System

Abstract

An object of a system according to an embodiment is to grasp a feeling or a state of a partner in real time and provide appropriate advice based on the grasped feeling or state.SOLUTION: A system according to an embodiment includes a reception unit, an estimation unit, a generation unit, and a display unit. The reception unit receives an input from a user. The estimation unit estimates a feeling (joy, sadness, anger, or the like) or a state (fatigue, tension, or the like) of the partner on the basis of the information received by the reception unit. The generation unit generates advice on a topic or behavior based on the emotion or the state estimated by the estimation unit. The display unit displays the advice generated by the generation unit by AR.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 technology has the problem that it is difficult to grasp the other person's emotions and state in real time and provide appropriate advice based on that.

[0005] The system according to the embodiment aims to grasp the emotions and state of the other party in real time and provide appropriate advice based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an estimation unit, a generation unit, and a display unit. The reception unit receives input from a user. The estimation unit estimates the other person's emotions (joy, sadness, anger, etc.) and state (fatigue, tension, etc.) based on the information received by the reception unit. The generation unit generates advice on topics and behavior based on the emotions and state estimated by the estimation unit. The display unit displays the advice generated by the generation unit in AR. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the emotions and state of the other party in real time and provide appropriate advice based on that. [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) An assistance system according to an embodiment of the present invention uses emotion estimation AI based on image and voice recognition and a large-scale language model to assist with dating and sales. The assistance system accepts input from a user, estimates the other person's emotions and state, generates advice on topics and behavior, and displays it in AR. For example, the assistance system uses a camera and microphone mounted on AR glasses to estimate the other person's current emotions and the state of the date or sales situation based on the face and voice of the other person. Next, based on the emotion estimation results, information about the current location obtained from the camera (current location estimation using VPS and surrounding objects), and past conversation logs, the system generates advice on topics and behavior and displays it in AR. This allows the user to respond appropriately to the other person's emotions and situation, thereby increasing the success rate of dates and sales. The assistance system allows the user to respond appropriately to the other person's emotions and situation, thereby increasing the success rate of dates and sales. For example, the assistance system can provide information about topics of interest to the dater or products that the salesperson is interested in.

[0029] The assistance system according to the embodiment includes a reception unit, an estimation unit, a generation unit, and a display unit. The reception unit receives input from a user. The input from the user includes, but is not limited to, voice input, text input, and gesture input. The reception unit receives voice input using, for example, voice recognition technology. The reception unit can also receive text input using a text box. The reception unit can also receive gesture input using gesture recognition technology. For example, when a user issues a voice instruction, the reception unit recognizes the voice and accepts it as input. When a user inputs text, the reception unit can accept the text as input. When a user performs a gesture, the reception unit can recognize the gesture and accept it as input. The estimation unit estimates the other party's emotion or state based on the information received by the reception unit. For example, the estimation unit estimates the other party's emotion or state by combining image recognition and voice recognition. For example, the estimation unit analyzes a facial image of the other party captured by a camera and estimates the emotion from the facial expression. The estimation unit can also analyze the other person's voice recorded by a microphone and estimate their emotions from the tone and speed of their voice. The estimation unit can also integrate the results of image recognition and voice recognition to more accurately estimate their emotions and state. For example, the estimation unit combines a facial image and a tone of voice to estimate the other person's emotions. The generation unit generates advice on topics and behavior based on the emotions and state estimated by the estimation unit. The generation unit generates advice by integrating, for example, the emotion estimation result, information about the area around the current location, and past conversation logs. For example, the generation unit provides topics that the other person is interested in based on the emotion estimation result. The generation unit can also suggest suitable places for dates or business trips based on information about the area around the current location. The generation unit can also provide information about products and services that the other person is interested in based on past conversation logs. For example, the generation unit refers to past conversation logs to provide topics that the other person has previously discussed. The display unit displays the advice generated by the generation unit using AR. The display unit displays the advice using, for example, AR glasses or AR contact lenses.For example, the display unit may display advice on AR glasses, allowing the user to visually receive the advice. Alternatively, the display unit may display advice using AR contact lenses. For example, the display unit may display advice on AR contact lenses, allowing the user to visually receive the advice. This allows the assist system according to the embodiment to estimate the emotions and state of the other person based on the user's input and provide appropriate advice, thereby increasing the success rate of dates and sales.

[0030] The estimation unit can estimate the other party's emotions and state by combining image recognition and voice recognition. For example, the estimation unit analyzes the other party's facial image captured by a camera and estimates the emotion from their facial expression. For example, the estimation unit extracts the other party's facial features using face recognition technology and estimates the emotion using facial expression recognition technology. The estimation unit can also analyze the other party's voice recorded by a microphone and estimate the emotion from the tone and speed of the voice. For example, the estimation unit extracts the other party's voice features using voice recognition technology and estimates the emotion using voice tone analysis technology. The estimation unit can also integrate the results of image recognition and voice recognition to estimate the other party's emotions and state more accurately. For example, the estimation unit combines the facial image and voice tone to estimate the other party's emotions. This combination of image recognition and voice recognition improves the accuracy of estimating emotions and states. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit can input the facial image and voice tone to a generation AI and have the generation AI perform emotion estimation.

[0031] The generation unit can generate advice by integrating the emotion estimation result, information about the area around the current location, and past conversation logs. The generation unit, for example, provides topics that the other party is interested in based on the emotion estimation result. For example, the generation unit may provide fun topics based on the emotion estimation result if the other party is having fun. The generation unit can also suggest places suitable for dates or business trips based on information about the area around the current location. For example, the generation unit provides information about nearby facilities and events based on the current location estimated by the VPS. The generation unit can also provide information about products and services that the other party is interested in based on past conversation logs. For example, the generation unit refers to past conversation logs to provide topics that the other party has previously discussed. This allows for the integration of the emotion estimation result, information about the area around the current location, and past conversation logs to provide more appropriate advice. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the emotion estimation result, information about the area around the current location, and past conversation logs into a generation AI and cause the generation AI to generate advice.

[0032] The display unit can display the advice using AR glasses or AR contact lenses. The display unit, for example, displays the advice on AR glasses, allowing the user to visually receive the advice. For example, the display unit can display the advice on AR glasses, allowing the user to visually receive the advice. The display unit can also display the advice using AR contact lenses. For example, the display unit can display the advice on AR contact lenses, allowing the user to visually receive the advice. This allows the user to visually receive the advice by using AR glasses or AR contact lenses. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the advice generated by the generation unit to a generation AI and cause the generation AI to execute processing for displaying the advice on AR glasses or AR contact lenses.

