system

Abstract

The system according to the embodiment aims to provide an assistant function that utilizes voice recognition in a moving space. [Solution] The system according to the embodiment includes a voice recognition unit, an analysis unit, a route guidance unit, an application operation unit, an API collaboration unit, and a suggestion unit. The voice recognition unit recognizes the user's voice. The analysis unit analyzes the voice recognized by the voice recognition unit. The route guidance unit provides route guidance based on the information analyzed by the analysis unit. The application operation unit operates smartphone applications based on the information analyzed by the analysis unit. The API collaboration unit acquires external information based on the information analyzed by the analysis unit. The suggestion unit makes specific suggestions to the user based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide assistant functions that utilize voice recognition in moving spaces, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an assistant function that utilizes voice recognition in a moving space. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, an analysis unit, a route guidance unit, an application operation unit, an API collaboration unit, and a suggestion unit. The voice recognition unit recognizes the user's voice. The analysis unit analyzes the voice recognized by the voice recognition unit. The route guidance unit provides route guidance based on the information analyzed by the analysis unit. The application operation unit operates smartphone applications based on the information analyzed by the analysis unit. The API collaboration unit acquires external information based on the information analyzed by the analysis unit. The suggestion unit makes specific suggestions to the user based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an assistant function that utilizes voice recognition in a moving space. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 assistant system according to an embodiment of the present invention integrates a smartphone equipped with a generative AI with a vehicle such as a car, providing it as an assistant in a moving space. When a user issues a voice command, the generative AI recognizes the voice and provides appropriate feedback. For example, if a user says, "Tell me where the nearest gas station is," the generative AI analyzes the command and provides directions to the nearest gas station. If a user says, "Play music," the generative AI controls a smartphone app to play music. Furthermore, the generative AI provides answers to user questions through API integration. For example, if a user asks, "What's the weather like today?", the generative AI obtains weather information and responds to the user. The generative AI also makes original suggestions based on the user's preferences and past behavioral history. For example, it may suggest a restaurant frequently visited by the user or introduce tourist spots that match the user's preferences. This system allows users to comfortably obtain information and operate the system while on the move. The integration of a smartphone equipped with a generative AI with a car makes moving spaces more convenient and comfortable. This allows the assistant system to recognize and analyze the user's voice, provide route guidance, operate apps, obtain external information, and make original suggestions, functioning as an assistant in the moving space.

[0029] The assistant system according to the embodiment includes a voice recognition unit, an analysis unit, a route guidance unit, an application operation unit, an API integration unit, and a suggestion unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit analyzes the user's voice using a voice recognition algorithm and converts the voice into text data. The voice recognition unit can adjust the accuracy of voice recognition depending on the type of microphone used. For example, using a noise-canceling microphone can eliminate ambient noise and improve the accuracy of voice recognition. The analysis unit analyzes the voice recognized by the voice recognition unit. For example, the analysis unit analyzes the voice data using natural language processing technology to understand the user's intent. The analysis unit can also extract the meaning of the voice data using an analysis algorithm and generate appropriate feedback. For example, if a user says, "Tell me the nearest gas station," the analysis unit understands the intent and obtains information about the nearest gas station. The route guidance unit provides route guidance based on the information analyzed by the analysis unit. For example, the route guidance unit calculates an optimal route using map data and provides guidance to the user. The route guidance unit can also update routes in real time using a navigation algorithm and provide the user with the latest route information. For example, the route guidance unit suggests an optimal route taking traffic congestion information into consideration. The app operation unit operates a smartphone app based on the information analyzed by the analysis unit. The app operation unit, for example, operates a music app on the smartphone to play music. The app operation unit can also operate a navigation app on the smartphone to provide route guidance. For example, when a user says, "Play music," the app operation unit launches a music app and plays the specified music. The API integration unit acquires external information based on the information analyzed by the analysis unit. For example, the API integration unit acquires current weather information using a weather information API. The API integration unit can also acquire the latest news information using a news API. For example, when a user asks, "What's the weather like today?", the API integration unit acquires current weather information using a weather information API and provides it to the user. The suggestion unit makes original suggestions to the user based on the information analyzed by the analysis unit.The suggestion unit may suggest restaurants based on the user's preferences and past behavior history, for example. The suggestion unit may also introduce tourist spots that match the user's preferences. For example, the suggestion unit may suggest restaurants that the user frequently visits and introduce new restaurants to the user. As a result, the assistant system according to the embodiment functions as an assistant in a moving space by recognizing and analyzing the user's voice, providing route guidance, operating apps, acquiring external information, and making original suggestions.

[0030] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech patterns. For example, the speech recognition unit prioritizes recognition of phrases that the user has frequently used in the past. The speech recognition unit can also improve recognition accuracy by learning the user's speaking rate and intonation. The speech recognition unit can also improve recognition accuracy by predicting words that the user will use in specific situations. This improves speech recognition accuracy by referring to the user's past speech patterns. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's past speech data into a generation AI and have the generation AI analyze the speech patterns.

[0031] The voice recognition unit can improve the recognition accuracy by filtering surrounding environmental sounds during voice recognition. For example, the voice recognition unit can filter engine noise inside the vehicle and recognize only the user's voice. The voice recognition unit can also maintain the accuracy of voice recognition by removing wind noise when the windows are open. The voice recognition unit can also filter passenger conversations as background noise and prioritize recognition of the user's voice. In this way, filtering the surrounding environmental sounds improves the accuracy of voice recognition. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input surrounding environmental sound data to a generation AI and cause the generation AI to filter the environmental sounds.

[0032] The speech recognition unit can improve the recognition accuracy by taking into account the user's geographical location information during speech recognition. For example, if the user is in a specific area, the speech recognition unit recognizes the speech taking into account the dialect and accent specific to that area. Furthermore, if the user is moving, the speech recognition unit can prioritize recognizing related information based on the user's current location. Furthermore, if the user is inside a specific facility, the speech recognition unit can prioritize recognizing terms related to that facility. This improves the accuracy of speech recognition by taking into account the user's geographical location information. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's geographical location information data into the generation AI and cause the generation AI to improve the recognition accuracy based on the location information.

