System

Abstract

An object of the system according to the embodiment is to safely check and reply to a message even during driving.SOLUTION: A system includes a voice input unit, a message reading unit, and a message reply unit. The voice input unit receives a voice instruction from a user. The message reading unit reads the message based on the instruction received by the voice input unit. The message replying unit replies with a message based on the instruction received by the voice input 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 to check and reply to smartphone notifications while driving, posing safety concerns.

[0005] The system according to the embodiment aims to enable safe message checking and replying while driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, a message reading unit, and a message reply unit. The voice input unit accepts voice instructions from a user. The message reading unit reads a message based on the instructions accepted by the voice input unit. The message reply unit replies to the message based on the instructions accepted by the voice input unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to safely check and reply to messages while driving. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A message processing system according to an embodiment of the present invention is a system that allows a user to check and reply to important notifications even when they are unable to operate a smartphone while driving. This system is realized using "Voice Assist AI." Specifically, a user can speak to "Voice Assist AI" to have messages read out and replies sent by voice. This allows the message processing system to safely and efficiently check and reply to messages while driving.

[0029] A message processing system according to an embodiment includes a voice input unit, a message reading unit, and a message reply unit. The voice input unit accepts voice instructions from a user. For example, if a user says, "Read my message," the voice input unit accepts the instruction. The voice input unit can also accept an instruction if a user says, "Reply." The message reading unit reads out a message based on the instruction accepted by the voice input unit. For example, a generation AI (text generation AI or multimodal generation AI) reads out a message received on a smartphone by voice. The generation AI reads out a message based on a user instruction. The message reply unit replies to a message based on the instruction accepted by the voice input unit. For example, the generation AI analyzes a user's voice and automatically replies to a message. The generation AI generates a reply message based on a user instruction. As a result, the message processing system according to an embodiment enables messages to be read out and replied to by voice instructions even while driving.

[0030] The message reading unit can refer to the user's past message history and optimize the reading order according to importance. For example, the message reading unit can analyze the user's past message history and prioritize reading out messages of high importance. For example, work-related messages are prioritized. The message reading unit can also postpone messages of low importance based on the user's message history. For example, advertising and promotional messages are postponed. This allows important messages to be prioritized when they are read out.

[0031] The message reading unit can automatically adjust the tone and speed of the reading voice depending on the content of the message. For example, the message reading unit analyzes the content of the message and adjusts the tone of the voice to emphasize important parts. For example, the voice can be raised to emphasize important information. The message reading unit can also adjust the reading speed depending on the content of the message. For example, an urgent message is read quickly. Furthermore, the message reading unit can also read emotional messages in an appropriate tone. For example, a message of gratitude is read in a warm tone. This makes it possible to read the message in an appropriate tone and speed depending on the content of the message.

[0032] In addition to reading messages, the message reading unit can also read related calendar events and reminders at the same time. For example, the message reading unit can automatically read related calendar events when reading a message. For example, it can read meeting details along with a meeting notification. The message reading unit can also automatically read reminders. For example, it can read reminders for important tasks. Furthermore, the message reading unit can read a combination of calendar events and reminders. For example, it can read a meeting notification and its preparation tasks at the same time. This allows the message and related information to be provided at the same time.

[0033] When the message reading unit reads out a message, if the user asks a question, the generation AI can instantly provide an answer to that question. When the message reading unit reads out a message, for example, if the user asks a question, the generation AI can instantly provide an answer. For example, if the user asks, "What time is the next meeting?", the time of the meeting will be read out. The message reading unit can also provide related information in response to a user's question. For example, if the user asks, "Where is the meeting?", the location of the meeting will be read out. The message reading unit can also provide detailed explanations in response to a user's question. For example, if the user asks, "What is the agenda for the meeting?", the details of the agenda will be read out. This allows the user's questions to be instantly responded to while the message is being read out.

[0034] The message reply unit can learn the user's past reply patterns and suggest the most appropriate reply content. The message reply unit, for example, analyzes the user's past reply patterns and automatically suggests the most appropriate reply content. For example, it prioritizes suggestions of frequently used phrases. The message reply unit can also generate appropriate reply content based on the user's reply patterns. For example, it can suggest reply content for business emails. Furthermore, the message reply unit can learn the user's reply patterns and customize the reply content. For example, it can suggest reply content for casual messages. This makes it possible to suggest the optimal reply content based on the user's past reply patterns.

