System

Abstract

An object of a system according to an embodiment is to grasp detailed needs of a user and propose an optimal destination.SOLUTION: A system according to an embodiment includes a chat function cooperation unit, a needs grasping unit, a destination suggestion unit, an event extraction unit, and a stop-off destination addition unit. The chat function cooperation unit causes the automobile Navi application and the chat function to cooperate with each other. The needs grasping unit grasps the needs of the user in stages through the chat function cooperation unit. The destination suggestion unit suggests a destination based on the need grasped by the need grasping unit. The event extraction unit extracts a preset event or various discount information. The stop-off destination adding unit adds a stop-off destination based on the information extracted by the event extracting 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] With conventional technology, it was difficult for car navigation apps to fully understand the user's detailed needs and suggest the optimal destination.

[0005] The system according to the embodiment aims to understand the detailed needs of the user and propose the most suitable destination. [Means for solving the problem]

[0006] The system according to the embodiment includes a chat function linking unit, a needs understanding unit, a destination suggestion unit, an event extraction unit, and a stop-off destination adding unit. The chat function linking unit links the car navigation app with the chat function. The needs understanding unit understands the user's needs in stages through the chat function linking unit. The destination suggestion unit suggests destinations based on the needs understood by the needs understanding unit. The event extraction unit extracts pre-set events or various discount information. The stop-off destination adding unit adds stop-off destinations based on the information extracted by the event extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the detailed needs of the user and suggest the most suitable destination. [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) The car navigation system according to an embodiment of the present invention is a system that gradually grasps the user's detailed needs in a chat-like format by linking a car navigation app with a chat function, and provides destinations similar to those found in online search results. Furthermore, it also has a function for extracting pre-set event and various discount information and adding them as stopover destinations. This allows the car navigation system to gradually grasp the user's detailed needs and propose optimal destinations. Furthermore, adding stopover destinations based on pre-set event and discount information can provide the user with a more fulfilling driving experience.

[0029] The car navigation system according to the embodiment includes a chat function linking unit, a needs understanding unit, a destination suggestion unit, an event extraction unit, and a stopover destination adding unit. The chat function linking unit links the car navigation app with the chat function. For example, the chat function allows the user to input questions and requests about destinations through chat. The needs understanding unit understands the user's needs step by step through the chat function linking unit. For example, the chat function allows the user to ask questions such as, "What kind of food do you want to eat?", "What is your budget?", and "How far can you travel?" to understand the user's specific needs. The destination suggestion unit suggests destinations based on the needs understood by the needs understanding unit. For example, if a user inputs "I'm looking for a tourist spot," the generation AI analyzes tourist information on the Internet and suggests the most suitable tourist spot for the user. The event extraction unit extracts pre-set events and various discount information. For example, if a user inputs "I want to go to a shopping mall," the generation AI searches for events and discount information held in the vicinity and suggests them to the user. The stopover destination adding unit adds stopover destinations based on the information extracted by the event extraction unit. For example, if a user inputs "I want to go to a restaurant," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user. This allows the automobile navigation system according to the embodiment to gradually grasp the user's detailed needs and suggest the most suitable destination. In addition, by adding stopover destinations based on pre-set event and discount information, the system can provide the user with a more fulfilling driving experience.

[0030] The chat function integration unit is equipped with a voice recognition function, allowing users to input questions and requests about their destination by voice. For example, the chat function integration unit adds a voice recognition function, allowing users to input questions and requests about their destination by voice. For example, if users input "Tell me about nearby cafes," the generation AI converts the voice into text and suggests appropriate cafes. This allows users to input questions and requests about their destination by voice.

[0031] The needs understanding unit analyzes the chat history, learns the user's preferences and patterns, and can reflect these in future suggestions. The needs understanding unit, for example, analyzes the chat history and learns the user's past visited places and preference patterns. For example, if the user frequently visits Italian restaurants, Italian restaurants will be displayed preferentially in future suggestions. This allows the user's preferences and patterns to be learned and reflected in future suggestions.

[0032] The chat function linkage unit has an image recognition function and can suggest destinations based on photos taken by the user. For example, the chat function linkage unit adds an image recognition function and analyzes photos taken by the user to suggest destinations. For example, it analyzes photos of food taken by the user and suggests nearby restaurants. This makes it possible to suggest destinations based on photos taken by the user.

