System

Abstract

An object of a system according to an embodiment is to flexibly set a route through a dialogue.SOLUTION: A system includes an interaction unit, an analysis unit, and a proposal unit. The interaction unit receives an instruction in a natural language from a user. The analysis unit analyzes the instruction received by the interaction unit. The proposal unit proposes an optimum route based on the instruction analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that the route settings of car navigation systems are fixed, making it difficult for users to change the details.

[0005] The system according to the embodiment aims to set routes flexibly through dialogue. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, and a proposal unit. The dialogue unit accepts instructions in natural language from a user. The analysis unit analyzes the instructions accepted by the dialogue unit. The proposal unit proposes an optimal route based on the instructions analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for flexible route setting through dialogue. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A car navigation system according to an embodiment of the present invention is a system in which a user creates a route while interacting with an AI assistant. This allows the car navigation system to create an optimal route according to the user's wishes through the interaction.

[0029] A car navigation system according to an embodiment includes a dialogue unit, an analysis unit, and a proposal unit. The dialogue unit accepts instructions in natural language from a user. For example, the dialogue unit accepts instructions from the user using voice input. The dialogue unit can also accept instructions from the user using text input. The dialogue unit can also accept instructions from the user using a specific command. The analysis unit analyzes the instructions accepted by the dialogue unit. For example, the analysis unit analyzes the user's instructions using voice recognition technology. The analysis unit can also analyze the user's instructions using text analysis technology. The analysis unit can also analyze the user's instructions using a machine learning algorithm. The proposal unit proposes an optimal route based on the instructions analyzed by the analysis unit. For example, the proposal unit proposes a route with the shortest distance. The proposal unit can also propose a route with the shortest time. The proposal unit can also propose a route that takes traffic conditions into consideration. This allows the car navigation system according to an embodiment to propose an optimal route based on instructions from a user in natural language.

[0030] The analysis unit can learn the user's past driving history and preferences and propose personalized routes. For example, the analysis unit analyzes the user's past driving history and learns frequently used roads and preferred routes. For example, the analysis unit records the user's frequently used roads and roads that the user avoids, and proposes an optimal route based on that. The analysis unit also learns the user's preferences and proposes personalized routes based on that. For example, the analysis unit learns the characteristics of the user's preferred routes and places that the user wants to avoid, and proposes an optimal route based on that. This makes it possible to propose personalized routes based on the user's past driving history and preferences.

[0031] The analysis unit can analyze specific keywords and phrases used by the user during a conversation and generate a more accurate route. The analysis unit, for example, analyzes specific keywords and phrases used by the user during a conversation and generates a route based on them. For example, it analyzes requests such as "I want to avoid traffic jams" or "I want to take a scenic road." The analysis unit also extracts important and frequently occurring phrases based on the context and generates a route based on them. For example, it suggests the optimal route based on the user's request. This makes it possible to generate a highly accurate route based on the content of the user's conversation.

[0032] The suggestion unit can refer to similar routes set by other users based on information provided by the user during the dialogue. For example, the suggestion unit searches a database for similar routes set by other users based on information provided by the user during the dialogue and uses them as reference. For example, it refers to routes taken by other users heading to the same destination. The suggestion unit also suggests similar routes based on the user's past route history or common destinations. For example, it suggests an optimal route by referring to routes taken by other users. In this way, a more optimal route can be suggested by referring to the routes taken by other users.

[0033] The suggestion unit can automatically add spot information of tourist attractions or restaurants based on conditions specified by the user. The suggestion unit automatically adds spot information such as tourist attractions and restaurants based on conditions specified by the user, for example. For example, it analyzes a request such as "I want to stop by a tourist attraction on the way." The suggestion unit also adds spot information based on the user's preferences. For example, it suggests tourist attractions that the user likes or highly rated restaurants. The suggestion unit also adds spot information based on conditions specified by the user. For example, it incorporates specific tourist attractions or restaurants into the route. This makes it possible to automatically add spot information based on conditions specified by the user.

