, and complaints by speaking. For example, it adds a voice input button to make it easy for users to input by voice. This reduces the effort required for input by allowing users to input complaints, dissatisfaction, and complaints by speaking.

[0032] The generation unit can automatically categorize content entered by a user and customize responses for specific categories. The generation unit, for example, develops an algorithm for automatically categorizing content entered by a user. For example, it performs text analysis and determines categories based on specific keywords. The generation unit also builds a system for customizing Tanuki-kun's responses according to the categorized content. For example, it sets responses such as "Uh huh" for work-related worries and "That's right" for relationship worries. The generation unit also designs an interface for automatically categorizing content entered by a user and customizing Tanuki-kun's responses for specific categories. For example, it displays different responses for each category. This allows the content entered by a user to be automatically categorized and Tanuki-kun's responses for specific categories to be customized, enabling more appropriate responses.

[0033] The response unit can diversify the nodding patterns and nod in different ways depending on the user's input. For example, the response unit programs multiple nodding patterns to diversify the raccoon's nodding patterns. For example, patterns such as a light nod, a deep nod, and a slow nod are prepared. The response unit also develops an algorithm that causes the raccoon to nod in different ways depending on the user's input. For example, it selects an appropriate nodding way depending on the intensity and content of the emotion. The response unit also designs an interface that diversifies the raccoon's nodding patterns and nods in different ways depending on the user's input. For example, it automatically changes the nodding way based on the input content. This diversifies the raccoon's nodding patterns and causes the raccoon to nod in different ways depending on the user's input, enabling a more natural response.

[0034] The response unit can change the facial expression and movements when nodding based on the user's input. For example, the response unit programs multiple facial expressions and movements to change the facial expression and movements of Mr. Tanuki when he nods. For example, variations such as nodding with a smiling expression and nodding with a serious expression are provided. The response unit also develops an algorithm to change the facial expression and movements of Mr. Tanuki when he nods based on the user's input. For example, it selects an appropriate facial expression and movement depending on the intensity and content of the emotion. The response unit also designs an interface to change the facial expression and movements of Mr. Tanuki when he nods. For example, it automatically changes the facial expression and movements based on the input. This allows for a response that is more in tune with the emotion by changing the facial expression and movements of Mr. Tanuki when he nods.

[0035] The response unit can add simple gestures and actions in addition to nodding to achieve more realistic reactions. For example, the response unit programs simple gestures and actions in addition to Mr. Tanuki's nodding. For example, actions such as waving, tilting the head, and shrugging the shoulders can be added. The response unit also develops an algorithm that adds simple gestures and actions in addition to Mr. Tanuki's nodding. For example, it selects appropriate gestures and actions depending on the user's input. The response unit also designs an interface that adds simple gestures and actions in addition to Mr. Tanuki's nodding. For example, it automatically changes gestures and actions based on the input. In this way, by adding simple gestures and actions in addition to Mr. Tanuki's nodding, more realistic reactions can be achieved.

[0036] The response unit can customize the sound of the nodding sound and enable the user to select it. For example, the response unit prepares a plurality of sound patterns to customize the sound of the raccoon dog's nodding sound. For example, the response unit allows the user to select sounds such as "uh-huh," "that's right," and "I understand." The response unit also designs an interface that allows the user to select the sound of the raccoon dog's nodding sound. For example, the response unit allows the user to select the sound pattern on a settings screen. The response unit also develops an algorithm to customize the sound of the raccoon dog's nodding sound. For example, the response unit builds a system that changes the sound pattern according to the user's preferences. This allows the sound of the raccoon dog's nodding sound to be customized and enabled for the user to select it, thereby enabling more personalized responses.

[0037] The generation unit can optimize the algorithm for the generation AI to only nod appropriately based on the user's input. For example, the generation unit optimizes the algorithm for the generation AI to only nod appropriately based on the user's input. For example, the generation unit adjusts the timing and strength of nodding based on the results of emotional analysis of the input. The generation unit also analyzes the user's input and develops an algorithm for only nodding appropriately. For example, it uses text mining technology to extract important points from the input and nods accordingly. The generation unit also collects user feedback to improve the accuracy of the algorithm in order to optimize the algorithm for the generation AI to only nod appropriately. For example, it evaluates the user's satisfaction with the timing and strength of nodding. This allows the generation AI to optimize the algorithm for only nodding appropriately, enabling more accurate responses.

[0038] The generation unit can refer to the user's past input history and learn the appropriate timing to nod without providing any advice. The generation unit, for example, stores the user's past input history in a database and builds a system that learns the appropriate timing to nod. For example, it analyzes past input content and the timing of nodding in response to it. The generation unit also refers to the user's past input history and develops an algorithm that learns the appropriate timing to nod without providing any advice. For example, it predicts the optimal timing to nod based on past data. The generation unit also designs an interface for learning the appropriate timing to nod based on the user's past input history. For example, it visualizes past input content and the timing of nodding in response to it. This allows the system to refer to the user's past input history and learn the appropriate timing to nod, enabling more natural responses.