[0035] The message reply unit can automatically adjust the tone and wording of a reply depending on the content of the message. The message reply unit, for example, analyzes the content of the message and automatically adjusts the tone and wording of a reply. For example, polite wording is used for formal messages. The message reply unit can also use friendly wording for casual messages. Furthermore, the message reply unit can also reply in an appropriate tone to emotional messages. For example, a message of gratitude is replied to in a warm tone. This makes it possible to reply in an appropriate tone and wording depending on the content of the message.

[0036] The message reply unit can automatically attach related files and links in addition to replying to a message. For example, the message reply unit analyzes the content of the message and automatically attaches related files and links. For example, meeting materials are automatically attached. The message reply unit can also attach specific files and links based on a user's instructions. For example, a project progress report is attached. Furthermore, the message reply unit can automatically attach related web links. For example, a link to reference materials is attached. This allows related information to be automatically attached when replying to a message.

[0037] The message reply unit allows the generation AI to check the user's schedule while replying to a message and suggest an appropriate timing for replying. For example, while replying to a message, the message reply unit allows the generation AI to check the user's schedule and suggest an appropriate timing for replying. For example, prompting the user to reply before a meeting. The message reply unit can also suggest the optimal time to reply based on the user's schedule. For example, prompting the user to reply during free time. Furthermore, the message reply unit can also support the user's task management. For example, prompting the user to reply after completing an important task. This makes it possible to suggest the optimal timing for replying based on the user's schedule.

[0038] The voice input unit can incorporate noise canceling technology to more accurately recognize a user's voice command during hands-free operation. For example, the voice input unit uses noise canceling technology to accurately recognize a user's voice command during hands-free operation. For example, noise inside the vehicle can be removed. The voice input unit can also filter external environmental sounds. For example, wind noise and engine noise can be removed. Furthermore, the voice input unit can also emphasize the user's voice. For example, the directivity of the microphone can be adjusted to emphasize the user's voice. This improves the accuracy of voice command recognition using noise canceling technology.

[0039] The voice input unit can monitor the user's driving status in real time and provide notifications at appropriate times. The voice input unit can, for example, monitor the user's driving status in real time and provide notifications at appropriate times. For example, it can provide notifications when waiting at a traffic light. The voice input unit can also adjust the timing of notifications depending on the driving situation. For example, it can refrain from providing notifications while driving on a highway. Furthermore, the voice input unit can provide notifications taking driving safety into consideration. For example, it can delay notifications in the event of sudden braking. This allows notifications to be provided at appropriate times depending on the driving situation.

[0040] The voice input unit can cooperate with the vehicle's navigation system to provide information about the destination during hands-free operation. For example, during hands-free operation, the voice input unit can cooperate with the vehicle's navigation system to automatically provide information about the destination. For example, it can read out the distance to the destination and the estimated time of arrival. The voice input unit can also suggest route changes based on instructions from the navigation system. For example, it can suggest an alternative route to avoid traffic congestion. Furthermore, the voice input unit can provide detailed information about the destination. For example, it can read out the address and contact information of the destination. This allows the voice input unit to cooperate with the navigation system to provide information about the destination.

[0041] The voice input unit can monitor the user's health condition during hands-free operation and issue an alert if an abnormality is detected. For example, the voice input unit can monitor the user's health condition in real time during hands-free operation and issue an alert if an abnormality is detected. For example, it can detect abnormalities in heart rate. The voice input unit can also provide advice based on the user's health condition. For example, it can suggest taking a break. Furthermore, the voice input unit can notify emergency contacts if an abnormality is detected. For example, it can contact family members or a medical institution. In this way, the user's health condition can be monitored and an alert can be issued if an abnormality is detected.