[0034] The proposal unit can collect traffic information in real time and immediately reflect congestion and accident information. The proposal unit, for example, collects traffic information in real time and immediately reflects congestion and accident information. For example, it obtains data from a traffic information service and reflects it in the route. The proposal unit also adjusts the route based on real-time traffic data. For example, it proposes the optimal route taking congestion information and accident information into consideration. The proposal unit also recalculates the route in response to changes in traffic conditions. For example, it updates the route based on real-time traffic information. In this way, the optimal route can be proposed by reflecting traffic information in real time.

[0035] The suggestion unit can analyze the user's driving style and suggest a route that matches it. For example, the suggestion unit analyzes the user's driving style and suggests a route that matches it. For example, the suggestion unit records the frequency of sudden braking and sudden acceleration and evaluates the driving style. The suggestion unit also suggests an optimal route based on the user's driving style. For example, it suggests a scenic route to a user who drives gently, and a route with little traffic to a user who drives recklessly. The suggestion unit also adjusts the route taking the user's driving style into consideration. For example, it adjusts the difficulty of the route depending on the driving style. This makes it possible to suggest a route that matches the user's driving style.

[0036] The suggestion unit can collect weather information in real time and suggest routes that avoid bad weather. The suggestion unit, for example, collects weather information in real time and suggests routes that avoid bad weather. For example, it obtains data from a weather information service and reflects it in the route. The suggestion unit also adjusts the route based on real-time weather data. For example, it suggests a route that avoids heavy rain or strong winds. The suggestion unit also recalculates the route according to changes in weather conditions. For example, it updates the route based on real-time weather information. In this way, it is possible to suggest routes that avoid bad weather by reflecting weather information in real time.

[0037] The suggestion unit can adjust the route based on feedback provided by other users in real time. The suggestion unit adjusts the route, for example, based on feedback provided by other users in real time. For example, it reflects traffic congestion information and accident information reported by other users. The suggestion unit also recalculates the route based on real-time feedback. For example, it adjusts the route taking into account the opinions and ratings of other users. The suggestion unit also proposes an optimal route based on feedback from other users. For example, it refers to routes recommended by other users. This makes it possible to adjust the route based on feedback from other users.

[0038] The suggestion unit can generate an optimal route based on detailed conditions specified by the user. The suggestion unit generates an optimal route based on, for example, detailed conditions specified by the user. For example, it analyzes requests such as "I want to avoid this road" or "I don't want to go through this area." The suggestion unit also adjusts the route based on the user's detailed conditions. For example, it proposes a route that avoids specific roads or areas. The suggestion unit also adjusts the difficulty of the route according to the user's conditions. For example, it proposes a route with less traffic or a route with beautiful scenery. In this way, it is possible to generate an optimal route based on detailed conditions specified by the user.

[0039] The suggestion unit can learn the user's past route setting history and suggest detailed settings that suit the user's preferences. For example, the suggestion unit analyzes the user's past route setting history and suggests detailed settings that suit the user's preferences. For example, the suggestion unit learns the user's preferences based on previously selected routes and evaluations. The suggestion unit also suggests an optimal route based on the user's past route setting history. For example, the suggestion unit learns the user's preferred route characteristics and places the user wants to avoid, and adjusts the route based on that. The suggestion unit also suggests detailed route settings according to the user's preferences. For example, the suggestion unit suggests a route that avoids specific roads or areas. This makes it possible to suggest detailed settings that suit the user's preferences based on the user's past route setting history.

[0040] The suggestion unit can refer to similar routes set by other users based on detailed conditions specified by the user. For example, the suggestion unit searches a database for similar routes set by other users based on detailed conditions specified by the user and uses them as reference. For example, it refers to routes set by other users with the same conditions. The suggestion unit also suggests similar routes based on the user's past route history and common destinations. For example, it suggests an optimal route by referring to routes set by other users. In this way, a more optimal route can be suggested by referring to the routes of other users.