[0033] The reception unit can analyze the user's past input history and select an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. For example, if the reception unit analyzes the user's past input history and has frequently used voice input, the reception unit preferentially suggests voice input. The reception unit can also preferentially suggest text input if the user has frequently used text input in the past. For example, if the reception unit analyzes the user's past input history and has frequently used text input, the reception unit preferentially suggests text input. The reception unit can also preferentially suggest gesture input if the user has frequently used gesture input in the past. For example, if the reception unit analyzes the user's past input history and has frequently used gesture input, the reception unit preferentially suggests gesture input. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0034] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, if the user is currently busy, the reception unit prioritizes receiving only important information. For example, if the reception unit analyzes the user's current situation and determines that the user is busy, it prioritizes receiving only important information. Furthermore, if the user is interested in a specific area of ​​interest, the reception unit can prioritize receiving information related to that area. For example, the reception unit analyzes the user's areas of interest and prioritizes receiving information related to the area of ​​interest. Furthermore, the reception unit can also receive a wide range of information if the user is relaxed. For example, if the reception unit analyzes the user's current situation and determines that the user is relaxed, it receives a wide range of information. By filtering based on the user's current situation and areas of interest, more appropriate information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's current situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0035] When accepting input, the acceptance unit can select an appropriate acceptance means depending on the user's input method (voice, text, gesture, etc.). For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition. For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the input using a text box. For example, if the user selects text input, the acceptance unit accepts the input using a text box. Furthermore, if the user selects gesture input, the acceptance unit can also accept the input using gesture recognition. For example, if the user selects gesture input, the acceptance unit accepts the input using gesture recognition technology. This allows for smoother input acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method to a generation AI and cause the generation AI to select the optimal acceptance means.

[0036] When receiving input, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving information related to that area. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is in a specific area, prioritizes receiving information related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is traveling, prioritizes receiving information related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving information about the area surrounding the user's home. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is at home, prioritizes receiving information about the area surrounding the user's home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0037] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit, for example, receives information related to places where the user has checked in on social media. For example, the reception unit analyzes the user's social media activity and receives information related to the checked-in places. The reception unit can also analyze the user's social media posts and receive related information. For example, the reception unit analyzes the user's social media posts and receive information about related tourist spots and stores. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the activities of the user's friends on social media and receives information about related places and events. In this way, the related information can be efficiently received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to a generation AI and cause the generation AI to receive related information.

[0038] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, preferentially suggests reception methods that the user has previously preferred. For example, the reception unit analyzes the user's past feedback and preferentially suggests the preferred reception methods. The reception unit can also eliminate reception methods that the user has previously avoided. For example, the reception unit analyzes the user's past feedback and eliminates the avoided reception methods. The reception unit can also suggest an optimal reception method based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and suggests an optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback to the generation AI and cause the generation AI to customize the reception method.

[0039] The estimation unit can improve the accuracy of estimation by integrating the results of image recognition and voice recognition during estimation. The estimation unit, for example, estimates emotions by integrating facial expression data obtained by image recognition and voice tone obtained by voice recognition. For example, the estimation unit estimates emotions by integrating facial expression data obtained using face recognition technology and voice tone obtained using voice tone analysis technology. The estimation unit can also estimate a state by integrating posture data obtained by image recognition and speaking style obtained by voice recognition. For example, the estimation unit estimates a state by integrating posture data obtained using posture recognition technology and speaking style obtained using voice recognition technology. The estimation unit can also estimate emotions by integrating eye movements obtained by image recognition and speech content obtained by speech recognition. For example, the estimation unit estimates emotions by integrating eye movements obtained using eye movement recognition technology and speech content obtained using voice recognition technology. In this way, integrating the results of image recognition and voice recognition improves the accuracy of estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the results of image recognition and voice recognition into the generation AI and have the generation AI improve the accuracy of the estimation.

[0040] During estimation, the estimation unit can improve the accuracy of the estimation by referring to the other party's past behavioral history. The estimation unit, for example, estimates the other party's current emotion by referring to an emotional pattern shown in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's current emotion based on the emotional pattern shown in the past. The estimation unit can also estimate the other party's current state based on the other party's past behavioral history. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's current state based on the past behavioral pattern. The estimation unit can also estimate the other party's emotion by comparing the other party's past behavior with their current behavior. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's emotion by comparing the past behavior with their current behavior. In this way, by referring to the other party's past behavioral history, the accuracy of the estimation is improved. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's past behavioral history into a generation AI and cause the generation AI to improve the accuracy of the estimation.

[0041] The estimation unit can make estimations taking into account the attribute information of the other party (such as age and gender). For example, the estimation unit makes estimations based on emotional patterns corresponding to the other party's age, taking into account the other party's age. For example, the estimation unit analyzes the other party's age and estimates emotions based on emotional patterns corresponding to the age. The estimation unit can also make estimations based on emotional patterns corresponding to the other party's gender, taking into account the other party's gender. For example, the estimation unit analyzes the other party's gender and estimates emotions based on emotional patterns corresponding to the gender. The estimation unit can also make estimations based on emotional patterns corresponding to the other party's occupation, taking into account the other party's occupation. For example, the estimation unit analyzes the other party's occupation and estimates emotions based on emotional patterns corresponding to the occupation. This enables more accurate estimations by taking into account the other party's attribute information. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's attribute information into a generation AI and cause the generation AI to perform estimation.

[0042] The estimation unit can make estimations taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the estimation unit makes estimations based on emotional patterns related to that area. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is in a specific area, estimates the emotion based on emotional patterns related to that area. Furthermore, if the other party is traveling, the estimation unit can make estimations based on emotional patterns related to the travel destination. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is traveling, estimates the emotion based on emotional patterns related to the travel destination. Furthermore, if the other party is at home, the estimation unit can make estimations based on emotional patterns around the home. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is at home, estimates the emotion based on emotional patterns around the home. This enables more accurate estimations by taking the other party's geographical location information into consideration. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's geographical location information to a generation AI and cause the generation AI to perform estimation.

[0043] The estimation unit can improve the accuracy of the estimation by referring to related literature and data of the other party during estimation. The estimation unit, for example, estimates emotions by referring to related literature read by the other party in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates emotions based on related literature read in the past. The estimation unit can also estimate a current state based on data referenced by the other party in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates a current state based on the data referenced in the past. The estimation unit can also estimate emotions based on literature and data in which the other party has been interested in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates emotions based on literature and data in which the other party has been interested in the past. In this way, by referring to related literature and data of the other party, the accuracy of the estimation is improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's related literature and data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0044] The estimation unit can make estimations taking into account the market value and business value of the other party. The estimation unit, for example, estimates business sentiments taking into account the market value of the other party. For example, the estimation unit analyzes the market value of the other party and estimates business sentiments according to the market value. The estimation unit can also estimate a business state taking into account the business value of the other party. For example, the estimation unit analyzes the business value of the other party and estimates a business state according to the business value. The estimation unit can also estimate business sentiments based on the market value and business value of the other party. For example, the estimation unit analyzes the market value and business value of the other party and estimates business sentiments based on the analysis. This enables more accurate estimation of business sentiments and states by taking into account the market value and business value of the other party. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the market value and business value of the other party into a generation AI and cause the generation AI to perform estimation.

[0045] When generating advice, the generation unit can improve the accuracy of the advice by integrating the emotion estimation result and information about the area around the current location. The generation unit, for example, integrates the emotion estimation result with information about the area around the current location to provide optimal advice. For example, the generation unit may provide fun topics based on the emotion estimation result if the other party is having fun. The generation unit may also suggest suitable places for dates or business trips based on information about the area around the current location. For example, the generation unit may provide information about nearby facilities and events based on the current location estimated by the VPS. The generation unit may also integrate the emotion estimation result with past conversation logs to provide optimal advice. For example, the generation unit may provide information about products and services that the other party is interested in based on the emotion estimation result. The generation unit may also integrate the emotion estimation result with information about objects near the current location to provide optimal advice. For example, the generation unit may provide topics that the other party is interested in based on the emotion estimation result. In this way, by integrating the emotion estimation result and information about the area around the current location, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input emotion estimation results and information about the current location into the generation AI and have the generation AI generate advice.