[0042] When supporting multiple languages, the message reading unit can select appropriate expressions according to the user's language proficiency. The message reading unit, for example, analyzes the user's language proficiency and automatically selects appropriate expressions. For example, it uses simple expressions for beginners. The message reading unit can also use complex expressions for advanced users. For example, it uses expressions including technical terms. Furthermore, the message reading unit can adjust the tone and wording according to the user's language proficiency. For example, it uses formal expressions. This makes it possible to select appropriate expressions according to the user's language proficiency.

[0043] The message reading unit takes cultural background and nuances into consideration when translating a message, allowing it to provide a more natural translation. The message reading unit, for example, takes cultural background and nuances into consideration when translating a message. For example, it appropriately translates expressions that are unique to a particular culture. The message reading unit can also take emotional nuances into consideration when translating. For example, it appropriately translates expressions of gratitude. Furthermore, the message reading unit can also provide translations that are appropriate to the context. For example, it performs translations that follow the flow of the conversation. This allows it to provide a natural translation that takes cultural background and nuances into consideration.

[0044] In addition to being multilingual, the message reading unit can automatically provide news and information in different languages. In addition to being multilingual, the message reading unit can automatically provide news and information in different languages. For example, English news can be translated and provided in Japanese. The message reading unit can also provide news that matches the user's interests. For example, it can provide sports news or entertainment news. Furthermore, the message reading unit can update news in real time. For example, it can automatically provide the latest news. This makes it possible to automatically provide news and information in different languages.

[0045] The message reading unit can provide feedback to correct the user's pronunciation or accent when supporting multiple languages. The message reading unit can provide feedback to correct the user's pronunciation or accent when supporting multiple languages, for example, by evaluating the accuracy of the pronunciation. The message reading unit can also provide advice to improve the user's pronunciation, for example, by providing guidance on correct pronunciation. Furthermore, the message reading unit can also provide feedback to correct the user's accent, for example, by pointing out the emphasis on the accent. This makes it possible to provide feedback to correct the user's pronunciation or accent.

[0046] The message reply unit can customize the tone and wording of the response according to the user's preferences. The message reply unit customizes the tone and wording of the response according to the user's preferences, for example. For example, a formal tone is used. The message reply unit can also use a casual tone. Furthermore, the message reply unit can use wording according to the user's preferences. For example, polite wording is used. This allows the response to be customized with a tone and wording according to the user's preferences.

[0047] The message reply unit can support a user's schedule and task management in addition to customizable responses. The message reply unit can, for example, support a user's schedule and task management in addition to customizable responses. For example, it can automatically set schedule reminders. The message reply unit can also support a user's task management. For example, it can manage a task list. Furthermore, the message reply unit can provide an appropriate response based on the user's schedule. For example, it can prompt a response before a meeting. This can support a user's schedule and task management.

[0048] The message reply unit can provide music and entertainment information according to the user's preferences when providing a customizable response. For example, the message reply unit can automatically provide music according to the user's preferences when providing a customizable response. For example, it can play songs by the user's favorite artist. The message reply unit can also provide entertainment information according to the user's preferences. For example, it can provide movie information and event information. Furthermore, the message reply unit can create a playlist according to the user's preferences. For example, it can provide a playlist of relaxing music. This makes it possible to provide music and entertainment information according to the user's preferences.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The message processing system can also monitor the user's health status and take appropriate action if an abnormality is detected. For example, it can monitor the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. It can also provide advice based on the user's health status, for example, suggesting that they take a break. Furthermore, it can notify emergency contacts if an abnormality is detected, for example, contacting family members or a medical institution.

[0051] The message processing system can also monitor the user's driving status in real time and send notifications at appropriate times. For example, it can send notifications when the user is waiting at a traffic light. It can also adjust the timing of notifications depending on the driving situation. For example, it can refrain from sending notifications when driving on a highway. It can also send notifications taking driving safety into consideration. For example, it can delay notifications when the driver brakes suddenly.

[0052] The message processing system can also learn the user's past message history and suggest the most appropriate reply content. For example, it can analyze the user's past reply patterns and automatically suggest the most appropriate reply content. For example, it can prioritize suggestions of frequently used phrases. It can also generate appropriate reply content based on the user's reply patterns. For example, it can suggest reply content for business emails. It can also learn the user's reply patterns and customize reply content. For example, it can suggest reply content for casual messages.