[0041] The suggestion unit can automatically add spot information such as tourist attractions and restaurants based on detailed conditions specified by the user. The suggestion unit automatically adds spot information such as tourist attractions and restaurants based on detailed conditions specified by the user, for example. For example, it analyzes a request such as "I want to stop by a tourist attraction on the way." The suggestion unit also adds spot information based on the user's preferences. For example, it suggests tourist attractions that the user likes and highly rated restaurants. The suggestion unit also adds spot information based on conditions specified by the user. For example, it incorporates specific tourist attractions and restaurants into the route. This makes it possible to automatically add spot information based on detailed conditions specified by the user.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The suggestion unit can analyze the user's music preferences while driving and suggest music according to the route. For example, it can suggest relaxing music for long-distance driving and music that improves concentration for driving in urban areas. The suggestion unit can also learn the user's past music playback history and suggest music that suits their preferences. Furthermore, the suggestion unit can adjust the music according to the weather and time of day while driving. This can make the user's driving experience more comfortable.

[0044] The suggestion unit can estimate the user's level of fatigue while driving and suggest taking a break. For example, if the user has been driving for a long time, it can display a message encouraging the user to take a break. The suggestion unit can also analyze the user's posture and eye movements while driving to estimate the user's level of fatigue. Furthermore, the suggestion unit can also suggest appropriate times to take a break based on the user's past driving history. This can support the user's safe driving.

[0045] The suggestion unit can monitor the user's health condition while driving and issue a warning if an abnormality is detected. For example, it can monitor the heart rate and blood pressure, and if an abnormality is detected, it can display a message encouraging the user to take a break. The suggestion unit can also analyze the user's posture and movements while driving to estimate the user's health condition. Furthermore, the suggestion unit can provide appropriate driving advice based on the user's past health data. This can support the user's health.

[0046] The suggestion unit can provide advice to support eco-driving while the user is driving. For example, it can display a message urging the user to avoid sudden acceleration and braking. The suggestion unit can also analyze the user's driving style and provide specific advice for eco-driving. Furthermore, the suggestion unit can evaluate the effectiveness of eco-driving based on the user's past driving data and provide feedback. This can support the user in improving fuel efficiency.

[0047] The suggestion unit can provide advice to support safe driving while the user is driving. For example, it can display a message warning the user of speeding or keeping an insufficient distance between vehicles. The suggestion unit can also analyze the user's driving style and provide specific advice for safe driving. Furthermore, the suggestion unit can evaluate the effectiveness of safe driving based on the user's past driving data and provide feedback. This can support the user's safe driving.

[0048] The suggestion unit can provide advice on providing entertainment for the user while driving. For example, it can suggest audiobooks or podcasts that can be enjoyed while driving. The suggestion unit can also learn the user's past entertainment history and suggest content that matches their preferences. Furthermore, the suggestion unit can adjust the entertainment depending on the weather and time of day while driving. This can make the user's driving experience more enjoyable.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The dialogue unit accepts instructions in natural language from the user. For example, the dialogue unit can accept instructions from the user using voice input, text input, or specific commands. Step 2: The analysis unit analyzes the instruction received by the dialogue unit. For example, the analysis unit can analyze the user's instruction using voice recognition technology, text analysis technology, or a machine learning algorithm. Step 3: The suggestion unit proposes an optimal route based on the instructions analyzed by the analysis unit. For example, the suggestion unit can propose the shortest route, the shortest time route, or a route that takes traffic conditions into consideration.

[0051] (Example 2) A car navigation system according to an embodiment of the present invention is a system in which a user creates a route while interacting with an AI assistant. This allows the car navigation system to create an optimal route according to the user's wishes through the interaction.