[0033] The chat function linkage unit has a multilingual support function, allowing foreign users to input questions and requests in their native language. The chat function linkage unit adds, for example, a multilingual support function, allowing foreign users to input questions and requests in their native language. For example, it supports multiple languages ​​such as English, Chinese, and Spanish. This allows foreign users to input questions and requests in their native language.

[0034] The needs understanding unit dynamically generates the next question based on the user's answer, and can elicit more specific needs. The needs understanding unit builds a system that dynamically generates the next question based on the user's answer, for example. For example, it asks, "What kind of food would you like to eat?" and continues, depending on the user's answer, with, "What is your budget?" This allows the next question to be dynamically generated based on the user's answer, and more specific needs to be elicited.

[0035] The needs understanding unit can refer to the user's past behavior history and present predicted needs in advance. The needs understanding unit, for example, analyzes the user's past behavior history and builds a system that presents predicted needs in advance. For example, the next suggestion is made based on the places the user has visited in the past and their search history. This makes it possible to refer to the user's past behavior history and present predicted needs in advance.

[0036] The needs understanding unit can group the needs of users and refer to suggestions from other users who have common needs. The needs understanding unit, for example, builds a system that groups the needs of users and refers to suggestions from other users who have common needs. For example, suggestions from users who have the same needs in the same area are shared. This allows the needs of users to be grouped and refer to suggestions from other users who have common needs.

[0037] The needs understanding unit can share the user's needs on the cloud and make them accessible from other devices. The needs understanding unit, for example, builds a system that shares the user's needs on the cloud and makes them accessible from other devices. For example, it makes it possible to access the user's needs from smartphones and tablets. This allows the user's needs to be shared on the cloud and made accessible from other devices.

[0038] The destination suggestion unit can analyze the user's search history and suggest the optimal destination based on past search results. The destination suggestion unit, for example, analyzes the user's search history and suggests the optimal destination based on keywords searched for in the past and places visited. For example, the next suggestion is made based on tourist spots and restaurants searched for by the user in the past. This makes it possible to analyze the user's search history and suggest the optimal destination based on past search results.

[0039] The destination suggestion unit can propose an optimal destination by combining the user's current location with weather information. The destination suggestion unit, for example, builds a system that proposes optimal destinations by combining the user's current location with weather information. For example, indoor tourist spots are proposed on rainy days. This makes it possible to propose optimal destinations by combining the user's current location with weather information.

[0040] The destination suggestion unit can link with the user's SNS account and suggest destinations based on posts by friends. The destination suggestion unit, for example, links with the user's SNS account and builds a system that suggests destinations based on posts by friends. For example, it can suggest tourist spots and restaurants that friends have visited. This allows the system to link with the user's SNS account and suggest destinations based on posts by friends.

[0041] The destination suggestion unit can provide a search filter that can be customized according to the user's preferences. The destination suggestion unit, for example, builds a system that provides a search filter that can be customized according to the user's preferences. For example, the system allows the user to set specific conditions for searching. This makes it possible to provide a search filter that can be customized according to the user's preferences.

[0042] The event extraction unit can work in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule. The event extraction unit, for example, builds a system that works in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule. For example, it suggests events that are being held nearby based on the user's schedule. This makes it possible to work in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule.

[0043] The event extraction unit can analyze the user's history of past events attended and suggest events that match their preferences. The event extraction unit, for example, builds a system that analyzes the user's history of past events attended and suggests events that match their preferences. For example, the next event is suggested based on the types of events the user has attended in the past. This makes it possible to analyze the user's history of past events attended and suggest events that match their preferences.

[0044] The event extraction unit can provide nearby event and discount information in real time based on the user's location information. The event extraction unit, for example, builds a system that provides nearby event and discount information in real time based on the user's location information. For example, the event extraction unit suggests events that are being held near the user's current location. This makes it possible to provide nearby event and discount information in real time based on the user's location information.

[0045] The event extraction unit can link with the user's SNS account and suggest events that friends are planning to attend. The event extraction unit, for example, links with the user's SNS account and builds a system that suggests events that friends are planning to attend. For example, the event extraction unit makes suggestions to the user based on events that friends are planning to attend. This allows the event extraction unit to link with the user's SNS account and suggest events that friends are planning to attend.