[0033] The speech recognition unit can analyze the user's social media activity during speech recognition to improve recognition accuracy. For example, the speech recognition unit prioritizes recognition of social media terms frequently used by the user. The speech recognition unit can also improve recognition accuracy by referring to the content of the user's social media posts. The speech recognition unit can also provide an appropriate response by taking into account the time period during which the user is active on social media. This improves speech recognition accuracy by analyzing the user's social media activity. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0034] During analysis, the analysis unit can improve analysis accuracy by referring to the user's past speech history. For example, the analysis unit prioritizes analysis of phrases that the user has frequently used in the past. The analysis unit can also improve analysis accuracy by learning the user's speech rate and intonation. The analysis unit can also improve analysis accuracy by predicting words that the user will use in specific situations. This improves analysis accuracy by referring to the user's past speech history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past speech data into the generation AI and have the generation AI analyze the speech history.

[0035] During analysis, the analysis unit can improve analysis accuracy by taking into account the user's current situation and context. For example, when the user is driving, the analysis unit prioritizes analyzing information related to driving. Furthermore, when the user is taking a break, the analysis unit can analyze information tailored to a relaxed situation. Furthermore, when the user is participating in a specific event, the analysis unit can prioritize analyzing information related to the event. This improves analysis accuracy by taking into account the user's current situation and context. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's current situation data into the generation AI and cause the generation AI to perform analysis based on the context.

[0036] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information specific to that area. Furthermore, if the user is moving, the analysis unit can prioritize analyzing information related to the user's current location. Furthermore, if the user is in a specific facility, the analysis unit can prioritize analyzing information related to the facility. This improves the accuracy of the analysis by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform analysis based on the location information.

[0037] During analysis, the analysis unit can analyze the user's social media activity to improve the accuracy of the analysis. For example, the analysis unit prioritizes analysis of social media terms frequently used by the user. The analysis unit can also improve the accuracy of the analysis by referring to the content of the user's social media posts. The analysis unit can also perform appropriate analysis by taking into account the time period during which the user is active on social media. This improves the accuracy of the analysis by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0038] When providing route guidance, the route guidance unit can suggest an optimal route by referring to the user's past movement history. The route guidance unit can suggest an optimal route, for example, based on routes the user has used in the past. The route guidance unit can also suggest a route that avoids congestion based on the user's past movement history. The route guidance unit can also analyze the user's past movement history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past movement history. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's past movement data into the generation AI and have the generation AI analyze the movement history.

[0039] The route guidance unit can adjust the route by taking into account traffic conditions and weather information in real time when providing route guidance. The route guidance unit can propose an optimal route based on, for example, real-time traffic congestion information. The route guidance unit can also propose an optimal route by taking into account real-time weather information. The route guidance unit can also propose a detour route based on real-time road construction information. This makes it possible to propose an optimal route by taking into account traffic conditions and weather information in real time. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input real-time traffic data to the generation AI and cause the generation AI to adjust the route based on the traffic conditions.

[0040] When providing route guidance, the route guidance unit can propose an optimal route by taking into account the user's geographical location information. For example, if the user is in a specific area, the route guidance unit proposes a route by taking into account information specific to that area. Furthermore, if the user is moving, the route guidance unit can propose an optimal route based on the user's current location. Furthermore, if the user is inside a specific facility, the route guidance unit can propose a route related to that facility. In this way, the optimal route can be proposed by taking into account the user's geographical location information. Some or all of the above-described processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's geographical location information data into a generation AI and cause the generation AI to propose a route based on the location information.

[0041] When providing route guidance, the route guidance unit can analyze the user's social media activity and suggest a route. The route guidance unit can suggest an optimal route based on, for example, places frequently visited by the user. The route guidance unit can also suggest a route by referring to the content of the user's social media posts. The route guidance unit can also suggest an appropriate route by taking into account the time period during which the user is active on social media. In this way, the optimal route can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0042] When operating an app, the app operation unit can suggest optimal operations by referring to the user's past app usage history. For example, the app operation unit can preferentially display apps that the user has used frequently in the past. The app operation unit can also learn the user's past operation patterns and suggest optimal operation methods. The app operation unit can also predict and suggest apps that the user will use during a specific time period. This makes it possible to suggest optimal operations by referring to the user's past app usage history. Some or all of the above-described processing in the app operation unit may be performed using, or without, AI, for example. For example, the app operation unit can input the user's past app usage data into a generation AI and have the generation AI analyze the usage history.

[0043] The app operation unit can adjust the operation when operating the app, taking into account the user's current situation and context. For example, when the user is driving, the app operation unit can preferentially suggest voice operation. Furthermore, when the user is taking a break, the app operation unit can provide detailed operation options. Furthermore, when the user is participating in a specific event, the app operation unit can suggest operations related to the event. This improves operability by taking into account the user's current situation and context. Some or all of the above-described processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's current situation data into the generation AI and cause the generation AI to adjust the operation based on the context.

[0044] When operating the app, the app operation unit can suggest optimal operations taking into account the user's geographical location information. For example, if the user is in a specific area, the app operation unit suggests operations taking into account information specific to that area. Furthermore, if the user is moving, the app operation unit can suggest related operations based on the user's current location. Furthermore, if the user is inside a specific facility, the app operation unit can suggest operations related to that facility. In this way, optimal operations can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's geographical location information data into a generation AI and cause the generation AI to suggest operations based on the location information.

[0045] When operating the app, the app operation unit can analyze the user's social media activity and suggest operations. For example, the app operation unit can prioritize suggestions of social media functions that the user uses frequently. The app operation unit can also suggest optimal operations by taking into account the content of the user's social media posts. The app operation unit can also suggest appropriate operations by taking into account the time period during which the user is active on social media. In this way, optimal operations can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0046] During API integration, the API integration unit can suggest optimal integration by referring to the user's past API usage history. For example, the API integration unit prioritizes integration of APIs that the user has used frequently in the past. The API integration unit can also learn the user's past API usage patterns and suggest optimal integration methods. The API integration unit can also predict and suggest APIs that the user will use during specific time periods. This makes it possible to suggest optimal integration by referring to the user's past API usage history. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI, for example. For example, the API integration unit can input the user's past API usage data into the generation AI and have the generation AI analyze the usage history.