[0052] A car navigation system according to an embodiment includes a dialogue unit, an analysis unit, and a proposal unit. The dialogue unit accepts instructions in natural language from a user. For example, the dialogue unit accepts instructions from the user using voice input. The dialogue unit can also accept instructions from the user using text input. The dialogue unit can also accept instructions from the user using a specific command. The analysis unit analyzes the instructions accepted by the dialogue unit. For example, the analysis unit analyzes the user's instructions using voice recognition technology. The analysis unit can also analyze the user's instructions using text analysis technology. The analysis unit can also analyze the user's instructions using a machine learning algorithm. The proposal unit proposes an optimal route based on the instructions analyzed by the analysis unit. For example, the proposal unit proposes a route with the shortest distance. The proposal unit can also propose a route with the shortest time. The proposal unit can also propose a route that takes traffic conditions into consideration. This allows the car navigation system according to an embodiment to propose an optimal route based on instructions from a user in natural language.

[0053] The analysis unit can learn the user's past driving history and preferences and propose personalized routes. For example, the analysis unit analyzes the user's past driving history and learns frequently used roads and preferred routes. For example, the analysis unit records the user's frequently used roads and roads that the user avoids, and proposes an optimal route based on that. The analysis unit also learns the user's preferences and proposes personalized routes based on that. For example, the analysis unit learns the characteristics of the user's preferred routes and places that the user wants to avoid, and proposes an optimal route based on that. This makes it possible to propose personalized routes based on the user's past driving history and preferences.

[0054] The analysis unit can infer emotions from the user's tone of voice and language, and suggest a relaxing route if the user is feeling stressed. The analysis unit, for example, analyzes the user's tone of voice and language to infer emotions. For example, it determines that the user is feeling stressed if the user's voice gets higher or the language gets rougher. The analysis unit also infers the user's emotions using an emotion recognition algorithm. For example, it analyzes voice frequency and volume to infer emotions. The analysis unit also suggests a relaxing route depending on the user's emotional state. For example, it suggests a route with beautiful scenery or a route with little traffic. This makes it possible to suggest a relaxing route depending on the user's emotional state.

[0055] The analysis unit can analyze specific keywords and phrases used by the user during a conversation and generate a more accurate route. The analysis unit, for example, analyzes specific keywords and phrases used by the user during a conversation and generates a route based on them. For example, it analyzes requests such as "I want to avoid traffic jams" or "I want to take a scenic road." The analysis unit also extracts important and frequently occurring phrases based on the context and generates a route based on them. For example, it suggests the optimal route based on the user's request. This makes it possible to generate a highly accurate route based on the content of the user's conversation.

[0056] The suggestion unit can refer to similar routes set by other users based on information provided by the user during the dialogue. For example, the suggestion unit searches a database for similar routes set by other users based on information provided by the user during the dialogue and uses them as reference. For example, it refers to routes taken by other users heading to the same destination. The suggestion unit also suggests similar routes based on the user's past route history or common destinations. For example, it suggests an optimal route by referring to routes taken by other users. In this way, a more optimal route can be suggested by referring to the routes taken by other users.

[0057] The suggestion unit can automatically add spot information of tourist attractions or restaurants based on conditions specified by the user. The suggestion unit automatically adds spot information such as tourist attractions and restaurants based on conditions specified by the user, for example. For example, it analyzes a request such as "I want to stop by a tourist attraction on the way." The suggestion unit also adds spot information based on the user's preferences. For example, it suggests tourist attractions that the user likes or highly rated restaurants. The suggestion unit also adds spot information based on conditions specified by the user. For example, it incorporates specific tourist attractions or restaurants into the route. This makes it possible to automatically add spot information based on conditions specified by the user.

[0058] The suggestion unit can use the emotion estimation function to analyze the emotions felt by the user during the conversation in real time and propose a route that elicits positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during the conversation in real time. For example, it analyzes the user's tone of voice and facial expression to estimate the emotion. The suggestion unit also proposes a route that elicits positive emotions according to the user's emotional state. For example, it proposes a scenic route or a relaxing route. The suggestion unit also proposes an optimal route based on the user's emotional state. For example, it adjusts the route taking the user's emotions into consideration. This makes it possible to propose a route that elicits positive emotions according to the user's emotional state.