[0046] The stop-off destination adding unit can analyze the user's travel route and suggest efficient stop-off destinations. The stop-off destination adding unit, for example, builds a system that analyzes the user's travel route and suggests efficient stop-off destinations. For example, it suggests places that the user can stop at on the way to the destination. This makes it possible to analyze the user's travel route and suggest efficient stop-off destinations.

[0047] The stop-off destination adding unit can refer to the user's past stop-off history and suggest stop-off destinations that suit the user's preferences. The stop-off destination adding unit, for example, analyzes the user's past stop-off history and builds a system that suggests stop-off destinations that suit the user's preferences. For example, the next suggestion is made based on the types of places the user has visited in the past. This makes it possible to refer to the user's past stop-off history and suggest stop-off destinations that suit the user's preferences.

[0048] The stop-off destination adding unit can suggest nearby stop-off destinations in real time based on the user's location information. The stop-off destination adding unit, for example, builds a system that suggests nearby stop-off destinations in real time based on the user's location information. For example, it suggests spots that the user can stop off at near their current location. This makes it possible to suggest nearby stop-off destinations in real time based on the user's location information.

[0049] The stop-off destination adding unit can link with the user's SNS account and suggest stop-off destinations that friends have visited. The stop-off destination adding unit, for example, links with the user's SNS account and builds a system that suggests stop-off destinations that friends have visited. For example, it suggests tourist spots and restaurants that friends have visited. This allows the unit to link with the user's SNS account and suggest stop-off destinations that friends have visited.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The car navigation system can further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit analyzes, for example, the user's driving speed and frequency of braking, and makes suggestions based on the user's driving style. For example, a message encouraging safe driving can be displayed to a user who frequently brakes suddenly. The system can also suggest driving routes that the user prefers based on the user's driving style. This allows suggestions to be made based on the user's driving style.

[0052] The car navigation system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's hobbies and interests based on, for example, places the user has visited or events the user has participated in in the past. For example, if the user frequently visits art museums, the hobby analysis unit can suggest museums and art galleries. It can also provide event information that matches the user's hobbies. This allows the system to make suggestions that match the user's hobbies and interests.

[0053] The car navigation system can further include an attention monitoring unit that monitors the user's attention while driving. The attention monitoring unit, for example, analyzes the user's line of sight and reaction time and displays a warning if the user's attention is declining. For example, if the user's line of sight is averted from the road, it can sound a warning. It can also display a message urging the user to take a break if reaction time is delayed. This allows the system to monitor the user's attention while driving and support safe driving.

[0054] The automobile navigation system can further include a fuel efficiency suggestion unit that analyzes the user's driving history and suggests fuel-efficient driving methods. The fuel efficiency suggestion unit, for example, suggests fuel-efficient driving methods based on the user's driving history. For example, it can advise the user to avoid sudden acceleration and braking. It can also provide tips for eco-driving. This makes it possible to analyze the user's driving history and suggest fuel-efficient driving methods.

[0055] The automotive navigation system may further include a maintenance management unit that manages the user's vehicle maintenance information. The maintenance management unit manages maintenance information such as vehicle oil changes and tire pressure checks, and notifies the user at the appropriate time. For example, it may display a notification when an oil change is required. It may also display a warning when tire pressure is low. This allows the system to manage the user's vehicle maintenance information and notify the user at the appropriate time.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The chat function integration unit integrates the car navigation app with the chat function, allowing users to, for example, input questions or requests about their destination through chat. Step 2: The needs understanding section gradually understands the user's needs through the chat function integration section. For example, it asks questions such as "What kind of food do you want to eat?", "What is your budget?", and "How far can you travel?" to understand the user's specific needs. Step 3: The destination suggestion unit suggests destinations based on the needs identified by the needs understanding unit. For example, if a user inputs "I'm looking for a tourist spot," the generation AI analyzes tourist information on the Internet and suggests the most suitable tourist spot for the user. Step 4: The event extraction unit extracts pre-set events and various discount information. For example, if a user inputs "I want to go to the shopping mall," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user. Step 5: The stopover destination addition unit adds stopover destinations based on the information extracted by the event extraction unit. For example, if a user inputs "I want to go to a restaurant," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user.

[0058] (Example 2) The car navigation system according to an embodiment of the present invention is a system that gradually grasps the user's detailed needs in a chat-like format by linking a car navigation app with a chat function, and provides destinations similar to those found in online search results. Furthermore, it also has a function for extracting pre-set event and various discount information and adding them as stopover destinations. This allows the car navigation system to gradually grasp the user's detailed needs and propose optimal destinations. Furthermore, adding stopover destinations based on pre-set event and discount information can provide the user with a more fulfilling driving experience.