[0039] The response unit can add a simple voice response in accordance with the user's input content in addition to a nod. For example, the response unit builds a system that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it programs voices such as "uh-huh" and "that's right." The response unit also develops an algorithm that adds a simple voice response in accordance with the user's input content. For example, it selects an appropriate voice response based on the results of an emotional analysis of the input content. The response unit also designs an interface that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it automatically plays a voice response based on the input content. In this way, by adding a simple voice response in addition to the raccoon's nod, more realistic responses are possible.

[0040] The response unit can customize the background image and animation when the raccoon nods in response to content input by the user. For example, the response unit builds a system that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image changes depending on the input content. The response unit also develops an algorithm that customizes the background image and animation when the raccoon nods. For example, the response unit selects an appropriate background image and animation based on the results of emotion analysis of the input content. The response unit also designs an interface that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image and animation change automatically based on the input content. This enables more personalized responses by customizing the background image and animation when the raccoon nods.

[0041] The generation unit can refer to the user's past input history and learn the responses of an attentive listening specialist. For example, the generation unit stores the user's past input history in a database and builds a system that learns Tanuki's responses as an attentive listening specialist. For example, it analyzes past input content and responses thereto. The generation unit also refers to the user's past input history and develops an algorithm that learns Tanuki's responses as an attentive listening specialist. For example, it predicts the optimal response based on past data. The generation unit also designs an interface for learning Tanuki's responses as an attentive listening specialist based on the user's past input history. For example, it visualizes past input content and responses thereto. This makes it possible to refer to the user's past input history and learn Tanuki's responses as an attentive listening specialist, thereby enabling more appropriate responses.

[0042] The response unit can add a simple voice response in accordance with the user's input content in addition to a nod. For example, the response unit builds a system that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it programs voices such as "uh-huh" and "that's right." The response unit also develops an algorithm that adds a simple voice response in accordance with the user's input content. For example, it selects an appropriate voice response based on the results of an emotional analysis of the input content. The response unit also designs an interface that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it automatically plays a voice response based on the input content. In this way, by adding a simple voice response in addition to the raccoon's nod, more realistic responses are possible.

[0043] The response unit can customize the background image and animation when the raccoon nods in response to content input by the user. For example, the response unit builds a system that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image changes depending on the input content. The response unit also develops an algorithm that customizes the background image and animation when the raccoon nods. For example, the response unit selects an appropriate background image and animation based on the results of emotion analysis of the input content. The response unit also designs an interface that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image and animation change automatically based on the input content. This enables more personalized responses by customizing the background image and animation when the raccoon nods.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The system may include an advice unit that provides appropriate advice based on user input. For example, if a user inputs "Things aren't going well at work," the advice unit may provide specific advice such as "I recommend taking a break" or "It would be good to break down your tasks into smaller pieces." The advice unit may also refer to the user's past input history and evaluate the effectiveness of previously provided advice. For example, if a user previously inputs "My relationship with my friends isn't going well," the advice unit may advise "Try to communicate more" and evaluate whether the relationship has improved based on subsequent input. Furthermore, the advice unit may provide advice incorporating the opinions of experts based on the user's input. For example, the advice unit may generate advice based on the opinions of psychologists or counselors. This allows the user to obtain specific and practical advice that helps solve problems.

[0046] The system may include a reminder module that sets appropriate reminders based on user input. For example, if a user inputs "I want to exercise every day," the reminder module may suggest, "Would you like to set a reminder to exercise every morning at 7:00?" The reminder module may also refer to the user's past input history and set periodic reminders. For example, if a user previously inputs, "I want to drink more water," the reminder module may suggest, "Would you like to set a reminder to drink water every two hours every day?" The reminder module may also provide customized reminders based on the user's input. For example, the reminder module may set a reminder such as, "Try meditating for five minutes every day to reduce stress." This allows the user to receive support in achieving their goals in their daily lives.

[0047] The system may include a relaxation unit that suggests appropriate relaxation methods based on user input. For example, if a user inputs "I'm stressed," the relaxation unit may suggest specific relaxation methods such as "Try taking deep breaths" or "Try taking a short walk." The relaxation unit may also refer to the user's past input history and evaluate the effectiveness of previously suggested relaxation methods. For example, if a user previously inputs "I feel anxious," the relaxation unit may suggest "Try meditation" and evaluate whether the user's anxiety has been reduced based on subsequent inputs. Furthermore, the relaxation unit may suggest relaxation methods incorporating expert opinions based on the user's input. For example, the relaxation unit may provide relaxation methods based on the opinions of psychologists and counselors. This allows users to obtain specific and practical relaxation methods, helping to reduce stress and anxiety.