[0059] The proposal unit can collect traffic information in real time and immediately reflect congestion and accident information. The proposal unit, for example, collects traffic information in real time and immediately reflects congestion and accident information. For example, it obtains data from a traffic information service and reflects it in the route. The proposal unit also adjusts the route based on real-time traffic data. For example, it proposes the optimal route taking congestion information and accident information into consideration. The proposal unit also recalculates the route in response to changes in traffic conditions. For example, it updates the route based on real-time traffic information. In this way, the optimal route can be proposed by reflecting traffic information in real time.

[0060] The suggestion unit can analyze the user's driving style and suggest a route that matches it. For example, the suggestion unit analyzes the user's driving style and suggests a route that matches it. For example, the suggestion unit records the frequency of sudden braking and sudden acceleration and evaluates the driving style. The suggestion unit also suggests an optimal route based on the user's driving style. For example, it suggests a scenic route to a user who drives gently, and a route with little traffic to a user who drives recklessly. The suggestion unit also adjusts the route taking the user's driving style into consideration. For example, it adjusts the difficulty of the route depending on the driving style. This makes it possible to suggest a route that matches the user's driving style.

[0061] The suggestion unit can estimate the user's emotional state and adjust the route to reduce stress. The suggestion unit, for example, estimates the user's emotional state and adjusts the route to reduce stress. For example, it analyzes the user's tone of voice and facial expression to estimate emotions. The suggestion unit also adjusts the route based on the user's emotional state. For example, if the user is feeling stressed, it suggests a route that will help them relax. The suggestion unit also recalculates the route taking the user's emotional state into consideration. For example, it adjusts the difficulty of the route depending on the emotional state. This makes it possible to adjust the route to reduce stress depending on the user's emotional state.

[0062] The suggestion unit can collect weather information in real time and suggest routes that avoid bad weather. The suggestion unit, for example, collects weather information in real time and suggests routes that avoid bad weather. For example, it obtains data from a weather information service and reflects it in the route. The suggestion unit also adjusts the route based on real-time weather data. For example, it suggests a route that avoids heavy rain or strong winds. The suggestion unit also recalculates the route according to changes in weather conditions. For example, it updates the route based on real-time weather information. In this way, it is possible to suggest routes that avoid bad weather by reflecting weather information in real time.

[0063] The suggestion unit can adjust the route based on feedback provided by other users in real time. The suggestion unit adjusts the route, for example, based on feedback provided by other users in real time. For example, it reflects traffic congestion information and accident information reported by other users. The suggestion unit also recalculates the route based on real-time feedback. For example, it adjusts the route taking into account the opinions and ratings of other users. The suggestion unit also proposes an optimal route based on feedback from other users. For example, it refers to routes recommended by other users. This makes it possible to adjust the route based on feedback from other users.

[0064] The suggestion unit can generate an optimal route based on detailed conditions specified by the user. The suggestion unit generates an optimal route based on, for example, detailed conditions specified by the user. For example, it analyzes requests such as "I want to avoid this road" or "I don't want to go through this area." The suggestion unit also adjusts the route based on the user's detailed conditions. For example, it proposes a route that avoids specific roads or areas. The suggestion unit also adjusts the difficulty of the route according to the user's conditions. For example, it proposes a route with less traffic or a route with beautiful scenery. In this way, it is possible to generate an optimal route based on detailed conditions specified by the user.

[0065] The suggestion unit can learn the user's past route setting history and suggest detailed settings that suit the user's preferences. For example, the suggestion unit analyzes the user's past route setting history and suggests detailed settings that suit the user's preferences. For example, the suggestion unit learns the user's preferences based on previously selected routes and evaluations. The suggestion unit also suggests an optimal route based on the user's past route setting history. For example, the suggestion unit learns the user's preferred route characteristics and places the user wants to avoid, and adjusts the route based on that. The suggestion unit also suggests detailed route settings according to the user's preferences. For example, the suggestion unit suggests a route that avoids specific roads or areas. This makes it possible to suggest detailed settings that suit the user's preferences based on the user's past route setting history.