[0046] When generating advice, the generation unit can improve the accuracy of the advice by referring to past conversation logs. For example, the generation unit can refer to past conversation logs and provide advice based on topics that the other party is interested in. For example, the generation unit can analyze past conversation logs and provide advice based on topics that the other party has previously discussed. The generation unit can also refer to past conversation logs and provide advice based on topics that the other party wants to avoid. For example, the generation unit can analyze past conversation logs and provide advice based on topics that the other party wants to avoid. The generation unit can also refer to past conversation logs and provide advice about products or services that the other party is interested in. For example, the generation unit can analyze past conversation logs and provide advice about products or services that the other party is interested in. In this way, more appropriate advice can be provided by referring to past conversation logs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past conversation logs into a generation AI and cause the generation AI to generate advice.

[0047] When generating advice, the generation unit can generate advice taking into account the other party's attribute information (age, gender, etc.). The generation unit, for example, takes into account the other party's age and provides advice appropriate to their age. For example, the generation unit analyzes the other party's age and provides advice appropriate to their age. The generation unit can also take into account the other party's gender and provide advice appropriate to their gender. For example, the generation unit analyzes the other party's gender and provides advice appropriate to their gender. The generation unit can also take into account the other party's occupation and provide advice appropriate to their occupation. For example, the generation unit analyzes the other party's occupation and provides advice appropriate to their occupation. In this way, more appropriate advice can be provided by taking into account the other party's attribute information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's attribute information into the generation AI and cause the generation AI to generate advice.

[0048] When generating advice, the generation unit can generate advice taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the generation unit provides advice related to that area. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is in a specific area, the generation unit provides advice related to that area. Furthermore, if the other party is traveling, the generation unit can provide advice related to the travel destination. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is traveling, the generation unit provides advice related to the travel destination. Furthermore, if the other party is at home, the generation unit can provide advice based on information about the area around the home. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is at home, the generation unit provides advice based on information about the area around the home. In this way, more appropriate advice can be provided by taking the other party's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's geographical location information to the generation AI and cause the generation AI to generate advice.

[0049] When generating advice, the generation unit can improve the accuracy of the advice by referring to related literature and data of the other party. The generation unit, for example, provides advice by referring to related literature that the other party has read in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice based on related literature that the other party has read in the past. The generation unit can also provide advice tailored to the other party's current situation based on data that the other party has referenced in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice tailored to the current situation based on the data that the other party has referenced in the past. The generation unit can also provide advice based on literature and data that the other party has been interested in in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice based on literature and data that the other party has been interested in in the past. In this way, by referring to the other party's related literature and data, the accuracy of the advice is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's related literature and data into a generation AI and cause the generation AI to generate advice.

[0050] When generating advice, the generation unit can generate advice taking into account the market value and business value of the other party. The generation unit, for example, provides business advice taking into account the market value of the other party. For example, the generation unit analyzes the market value of the other party and provides business advice based on the market value. The generation unit can also provide business advice taking into account the business value of the other party. For example, the generation unit analyzes the business value of the other party and provides business advice based on the business value. The generation unit can also provide business advice based on the market value and business value of the other party. For example, the generation unit analyzes the market value and business value of the other party and provides business advice based on the analysis. This makes it possible to provide more appropriate business advice by taking into account the market value and business value of the other party. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the market value and business value of the other party into a generation AI and cause the generation AI to generate advice.

[0051] When displaying, the display unit can select an appropriate display method by referring to the user's past display history. The display unit, for example, prioritizes providing display methods that the user has previously preferred. For example, the display unit analyzes the user's past display history and prioritizes providing the preferred display method. The display unit can also eliminate display methods that the user has avoided in the past. For example, the display unit analyzes the user's past display history and eliminates the avoided display method. The display unit can also suggest an optimal display method based on the user's past display history. For example, the display unit analyzes the user's past display history and suggests an optimal display method. In this way, the optimal display method can be provided by referring to the user's past display history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display history to a generation AI and cause the generation AI to select a display method.

[0052] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is on a date, the display unit prioritizes displaying information related to the date. For example, the display unit analyzes the user's current task and determines that the user is on a date, and prioritizes displaying information related to the date. Furthermore, the display unit can also prioritize displaying information related to business if the user is working. For example, the display unit analyzes the user's current task and determines that the user is working, and prioritizes displaying information related to business. Furthermore, the display unit can also prioritize displaying information that helps the user relax if the user is on a break. For example, the display unit analyzes the user's current task and determines that the user is on a break, and prioritizes displaying information that helps the user relax. This allows the display content to be customized according to the user's current task, thereby providing more appropriate information. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the user's current task into a generation AI and have the generation AI customize the display content.

[0053] The display unit can select an appropriate display method by taking into consideration the user's device information (e.g., AR glasses, AR contact lenses, etc.) when displaying. For example, if the user is using AR glasses, the display unit provides a display method optimized for the AR glasses. For example, the display unit analyzes the user's device information and, if it determines that the user is using AR glasses, provides a display method optimized for the AR glasses. Furthermore, if the user is using AR contact lenses, the display unit can also provide a display method optimized for the AR contact lenses. For example, the display unit analyzes the user's device information and, if it determines that the user is using AR contact lenses, provides a display method optimized for the AR contact lenses. Furthermore, the display unit can also provide a display method optimized for the smartphone when the user is using a smartphone. For example, the display unit analyzes the user's device information and, if it determines that the user is using a smartphone, provides a display method optimized for the smartphone. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information to a generation AI and cause the generation AI to select a display method.

[0054] The display unit can select an appropriate display method by taking into account the user's geographical location information when displaying information. For example, if the user is in a specific area, the display unit prioritizes displaying information related to that area. For example, if the display unit analyzes the user's geographical location information and determines that the user is in a specific area, it prioritizes displaying information related to that area. Furthermore, if the user is traveling, the display unit can prioritize displaying information related to the travel destination. For example, if the display unit analyzes the user's geographical location information and determines that the user is traveling, it prioritizes displaying information related to the travel destination. Furthermore, if the user is at home, the display unit can prioritize displaying information about the area surrounding the user's home. For example, if the display unit analyzes the user's geographical location information and determines that the user is at home, it prioritizes displaying information about the area surrounding the user's home. This allows for providing an optimal display method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's geographical location information to a generation AI and cause the generation AI to select a display method.

[0055] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the display unit analyzes the language setting of the user's device and automatically sets the display content based on the language. The display unit can also provide a language switching function when the user uses multiple languages. For example, the display unit provides a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. For example, the display unit provides the display content in that language when the user selects a specific language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0056] The display unit can analyze the user's social media activity and display related information when displaying the information. The display unit, for example, displays information about places where the user has checked in on social media. For example, the display unit analyzes the user's social media activity and displays information about the checked-in places. The display unit can also analyze the user's social media posts and display information about related tourist spots and stores. For example, the display unit analyzes the user's social media posts and displays information about related tourist spots and stores. The display unit can also display information about related places and events based on the activities of the user's friends on social media. For example, the display unit analyzes the activities of the user's friends on social media and displays information about related places and events. This allows the user's social media activity to be analyzed and related information to be displayed efficiently. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit can input the user's social media activity into a generation AI and cause the generation AI to display related information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used gesture input in the past, the reception unit can preferentially suggest gesture input. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0059] The estimation unit can improve the accuracy of estimation by referring to the other person's past behavioral history. For example, the current emotion can be estimated by referring to the emotion patterns shown by the other person in the past. The current state can also be estimated based on the other person's past behavioral history. Furthermore, the emotion can be estimated by comparing the other person's past behavior with their current behavior. In this way, the accuracy of estimation can be improved by referring to the other person's past behavioral history.