[0048] The system may include a goal setting unit that supports appropriate goal setting based on user input. For example, if a user inputs "I want to exercise more," the goal setting unit may suggest, "Set a goal of exercising for 30 minutes three times a week." The goal setting unit may also refer to the user's past input history to set more realistic goals. For example, if a user previously inputs, "I want to go on a diet," the goal setting unit may suggest, "Aim to lose 2 kg in one month." Furthermore, the goal setting unit may provide specific steps for achieving a goal based on the user's input. For example, it may suggest a specific action plan, such as, "Start by walking for 10 minutes every day." This allows the user to set specific, achievable goals and receive support toward achieving them.

[0049] The system may include a learning support unit that provides appropriate learning resources based on user input. For example, if a user inputs "I want to learn a new skill," the learning support unit may suggest specific learning resources such as "Try taking an online course" or "Try reading related books." The learning support unit may also refer to the user's past input history and evaluate the effectiveness of previously provided learning resources. For example, if a user previously inputs "I want to learn programming," the learning support unit may suggest "Try taking this online course" and evaluate the user's learning progress based on subsequent input. Furthermore, the learning support unit may provide learning resources that incorporate expert opinions based on the user's input. For example, learning resources that reference the opinions of educators and experts may be provided. This allows the user to obtain specific and effective learning resources, helping to improve their skills.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The input section accepts the user's complaints, dissatisfaction, and grievances. For example, the user enters content such as "My work isn't going well" or "My relationship with my friend isn't going well" into the app. Step 2: The generator analyzes the content received by the input unit. For example, the generator AI performs text analysis to evaluate the emotional intensity of the input content. The generator can also refer to the user's past input history and suggest solutions from other users with similar concerns. Step 3: The response unit generates a raccoon dog nod based on the analysis results generated by the generation unit. For example, the raccoon dog nods in a way that indicates "yes, yes" or "that's right." This allows the system to help the user organize their own problems and find solutions.

[0052] (Example 2) A system according to an embodiment of the present invention uses generative AI to encourage people to confide their worries to Mr. Tanuki, providing hints on how to be more positive and helping them realize that the world isn't so bad. This system allows users to launch an app and write down their complaints, dissatisfaction, and grievances. Mr. Tanuki responds by nodding, allowing the user to organize their problems and find solutions. This allows the user to confide their worries to Mr. Tanuki and receive hints on how to be more positive. For example, by talking to Mr. Tanuki about work stress or interpersonal worries, the user can organize their problems and find solutions. Furthermore, by supporting anger management and a bird's-eye view of things, the system allows users to calmly deal with problems.

[0053] The system according to the embodiment includes an input unit, a generation unit, and a response unit. The input unit accepts user complaints, dissatisfaction, and groaning. For example, a user may enter content such as "My work isn't going well" or "My relationship with my friend isn't going well" into the app. The generation unit analyzes the content accepted by the input unit. For example, a generation AI performs text analysis to evaluate the emotional intensity of the input content. The generation unit can also refer to the user's past input history and suggest solutions from other users with similar concerns. The response unit generates a nod for the raccoon dog based on the analysis results generated by the generation unit. For example, the raccoon dog nods in a way that indicates "yes, yes" or "that's right." This allows the system to help the user organize their problems and find a solution. For example, the generation AI performs real-time emotional analysis of the complaints, dissatisfaction, and groaning entered by the user into the app and quantifies the emotional intensity. This allows the raccoon dog's response to be adjusted. It is also possible to develop an algorithm for analyzing user input and evaluating the emotional intensity. Furthermore, we can build a system that adjusts the intensity and frequency of Tanuki's nods based on the results of emotion analysis. This allows us to adjust the intensity and frequency of Tanuki's nods by evaluating the emotional intensity of the user's input.

[0054] The generation unit can perform emotion analysis based on the input content and evaluate the emotional intensity of the input content. For example, the generation AI analyzes the content of complaints, dissatisfaction, and grumbling entered by the user into the app in real time and quantifies the intensity of the emotion. For example, it evaluates the intensity of anger or sadness and adjusts Tanuki's response. The generation unit also analyzes the user's input content and develops an algorithm to evaluate the intensity of the emotion. For example, it measures the intensity of emotion based on specific keywords or phrases in the text. The generation unit also builds a system that adjusts the intensity and frequency of Tanuki's nods based on the results of the emotion analysis. For example, if the intensity of the emotion is high, Tanuki will nod more vigorously. This allows the intensity and frequency of Tanuki's nods to be adjusted by evaluating the emotional intensity of the user's input content.