[0047] During API integration, the API integration unit can adjust integration taking into account the user's current situation and context. For example, if the user is driving, the API integration unit prioritizes integration of driving-related information. Furthermore, if the user is taking a break, the API integration unit can prioritize integration of information suited to a relaxed situation. Furthermore, if the user is participating in a specific event, the API integration unit can prioritize integration of information related to the event. This enables optimal integration by taking into account the user's current situation and context. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI. For example, the API integration unit can input the user's current situation data into the generation AI and cause the generation AI to adjust integration based on the context.

[0048] During API integration, the API integration unit can propose optimal integration taking into account the user's geographical location information. For example, if the user is in a specific area, the API integration unit considers information specific to that area when integrating. Furthermore, if the user is on the move, the API integration unit can integrate related information based on the user's current location. Furthermore, if the user is in a specific facility, the API integration unit can integrate information related to the facility. This makes it possible to propose optimal integration by considering the user's geographical location information. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI. For example, the API integration unit can input the user's geographical location information data into the generation AI and cause the generation AI to execute integration proposals based on the location information.

[0049] During API integration, the API integration unit can analyze the user's social media activity and suggest integrations. For example, the API integration unit prioritizes integration of information from social media that the user uses frequently. The API integration unit can also suggest optimal integrations by taking into account the content of the user's social media posts. The API integration unit can also suggest appropriate integrations by taking into account the time periods during which the user is active on social media. This makes it possible to suggest optimal integrations by analyzing the user's social media activity. Some or all of the above-described processing in the API integration unit may be performed using AI, for example, or may be performed without using AI. For example, the API integration unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0050] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past behavioral history. The suggestion unit can make optimal suggestions based on, for example, places that the user has frequently visited in the past. The suggestion unit can also learn the user's past behavioral patterns and make optimal suggestions. The suggestion unit can also predict and suggest actions that the user will take during a specific time period. This allows optimal suggestions to be made by referring to the user's past behavioral history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past behavioral data into the generation AI and cause the generation AI to analyze the behavioral history.

[0051] When making a suggestion, the suggestion unit can adjust the suggestion taking into account the user's current situation and context. For example, if the user is driving, the suggestion unit can prioritize suggestions related to driving. Furthermore, if the user is taking a break, the suggestion unit can make suggestions tailored to a relaxed situation. Furthermore, if the user is participating in a specific event, the suggestion unit can make suggestions related to the event. This allows optimal suggestions to be made by taking into account the user's current situation and context. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's current situation to the generation AI and cause the generation AI to adjust the suggestion based on the context.

[0052] When making a proposal, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a specific area, the suggestion unit makes suggestions by taking into account information specific to that area. Furthermore, if the user is moving, the suggestion unit can make relevant suggestions based on the user's current location. Furthermore, if the user is in a specific facility, the suggestion unit can make suggestions related to that facility. In this way, optimal suggestions can be made by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into the generation AI and cause the generation AI to make suggestions based on the location information.

[0053] When making a suggestion, the suggestion unit can analyze the user's social media activity and make the suggestion. The suggestion unit can make the optimal suggestion based on, for example, information about social media that the user frequently uses. The suggestion unit can also make the optimal suggestion by referring to the content of the user's comments on social media. The suggestion unit can also make an appropriate suggestion by taking into account the time period during which the user is active on social media. In this way, the optimal suggestion can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's social media data into the generation AI and cause the generation AI to analyze the social media activity.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The assistant system can further include a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate and blood pressure and sends it to the analysis unit. The analysis unit can evaluate the user's health condition based on this data and suggest a break if necessary. For example, if the user's heart rate is high, the analysis unit can suggest the user to take a break. Also, if the user's blood pressure is abnormally high, the analysis unit can guide the user to the nearest medical institution. This allows the user's health condition to be monitored in real time and appropriate advice to be provided.

[0056] The assistant system can further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit acquires the user's driving data and sends it to the analysis unit. The analysis unit can evaluate the user's driving style based on this data and provide advice on safe driving. For example, if the user frequently brakes or accelerates suddenly, the analysis unit can suggest that the user drive more calmly. Furthermore, if the user continues driving for a long period of time, the analysis unit can suggest that the user take a break. This can improve the user's driving style and promote safe driving.

[0057] The assistant system can further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes the user's past behavioral history and statements to identify the user's hobbies and interests. The analysis unit can provide information related to the user based on this data. For example, if the user is interested in a particular sport, the analysis unit can provide the latest news and event information related to that sport. Also, if the user is interested in a particular music genre, the analysis unit can provide information on new songs and artists in that genre. This allows the system to provide information tailored to the user's hobbies and interests, increasing enjoyment while traveling.

[0058] The assistant system can further include a driving history learning unit that learns the user's driving history. The driving history learning unit analyzes the user's past driving data and identifies the user's driving patterns. The analysis unit can provide the user with optimal driving advice based on this data. For example, if the user frequently uses a specific route, the analysis unit can suggest the optimal driving method for that route. Also, if the user drives during a specific time period, the analysis unit can provide driving advice that takes into account the traffic conditions during that time period. This makes it possible to provide optimal driving advice based on the user's driving history and support safe and efficient driving.