[0060] When generating advice, the generation unit can generate advice taking into consideration the attribute information (age, gender, etc.) of the other party. For example, advice appropriate to the other party's age can be provided by taking into consideration the other party's age. Also, advice appropriate to the other party's gender can be provided by taking into consideration the other party's gender. Furthermore, advice appropriate to the other party's occupation can be provided by taking into consideration the other party's occupation. In this way, more appropriate advice can be provided by taking into consideration the other party's attribute information.

[0061] The display unit can select an appropriate display method by taking into consideration the user's device information (AR glasses, AR contact lenses, etc.). For example, if the user is using AR glasses, a display method optimized for the AR glasses can be provided. Also, if the user is using AR contact lenses, a display method optimized for the AR contact lenses can be provided. Furthermore, if the user is using a smartphone, a display method optimized for the smartphone can be provided. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0062] The reception unit can analyze the user's social media activity and receive related information when receiving input. For example, it can receive information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and receive related information. It can also receive related information by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently receive related information by analyzing the user's social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives input from a user. The input from a user includes voice input, text input, gesture input, etc. For example, voice input can be received using voice recognition technology, text input can be received using a text box, and gesture input can be received using gesture recognition technology. Step 2: The estimation unit estimates the other person's emotions and state based on the information received by the reception unit. The estimation unit estimates the other person's emotions and state by combining image recognition and voice recognition. For example, it can analyze the other person's facial image captured by a camera and estimate emotions from their facial expressions, or it can analyze the other person's voice recorded by a microphone and estimate emotions from the tone and speed of their voice. It is also possible to integrate the results of image recognition and voice recognition to estimate emotions and states more accurately. Step 3: The generation unit generates advice on topics and behavior based on the emotions and state estimated by the estimation unit. The generation unit generates advice by integrating the emotion estimation results, information about the area around the current location, and past conversation logs. For example, it can provide topics that the other person is interested in based on the emotion estimation results, suggest suitable places for dates or business trips based on information about the area around the current location, or provide information about products and services that the other person is interested in based on past conversation logs. Step 4: The display unit displays the advice generated by the generation unit using AR. The display unit displays the advice using AR glasses or AR contact lenses. For example, the advice can be displayed on AR glasses, allowing the user to visually receive the advice. Alternatively, the advice can be displayed using AR contact lenses.

[0065] (Example 2) An assistance system according to an embodiment of the present invention uses emotion estimation AI based on image and voice recognition and a large-scale language model to assist with dating and sales. The assistance system accepts input from a user, estimates the other person's emotions and state, generates advice on topics and behavior, and displays it in AR. For example, the assistance system uses a camera and microphone mounted on AR glasses to estimate the other person's current emotions and the state of the date or sales situation based on the face and voice of the other person. Next, based on the emotion estimation results, information about the current location obtained from the camera (current location estimation using VPS and surrounding objects), and past conversation logs, the system generates advice on topics and behavior and displays it in AR. This allows the user to respond appropriately to the other person's emotions and situation, thereby increasing the success rate of dates and sales. The assistance system allows the user to respond appropriately to the other person's emotions and situation, thereby increasing the success rate of dates and sales. For example, the assistance system can provide information about topics of interest to the dater or products that the salesperson is interested in.

[0066] The assistance system according to the embodiment includes a reception unit, an estimation unit, a generation unit, and a display unit. The reception unit receives input from a user. The input from the user includes, but is not limited to, voice input, text input, and gesture input. The reception unit receives voice input using, for example, voice recognition technology. The reception unit can also receive text input using a text box. The reception unit can also receive gesture input using gesture recognition technology. For example, when a user issues a voice instruction, the reception unit recognizes the voice and accepts it as input. When a user inputs text, the reception unit can accept the text as input. When a user performs a gesture, the reception unit can recognize the gesture and accept it as input. The estimation unit estimates the other party's emotion or state based on the information received by the reception unit. For example, the estimation unit estimates the other party's emotion or state by combining image recognition and voice recognition. For example, the estimation unit analyzes a facial image of the other party captured by a camera and estimates the emotion from the facial expression. The estimation unit can also analyze the other person's voice recorded by a microphone and estimate their emotions from the tone and speed of their voice. The estimation unit can also integrate the results of image recognition and voice recognition to more accurately estimate their emotions and state. For example, the estimation unit combines a facial image and a tone of voice to estimate the other person's emotions. The generation unit generates advice on topics and behavior based on the emotions and state estimated by the estimation unit. The generation unit generates advice by integrating, for example, the emotion estimation result, information about the area around the current location, and past conversation logs. For example, the generation unit provides topics that the other person is interested in based on the emotion estimation result. The generation unit can also suggest suitable places for dates or business trips based on information about the area around the current location. The generation unit can also provide information about products and services that the other person is interested in based on past conversation logs. For example, the generation unit refers to past conversation logs to provide topics that the other person has previously discussed. The display unit displays the advice generated by the generation unit using AR. The display unit displays the advice using, for example, AR glasses or AR contact lenses.For example, the display unit may display advice on AR glasses, allowing the user to visually receive the advice. Alternatively, the display unit may display advice using AR contact lenses. For example, the display unit may display advice on AR contact lenses, allowing the user to visually receive the advice. This allows the assist system according to the embodiment to estimate the emotions and state of the other person based on the user's input and provide appropriate advice, thereby increasing the success rate of dates and sales.

[0067] The estimation unit can estimate the other party's emotions and state by combining image recognition and voice recognition. For example, the estimation unit analyzes the other party's facial image captured by a camera and estimates the emotion from their facial expression. For example, the estimation unit extracts the other party's facial features using face recognition technology and estimates the emotion using facial expression recognition technology. The estimation unit can also analyze the other party's voice recorded by a microphone and estimate the emotion from the tone and speed of the voice. For example, the estimation unit extracts the other party's voice features using voice recognition technology and estimates the emotion using voice tone analysis technology. The estimation unit can also integrate the results of image recognition and voice recognition to estimate the other party's emotions and state more accurately. For example, the estimation unit combines the facial image and voice tone to estimate the other party's emotions. This combination of image recognition and voice recognition improves the accuracy of estimating emotions and states. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit can input the facial image and voice tone to a generation AI and have the generation AI perform emotion estimation.