[0053] The message processing system can further support the user's schedule and task management, for example by automatically setting schedule reminders. It can also support the user's task management, for example by managing a task list. It can also provide appropriate responses based on the user's schedule, for example by prompting a response before a meeting.

[0054] The message processing system can further select appropriate expressions according to the user's language proficiency. For example, the system can analyze the user's language proficiency and automatically select appropriate expressions. For example, the system can use simple expressions for beginners. Alternatively, the system can use complex expressions for advanced users. For example, the system can use expressions containing technical terms. Furthermore, the system can adjust the tone and wording according to the user's language proficiency. For example, the system can use formal expressions.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The voice input unit accepts voice instructions from the user. For example, if the user says "Read the message," the voice input unit accepts that instruction. Also, if the user says "Reply," the voice input unit can accept that instruction as well. Step 2: The message reading unit reads out the message based on the instructions received by the voice input unit. For example, the generation AI reads out a message received on a smartphone. The generation AI reads out the message based on the user's instructions. Step 3: The message reply unit sends a reply message based on the instructions received by the voice input unit. For example, the generation AI analyzes the user's voice and automatically sends a reply message. The generation AI generates a reply message based on the user's instructions.

[0057] (Example 2) A message processing system according to an embodiment of the present invention is a system that allows a user to check and reply to important notifications even when they are unable to operate a smartphone while driving. This system is realized using "Voice Assist AI." Specifically, a user can speak to "Voice Assist AI" to have messages read out and replies sent by voice. This allows the message processing system to safely and efficiently check and reply to messages while driving.

[0058] A message processing system according to an embodiment includes a voice input unit, a message reading unit, and a message reply unit. The voice input unit accepts voice instructions from a user. For example, if a user says, "Read my message," the voice input unit accepts the instruction. The voice input unit can also accept an instruction if a user says, "Reply." The message reading unit reads out a message based on the instruction accepted by the voice input unit. For example, a generation AI (text generation AI or multimodal generation AI) reads out a message received on a smartphone by voice. The generation AI reads out a message based on a user instruction. The message reply unit replies to a message based on the instruction accepted by the voice input unit. For example, the generation AI analyzes a user's voice and automatically replies to a message. The generation AI generates a reply message based on a user instruction. As a result, the message processing system according to an embodiment enables messages to be read out and replied to by voice instructions even while driving.

[0059] The message reading unit can refer to the user's past message history and optimize the reading order according to importance. For example, the message reading unit can analyze the user's past message history and prioritize reading out messages of high importance. For example, work-related messages are prioritized. The message reading unit can also postpone messages of low importance based on the user's message history. For example, advertising and promotional messages are postponed. This allows important messages to be prioritized when they are read out.

[0060] The message reading unit can automatically adjust the tone and speed of the reading voice depending on the content of the message. For example, the message reading unit analyzes the content of the message and adjusts the tone of the voice to emphasize important parts. For example, the voice can be raised to emphasize important information. The message reading unit can also adjust the reading speed depending on the content of the message. For example, an urgent message is read quickly. Furthermore, the message reading unit can also read emotional messages in an appropriate tone. For example, a message of gratitude is read in a warm tone. This makes it possible to read the message in an appropriate tone and speed depending on the content of the message.

[0061] The message reading unit can use the emotion estimation function to read the emotional nuances of the message and read it out with appropriate emotion. The message reading unit, for example, analyzes the emotion of the message and reads it out in a tone of voice that corresponds to the emotion. For example, a joyful message is read out in a bright tone. The message reading unit can also read a sad message in a calm tone. Furthermore, the message reading unit can also read an angry message in a strong tone. This makes it possible to appropriately convey the emotional nuances of the message.

[0062] In addition to reading messages, the message reading unit can also read related calendar events and reminders at the same time. For example, the message reading unit can automatically read related calendar events when reading a message. For example, it can read meeting details along with a meeting notification. The message reading unit can also automatically read reminders. For example, it can read reminders for important tasks. Furthermore, the message reading unit can read a combination of calendar events and reminders. For example, it can read a meeting notification and its preparation tasks at the same time. This allows the message and related information to be provided at the same time.