[0059] The assistant system can further include a food learning unit that learns the user's food preferences. The food learning unit analyzes the user's past dining history and comments to identify the user's food preferences. The analysis unit can suggest restaurants and menus relevant to the user based on this data. For example, if the user likes a particular dish, the analysis unit can suggest restaurants that serve that dish. Also, if the user likes a particular ingredient, the analysis unit can suggest menus that use that ingredient. This allows the system to make suggestions based on the user's food preferences, improving the dining experience while on the move.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The voice recognition unit recognizes the user's voice. The voice recognition unit uses a voice recognition algorithm to analyze the user's voice and convert it into text data. The accuracy of voice recognition can also be adjusted depending on the type of microphone used. For example, using a noise-canceling microphone can eliminate ambient noise and improve the accuracy of voice recognition. Step 2: The analysis unit analyzes the voice recognized by the voice recognition unit. The analysis unit uses natural language processing technology to analyze the voice data and understand the user's intent. It also uses an analysis algorithm to extract the meaning of the voice data and generate appropriate feedback. For example, if a user says, "Tell me where the nearest gas station is," the analysis unit understands the user's intent and obtains information about the nearest gas station. Step 3: The route guidance unit provides route guidance based on the information analyzed by the analysis unit. The route guidance unit uses map data to calculate the optimal route and guides the user. It can also update routes in real time using navigation algorithms to provide the user with the latest route information. For example, it can propose the optimal route taking traffic congestion information into account. Step 4: The app operation unit operates the smartphone app based on the information analyzed by the analysis unit. The app operation unit operates the smartphone's music app to play music. It can also operate the smartphone's navigation app to provide route guidance. For example, if the user says "play music," the music app will be launched and the specified music will be played. Step 5: The API integration unit obtains external information based on the information analyzed by the analysis unit. The API integration unit obtains current weather information using the weather information API. It can also obtain the latest news information using the news API. For example, if a user asks, "What's the weather like today?", the API integration unit obtains current weather information using the weather information API and provides it to the user. Step 6: The suggestion unit makes original suggestions to the user based on the information analyzed by the analysis unit. The suggestion unit suggests restaurants based on the user's preferences and past behavior history. It can also introduce tourist spots that match the user's preferences. For example, it can suggest restaurants that the user frequently visits and introduce new restaurants.

[0062] (Example 2) An assistant system according to an embodiment of the present invention integrates a smartphone equipped with a generative AI with a vehicle such as a car, providing it as an assistant in a moving space. When a user issues a voice command, the generative AI recognizes the voice and provides appropriate feedback. For example, if a user says, "Tell me where the nearest gas station is," the generative AI analyzes the command and provides directions to the nearest gas station. If a user says, "Play music," the generative AI controls a smartphone app to play music. Furthermore, the generative AI provides answers to user questions through API integration. For example, if a user asks, "What's the weather like today?", the generative AI obtains weather information and responds to the user. The generative AI also makes original suggestions based on the user's preferences and past behavioral history. For example, it may suggest a restaurant frequently visited by the user or introduce tourist spots that match the user's preferences. This system allows users to comfortably obtain information and operate the system while on the move. The integration of a smartphone equipped with a generative AI with a car makes moving spaces more convenient and comfortable. This allows the assistant system to recognize and analyze the user's voice, provide route guidance, operate apps, obtain external information, and make original suggestions, functioning as an assistant in the moving space.

[0063] The assistant system according to the embodiment includes a voice recognition unit, an analysis unit, a route guidance unit, an application operation unit, an API integration unit, and a suggestion unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit analyzes the user's voice using a voice recognition algorithm and converts the voice into text data. The voice recognition unit can adjust the accuracy of voice recognition depending on the type of microphone used. For example, using a noise-canceling microphone can eliminate ambient noise and improve the accuracy of voice recognition. The analysis unit analyzes the voice recognized by the voice recognition unit. For example, the analysis unit analyzes the voice data using natural language processing technology to understand the user's intent. The analysis unit can also extract the meaning of the voice data using an analysis algorithm and generate appropriate feedback. For example, if a user says, "Tell me the nearest gas station," the analysis unit understands the intent and obtains information about the nearest gas station. The route guidance unit provides route guidance based on the information analyzed by the analysis unit. For example, the route guidance unit calculates an optimal route using map data and provides guidance to the user. The route guidance unit can also update routes in real time using a navigation algorithm and provide the user with the latest route information. For example, the route guidance unit suggests an optimal route taking traffic congestion information into consideration. The app operation unit operates a smartphone app based on the information analyzed by the analysis unit. The app operation unit, for example, operates a music app on the smartphone to play music. The app operation unit can also operate a navigation app on the smartphone to provide route guidance. For example, when a user says, "Play music," the app operation unit launches a music app and plays the specified music. The API integration unit acquires external information based on the information analyzed by the analysis unit. For example, the API integration unit acquires current weather information using a weather information API. The API integration unit can also acquire the latest news information using a news API. For example, when a user asks, "What's the weather like today?", the API integration unit acquires current weather information using a weather information API and provides it to the user. The suggestion unit makes original suggestions to the user based on the information analyzed by the analysis unit.The suggestion unit may suggest restaurants based on the user's preferences and past behavior history, for example. The suggestion unit may also introduce tourist spots that match the user's preferences. For example, the suggestion unit may suggest restaurants that the user frequently visits and introduce new restaurants to the user. As a result, the assistant system according to the embodiment functions as an assistant in a moving space by recognizing and analyzing the user's voice, providing route guidance, operating apps, acquiring external information, and making original suggestions.

[0064] The speech recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions. For example, if the user is nervous, the speech recognition unit can increase the sensitivity of speech recognition to more accurately recognize speech. Furthermore, if the user is relaxed, the speech recognition unit can return the sensitivity of speech recognition to normal, allowing for natural conversation. Furthermore, if the user is in a hurry, the speech recognition unit can increase the response speed of speech recognition to quickly process instructions. This improves recognition accuracy by adjusting the accuracy of speech recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit can be performed using, for example, AI, or without AI. For example, the speech recognition unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0065] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech patterns. For example, the speech recognition unit prioritizes recognition of phrases that the user has frequently used in the past. The speech recognition unit can also improve recognition accuracy by learning the user's speaking rate and intonation. The speech recognition unit can also improve recognition accuracy by predicting words that the user will use in specific situations. This improves speech recognition accuracy by referring to the user's past speech patterns. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's past speech data into a generation AI and have the generation AI analyze the speech patterns.

[0066] The voice recognition unit can improve the recognition accuracy by filtering surrounding environmental sounds during voice recognition. For example, the voice recognition unit can filter engine noise inside the vehicle and recognize only the user's voice. The voice recognition unit can also maintain the accuracy of voice recognition by removing wind noise when the windows are open. The voice recognition unit can also filter passenger conversations as background noise and prioritize recognition of the user's voice. In this way, filtering the surrounding environmental sounds improves the accuracy of voice recognition. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition unit can input surrounding environmental sound data to a generation AI and cause the generation AI to filter the environmental sounds.