[0068] The generation unit can generate advice by integrating the emotion estimation result, information about the area around the current location, and past conversation logs. The generation unit, for example, provides topics that the other party is interested in based on the emotion estimation result. For example, the generation unit may provide fun topics based on the emotion estimation result if the other party is having fun. The generation unit can also suggest places suitable for dates or business trips based on information about the area around the current location. For example, the generation unit provides information about nearby facilities and events based on the current location estimated by the VPS. The generation unit can also provide information about products and services that the other party is interested in based on past conversation logs. For example, the generation unit refers to past conversation logs to provide topics that the other party has previously discussed. This allows for the integration of the emotion estimation result, information about the area around the current location, and past conversation logs to provide more appropriate advice. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the emotion estimation result, information about the area around the current location, and past conversation logs into a generation AI and cause the generation AI to generate advice.

[0069] The display unit can display the advice using AR glasses or AR contact lenses. The display unit, for example, displays the advice on AR glasses, allowing the user to visually receive the advice. For example, the display unit can display the advice on AR glasses, allowing the user to visually receive the advice. The display unit can also display the advice using AR contact lenses. For example, the display unit can display the advice on AR contact lenses, allowing the user to visually receive the advice. This allows the user to visually receive the advice by using AR glasses or AR contact lenses. Some or all of the above-described processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the advice generated by the generation unit to a generation AI and cause the generation AI to execute processing for displaying the advice on AR glasses or AR contact lenses.

[0070] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. For example, if the user is nervous, the reception unit delays input reception until the user relaxes. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, delays input reception until the user relaxes. Furthermore, if the user is excited, the reception unit can immediately accept input and respond quickly. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is excited, and immediately accepts input. Furthermore, if the user is tired, the reception unit can encourage the user to take a break and accept input after the break. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is tired, and encourages the user to take a break and accepts input after the break. This allows the timing of input reception to be adjusted according to the user's emotion, thereby accepting input at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression and tone of voice into the generation AI and cause the generation AI to estimate the user's emotion.

[0071] The reception unit can analyze the user's past input history and select an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit preferentially suggests voice input. For example, if the reception unit analyzes the user's past input history and has frequently used voice input, the reception unit preferentially suggests voice input. The reception unit can also preferentially suggest text input if the user has frequently used text input in the past. For example, if the reception unit analyzes the user's past input history and has frequently used text input, the reception unit preferentially suggests text input. The reception unit can also preferentially suggest gesture input if the user has frequently used gesture input in the past. For example, if the reception unit analyzes the user's past input history and has frequently used gesture input, the reception unit preferentially suggests gesture input. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0072] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. For example, if the user is currently busy, the reception unit prioritizes receiving only important information. For example, if the reception unit analyzes the user's current situation and determines that the user is busy, it prioritizes receiving only important information. Furthermore, if the user is interested in a specific area of ​​interest, the reception unit can prioritize receiving information related to that area. For example, the reception unit analyzes the user's areas of interest and prioritizes receiving information related to the area of ​​interest. Furthermore, the reception unit can also receive a wide range of information if the user is relaxed. For example, if the reception unit analyzes the user's current situation and determines that the user is relaxed, it receives a wide range of information. By filtering based on the user's current situation and areas of interest, more appropriate information can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's current situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0073] When accepting input, the acceptance unit can select an appropriate acceptance means depending on the user's input method (voice, text, gesture, etc.). For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition. For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the input using a text box. For example, if the user selects text input, the acceptance unit accepts the input using a text box. Furthermore, if the user selects gesture input, the acceptance unit can also accept the input using gesture recognition. For example, if the user selects gesture input, the acceptance unit accepts the input using gesture recognition technology. This allows for smoother input acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input method to a generation AI and cause the generation AI to select the optimal acceptance means.

[0074] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit prioritizes receiving important information. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, and then prioritizes receiving important information. The reception unit can also receive a wide range of information if the user is relaxed. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is relaxed, and then prioritizes receiving a wide range of information. The reception unit can also prioritize receiving information requiring a prompt response if the user is excited. For example, the reception unit analyzes the user's facial expression and tone of voice and determines that the user is excited, and then prioritizes receiving information requiring a prompt response. In this way, by determining the priority of information based on the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression and tone of voice into the generation AI and cause the generation AI to estimate the user's emotion.

[0075] When receiving input, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving information related to that area. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is in a specific area, prioritizes receiving information related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is traveling, prioritizes receiving information related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving information about the area surrounding the user's home. For example, the reception unit analyzes the user's geographical location information and, if it determines that the user is at home, prioritizes receiving information about the area surrounding the user's home. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0076] The reception unit can analyze the user's social media activity and receive related information when receiving input. The reception unit, for example, receives information related to places where the user has checked in on social media. For example, the reception unit analyzes the user's social media activity and receives information related to the checked-in places. The reception unit can also analyze the user's social media posts and receive related information. For example, the reception unit analyzes the user's social media posts and receive information about related tourist spots and stores. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the activities of the user's friends on social media and receives information about related places and events. In this way, the related information can be efficiently received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity to a generation AI and cause the generation AI to receive related information.

[0077] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, preferentially suggests reception methods that the user has previously preferred. For example, the reception unit analyzes the user's past feedback and preferentially suggests the preferred reception methods. The reception unit can also eliminate reception methods that the user has previously avoided. For example, the reception unit analyzes the user's past feedback and eliminates the avoided reception methods. The reception unit can also suggest an optimal reception method based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and suggests an optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback to the generation AI and cause the generation AI to customize the reception method.

[0078] The estimation unit can estimate the user's emotion and adjust the emotion and state estimation method based on the estimated user emotion. For example, if the user is nervous, the estimation unit performs a detailed analysis to improve the accuracy of emotion estimation. For example, the estimation unit analyzes the user's facial expression and tone of voice in detail and, if it determines that the user is nervous, performs a detailed analysis to improve the accuracy of emotion estimation. The estimation unit can also estimate the user's emotion through a simple analysis if the user is relaxed. For example, the estimation unit can simply analyze the user's facial expression and tone of voice and, if it determines that the user is relaxed, estimate the emotion through a simple analysis. The estimation unit can also quickly estimate the user's emotion if the user is excited. For example, the estimation unit can quickly analyze the user's facial expression and tone of voice and, if it determines that the user is excited, quickly estimate the emotion. This allows for more accurate estimation of the user's emotion and state by adjusting the estimation method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit may input the user's facial expression and tone of voice into the generation AI and cause the generation AI to estimate emotions.

[0079] The estimation unit can improve the accuracy of estimation by integrating the results of image recognition and voice recognition during estimation. The estimation unit, for example, estimates emotions by integrating facial expression data obtained by image recognition and voice tone obtained by voice recognition. For example, the estimation unit estimates emotions by integrating facial expression data obtained using face recognition technology and voice tone obtained using voice tone analysis technology. The estimation unit can also estimate a state by integrating posture data obtained by image recognition and speaking style obtained by voice recognition. For example, the estimation unit estimates a state by integrating posture data obtained using posture recognition technology and speaking style obtained using voice recognition technology. The estimation unit can also estimate emotions by integrating eye movements obtained by image recognition and speech content obtained by speech recognition. For example, the estimation unit estimates emotions by integrating eye movements obtained using eye movement recognition technology and speech content obtained using voice recognition technology. In this way, integrating the results of image recognition and voice recognition improves the accuracy of estimation. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the results of image recognition and voice recognition into the generation AI and have the generation AI improve the accuracy of the estimation.