[0055] The generation unit can refer to the input history and present solutions from other users with similar problems. For example, the generation unit stores the user's past input history in a database and builds a system that searches for solutions from other users with similar problems. For example, it presents how users with similar problems have solved them in the past. The generation unit also develops an algorithm that analyzes the user's input content and compares it with the past input history. For example, it uses text mining technology to extract similar problems. The generation unit also designs an interface for presenting solutions from other users with similar problems. For example, it displays the solutions in list format so that the user can refer to them. This allows the user to refer to the user's past input history and present solutions from other users with similar problems.

[0056] The generation unit uses an emotion estimation function to analyze the user's emotional state in real time when inputting text and nod at the appropriate timing. The generation unit is equipped with an emotion estimation function to analyze the user's emotional state in real time when inputting text. For example, it performs text analysis of the input content and detects changes in emotion. The generation unit also uses the emotion estimation function to build a system that optimizes the timing of Tanuki's nod according to the user's emotional state. For example, it makes Tanuki nod at the moment when emotions are heightened. The generation unit also monitors the user's emotional state in real time and develops an algorithm for making Tanuki nod at the appropriate timing. For example, it emphasizes nodding at the peak of emotion. This allows the system to analyze the user's emotional state in real time and make Tanuki nod at the appropriate timing, thereby responding in line with the user's emotions.

[0057] The input unit is equipped with a voice input function, allowing users to input complaints, dissatisfaction, and complaints by speaking. For example, the input unit adds a voice input function to an app, allowing users to input complaints, dissatisfaction, and complaints by speaking. For example, it uses a microphone to convert speech to text. The input unit also builds a system that uses voice recognition technology to convert what the user says into text in real time. For example, it automatically converts voice input content into text. The input unit also uses the voice input function to design an interface that allows users to input complaints, dissatisfaction, and complaints by speaking. For example, it adds a voice input button to make it easy for users to input by voice. This reduces the effort required for input by allowing users to input complaints, dissatisfaction, and complaints by speaking.

[0058] The generation unit can automatically categorize content entered by a user and customize responses for specific categories. The generation unit, for example, develops an algorithm for automatically categorizing content entered by a user. For example, it performs text analysis and determines categories based on specific keywords. The generation unit also builds a system for customizing Tanuki-kun's responses according to the categorized content. For example, it sets responses such as "Uh huh" for work-related worries and "That's right" for relationship worries. The generation unit also designs an interface for automatically categorizing content entered by a user and customizing Tanuki-kun's responses for specific categories. For example, it displays different responses for each category. This allows the content entered by a user to be automatically categorized and Tanuki-kun's responses for specific categories to be customized, enabling more appropriate responses.

[0059] The generation unit uses the emotion estimation function to automatically generate background music and sound effects according to the input content, thereby soothing the user's emotions. For example, the generation unit uses the emotion estimation function to build a system that automatically generates background music and sound effects according to the user's input content. For example, calm music is played for sad content, and relaxing music is played for angry content. The generation unit also analyzes the user's emotional state in real time and develops an algorithm that automatically generates background music and sound effects according to the user's input content. For example, the tempo and volume of the music are adjusted according to the intensity of the emotion. The generation unit also uses the emotion estimation function to design an interface that automatically generates background music and sound effects according to the input content. For example, appropriate music is played as soon as the user starts input. In this way, the user's emotions can be soothed by automatically generating background music and sound effects according to the user's input content.

[0060] The response unit can diversify the nodding patterns and nod in different ways depending on the user's input. For example, the response unit programs multiple nodding patterns to diversify the raccoon's nodding patterns. For example, patterns such as a light nod, a deep nod, and a slow nod are prepared. The response unit also develops an algorithm that causes the raccoon to nod in different ways depending on the user's input. For example, it selects an appropriate nodding way depending on the intensity and content of the emotion. The response unit also designs an interface that diversifies the raccoon's nodding patterns and nods in different ways depending on the user's input. For example, it automatically changes the nodding way based on the input content. This diversifies the raccoon's nodding patterns and causes the raccoon to nod in different ways depending on the user's input, enabling a more natural response.

[0061] The response unit can change the facial expression and movements when nodding based on the user's input. For example, the response unit programs multiple facial expressions and movements to change the facial expression and movements of Mr. Tanuki when he nods. For example, variations such as nodding with a smiling expression and nodding with a serious expression are provided. The response unit also develops an algorithm to change the facial expression and movements of Mr. Tanuki when he nods based on the user's input. For example, it selects an appropriate facial expression and movement depending on the intensity and content of the emotion. The response unit also designs an interface to change the facial expression and movements of Mr. Tanuki when he nods. For example, it automatically changes the facial expression and movements based on the input. This allows for a response that is more in tune with the emotion by changing the facial expression and movements of Mr. Tanuki when he nods.