[0059] The car navigation system according to the embodiment includes a chat function linking unit, a needs understanding unit, a destination suggestion unit, an event extraction unit, and a stopover destination adding unit. The chat function linking unit links the car navigation app with the chat function. For example, the chat function allows the user to input questions and requests about destinations through chat. The needs understanding unit understands the user's needs step by step through the chat function linking unit. For example, the chat function allows the user to ask questions such as, "What kind of food do you want to eat?", "What is your budget?", and "How far can you travel?" to understand the user's specific needs. The destination suggestion unit suggests destinations based on the needs understood by the needs understanding unit. For example, if a user inputs "I'm looking for a tourist spot," the generation AI analyzes tourist information on the Internet and suggests the most suitable tourist spot for the user. The event extraction unit extracts pre-set events and various discount information. For example, if a user inputs "I want to go to a shopping mall," the generation AI searches for events and discount information held in the vicinity and suggests them to the user. The stopover destination adding unit adds stopover destinations based on the information extracted by the event extraction unit. For example, if a user inputs "I want to go to a restaurant," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user. This allows the automobile navigation system according to the embodiment to gradually grasp the user's detailed needs and suggest the most suitable destination. In addition, by adding stopover destinations based on pre-set event and discount information, the system can provide the user with a more fulfilling driving experience.

[0060] The chat function integration unit is equipped with a voice recognition function, allowing users to input questions and requests about their destination by voice. For example, the chat function integration unit adds a voice recognition function, allowing users to input questions and requests about their destination by voice. For example, if users input "Tell me about nearby cafes," the generation AI converts the voice into text and suggests appropriate cafes. This allows users to input questions and requests about their destination by voice.

[0061] The needs understanding unit analyzes the chat history, learns the user's preferences and patterns, and can reflect these in future suggestions. The needs understanding unit, for example, analyzes the chat history and learns the user's past visited places and preference patterns. For example, if the user frequently visits Italian restaurants, Italian restaurants will be displayed preferentially in future suggestions. This allows the user's preferences and patterns to be learned and reflected in future suggestions.

[0062] The needs understanding unit can use the emotion estimation function to make suggestions according to the user's emotions. For example, the needs understanding unit uses the emotion estimation function to analyze the user's emotional state in real time and make appropriate suggestions. For example, if the user is feeling stressed, the needs understanding unit can suggest a park or cafe where the user can relax. This makes it possible to make suggestions according to the user's emotions.

[0063] The chat function linkage unit has an image recognition function and can suggest destinations based on photos taken by the user. For example, the chat function linkage unit adds an image recognition function and analyzes photos taken by the user to suggest destinations. For example, it analyzes photos of food taken by the user and suggests nearby restaurants. This makes it possible to suggest destinations based on photos taken by the user.

[0064] The chat function linkage unit has a multilingual support function, allowing foreign users to input questions and requests in their native language. The chat function linkage unit adds, for example, a multilingual support function, allowing foreign users to input questions and requests in their native language. For example, it supports multiple languages ​​such as English, Chinese, and Spanish. This allows foreign users to input questions and requests in their native language.

[0065] The needs understanding unit uses the emotion estimation function to analyze the emotions felt by the user during a chat in real time and can conduct a dialogue that draws out positive emotions. The needs understanding unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during a chat in real time and can conduct a dialogue that draws out positive emotions. For example, if the user is feeling anxious, the needs understanding unit sends an encouraging message. This allows the emotions felt by the user during a chat to be analyzed in real time and can conduct a dialogue that draws out positive emotions.

[0066] The needs understanding unit dynamically generates the next question based on the user's answer, and can elicit more specific needs. The needs understanding unit builds a system that dynamically generates the next question based on the user's answer, for example. For example, it asks, "What kind of food would you like to eat?" and continues, depending on the user's answer, with, "What is your budget?" This allows the next question to be dynamically generated based on the user's answer, and more specific needs to be elicited.

[0067] The needs understanding unit can refer to the user's past behavior history and present predicted needs in advance. The needs understanding unit, for example, analyzes the user's past behavior history and builds a system that presents predicted needs in advance. For example, the next suggestion is made based on the places the user has visited in the past and their search history. This makes it possible to refer to the user's past behavior history and present predicted needs in advance.