[0080] During estimation, the estimation unit can improve the accuracy of the estimation by referring to the other party's past behavioral history. The estimation unit, for example, estimates the other party's current emotion by referring to an emotional pattern shown in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's current emotion based on the emotional pattern shown in the past. The estimation unit can also estimate the other party's current state based on the other party's past behavioral history. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's current state based on the past behavioral pattern. The estimation unit can also estimate the other party's emotion by comparing the other party's past behavior with their current behavior. For example, the estimation unit analyzes the other party's past behavioral history and estimates the other party's emotion by comparing the past behavior with their current behavior. In this way, by referring to the other party's past behavioral history, the accuracy of the estimation is improved. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's past behavioral history into a generation AI and cause the generation AI to improve the accuracy of the estimation.

[0081] The estimation unit can make estimations taking into account the attribute information of the other party (such as age and gender). For example, the estimation unit makes estimations based on emotional patterns corresponding to the other party's age, taking into account the other party's age. For example, the estimation unit analyzes the other party's age and estimates emotions based on emotional patterns corresponding to the age. The estimation unit can also make estimations based on emotional patterns corresponding to the other party's gender, taking into account the other party's gender. For example, the estimation unit analyzes the other party's gender and estimates emotions based on emotional patterns corresponding to the gender. The estimation unit can also make estimations based on emotional patterns corresponding to the other party's occupation, taking into account the other party's occupation. For example, the estimation unit analyzes the other party's occupation and estimates emotions based on emotional patterns corresponding to the occupation. This enables more accurate estimations by taking into account the other party's attribute information. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's attribute information into a generation AI and cause the generation AI to perform estimation.

[0082] The estimation unit can estimate the user's emotions and adjust the display method of the estimation results based on the estimated user emotions. For example, if the user is nervous, the estimation unit provides a simple, highly visible display method. For example, if the estimation unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, it provides a simple, highly visible display method. The estimation unit can also provide a display method including detailed information if the user is relaxed. For example, if the estimation unit analyzes the user's facial expression and tone of voice and determines that the user is relaxed, it provides a display method including detailed information. The estimation unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, if the estimation unit analyzes the user's facial expression and tone of voice and determines that the user is in a hurry, it provides a display method that focuses on the main points. This enables a more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit may input the user's facial expression and tone of voice into the generation AI and have the generation AI estimate the emotion.

[0083] The estimation unit can make estimations taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the estimation unit makes estimations based on emotional patterns related to that area. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is in a specific area, estimates the emotion based on emotional patterns related to that area. Furthermore, if the other party is traveling, the estimation unit can make estimations based on emotional patterns related to the travel destination. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is traveling, estimates the emotion based on emotional patterns related to the travel destination. Furthermore, if the other party is at home, the estimation unit can make estimations based on emotional patterns around the home. For example, the estimation unit analyzes the other party's geographical location information and, if it determines that the other party is at home, estimates the emotion based on emotional patterns around the home. This enables more accurate estimations by taking the other party's geographical location information into consideration. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's geographical location information to a generation AI and cause the generation AI to perform estimation.

[0084] The estimation unit can improve the accuracy of the estimation by referring to related literature and data of the other party during estimation. The estimation unit, for example, estimates emotions by referring to related literature read by the other party in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates emotions based on related literature read in the past. The estimation unit can also estimate a current state based on data referenced by the other party in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates a current state based on the data referenced in the past. The estimation unit can also estimate emotions based on literature and data in which the other party has been interested in the past. For example, the estimation unit analyzes the other party's past behavioral history and estimates emotions based on literature and data in which the other party has been interested in the past. In this way, by referring to related literature and data of the other party, the accuracy of the estimation is improved. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the other party's related literature and data into the generation AI and cause the generation AI to improve the accuracy of the estimation.

[0085] The estimation unit can make estimations taking into account the market value and business value of the other party. The estimation unit, for example, estimates business sentiments taking into account the market value of the other party. For example, the estimation unit analyzes the market value of the other party and estimates business sentiments according to the market value. The estimation unit can also estimate a business state taking into account the business value of the other party. For example, the estimation unit analyzes the business value of the other party and estimates a business state according to the business value. The estimation unit can also estimate business sentiments based on the market value and business value of the other party. For example, the estimation unit analyzes the market value and business value of the other party and estimates business sentiments based on the analysis. This enables more accurate estimation of business sentiments and states by taking into account the market value and business value of the other party. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the market value and business value of the other party into a generation AI and cause the generation AI to perform estimation.

[0086] The generation unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user emotions. For example, if the user is nervous, the generation unit provides advice to relax. For example, the generation unit analyzes the user's facial expression and tone of voice and, if it determines that the user is nervous, provides advice to relax. The generation unit can also provide detailed advice if the user is relaxed. For example, the generation unit analyzes the user's facial expression and tone of voice and, if it determines that the user is relaxed, provides detailed advice. The generation unit can also provide advice to respond quickly if the user is excited. For example, the generation unit analyzes the user's facial expression and tone of voice and, if it determines that the user is excited, provides advice to respond quickly. This allows the user to provide more appropriate advice by adjusting the way in which advice is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression and tone of voice into the generation AI and have the generation AI estimate the emotion.

[0087] When generating advice, the generation unit can improve the accuracy of the advice by integrating the emotion estimation result and information about the area around the current location. The generation unit, for example, integrates the emotion estimation result with information about the area around the current location to provide optimal advice. For example, the generation unit may provide fun topics based on the emotion estimation result if the other party is having fun. The generation unit may also suggest suitable places for dates or business trips based on information about the area around the current location. For example, the generation unit may provide information about nearby facilities and events based on the current location estimated by the VPS. The generation unit may also integrate the emotion estimation result with past conversation logs to provide optimal advice. For example, the generation unit may provide information about products and services that the other party is interested in based on the emotion estimation result. The generation unit may also integrate the emotion estimation result with information about objects near the current location to provide optimal advice. For example, the generation unit may provide topics that the other party is interested in based on the emotion estimation result. In this way, by integrating the emotion estimation result and information about the area around the current location, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input emotion estimation results and information about the current location into the generation AI and have the generation AI generate advice.

[0088] When generating advice, the generation unit can improve the accuracy of the advice by referring to past conversation logs. For example, the generation unit can refer to past conversation logs and provide advice based on topics that the other party is interested in. For example, the generation unit can analyze past conversation logs and provide advice based on topics that the other party has previously discussed. The generation unit can also refer to past conversation logs and provide advice based on topics that the other party wants to avoid. For example, the generation unit can analyze past conversation logs and provide advice based on topics that the other party wants to avoid. The generation unit can also refer to past conversation logs and provide advice about products or services that the other party is interested in. For example, the generation unit can analyze past conversation logs and provide advice about products or services that the other party is interested in. In this way, more appropriate advice can be provided by referring to past conversation logs. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past conversation logs into a generation AI and cause the generation AI to generate advice.

[0089] When generating advice, the generation unit can generate advice taking into account the other party's attribute information (age, gender, etc.). The generation unit, for example, takes into account the other party's age and provides advice appropriate to their age. For example, the generation unit analyzes the other party's age and provides advice appropriate to their age. The generation unit can also take into account the other party's gender and provide advice appropriate to their gender. For example, the generation unit analyzes the other party's gender and provides advice appropriate to their gender. The generation unit can also take into account the other party's occupation and provide advice appropriate to their occupation. For example, the generation unit analyzes the other party's occupation and provides advice appropriate to their occupation. In this way, more appropriate advice can be provided by taking into account the other party's attribute information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's attribute information into the generation AI and cause the generation AI to generate advice.