[0062] The response unit can use the emotion estimation function to adjust the intensity and frequency of nodding according to the user's emotional state. For example, the response unit uses the emotion estimation function to build a system that adjusts the intensity and frequency of nodding according to the user's emotional state. For example, when the intensity of emotion is high, Tanuki nods more strongly and frequently. The response unit also analyzes the user's emotional state in real time and develops an algorithm that adjusts the intensity and frequency of nodding accordingly. For example, it dynamically changes the nodding pattern according to changes in emotion. The response unit also uses the emotion estimation function to design an interface that adjusts the intensity and frequency of nodding according to the user's emotional state. For example, it automatically adjusts the intensity and frequency of nodding according to the intensity of emotion. This enables more appropriate responses by adjusting the intensity and frequency of nodding according to the user's emotional state.

[0063] The response unit can add simple gestures and actions in addition to nodding to achieve more realistic reactions. For example, the response unit programs simple gestures and actions in addition to Mr. Tanuki's nodding. For example, actions such as waving, tilting the head, and shrugging the shoulders can be added. The response unit also develops an algorithm that adds simple gestures and actions in addition to Mr. Tanuki's nodding. For example, it selects appropriate gestures and actions depending on the user's input. The response unit also designs an interface that adds simple gestures and actions in addition to Mr. Tanuki's nodding. For example, it automatically changes gestures and actions based on the input. In this way, by adding simple gestures and actions in addition to Mr. Tanuki's nodding, more realistic reactions can be achieved.

[0064] The response unit can customize the sound of the nodding sound and enable the user to select it. For example, the response unit prepares a plurality of sound patterns to customize the sound of the raccoon dog's nodding sound. For example, the response unit allows the user to select sounds such as "uh-huh," "that's right," and "I understand." The response unit also designs an interface that allows the user to select the sound of the raccoon dog's nodding sound. For example, the response unit allows the user to select the sound pattern on a settings screen. The response unit also develops an algorithm to customize the sound of the raccoon dog's nodding sound. For example, the response unit builds a system that changes the sound pattern according to the user's preferences. This allows the sound of the raccoon dog's nodding sound to be customized and enabled for the user to select it, thereby enabling more personalized responses.

[0065] The response unit can use the emotion estimation function to increase the variety of nods according to the user's emotional state. For example, the response unit uses the emotion estimation function to build a system that increases the variety of raccoon dog nods according to the user's emotional state. For example, different nodding patterns are selected depending on the intensity and type of emotion. The response unit also analyzes the user's emotional state in real time and develops an algorithm that increases the variety of raccoon dog nods accordingly. For example, the nodding pattern is dynamically changed depending on changes in emotion. The response unit also uses the emotion estimation function to design an interface that increases the variety of raccoon dog nods according to the user's emotional state. For example, the nodding variation is automatically changed depending on the intensity and type of emotion. This increases the variety of raccoon dog nods according to the user's emotional state, enabling more appropriate responses.

[0066] The generation unit can optimize the algorithm for the generation AI to only nod appropriately based on the user's input. For example, the generation unit optimizes the algorithm for the generation AI to only nod appropriately based on the user's input. For example, the generation unit adjusts the timing and strength of nodding based on the results of emotional analysis of the input. The generation unit also analyzes the user's input and develops an algorithm for only nodding appropriately. For example, it uses text mining technology to extract important points from the input and nods accordingly. The generation unit also collects user feedback to improve the accuracy of the algorithm in order to optimize the algorithm for the generation AI to only nod appropriately. For example, it evaluates the user's satisfaction with the timing and strength of nodding. This allows the generation AI to optimize the algorithm for only nodding appropriately, enabling more accurate responses.

[0067] The generation unit can refer to the user's past input history and learn the appropriate timing to nod without providing any advice. The generation unit, for example, stores the user's past input history in a database and builds a system that learns the appropriate timing to nod. For example, it analyzes past input content and the timing of nodding in response to it. The generation unit also refers to the user's past input history and develops an algorithm that learns the appropriate timing to nod without providing any advice. For example, it predicts the optimal timing to nod based on past data. The generation unit also designs an interface for learning the appropriate timing to nod based on the user's past input history. For example, it visualizes past input content and the timing of nodding in response to it. This allows the system to refer to the user's past input history and learn the appropriate timing to nod, enabling more natural responses.