[0063] When the message reading unit reads out a message, if the user asks a question, the generation AI can instantly provide an answer to that question. When the message reading unit reads out a message, for example, if the user asks a question, the generation AI can instantly provide an answer. For example, if the user asks, "What time is the next meeting?", the time of the meeting will be read out. The message reading unit can also provide related information in response to a user's question. For example, if the user asks, "Where is the meeting?", the location of the meeting will be read out. The message reading unit can also provide detailed explanations in response to a user's question. For example, if the user asks, "What is the agenda for the meeting?", the details of the agenda will be read out. This allows the user's questions to be instantly responded to while the message is being read out.

[0064] The message reading unit can use the emotion estimation function to read out relaxing music or encouraging words according to the user's emotional state. For example, the message reading unit can use the emotion estimation function to automatically play relaxing music according to the user's emotional state. For example, relaxing music is played when the user is feeling stressed. The message reading unit can also read out encouraging words according to the user's emotional state. For example, encouraging words are read out when the user is feeling depressed. Furthermore, the message reading unit can also suggest relaxation methods according to the user's emotional state. For example, it can provide guidance on deep breathing. This makes it possible to respond appropriately according to the user's emotional state.

[0065] The message reply unit can learn the user's past reply patterns and suggest the most appropriate reply content. The message reply unit, for example, analyzes the user's past reply patterns and automatically suggests the most appropriate reply content. For example, it prioritizes suggestions of frequently used phrases. The message reply unit can also generate appropriate reply content based on the user's reply patterns. For example, it can suggest reply content for business emails. Furthermore, the message reply unit can learn the user's reply patterns and customize the reply content. For example, it can suggest reply content for casual messages. This makes it possible to suggest the optimal reply content based on the user's past reply patterns.

[0066] The message reply unit can automatically adjust the tone and wording of a reply depending on the content of the message. The message reply unit, for example, analyzes the content of the message and automatically adjusts the tone and wording of a reply. For example, polite wording is used for formal messages. The message reply unit can also use friendly wording for casual messages. Furthermore, the message reply unit can also reply in an appropriate tone to emotional messages. For example, a message of gratitude is replied to in a warm tone. This makes it possible to reply in an appropriate tone and wording depending on the content of the message.

[0067] The message reply unit uses the emotion estimation function to generate reply content according to the user's emotion and can show emotional empathy. The message reply unit, for example, uses the emotion estimation function to automatically generate reply content according to the user's emotion. For example, a congratulatory message is returned in response to an emotion of joy. The message reply unit can also return a comforting message in response to an emotion of sadness. Furthermore, the message reply unit can also return a message showing a calm response in response to an emotion of anger. This makes it possible to provide an appropriate reply according to the user's emotion.

[0068] The message reply unit can automatically attach related files and links in addition to replying to a message. For example, the message reply unit analyzes the content of the message and automatically attaches related files and links. For example, meeting materials are automatically attached. The message reply unit can also attach specific files and links based on a user's instructions. For example, a project progress report is attached. Furthermore, the message reply unit can automatically attach related web links. For example, a link to reference materials is attached. This allows related information to be automatically attached when replying to a message.

[0069] The message reply unit allows the generation AI to check the user's schedule while replying to a message and suggest an appropriate timing for replying. For example, while replying to a message, the message reply unit allows the generation AI to check the user's schedule and suggest an appropriate timing for replying. For example, prompting the user to reply before a meeting. The message reply unit can also suggest the optimal time to reply based on the user's schedule. For example, prompting the user to reply during free time. Furthermore, the message reply unit can also support the user's task management. For example, prompting the user to reply after completing an important task. This makes it possible to suggest the optimal timing for replying based on the user's schedule.

[0070] The message reply unit can use the emotion estimation function to include in the reply encouraging words or advice according to the user's emotional state. The message reply unit can, for example, use the emotion estimation function to include in the reply encouraging words according to the user's emotional state. For example, it can reply encouraging words when the user is feeling stressed. The message reply unit can also include in the reply advice according to the user's emotional state. For example, it can reply with a suggestion for solving a problem. Furthermore, the message reply unit can also include in the reply encouraging words according to the user's emotional state. For example, it can reply with encouraging words for a difficult situation. This makes it possible to provide appropriate encouragement or advice according to the user's emotional state.