[0067] The speech recognition unit can estimate the user's emotions and adjust the speech recognition response speed based on the estimated user's emotions. For example, if the user is nervous, the speech recognition unit can increase the response speed of the generation AI and provide quick feedback. Furthermore, if the user is relaxed, the speech recognition unit can return the response speed to normal and maintain natural conversation. Furthermore, if the user is in a hurry, the speech recognition unit can maximize the response speed and process instructions immediately. This enables quick feedback by adjusting the speech recognition response speed according to the user's emotions. Emotion estimation is achieved 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 speech recognition unit can be performed using, for example, an AI. For example, the speech recognition unit can input the user's voice data into the generation AI and have the generation AI perform emotion estimation.

[0068] The speech recognition unit can improve the recognition accuracy by taking into account the user's geographical location information during speech recognition. For example, if the user is in a specific area, the speech recognition unit recognizes the speech taking into account the dialect and accent specific to that area. Furthermore, if the user is moving, the speech recognition unit can prioritize recognizing related information based on the user's current location. Furthermore, if the user is inside a specific facility, the speech recognition unit can prioritize recognizing terms related to that facility. This improves the accuracy of speech recognition by taking into account the user's geographical location information. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's geographical location information data into the generation AI and cause the generation AI to improve the recognition accuracy based on the location information.

[0069] The speech recognition unit can analyze the user's social media activity during speech recognition to improve recognition accuracy. For example, the speech recognition unit prioritizes recognition of social media terms frequently used by the user. The speech recognition unit can also improve recognition accuracy by referring to the content of the user's social media posts. The speech recognition unit can also provide an appropriate response by taking into account the time period during which the user is active on social media. This improves speech recognition accuracy by analyzing the user's social media activity. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is nervous, the analysis unit can cause the generation AI to speed up the analysis algorithm and provide immediate feedback. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to return the analysis algorithm to normal and perform a natural analysis. Furthermore, if the user is in a hurry, the analysis unit can cause the generation AI to maximize the speed of the analysis algorithm and provide immediate analysis results. This improves analysis accuracy by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into the generation AI and cause the generation AI to adjust the analysis algorithm based on the emotion.

[0071] During analysis, the analysis unit can improve analysis accuracy by referring to the user's past speech history. For example, the analysis unit prioritizes analysis of phrases that the user has frequently used in the past. The analysis unit can also improve analysis accuracy by learning the user's speech rate and intonation. The analysis unit can also improve analysis accuracy by predicting words that the user will use in specific situations. This improves analysis accuracy by referring to the user's past speech history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past speech data into the generation AI and have the generation AI analyze the speech history.

[0072] During analysis, the analysis unit can improve analysis accuracy by taking into account the user's current situation and context. For example, when the user is driving, the analysis unit prioritizes analyzing information related to driving. Furthermore, when the user is taking a break, the analysis unit can analyze information tailored to a relaxed situation. Furthermore, when the user is participating in a specific event, the analysis unit can prioritize analyzing information related to the event. This improves analysis accuracy by taking into account the user's current situation and context. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's current situation data into the generation AI and cause the generation AI to perform analysis based on the context.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. The 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method based on the emotion.

[0074] The analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information during analysis. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information specific to that area. Furthermore, if the user is moving, the analysis unit can prioritize analyzing information related to the user's current location. Furthermore, if the user is in a specific facility, the analysis unit can prioritize analyzing information related to the facility. This improves the accuracy of the analysis by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform analysis based on the location information.

[0075] During analysis, the analysis unit can analyze the user's social media activity to improve the accuracy of the analysis. For example, the analysis unit prioritizes analysis of social media terms frequently used by the user. The analysis unit can also improve the accuracy of the analysis by referring to the content of the user's social media posts. The analysis unit can also perform appropriate analysis by taking into account the time period during which the user is active on social media. This improves the accuracy of the analysis by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0076] The route guidance unit can estimate the user's emotions and adjust the route guidance method based on the estimated user's emotions. For example, if the user is nervous, the route guidance unit can provide simple, highly visible route guidance. Furthermore, if the user is relaxed, the route guidance unit can provide route guidance including detailed information. Furthermore, if the user is in a hurry, the route guidance unit can provide route guidance that emphasizes the shortest route. This improves visibility by adjusting the route guidance method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 route guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route guidance unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the route guidance based on the emotion.

[0077] When providing route guidance, the route guidance unit can suggest an optimal route by referring to the user's past movement history. The route guidance unit can suggest an optimal route, for example, based on routes the user has used in the past. The route guidance unit can also suggest a route that avoids congestion based on the user's past movement history. The route guidance unit can also analyze the user's past movement history and suggest the most efficient route. In this way, the optimal route can be suggested by referring to the user's past movement history. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's past movement data into the generation AI and have the generation AI analyze the movement history.

[0078] The route guidance unit can adjust the route by taking into account traffic conditions and weather information in real time when providing route guidance. The route guidance unit can propose an optimal route based on, for example, real-time traffic congestion information. The route guidance unit can also propose an optimal route by taking into account real-time weather information. The route guidance unit can also propose a detour route based on real-time road construction information. This makes it possible to propose an optimal route by taking into account traffic conditions and weather information in real time. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input real-time traffic data to the generation AI and cause the generation AI to adjust the route based on the traffic conditions.

[0079] The route guidance unit can estimate the user's emotions and determine the priority of route guidance based on the estimated user emotions. For example, if the user is nervous, the route guidance unit can prioritize providing important route guidance. Furthermore, if the user is relaxed, the route guidance unit can prioritize providing detailed route guidance. Furthermore, if the user is in a hurry, the route guidance unit can prioritize providing the shortest route. This allows important information to be provided preferentially by determining the priority of route guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 route guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the route guidance unit can input user emotion data into the generation AI and cause the generation AI to determine the priority of route guidance based on emotions.