[0068] The generation unit can use the emotion estimation function to adjust the frequency and intensity of nodding according to the user's emotional state. For example, the generation unit uses the emotion estimation function to build a system that adjusts the frequency and intensity of nodding according to the user's emotional state. For example, when the intensity of emotion is high, Tanuki nods more strongly and frequently. The generation unit also analyzes the user's emotional state in real time and develops an algorithm that adjusts the frequency and intensity of nodding accordingly. For example, it dynamically changes the nodding pattern according to changes in emotion. The generation unit also uses the emotion estimation function to design an interface that adjusts the frequency and intensity of nodding according to the user's emotional state. For example, it automatically adjusts the frequency and intensity of nodding according to the intensity of emotion. This enables more appropriate responses by adjusting the frequency and intensity of nodding according to the user's emotional state.

[0069] The response unit can add a simple voice response in accordance with the user's input content in addition to a nod. For example, the response unit builds a system that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it programs voices such as "uh-huh" and "that's right." The response unit also develops an algorithm that adds a simple voice response in accordance with the user's input content. For example, it selects an appropriate voice response based on the results of an emotional analysis of the input content. The response unit also designs an interface that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it automatically plays a voice response based on the input content. In this way, by adding a simple voice response in addition to the raccoon's nod, more realistic responses are possible.

[0070] The response unit can customize the background image and animation when the raccoon nods in response to content input by the user. For example, the response unit builds a system that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image changes depending on the input content. The response unit also develops an algorithm that customizes the background image and animation when the raccoon nods. For example, the response unit selects an appropriate background image and animation based on the results of emotion analysis of the input content. The response unit also designs an interface that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image and animation change automatically based on the input content. This enables more personalized responses by customizing the background image and animation when the raccoon nods.

[0071] The response unit can use the emotion estimation function to increase the variety of nods according to the user's emotional state. For example, the response unit uses the emotion estimation function to build a system that increases the variety of raccoon dog nods according to the user's emotional state. For example, different nodding patterns are selected depending on the intensity and type of emotion. The response unit also analyzes the user's emotional state in real time and develops an algorithm that increases the variety of raccoon dog nods accordingly. For example, the nodding pattern is dynamically changed depending on changes in emotion. The response unit also uses the emotion estimation function to design an interface that increases the variety of raccoon dog nods according to the user's emotional state. For example, the nodding variation is automatically changed depending on the intensity and type of emotion. This increases the variety of raccoon dog nods according to the user's emotional state, enabling more appropriate responses.

[0072] The generation unit allows the generation AI to perform emotion analysis based on the user's input content and optimize the appropriate timing of nodding. The generation unit, for example, builds a system in which the generation AI performs emotion analysis based on the user's input content and optimizes the appropriate timing of nodding. For example, the timing of nodding is adjusted according to the intensity and type of emotion. The generation unit also analyzes the user's input content and develops an algorithm that optimizes the appropriate timing of nodding. For example, it uses text mining technology to extract important points from the input content and nods accordingly. The generation unit also designs an interface that allows the generation AI to perform emotion analysis and optimize the appropriate timing of nodding. For example, it automatically adjusts the timing of nodding based on the input content. This allows the generation AI to perform emotion analysis and optimize the appropriate timing of nodding, enabling more accurate responses.

[0073] The generation unit can refer to the user's past input history and learn the responses of an attentive listening specialist. For example, the generation unit stores the user's past input history in a database and builds a system that learns Tanuki's responses as an attentive listening specialist. For example, it analyzes past input content and responses thereto. The generation unit also refers to the user's past input history and develops an algorithm that learns Tanuki's responses as an attentive listening specialist. For example, it predicts the optimal response based on past data. The generation unit also designs an interface for learning Tanuki's responses as an attentive listening specialist based on the user's past input history. For example, it visualizes past input content and responses thereto. This makes it possible to refer to the user's past input history and learn Tanuki's responses as an attentive listening specialist, thereby enabling more appropriate responses.

[0074] The generation unit can use the emotion estimation function to adjust the frequency and intensity of nodding according to the user's emotional state. For example, the generation unit uses the emotion estimation function to build a system that adjusts the frequency and intensity of nodding according to the user's emotional state. For example, when the intensity of emotion is high, Tanuki nods more strongly and frequently. The generation unit also analyzes the user's emotional state in real time and develops an algorithm that adjusts the frequency and intensity of nodding accordingly. For example, it dynamically changes the nodding pattern according to changes in emotion. The generation unit also uses the emotion estimation function to design an interface that adjusts the frequency and intensity of nodding according to the user's emotional state. For example, it automatically adjusts the frequency and intensity of nodding according to the intensity of emotion. This enables more appropriate responses by adjusting the frequency and intensity of nodding according to the user's emotional state.

[0075] The response unit can add a simple voice response in accordance with the user's input content in addition to a nod. For example, the response unit builds a system that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it programs voices such as "uh-huh" and "that's right." The response unit also develops an algorithm that adds a simple voice response in accordance with the user's input content. For example, it selects an appropriate voice response based on the results of an emotional analysis of the input content. The response unit also designs an interface that adds a simple voice response in accordance with the user's input content in addition to the raccoon's nod. For example, it automatically plays a voice response based on the input content. In this way, by adding a simple voice response in addition to the raccoon's nod, more realistic responses are possible.