[0068] The needs understanding unit uses the emotion estimation function to ask questions according to the user's emotions and make more appropriate suggestions. For example, the needs understanding unit uses the emotion estimation function to analyze the user's emotional state in real time and ask appropriate questions. For example, if the user is relaxed, the needs understanding unit asks questions suggesting places where the user can relax. This allows the needs understanding unit to ask questions according to the user's emotions and make more appropriate suggestions.

[0069] The needs understanding unit can group the needs of users and refer to suggestions from other users who have common needs. The needs understanding unit, for example, builds a system that groups the needs of users and refers to suggestions from other users who have common needs. For example, suggestions from users who have the same needs in the same area are shared. This allows the needs of users to be grouped and refer to suggestions from other users who have common needs.

[0070] The needs understanding unit can share the user's needs on the cloud and make them accessible from other devices. The needs understanding unit, for example, builds a system that shares the user's needs on the cloud and makes them accessible from other devices. For example, it makes it possible to access the user's needs from smartphones and tablets. This allows the user's needs to be shared on the cloud and made accessible from other devices.

[0071] The needs understanding unit uses the emotion estimation function to set priorities of needs based on the user's emotions and can make suggestions starting with the most important need. The needs understanding unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and set priorities of needs. For example, if the user is feeling stressed, the unit preferentially suggests places where the user can relax. This allows the needs understanding unit to set priorities of needs based on the user's emotions and can make suggestions starting with the most important need.

[0072] The destination suggestion unit can analyze the user's search history and suggest the optimal destination based on past search results. The destination suggestion unit, for example, analyzes the user's search history and suggests the optimal destination based on keywords searched for in the past and places visited. For example, the next suggestion is made based on tourist spots and restaurants searched for by the user in the past. This makes it possible to analyze the user's search history and suggest the optimal destination based on past search results.

[0073] The destination suggestion unit can propose an optimal destination by combining the user's current location with weather information. The destination suggestion unit, for example, builds a system that proposes optimal destinations by combining the user's current location with weather information. For example, indoor tourist spots are proposed on rainy days. This makes it possible to propose optimal destinations by combining the user's current location with weather information.

[0074] The destination suggestion unit can link with the user's SNS account and suggest destinations based on posts by friends. The destination suggestion unit, for example, links with the user's SNS account and builds a system that suggests destinations based on posts by friends. For example, it can suggest tourist spots and restaurants that friends have visited. This allows the system to link with the user's SNS account and suggest destinations based on posts by friends.

[0075] The destination suggestion unit can provide a search filter that can be customized according to the user's preferences. The destination suggestion unit, for example, builds a system that provides a search filter that can be customized according to the user's preferences. For example, the system allows the user to set specific conditions for searching. This makes it possible to provide a search filter that can be customized according to the user's preferences.

[0076] The destination suggestion unit can use the emotion estimation function to preferentially display search results based on the user's emotion. For example, the destination suggestion unit uses the emotion estimation function to analyze the user's emotional state in real time and build a system that preferentially displays search results. For example, if the user wants to relax, places where they can relax are preferentially displayed. This makes it possible to preferentially display search results based on the user's emotion.

[0077] The event extraction unit can work in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule. The event extraction unit, for example, builds a system that works in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule. For example, it suggests events that are being held nearby based on the user's schedule. This makes it possible to work in conjunction with the user's calendar to suggest events and discount information that fit the user's schedule.

[0078] The event extraction unit can analyze the user's history of past events attended and suggest events that match their preferences. The event extraction unit, for example, builds a system that analyzes the user's history of past events attended and suggests events that match their preferences. For example, the next event is suggested based on the types of events the user has attended in the past. This makes it possible to analyze the user's history of past events attended and suggest events that match their preferences.

[0079] The event extraction unit can use the emotion estimation function to suggest events and discount information according to the user's emotions. For example, the event extraction unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate events and discount information. For example, if the user wants to have fun, entertainment events are suggested. This makes it possible to suggest events and discount information according to the user's emotions.

[0080] The event extraction unit can provide nearby event and discount information in real time based on the user's location information. The event extraction unit, for example, builds a system that provides nearby event and discount information in real time based on the user's location information. For example, the event extraction unit suggests events that are being held near the user's current location. This makes it possible to provide nearby event and discount information in real time based on the user's location information.