[0080] When providing route guidance, the route guidance unit can propose an optimal route by taking into account the user's geographical location information. For example, if the user is in a specific area, the route guidance unit proposes a route by taking into account information specific to that area. Furthermore, if the user is moving, the route guidance unit can propose an optimal route based on the user's current location. Furthermore, if the user is inside a specific facility, the route guidance unit can propose a route related to that facility. In this way, the optimal route can be proposed by taking into account the user's geographical location information. Some or all of the above-described processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's geographical location information data into a generation AI and cause the generation AI to propose a route based on the location information.

[0081] When providing route guidance, the route guidance unit can analyze the user's social media activity and suggest a route. The route guidance unit can suggest an optimal route based on, for example, places frequently visited by the user. The route guidance unit can also suggest a route by referring to the content of the user's social media posts. The route guidance unit can also suggest an appropriate route by taking into account the time period during which the user is active on social media. In this way, the optimal route can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the route guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the route guidance unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0082] The app operation unit can estimate the user's emotions and adjust the app operation method based on the estimated user emotions. For example, if the user is nervous, the app operation unit can provide a simple, highly visible operation method. Furthermore, if the user is relaxed, the app operation unit can provide detailed operation options. Furthermore, if the user is in a hurry, the app operation unit can provide a quick operation method. This improves operability by adjusting the app operation method according to the user's emotions. Emotion estimation is achieved 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 app operation unit can be performed using, for example, AI, or without AI. For example, the app operation unit can input the user's emotion data into the generation AI and have the generation AI adjust the operation method based on the emotion.

[0083] When operating an app, the app operation unit can suggest optimal operations by referring to the user's past app usage history. For example, the app operation unit can preferentially display apps that the user has used frequently in the past. The app operation unit can also learn the user's past operation patterns and suggest optimal operation methods. The app operation unit can also predict and suggest apps that the user will use during a specific time period. This makes it possible to suggest optimal operations by referring to the user's past app usage history. Some or all of the above-described processing in the app operation unit may be performed using, or without, AI, for example. For example, the app operation unit can input the user's past app usage data into a generation AI and have the generation AI analyze the usage history.

[0084] The app operation unit can adjust the operation when operating the app, taking into account the user's current situation and context. For example, when the user is driving, the app operation unit can preferentially suggest voice operation. Furthermore, when the user is taking a break, the app operation unit can provide detailed operation options. Furthermore, when the user is participating in a specific event, the app operation unit can suggest operations related to the event. This improves operability by taking into account the user's current situation and context. Some or all of the above-described processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's current situation data into the generation AI and cause the generation AI to adjust the operation based on the context.

[0085] The app operation unit can estimate the user's emotions and determine the priority of app operations based on the estimated user emotions. For example, if the user is nervous, the app operation unit can prioritize providing important operations. Furthermore, if the user is relaxed, the app operation unit can prioritize providing detailed operation options. Furthermore, if the user is in a hurry, the app operation unit can prioritize providing methods that allow for quick operations. Thus, by determining the priority of app operations according to the user's emotions, important operations can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 app operation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the app operation unit can input the user's emotion data into the generation AI and cause the generation AI to determine the operation priority based on the emotion.

[0086] When operating the app, the app operation unit can suggest optimal operations taking into account the user's geographical location information. For example, if the user is in a specific area, the app operation unit suggests operations taking into account information specific to that area. Furthermore, if the user is moving, the app operation unit can suggest related operations based on the user's current location. Furthermore, if the user is inside a specific facility, the app operation unit can suggest operations related to that facility. In this way, optimal operations can be suggested by taking the user's geographical location information into account. Some or all of the above-described processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's geographical location information data into a generation AI and cause the generation AI to suggest operations based on the location information.

[0087] When operating the app, the app operation unit can analyze the user's social media activity and suggest operations. For example, the app operation unit can prioritize suggestions of social media functions that the user uses frequently. The app operation unit can also suggest optimal operations by taking into account the content of the user's social media posts. The app operation unit can also suggest appropriate operations by taking into account the time period during which the user is active on social media. In this way, optimal operations can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the app operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the app operation unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0088] The API integration unit can estimate the user's emotions and adjust the API integration method based on the estimated user emotions. For example, if the user is nervous, the API integration unit can quickly perform API integration and provide information immediately. Furthermore, if the user is relaxed, the API integration unit can perform API integration at a normal speed and provide natural information. Furthermore, if the user is in a hurry, the API integration unit can perform API integration in the shortest time possible and provide information quickly. This enables prompt information provision by adjusting the API integration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation 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 API integration unit may be performed using, for example, an AI. For example, the API integration unit can input user emotion data into the generation AI and cause the generation AI to adjust API integration based on the emotion.

[0089] During API integration, the API integration unit can suggest optimal integration by referring to the user's past API usage history. For example, the API integration unit prioritizes integration of APIs that the user has used frequently in the past. The API integration unit can also learn the user's past API usage patterns and suggest optimal integration methods. The API integration unit can also predict and suggest APIs that the user will use during specific time periods. This makes it possible to suggest optimal integration by referring to the user's past API usage history. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI, for example. For example, the API integration unit can input the user's past API usage data into the generation AI and have the generation AI analyze the usage history.

[0090] During API integration, the API integration unit can adjust integration taking into account the user's current situation and context. For example, if the user is driving, the API integration unit prioritizes integration of driving-related information. Furthermore, if the user is taking a break, the API integration unit can prioritize integration of information suited to a relaxed situation. Furthermore, if the user is participating in a specific event, the API integration unit can prioritize integration of information related to the event. This enables optimal integration by taking into account the user's current situation and context. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI. For example, the API integration unit can input the user's current situation data into the generation AI and cause the generation AI to adjust integration based on the context.

[0091] The API integration unit can estimate the user's emotions and determine the priority of API integration based on the estimated user emotions. For example, if the user is nervous, the API integration unit prioritizes the integration of important information. Furthermore, if the user is relaxed, the API integration unit can integrate detailed information. Furthermore, if the user is in a hurry, the API integration unit can quickly integrate information. Thus, by determining the priority of API integration according to the user's emotions, important information can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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 API integration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the API integration unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of API integration based on the emotion.