[0076] The response unit can customize the background image and animation when the raccoon nods in response to content input by the user. For example, the response unit builds a system that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image changes depending on the input content. The response unit also develops an algorithm that customizes the background image and animation when the raccoon nods. For example, the response unit selects an appropriate background image and animation based on the results of emotion analysis of the input content. The response unit also designs an interface that customizes the background image and animation when the raccoon nods in response to content input by the user. For example, the background image and animation change automatically based on the input content. This enables more personalized responses by customizing the background image and animation when the raccoon nods.

[0077] The response unit can use the emotion estimation function to increase the variety of nods according to the user's emotional state. For example, the response unit uses the emotion estimation function to build a system that increases the variety of raccoon dog nods according to the user's emotional state. For example, different nodding patterns are selected depending on the intensity and type of emotion. The response unit also analyzes the user's emotional state in real time and develops an algorithm that increases the variety of raccoon dog nods accordingly. For example, the nodding pattern is dynamically changed depending on changes in emotion. The response unit also uses the emotion estimation function to design an interface that increases the variety of raccoon dog nods according to the user's emotional state. For example, the nodding variation is automatically changed depending on the intensity and type of emotion. This increases the variety of raccoon dog nods according to the user's emotional state, enabling more appropriate responses.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The system may include an advice unit that provides appropriate advice based on user input. For example, if a user inputs "Things aren't going well at work," the advice unit may provide specific advice such as "I recommend taking a break" or "It would be good to break down your tasks into smaller pieces." The advice unit may also refer to the user's past input history and evaluate the effectiveness of previously provided advice. For example, if a user previously inputs "My relationship with my friends isn't going well," the advice unit may advise "Try to communicate more" and evaluate whether the relationship has improved based on subsequent input. Furthermore, the advice unit may provide advice incorporating the opinions of experts based on the user's input. For example, the advice unit may generate advice based on the opinions of psychologists or counselors. This allows the user to obtain specific and practical advice that helps solve problems.

[0080] The system may include a reminder module that sets appropriate reminders based on user input. For example, if a user inputs "I want to exercise every day," the reminder module may suggest, "Would you like to set a reminder to exercise every morning at 7:00?" The reminder module may also refer to the user's past input history and set periodic reminders. For example, if a user previously inputs, "I want to drink more water," the reminder module may suggest, "Would you like to set a reminder to drink water every two hours every day?" The reminder module may also provide customized reminders based on the user's input. For example, the reminder module may set a reminder such as, "Try meditating for five minutes every day to reduce stress." This allows the user to receive support in achieving their goals in their daily lives.

[0081] The system may include a relaxation unit that suggests appropriate relaxation methods based on user input. For example, if a user inputs "I'm stressed," the relaxation unit may suggest specific relaxation methods such as "Try taking deep breaths" or "Try taking a short walk." The relaxation unit may also refer to the user's past input history and evaluate the effectiveness of previously suggested relaxation methods. For example, if a user previously inputs "I feel anxious," the relaxation unit may suggest "Try meditation" and evaluate whether the user's anxiety has been reduced based on subsequent inputs. Furthermore, the relaxation unit may suggest relaxation methods incorporating expert opinions based on the user's input. For example, the relaxation unit may provide relaxation methods based on the opinions of psychologists and counselors. This allows users to obtain specific and practical relaxation methods, helping to reduce stress and anxiety.

[0082] The system may include a goal setting unit that supports appropriate goal setting based on user input. For example, if a user inputs "I want to exercise more," the goal setting unit may suggest, "Set a goal of exercising for 30 minutes three times a week." The goal setting unit may also refer to the user's past input history to set more realistic goals. For example, if a user previously inputs, "I want to go on a diet," the goal setting unit may suggest, "Aim to lose 2 kg in one month." Furthermore, the goal setting unit may provide specific steps for achieving a goal based on the user's input. For example, it may suggest a specific action plan, such as, "Start by walking for 10 minutes every day." This allows the user to set specific, achievable goals and receive support toward achieving them.

[0083] The system may include a learning support unit that provides appropriate learning resources based on user input. For example, if a user inputs "I want to learn a new skill," the learning support unit may suggest specific learning resources such as "Try taking an online course" or "Try reading related books." The learning support unit may also refer to the user's past input history and evaluate the effectiveness of previously provided learning resources. For example, if a user previously inputs "I want to learn programming," the learning support unit may suggest "Try taking this online course" and evaluate the user's learning progress based on subsequent input. Furthermore, the learning support unit may provide learning resources that incorporate expert opinions based on the user's input. For example, learning resources that reference the opinions of educators and experts may be provided. This allows the user to obtain specific and effective learning resources, helping to improve their skills.