[0081] The event extraction unit can link with the user's SNS account and suggest events that friends are planning to attend. The event extraction unit, for example, links with the user's SNS account and builds a system that suggests events that friends are planning to attend. For example, the event extraction unit makes suggestions to the user based on events that friends are planning to attend. This allows the event extraction unit to link with the user's SNS account and suggest events that friends are planning to attend.

[0082] The event extraction unit can use the emotion estimation function to prioritize display of events and discount information based on the user's emotion. The event extraction unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and build a system that prioritizes display of events and discount information. For example, if the user wants to have fun, entertainment events are prioritized. This makes it possible to prioritize display of events and discount information based on the user's emotion.

[0083] The stop-off destination adding unit can analyze the user's travel route and suggest efficient stop-off destinations. The stop-off destination adding unit, for example, builds a system that analyzes the user's travel route and suggests efficient stop-off destinations. For example, it suggests places that the user can stop at on the way to the destination. This makes it possible to analyze the user's travel route and suggest efficient stop-off destinations.

[0084] The stop-off destination adding unit can refer to the user's past stop-off history and suggest stop-off destinations that suit the user's preferences. The stop-off destination adding unit, for example, analyzes the user's past stop-off history and builds a system that suggests stop-off destinations that suit the user's preferences. For example, the next suggestion is made based on the types of places the user has visited in the past. This makes it possible to refer to the user's past stop-off history and suggest stop-off destinations that suit the user's preferences.

[0085] The stop-off destination adding unit can use the emotion estimation function to suggest stop-off destinations that correspond to the user's emotions. For example, the stop-off destination adding unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest appropriate stop-off destinations. For example, if the user wants to relax, a park or a cafe can be suggested. This makes it possible to suggest stop-off destinations that correspond to the user's emotions.

[0086] The stop-off destination adding unit can suggest nearby stop-off destinations in real time based on the user's location information. The stop-off destination adding unit, for example, builds a system that suggests nearby stop-off destinations in real time based on the user's location information. For example, it suggests spots that the user can stop off at near their current location. This makes it possible to suggest nearby stop-off destinations in real time based on the user's location information.

[0087] The stop-off destination adding unit can link with the user's SNS account and suggest stop-off destinations that friends have visited. The stop-off destination adding unit, for example, links with the user's SNS account and builds a system that suggests stop-off destinations that friends have visited. For example, it suggests tourist spots and restaurants that friends have visited. This allows the unit to link with the user's SNS account and suggest stop-off destinations that friends have visited.

[0088] The stop-off destination adding unit can use the emotion estimation function to preferentially display stop-off destinations based on the user's emotion. The stop-off destination adding unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and build a system that preferentially displays stop-off destinations. For example, if the user wants to relax, places where they can relax are preferentially displayed. This makes it possible to preferentially display stop-off destinations based on the user's emotion.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The car navigation system can further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit analyzes, for example, the user's driving speed and frequency of braking, and makes suggestions based on the user's driving style. For example, a message encouraging safe driving can be displayed to a user who frequently brakes suddenly. The system can also suggest driving routes that the user prefers based on the user's driving style. This allows suggestions to be made based on the user's driving style.

[0091] The car navigation system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures, for example, the user's heart rate and stress level and makes suggestions based on the user's health condition. For example, if the user's heart rate is high, the unit can suggest a place where the user can relax. It can also display a message encouraging the user to take a break to reduce fatigue caused by long driving hours. This allows the system to make suggestions based on the user's health condition.

[0092] The car navigation system can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's hobbies and interests based on, for example, places the user has visited or events the user has participated in in the past. For example, if the user frequently visits art museums, the hobby analysis unit can suggest museums and art galleries. It can also provide event information that matches the user's hobbies. This allows the system to make suggestions that match the user's hobbies and interests.

[0093] The car navigation system may further include a music suggestion unit that estimates the user's emotions and suggests music based on the emotions. The music suggestion unit, for example, analyzes the user's emotional state in real time and suggests appropriate music. For example, if the user wants to relax, relaxing music may be suggested. Also, if the user wants to cheer up, upbeat music may be suggested. In this way, music can be suggested based on the user's emotions.