[0092] During API integration, the API integration unit can propose optimal integration taking into account the user's geographical location information. For example, if the user is in a specific area, the API integration unit considers information specific to that area when integrating. Furthermore, if the user is on the move, the API integration unit can integrate related information based on the user's current location. Furthermore, if the user is in a specific facility, the API integration unit can integrate information related to the facility. This makes it possible to propose optimal integration by considering the user's geographical location information. Some or all of the above-described processing in the API integration unit may be performed using, or without, AI. For example, the API integration unit can input the user's geographical location information data into the generation AI and cause the generation AI to execute integration proposals based on the location information.

[0093] During API integration, the API integration unit can analyze the user's social media activity and suggest integrations. For example, the API integration unit prioritizes integration of information from social media that the user uses frequently. The API integration unit can also suggest optimal integrations by taking into account the content of the user's social media posts. The API integration unit can also suggest appropriate integrations by taking into account the time periods during which the user is active on social media. This makes it possible to suggest optimal integrations by analyzing the user's social media activity. Some or all of the above-described processing in the API integration unit may be performed using AI, for example, or may be performed without using AI. For example, the API integration unit can input the user's social media data into the generation AI and have the generation AI analyze the social media activity.

[0094] The suggestion unit can estimate the user's emotions and adjust the suggestion method based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This improves visibility by adjusting the suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the suggestion method based on the emotion.

[0095] When making a suggestion, the suggestion unit can make optimal suggestions by referring to the user's past behavioral history. The suggestion unit can make optimal suggestions based on, for example, places that the user has frequently visited in the past. The suggestion unit can also learn the user's past behavioral patterns and make optimal suggestions. The suggestion unit can also predict and suggest actions that the user will take during a specific time period. This allows optimal suggestions to be made by referring to the user's past behavioral history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past behavioral data into the generation AI and cause the generation AI to analyze the behavioral history.

[0096] When making a suggestion, the suggestion unit can adjust the suggestion taking into account the user's current situation and context. For example, if the user is driving, the suggestion unit can prioritize suggestions related to driving. Furthermore, if the user is taking a break, the suggestion unit can make suggestions tailored to a relaxed situation. Furthermore, if the user is participating in a specific event, the suggestion unit can make suggestions related to the event. This allows optimal suggestions to be made by taking into account the user's current situation and context. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's current situation to the generation AI and cause the generation AI to adjust the suggestion based on the context.

[0097] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can prioritize important suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can quickly provide suggestions. This allows important suggestions to be prioritized by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of suggestions based on emotions.

[0098] When making a proposal, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. For example, if the user is in a specific area, the suggestion unit makes suggestions by taking into account information specific to that area. Furthermore, if the user is moving, the suggestion unit can make relevant suggestions based on the user's current location. Furthermore, if the user is in a specific facility, the suggestion unit can make suggestions related to that facility. In this way, optimal suggestions can be made by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into the generation AI and cause the generation AI to make suggestions based on the location information.

[0099] When making a suggestion, the suggestion unit can analyze the user's social media activity and make the suggestion. The suggestion unit can make the optimal suggestion based on, for example, information about social media that the user frequently uses. The suggestion unit can also make the optimal suggestion by referring to the content of the user's comments on social media. The suggestion unit can also make an appropriate suggestion by taking into account the time period during which the user is active on social media. In this way, the optimal suggestion can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the user's social media data into the generation AI and cause the generation AI to analyze the social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the voice recognition unit, analysis unit, route guidance unit, application operation unit, API linkage unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit is realized by the microphone 38B and control unit 46A of the smart device 14 and recognizes the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice data to understand the user's intention. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using map data and provides guidance to the user. The application operation unit is realized, for example, by the control unit 46A of the smart device 14 and operates the smartphone application. The API linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires external information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides original suggestions to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described voice recognition unit, analysis unit, route guidance unit, application operation unit, API collaboration unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit is realized by the microphone 238 and control unit 46A of the smart glasses 214 and recognizes the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes voice data to understand the user's intention. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using map data and provides guidance to the user. The application operation unit is realized, for example, by the control unit 46A of the smart glasses 214 and operates a smartphone application. The API collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires external information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides original suggestions to the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described voice recognition unit, analysis unit, route guidance unit, application operation unit, API collaboration unit, and suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the voice recognition unit is realized by the microphone 238 and control unit 46A of the headset-type terminal 314 and recognizes the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice data to understand the user's intention. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using map data and provides guidance to the user. The application operation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and operates a smartphone application. The API collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires external information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides original suggestions to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned voice recognition unit, analysis unit, route guidance unit, application operation unit, API collaboration unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit is realized by the microphone 238 and control unit 46A of the robot 414 and recognizes the user's voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the voice data to understand the user's intention. The route guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal route using map data and guides the user therethrough. The application operation unit is realized, for example, by the control unit 46A of the robot 414 and operates a smartphone application. The API collaboration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires external information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes original suggestions to the user.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The assistant system can further include a health management unit that monitors the user's health condition. The health management unit acquires vital data such as the user's heart rate and blood pressure and sends it to the analysis unit. The analysis unit can evaluate the user's health condition based on this data and suggest a break if necessary. For example, if the user's heart rate is high, the analysis unit can suggest the user to take a break. Also, if the user's blood pressure is abnormally high, the analysis unit can guide the user to the nearest medical institution. This allows the user's health condition to be monitored in real time and appropriate advice to be provided.

[0102] The assistant system can further include a music suggestion unit that estimates the user's emotions and selects music based on the estimated emotions. The music suggestion unit can suggest relaxing music if the user is relaxed. Also, if the user is feeling stressed, it can suggest music that will reduce stress. Furthermore, if the user wants to cheer up, it can suggest uplifting music. In this way, music that matches the user's emotions can be provided, improving the user's mood while traveling.

[0103] The assistant system can further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit acquires the user's driving data and sends it to the analysis unit. The analysis unit can evaluate the user's driving style based on this data and provide advice on safe driving. For example, if the user frequently brakes or accelerates suddenly, the analysis unit can suggest that the user drive more calmly. Furthermore, if the user continues driving for a long period of time, the analysis unit can suggest that the user take a break. This can improve the user's driving style and promote safe driving.