[0084] The system can suggest appropriate relaxation methods based on the user's emotional state. For example, if the system estimates that the user is feeling stressed based on their input, the relaxation module can suggest specific relaxation methods such as "Try taking deep breaths" or "Try taking a short walk." The relaxation module can also monitor the user's emotional state in real time and adjust the relaxation method according to changes in the user's emotions. For example, if the intensity of the emotion increases, the relaxation module can suggest a method with a more effective relaxation effect. Furthermore, the relaxation module can suggest relaxation methods based on the user's emotional state, incorporating expert opinions. For example, it can provide relaxation methods based on the opinions of psychologists and counselors. This allows the user to obtain specific and effective relaxation methods according to their emotional state, helping to reduce stress and anxiety.

[0085] The system can provide appropriate music and audio depending on the user's emotional state. For example, if the user's input indicates that they are feeling sad, the music providing unit will suggest, "Would you like to play some calming music?" The music providing unit can also monitor the user's emotional state in real time and adjust the music and audio according to changes in emotion. For example, if the intensity of the emotion increases, music with a more relaxing effect will be played. Furthermore, the music providing unit can provide music and audio incorporating expert opinions based on the user's emotional state. For example, music based on the opinions of music therapists and psychologists can be provided. This allows the user to obtain specific and effective music and audio according to their emotional state, helping to stabilize their emotions.

[0086] The system can suggest appropriate exercises and activities based on the user's emotional state. For example, if the system estimates that the user is feeling angry based on their input, the exercise suggestion unit can suggest specific exercises and activities, such as "try a light jog" or "try yoga." The exercise suggestion unit can also monitor the user's emotional state in real time and adjust exercises and activities according to changes in emotion. For example, if the intensity of an emotion increases, the system can suggest an exercise that has a more relaxing effect. Furthermore, the exercise suggestion unit can suggest exercises and activities based on the user's emotional state, incorporating expert advice. For example, the system can provide exercises based on the advice of fitness instructors and psychologists. This allows the user to find specific and effective exercises and activities that match their emotional state, helping to stabilize their emotions.

[0087] The system can provide appropriate mental health resources according to the user's emotional state. For example, if the system infers that the user is feeling anxious based on their input, the mental health resource unit can suggest specific resources such as "try online counseling" or "read articles about mental health." The mental health resource unit can also monitor the user's emotional state in real time and adjust resources according to changes in their emotions. For example, if the intensity of their emotions increases, more specialized resources can be provided. Furthermore, the mental health resource unit can provide resources that incorporate expert opinions based on the user's emotional state. For example, it can provide resources that incorporate the opinions of psychologists and counselors. This allows the user to obtain specific and effective mental health resources according to their emotional state, which helps stabilize their emotions.

[0088] The system can suggest appropriate communication methods based on the user's emotional state. For example, if the system estimates that the user feels lonely based on their input, the communication suggestion unit will suggest specific communication methods such as "contact a friend" or "join an online community." The communication suggestion unit can also monitor the user's emotional state in real time and adjust the communication method according to changes in their emotions. For example, if the intensity of their emotions increases, it will suggest a more proactive communication method. Furthermore, the communication suggestion unit can suggest communication methods that incorporate the opinions of experts based on the user's emotional state. For example, it can provide communication methods that take into account the opinions of psychologists and counselors. This allows the user to find specific and effective communication methods that suit their emotional state, helping to stabilize their emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The input section accepts the user's complaints, dissatisfaction, and grievances. For example, the user enters content such as "My work isn't going well" or "My relationship with my friend isn't going well" into the app. Step 2: The generator analyzes the content received by the input unit. For example, the generator AI performs text analysis to evaluate the emotional intensity of the input content. The generator can also refer to the user's past input history and suggest solutions from other users with similar concerns. Step 3: The response unit generates a raccoon dog nod based on the analysis results generated by the generation unit. For example, the raccoon dog nods in a way that indicates "yes, yes" or "that's right." This allows the system to help the user organize their own problems and find solutions.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for receiving user complaints, dissatisfaction, and grumbling; a generation unit that analyzes the content received by the input unit; a response unit that generates a nod based on the analysis result generated by the generation unit. A system characterized by:

2. The generation unit Performing sentiment analysis based on input to assess the emotional intensity of the input The system of claim 1 .

3. The generation unit Refer to input history and suggest solutions from other users with similar problems 2. The system of claim 1.

4. The generation unit Analyzes the user's emotional state in real time as they type, and nods at the appropriate time 2. The system of claim 1.

5. The input unit It has a voice input function, allowing users to input complaints, dissatisfaction, and complaints by speaking.

2. The system of claim 1.

Images