[0090] The generation unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is in a hurry, the generation unit provides short, to-the-point advice. For example, the generation unit analyzes the user's facial expression and tone of voice and determines that the user is in a hurry, and provides short, to-the-point advice. The generation unit can also provide longer advice with detailed explanations if the user is relaxed. For example, the generation unit analyzes the user's facial expression and tone of voice and determines that the user is relaxed, and provides longer advice with detailed explanations. The generation unit can also provide advice with visually stimulating effects if the user is excited. For example, the generation unit analyzes the user's facial expression and tone of voice and determines that the user is excited, and provides advice with visually stimulating effects. This allows the length of advice to be adjusted based on the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's facial expression and tone of voice into the generation AI and have the generation AI estimate the emotion.

[0091] When generating advice, the generation unit can generate advice taking into account the geographical location information of the other party. For example, if the other party is in a specific area, the generation unit provides advice related to that area. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is in a specific area, the generation unit provides advice related to that area. Furthermore, if the other party is traveling, the generation unit can provide advice related to the travel destination. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is traveling, the generation unit provides advice related to the travel destination. Furthermore, if the other party is at home, the generation unit can provide advice based on information about the area around the home. For example, if the generation unit analyzes the geographical location information of the other party and determines that the other party is at home, the generation unit provides advice based on information about the area around the home. In this way, more appropriate advice can be provided by taking the other party's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's geographical location information to the generation AI and cause the generation AI to generate advice.

[0092] When generating advice, the generation unit can improve the accuracy of the advice by referring to related literature and data of the other party. The generation unit, for example, provides advice by referring to related literature that the other party has read in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice based on related literature that the other party has read in the past. The generation unit can also provide advice tailored to the other party's current situation based on data that the other party has referenced in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice tailored to the current situation based on the data that the other party has referenced in the past. The generation unit can also provide advice based on literature and data that the other party has been interested in in the past. For example, the generation unit analyzes the other party's past behavioral history and provides advice based on literature and data that the other party has been interested in in the past. In this way, by referring to the other party's related literature and data, the accuracy of the advice is improved. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the other party's related literature and data into a generation AI and cause the generation AI to generate advice.

[0093] When generating advice, the generation unit can generate advice taking into account the market value and business value of the other party. The generation unit, for example, provides business advice taking into account the market value of the other party. For example, the generation unit analyzes the market value of the other party and provides business advice based on the market value. The generation unit can also provide business advice taking into account the business value of the other party. For example, the generation unit analyzes the business value of the other party and provides business advice based on the business value. The generation unit can also provide business advice based on the market value and business value of the other party. For example, the generation unit analyzes the market value and business value of the other party and provides business advice based on the analysis. This makes it possible to provide more appropriate business advice by taking into account the market value and business value of the other party. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the market value and business value of the other party into a generation AI and cause the generation AI to generate advice.

[0094] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, the display unit provides a simple, highly visible display method. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, it provides a simple, highly visible display method. The display unit can also provide a display method including detailed information if the user is relaxed. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is relaxed, it provides a display method including detailed information. The display unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is in a hurry, it provides a display method that focuses on the main points. This enables a more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's facial expression and tone of voice into the generation AI and cause the generation AI to estimate emotions.

[0095] When displaying, the display unit can select an appropriate display method by referring to the user's past display history. The display unit, for example, prioritizes providing display methods that the user has previously preferred. For example, the display unit analyzes the user's past display history and prioritizes providing the preferred display method. The display unit can also eliminate display methods that the user has avoided in the past. For example, the display unit analyzes the user's past display history and eliminates the avoided display method. The display unit can also suggest an optimal display method based on the user's past display history. For example, the display unit analyzes the user's past display history and suggests an optimal display method. In this way, the optimal display method can be provided by referring to the user's past display history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's past display history to a generation AI and cause the generation AI to select a display method.

[0096] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is on a date, the display unit prioritizes displaying information related to the date. For example, the display unit analyzes the user's current task and determines that the user is on a date, and prioritizes displaying information related to the date. Furthermore, the display unit can also prioritize displaying information related to business if the user is working. For example, the display unit analyzes the user's current task and determines that the user is working, and prioritizes displaying information related to business. Furthermore, the display unit can also prioritize displaying information that helps the user relax if the user is on a break. For example, the display unit analyzes the user's current task and determines that the user is on a break, and prioritizes displaying information that helps the user relax. This allows the display content to be customized according to the user's current task, thereby providing more appropriate information. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the user's current task into a generation AI and have the generation AI customize the display content.

[0097] The display unit can select an appropriate display method by taking into consideration the user's device information (e.g., AR glasses, AR contact lenses, etc.) when displaying. For example, if the user is using AR glasses, the display unit provides a display method optimized for the AR glasses. For example, the display unit analyzes the user's device information and, if it determines that the user is using AR glasses, provides a display method optimized for the AR glasses. Furthermore, if the user is using AR contact lenses, the display unit can also provide a display method optimized for the AR contact lenses. For example, the display unit analyzes the user's device information and, if it determines that the user is using AR contact lenses, provides a display method optimized for the AR contact lenses. Furthermore, the display unit can also provide a display method optimized for the smartphone when the user is using a smartphone. For example, the display unit analyzes the user's device information and, if it determines that the user is using a smartphone, provides a display method optimized for the smartphone. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the user's device information to a generation AI and cause the generation AI to select a display method.

[0098] The display unit can estimate the user's emotions and prioritize display content based on the estimated user emotions. For example, if the user is nervous, the display unit prioritizes displaying important information. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, it prioritizes displaying important information. The display unit can also display a wide range of information if the user is relaxed. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is relaxed, it displays a wide range of information. The display unit can also prioritize displaying information requiring a prompt response if the user is excited. For example, if the display unit analyzes the user's facial expression and tone of voice and determines that the user is excited, it prioritizes displaying information requiring a prompt response. In this way, by prioritizing display content based on the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's facial expression and tone of voice into the generation AI and cause the generation AI to estimate emotions.

[0099] The display unit can select an appropriate display method by taking into account the user's geographical location information when displaying information. For example, if the user is in a specific area, the display unit prioritizes displaying information related to that area. For example, if the display unit analyzes the user's geographical location information and determines that the user is in a specific area, it prioritizes displaying information related to that area. Furthermore, if the user is traveling, the display unit can prioritize displaying information related to the travel destination. For example, if the display unit analyzes the user's geographical location information and determines that the user is traveling, it prioritizes displaying information related to the travel destination. Furthermore, if the user is at home, the display unit can prioritize displaying information about the area surrounding the user's home. For example, if the display unit analyzes the user's geographical location information and determines that the user is at home, it prioritizes displaying information about the area surrounding the user's home. This allows for providing an optimal display method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the user's geographical location information to a generation AI and cause the generation AI to select a display method.

[0100] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. For example, the display unit analyzes the language setting of the user's device and automatically sets the display content based on the language. The display unit can also provide a language switching function when the user uses multiple languages. For example, the display unit provides a language switching function when the user uses multiple languages. The display unit can also provide the display content in a specific language when the user selects that language. For example, the display unit provides the display content in that language when the user selects a specific language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input the user's language setting to a generation AI and cause the generation AI to perform multilingual support for the display content.