[0104] The assistant system can further estimate the user's emotions and adjust the voice tone of the route guidance based on the estimated emotions. For example, if the user is nervous, the voice tone of the route guidance can be made gentler to relax the user. Also, if the user is relaxed, the voice tone of the route guidance can be made normal. Furthermore, if the user is in a hurry, the voice tone of the route guidance can be made faster to provide prompt guidance. This allows route guidance to be provided with a voice tone that corresponds to the user's emotions, supporting a comfortable journey.

[0105] The assistant system can further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes the user's past behavioral history and statements to identify the user's hobbies and interests. The analysis unit can provide information related to the user based on this data. For example, if the user is interested in a particular sport, the analysis unit can provide the latest news and event information related to that sport. Also, if the user is interested in a particular music genre, the analysis unit can provide information on new songs and artists in that genre. This allows the system to provide information tailored to the user's hobbies and interests, increasing enjoyment while traveling.

[0106] The assistant system can also estimate the user's emotions and adjust the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxing places and activities. If the user wants to cheer up, the system can suggest energetic places and activities. If the user is relaxed, the system can provide regular suggestions. This allows the system to provide suggestions tailored to the user's emotions and improve the user's experience while traveling.

[0107] The assistant system can further include a driving history learning unit that learns the user's driving history. The driving history learning unit analyzes the user's past driving data and identifies the user's driving patterns. The analysis unit can provide the user with optimal driving advice based on this data. For example, if the user frequently uses a specific route, the analysis unit can suggest the optimal driving method for that route. Also, if the user drives during a specific time period, the analysis unit can provide driving advice that takes into account the traffic conditions during that time period. This makes it possible to provide optimal driving advice based on the user's driving history and support safe and efficient driving.

[0108] The assistant system can further estimate the user's emotions and adjust the app interface based on the estimated emotions. For example, if the user is nervous, a simple, highly visible interface can be provided. If the user is relaxed, an interface containing detailed information can be provided. Furthermore, if the user is in a hurry, an interface that can be operated quickly can be provided. This allows the system to provide an interface that corresponds to the user's emotions and improves operability.

[0109] The assistant system can further include a food learning unit that learns the user's food preferences. The food learning unit analyzes the user's past dining history and comments to identify the user's food preferences. The analysis unit can suggest restaurants and menus relevant to the user based on this data. For example, if the user likes a particular dish, the analysis unit can suggest restaurants that serve that dish. Also, if the user likes a particular ingredient, the analysis unit can suggest menus that use that ingredient. This allows the system to make suggestions based on the user's food preferences, improving the dining experience while on the move.

[0110] The assistant system can further estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is feeling stressed, the frequency of notifications can be reduced to reduce the user's burden. If the user is relaxed, notifications can be sent at a normal frequency. Furthermore, if the user is in a hurry, only important notifications can be sent with priority. This allows the frequency of notifications to be adjusted according to the user's emotions, providing a comfortable user experience.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The voice recognition unit recognizes the user's voice. The voice recognition unit uses a voice recognition algorithm to analyze the user's voice and convert it into text data. The accuracy of voice recognition can also be adjusted depending on the type of microphone used. For example, using a noise-canceling microphone can eliminate ambient noise and improve the accuracy of voice recognition. Step 2: The analysis unit analyzes the voice recognized by the voice recognition unit. The analysis unit uses natural language processing technology to analyze the voice data and understand the user's intent. It also uses an analysis algorithm to extract the meaning of the voice data and generate appropriate feedback. For example, if a user says, "Tell me where the nearest gas station is," the analysis unit understands the user's intent and obtains information about the nearest gas station. Step 3: The route guidance unit provides route guidance based on the information analyzed by the analysis unit. The route guidance unit uses map data to calculate the optimal route and guides the user. It can also update routes in real time using navigation algorithms to provide the user with the latest route information. For example, it can propose the optimal route taking traffic congestion information into account. Step 4: The app operation unit operates the smartphone app based on the information analyzed by the analysis unit. The app operation unit operates the smartphone's music app to play music. It can also operate the smartphone's navigation app to provide route guidance. For example, if the user says "play music," the music app will be launched and the specified music will be played. Step 5: The API integration unit obtains external information based on the information analyzed by the analysis unit. The API integration unit obtains current weather information using the weather information API. It can also obtain the latest news information using the news API. For example, if a user asks, "What's the weather like today?", the API integration unit obtains current weather information using the weather information API and provides it to the user. Step 6: The suggestion unit makes original suggestions to the user based on the information analyzed by the analysis unit. The suggestion unit suggests restaurants based on the user's preferences and past behavior history. It can also introduce tourist spots that match the user's preferences. For example, it can suggest restaurants that the user frequently visits and introduce new restaurants.

[0113] 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.

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 speech recognition unit that recognizes a user's speech; an analysis unit that analyzes the voice recognized by the voice recognition unit; a route guidance unit that provides route guidance based on the information analyzed by the analysis unit; an application operation unit that operates an application of the smartphone based on the information analyzed by the analysis unit; an API linking unit that acquires external information based on the information analyzed by the analysis unit; a suggestion unit that makes a specific suggestion to the user based on the information analyzed by the analysis unit. A system characterized by:

2. The voice recognition unit Estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions.

2. The system of claim 1.

3. The voice recognition unit During speech recognition, the accuracy of recognition is improved by referring to the user's past speech patterns.

2. The system of claim 1.

4. The voice recognition unit During voice recognition, filtering out ambient sounds improves recognition accuracy.

2. The system of claim 1.

5. The voice recognition unit Estimate the user's emotion and adjust the response speed of the speech recognition based on the estimated user's emotion.

2. The system of claim 1.

6. The voice recognition unit Improve speech recognition accuracy based on the user's geographic location 2. The system of claim 1.

7. The voice recognition unit During voice recognition, the system analyzes users' social media activity to improve recognition accuracy.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting analysis algorithms based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit During analysis, the accuracy of analysis is improved by referring to the user's past speech history.

2. The system of claim 1.

10. The analysis unit At the time of analysis, improve accuracy based on the user's current situation and context 2. The system of claim 1.

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