[0094] The car navigation system can further include an attention monitoring unit that monitors the user's attention while driving. The attention monitoring unit, for example, analyzes the user's line of sight and reaction time and displays a warning if the user's attention is declining. For example, if the user's line of sight is averted from the road, it can sound a warning. It can also display a message urging the user to take a break if reaction time is delayed. This allows the system to monitor the user's attention while driving and support safe driving.

[0095] The car navigation system can further include a rest suggestion unit that estimates the user's emotions and suggests rest spots based on the emotions. The rest suggestion unit, for example, analyzes the user's emotional state in real time and suggests appropriate rest spots. For example, if the user is tired, it can suggest a cafe or park where the user can relax. Also, if the user is feeling stressed, it can suggest a place where the user can refresh themselves. In this way, it is possible to suggest rest spots based on the user's emotions.

[0096] The automobile navigation system can further include a fuel efficiency suggestion unit that analyzes the user's driving history and suggests fuel-efficient driving methods. The fuel efficiency suggestion unit, for example, suggests fuel-efficient driving methods based on the user's driving history. For example, it can advise the user to avoid sudden acceleration and braking. It can also provide tips for eco-driving. This makes it possible to analyze the user's driving history and suggest fuel-efficient driving methods.

[0097] The car navigation system may further include a driving course suggestion unit that estimates the user's emotions and suggests driving courses based on the emotions. The driving course suggestion unit, for example, analyzes the user's emotional state in real time and suggests appropriate driving courses. For example, if the user wants to relax, it may suggest a scenic course. Also, if the user is feeling adventurous, it may suggest unexplored areas. In this way, it is possible to suggest driving courses based on the user's emotions.

[0098] The automotive navigation system may further include a maintenance management unit that manages the user's vehicle maintenance information. The maintenance management unit manages maintenance information such as vehicle oil changes and tire pressure checks, and notifies the user at the appropriate time. For example, it may display a notification when an oil change is required. It may also display a warning when tire pressure is low. This allows the system to manage the user's vehicle maintenance information and notify the user at the appropriate time.

[0099] The car navigation system may further include a driving advice unit that estimates the user's emotions and provides driving advice based on the emotions. The driving advice unit may, for example, analyze the user's emotional state in real time and provide appropriate driving advice. For example, if the user is nervous, the driving advice unit may provide advice to relax. Also, if the user is tired, the driving advice unit may provide advice to take a break. In this way, driving advice based on the user's emotions can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The chat function integration unit integrates the car navigation app with the chat function, allowing users to, for example, input questions or requests about their destination through chat. Step 2: The needs understanding section gradually understands the user's needs through the chat function integration section. For example, it asks questions such as "What kind of food do you want to eat?", "What is your budget?", and "How far can you travel?" to understand the user's specific needs. Step 3: The destination suggestion unit suggests destinations based on the needs identified by the needs understanding unit. For example, if a user inputs "I'm looking for a tourist spot," the generation AI analyzes tourist information on the Internet and suggests the most suitable tourist spot for the user. Step 4: The event extraction unit extracts pre-set events and various discount information. For example, if a user inputs "I want to go to the shopping mall," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user. Step 5: The stopover destination addition unit adds stopover destinations based on the information extracted by the event extraction unit. For example, if a user inputs "I want to go to a restaurant," the generation AI searches for events and discount information being held in the vicinity and suggests them to the user.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 chat function linking unit that links the car navigation app with the chat function, a needs grasping unit that gradually grasps the needs of users through the chat function linkage unit; a destination suggestion unit that suggests destinations based on the needs identified by the needs identification unit; an event extraction unit that extracts pre-set events or various discount information; a stop destination addition unit that adds a stop destination based on the information extracted by the event extraction unit. A system characterized by:

2. The chat function linking unit A voice recognition function is provided, allowing the user to input questions or requests about the destination by voice.

2. The system of claim 1.

3. The needs understanding unit Based on the user's answer, the next question is dynamically generated to elicit more specific needs.

2. The system of claim 1.

4. The destination suggestion unit Analyze the user's search history and suggest the best destination based on past search results 2. The system of claim 1.

5. The event extraction unit Linking with the user's calendar to suggest events or discount information that fit the user's schedule 2. The system of claim 1.

6. The stop destination addition unit Analyze the user's travel route and suggest efficient stops 2. The system of claim 1.

7. The needs understanding unit Providing suggestions according to the user's emotions 2. The system of claim 1.

8. The destination suggestion unit Suggesting destinations according to the user's emotions 2. The system of claim 1.

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