[0101] The display unit can analyze the user's social media activity and display related information when displaying the information. The display unit, for example, displays information about places where the user has checked in on social media. For example, the display unit analyzes the user's social media activity and displays information about the checked-in places. The display unit can also analyze the user's social media posts and display information about related tourist spots and stores. For example, the display unit analyzes the user's social media posts and displays information about related tourist spots and stores. The display unit can also display information about related places and events based on the activities of the user's friends on social media. For example, the display unit analyzes the activities of the user's friends on social media and displays information about related places and events. This allows the user's social media activity to be analyzed and related information to be displayed efficiently. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit can input the user's social media activity into a generation AI and cause the generation AI to display related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input or text input from the user using the microphone 38B or touch panel 38A of the smart device 14. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes face images captured by the camera 42 and voices recorded by the microphone 38B to estimate emotions and states. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice based on emotion estimation results, information about the surrounding area of ​​the current location, and past conversation logs. The display unit displays advice in AR using, for example, the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes face images captured by the camera 42 and voices recorded by the microphone 238 to estimate emotions and states. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice based on emotion estimation results, information about the surrounding area of ​​the current location, and past conversation logs. The display unit displays advice in AR using, for example, the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset-type terminal 314. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes face images captured by the camera 42 and voices recorded by the microphone 238 to estimate emotions and states. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice based on emotion estimation results, information about the surrounding area of ​​the current location, and past conversation logs. The display unit displays advice in AR using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes face images captured by the camera 42 and voices recorded by the microphone 238 to estimate emotions and states. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice based on emotion estimation results, information about the surrounding area of ​​the current location, and past conversation logs. The display unit displays advice in AR using, for example, the display of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used gesture input in the past, the reception unit can preferentially suggest gesture input. In this way, the optimal input method can be suggested by analyzing the user's past input history.

[0104] The estimation unit can improve the accuracy of estimation by referring to the other person's past behavioral history. For example, the current emotion can be estimated by referring to the emotion patterns shown by the other person in the past. The current state can also be estimated based on the other person's past behavioral history. Furthermore, the emotion can be estimated by comparing the other person's past behavior with their current behavior. In this way, the accuracy of estimation can be improved by referring to the other person's past behavioral history.

[0105] When generating advice, the generation unit can generate advice taking into consideration the attribute information (age, gender, etc.) of the other party. For example, advice appropriate to the other party's age can be provided by taking into consideration the other party's age. Also, advice appropriate to the other party's gender can be provided by taking into consideration the other party's gender. Furthermore, advice appropriate to the other party's occupation can be provided by taking into consideration the other party's occupation. In this way, more appropriate advice can be provided by taking into consideration the other party's attribute information.

[0106] The display unit can select an appropriate display method by taking into consideration the user's device information (AR glasses, AR contact lenses, etc.). For example, if the user is using AR glasses, a display method optimized for the AR glasses can be provided. Also, if the user is using AR contact lenses, a display method optimized for the AR contact lenses can be provided. Furthermore, if the user is using a smartphone, a display method optimized for the smartphone can be provided. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0107] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated user emotions. For example, if the user is nervous, the reception of input can be delayed until the user is relaxed. Also, if the user is excited, the reception of input can be started immediately to respond quickly. Furthermore, if the user is tired, the reception of input can be started after the break. In this way, by adjusting the timing of input reception according to the user's emotions, the input can be received at a more appropriate timing.

[0108] The estimation unit can estimate the user's emotion and adjust the emotion and state estimation method based on the estimated user emotion. For example, if the user is nervous, a detailed analysis can be performed to improve the accuracy of emotion estimation. If the user is relaxed, the emotion can be estimated with a simple analysis. Furthermore, if the user is excited, the emotion can be estimated quickly. In this way, by adjusting the estimation method based on the user's emotion, more accurate emotion and state estimation becomes possible.

[0109] The generation unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is nervous, advice to help them relax can be provided. Also, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is excited, advice to help them respond quickly can be provided. In this way, by adjusting the way in which advice is expressed based on the user's emotions, more appropriate advice can be provided.

[0110] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method based on the user's emotions.

[0111] The display unit can estimate the user's emotions and determine the priority of display content based on the estimated user's emotions. For example, if the user is nervous, important information can be displayed with priority. Also, if the user is relaxed, a wide range of information can be displayed. Furthermore, if the user is excited, information that requires a quick response can be displayed with priority. In this way, by determining the priority of display content based on the user's emotions, important information can be displayed with priority.

[0112] The reception unit can analyze the user's social media activity and receive related information when receiving input. For example, it can receive information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and receive related information. It can also receive related information by referring to the activities of the user's friends on social media. In this way, it is possible to efficiently receive related information by analyzing the user's social media activity.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit receives input from a user. The input from a user includes voice input, text input, gesture input, etc. For example, voice input can be received using voice recognition technology, text input can be received using a text box, and gesture input can be received using gesture recognition technology. Step 2: The estimation unit estimates the other person's emotions and state based on the information received by the reception unit. The estimation unit estimates the other person's emotions and state by combining image recognition and voice recognition. For example, it can analyze the other person's facial image captured by a camera and estimate emotions from their facial expressions, or it can analyze the other person's voice recorded by a microphone and estimate emotions from the tone and speed of their voice. It is also possible to integrate the results of image recognition and voice recognition to estimate emotions and states more accurately. Step 3: The generation unit generates advice on topics and behavior based on the emotions and state estimated by the estimation unit. The generation unit generates advice by integrating the emotion estimation results, information about the area around the current location, and past conversation logs. For example, it can provide topics that the other person is interested in based on the emotion estimation results, suggest suitable places for dates or business trips based on information about the area around the current location, or provide information about products and services that the other person is interested in based on past conversation logs. Step 4: The display unit displays the advice generated by the generation unit using AR. The display unit displays the advice using AR glasses or AR contact lenses. For example, the advice can be displayed on AR glasses, allowing the user to visually receive the advice. Alternatively, the advice can be displayed using AR contact lenses.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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. a reception unit that receives input from a user; an estimation unit that estimates the emotion or state of the other person based on the information received by the reception unit; a generation unit that generates advice on topics and behavior based on the emotion and state estimated by the estimation unit; a display unit that displays the advice generated by the generation unit in AR; Equipped with A system characterized by:

2. The estimation unit Combining image and voice recognition to estimate the other person's emotions (happiness, sadness, anger, etc.) and state (fatigue, tension, etc.) 2. The system of claim 1.

3. The generation unit Generates advice by integrating emotion estimation results, information about the current location, and past conversation logs 2. The system of claim 1.

4. The display unit Displaying advice using AR glasses or AR contact lenses 2. The system of claim 1.

5. The reception unit Estimate the user's emotions (happiness, sadness, anger, etc.) and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

7. The reception unit Filtering based on the user's current situation (e.g., at work, on break, etc.) and interests (e.g., sports, music, etc.) 2. The system of claim 1.

8. The reception unit When accepting input, select an appropriate acceptance method depending on the user's input method (e.g., voice, text, gesture, etc.) 2. The system of claim 1.

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