[0066] The suggestion unit can estimate the user's emotional state and suggest a relaxing route or a scenic route. The suggestion unit, for example, estimates the user's emotional state and suggests a relaxing route or a scenic route. For example, the suggestion unit analyzes the user's tone of voice and facial expression to estimate emotions. The suggestion unit also suggests an optimal route based on the user's emotional state. For example, it suggests a relaxing route or a scenic route. The suggestion unit also adjusts the route taking the user's emotional state into consideration. For example, it adjusts the difficulty of the route depending on the emotional state. This makes it possible to suggest a relaxing route or a scenic route depending on the user's emotional state.

[0067] The suggestion unit can refer to similar routes set by other users based on detailed conditions specified by the user. For example, the suggestion unit searches a database for similar routes set by other users based on detailed conditions specified by the user and uses them as reference. For example, it refers to routes set by other users with the same conditions. The suggestion unit also suggests similar routes based on the user's past route history and common destinations. For example, it suggests an optimal route by referring to routes set by other users. In this way, a more optimal route can be suggested by referring to the routes of other users.

[0068] The suggestion unit can automatically add spot information such as tourist attractions and restaurants based on detailed conditions specified by the user. The suggestion unit automatically adds spot information such as tourist attractions and restaurants based on detailed conditions specified by the user, for example. For example, it analyzes a request such as "I want to stop by a tourist attraction on the way." The suggestion unit also adds spot information based on the user's preferences. For example, it suggests tourist attractions that the user likes and highly rated restaurants. The suggestion unit also adds spot information based on conditions specified by the user. For example, it incorporates specific tourist attractions and restaurants into the route. This makes it possible to automatically add spot information based on detailed conditions specified by the user.

[0069] The suggestion unit can use the emotion estimation function to analyze the emotions felt by the user while configuring the detailed settings and suggest detailed settings that elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions felt by the user while configuring the detailed settings. For example, it analyzes the user's tone of voice and facial expression to estimate the emotion. The suggestion unit also suggests detailed settings that elicit positive emotions according to the user's emotional state. For example, it suggests a relaxing route or a scenic route. The suggestion unit also suggests an optimal route based on the user's emotional state. For example, it adjusts the route taking the user's emotions into consideration. This makes it possible to provide detailed settings that elicit positive emotions according to the user's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The suggestion unit can analyze the user's music preferences while driving and suggest music according to the route. For example, it can suggest relaxing music for long-distance driving and music that improves concentration for driving in urban areas. The suggestion unit can also learn the user's past music playback history and suggest music that suits their preferences. Furthermore, the suggestion unit can adjust the music according to the weather and time of day while driving. This can make the user's driving experience more comfortable.

[0072] The suggestion unit can estimate the user's level of fatigue while driving and suggest taking a break. For example, if the user has been driving for a long time, it can display a message encouraging the user to take a break. The suggestion unit can also analyze the user's posture and eye movements while driving to estimate the user's level of fatigue. Furthermore, the suggestion unit can also suggest appropriate times to take a break based on the user's past driving history. This can support the user's safe driving.

[0073] The suggestion unit can estimate the user's emotional state and suggest relaxing music. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing music. The suggestion unit can also analyze the user's tone of voice and facial expression to estimate the user's emotions. Furthermore, the suggestion unit can adjust the genre and tempo of the music based on the user's emotional state. This makes it possible to provide music that suits the user's emotional state.

[0074] The suggestion unit can monitor the user's health condition while driving and issue a warning if an abnormality is detected. For example, it can monitor the heart rate and blood pressure, and if an abnormality is detected, it can display a message encouraging the user to take a break. The suggestion unit can also analyze the user's posture and movements while driving to estimate the user's health condition. Furthermore, the suggestion unit can provide appropriate driving advice based on the user's past health data. This can support the user's health.

[0075] The suggestion unit can estimate the user's emotional state and suggest activities to reduce stress while driving. For example, if the user is feeling stressed, the suggestion unit can suggest deep breathing or simple stretching. The suggestion unit can also analyze the user's tone of voice and facial expression to estimate the user's emotions. Furthermore, the suggestion unit can suggest relaxing activities based on the user's emotional state. This makes it possible to provide stress reduction measures tailored to the user's emotional state.

[0076] The suggestion unit can provide advice to support eco-driving while the user is driving. For example, it can display a message urging the user to avoid sudden acceleration and braking. The suggestion unit can also analyze the user's driving style and provide specific advice for eco-driving. Furthermore, the suggestion unit can evaluate the effectiveness of eco-driving based on the user's past driving data and provide feedback. This can support the user in improving fuel efficiency.

[0077] The suggestion unit can estimate the user's emotional state and provide advice to improve concentration while driving. For example, if the user is tired, the suggestion unit can suggest a simple exercise to improve concentration. The suggestion unit can also analyze the user's tone of voice and facial expression to estimate the user's emotions. Furthermore, the suggestion unit can provide advice to improve concentration based on the user's emotional state. This makes it possible to provide measures to improve concentration according to the user's emotional state.

[0078] The suggestion unit can provide advice to support safe driving while the user is driving. For example, it can display a message warning the user of speeding or keeping an insufficient distance between vehicles. The suggestion unit can also analyze the user's driving style and provide specific advice for safe driving. Furthermore, the suggestion unit can evaluate the effectiveness of safe driving based on the user's past driving data and provide feedback. This can support the user's safe driving.

[0079] The suggestion unit can estimate the user's emotional state and provide advice to promote relaxation while driving. For example, if the user is nervous, the suggestion unit can suggest breathing techniques to help the user relax. The suggestion unit can also analyze the user's tone of voice and facial expressions to estimate the user's emotions. Furthermore, the suggestion unit can provide advice to help the user relax based on the user's emotional state. This makes it possible to provide relaxation measures that correspond to the user's emotional state.

[0080] The suggestion unit can provide advice on providing entertainment for the user while driving. For example, it can suggest audiobooks or podcasts that can be enjoyed while driving. The suggestion unit can also learn the user's past entertainment history and suggest content that matches their preferences. Furthermore, the suggestion unit can adjust the entertainment depending on the weather and time of day while driving. This can make the user's driving experience more enjoyable.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The dialogue unit accepts instructions in natural language from the user. For example, the dialogue unit can accept instructions from the user using voice input, text input, or specific commands. Step 2: The analysis unit analyzes the instruction received by the dialogue unit. For example, the analysis unit can analyze the user's instruction using voice recognition technology, text analysis technology, or a machine learning algorithm. Step 3: The suggestion unit proposes an optimal route based on the instructions analyzed by the analysis unit. For example, the suggestion unit can propose the shortest route, the shortest time route, or a route that takes traffic conditions into consideration.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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, in order to avoid confusion and to 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 dialogue unit that accepts instructions in natural language from a user; an analysis unit that analyzes the instruction received by the dialogue unit; a proposal unit that proposes an optimal route based on the instructions analyzed by the analysis unit. A system characterized by:

2. The analysis unit Learns the user's driving history and preferences to provide personalized route suggestions 2. The system of claim 1.

3. The proposal unit Based on information provided by the user during the interaction, the app references similar routes set by other users.

2. The system of claim 1.

4. The proposal unit Collects traffic information in real time and immediately reflects congestion and accident information 2. The system of claim 1.

5. The analysis unit It estimates the user's emotions from their tone of voice and vocabulary, and suggests a relaxing route if they are feeling stressed.

2. The system of claim 1.

6. The proposal unit Analyzes the emotions felt by the user in real time during the conversation and suggests routes that elicit positive emotions 2. The system of claim 1.

7. The proposal unit Estimate the user's emotional state and adjust the route to reduce stress 2. The system of claim 1.

8. The proposal unit Estimates the user's emotional state and suggests relaxing or scenic routes 2. The system of claim 1.

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