[0071] The voice input unit can incorporate noise canceling technology to more accurately recognize a user's voice command during hands-free operation. For example, the voice input unit uses noise canceling technology to accurately recognize a user's voice command during hands-free operation. For example, noise inside the vehicle can be removed. The voice input unit can also filter external environmental sounds. For example, wind noise and engine noise can be removed. Furthermore, the voice input unit can also emphasize the user's voice. For example, the directivity of the microphone can be adjusted to emphasize the user's voice. This improves the accuracy of voice command recognition using noise canceling technology.

[0072] The voice input unit can monitor the user's driving status in real time and provide notifications at appropriate times. The voice input unit can, for example, monitor the user's driving status in real time and provide notifications at appropriate times. For example, it can provide notifications when waiting at a traffic light. The voice input unit can also adjust the timing of notifications depending on the driving situation. For example, it can refrain from providing notifications while driving on a highway. Furthermore, the voice input unit can provide notifications taking driving safety into consideration. For example, it can delay notifications in the event of sudden braking. This allows notifications to be provided at appropriate times depending on the driving situation.

[0073] The voice input unit can use the emotion estimation function to detect the user's stress level and provide voice guidance for relaxation. The voice input unit can, for example, use the emotion estimation function to detect the user's stress level in real time and provide voice guidance for relaxation. For example, it can provide guidance for deep breathing. The voice input unit can also suggest relaxation methods according to the user's stress level. For example, it can provide guidance for stretching. Furthermore, the voice input unit can provide advice for reducing the user's stress level. For example, it can suggest playing relaxing music. This makes it possible to provide relaxation guidance according to the user's stress level.

[0074] The voice input unit can cooperate with the vehicle's navigation system to provide information about the destination during hands-free operation. For example, during hands-free operation, the voice input unit can cooperate with the vehicle's navigation system to automatically provide information about the destination. For example, it can read out the distance to the destination and the estimated time of arrival. The voice input unit can also suggest route changes based on instructions from the navigation system. For example, it can suggest an alternative route to avoid traffic congestion. Furthermore, the voice input unit can provide detailed information about the destination. For example, it can read out the address and contact information of the destination. This allows the voice input unit to cooperate with the navigation system to provide information about the destination.

[0075] The voice input unit can monitor the user's health condition during hands-free operation and issue an alert if an abnormality is detected. For example, the voice input unit can monitor the user's health condition in real time during hands-free operation and issue an alert if an abnormality is detected. For example, it can detect abnormalities in heart rate. The voice input unit can also provide advice based on the user's health condition. For example, it can suggest taking a break. Furthermore, the voice input unit can notify emergency contacts if an abnormality is detected. For example, it can contact family members or a medical institution. In this way, the user's health condition can be monitored and an alert can be issued if an abnormality is detected.

[0076] The voice input unit can use the emotion estimation function to provide driving advice and relaxation methods according to the user's emotional state. The voice input unit can, for example, use the emotion estimation function to provide driving advice according to the user's emotional state. For example, it can provide advice on how to relax when the user is feeling stressed. The voice input unit can also suggest relaxation methods according to the user's emotional state. For example, it can provide guidance on deep breathing. Furthermore, the voice input unit can also provide driving tips according to the user's emotional state. For example, it can provide advice on safe driving. This makes it possible to provide driving advice and relaxation methods according to the user's emotional state.

[0077] When supporting multiple languages, the message reading unit can select appropriate expressions according to the user's language proficiency. The message reading unit, for example, analyzes the user's language proficiency and automatically selects appropriate expressions. For example, it uses simple expressions for beginners. The message reading unit can also use complex expressions for advanced users. For example, it uses expressions including technical terms. Furthermore, the message reading unit can adjust the tone and wording according to the user's language proficiency. For example, it uses formal expressions. This makes it possible to select appropriate expressions according to the user's language proficiency.

[0078] The message reading unit takes cultural background and nuances into consideration when translating a message, allowing it to provide a more natural translation. The message reading unit, for example, takes cultural background and nuances into consideration when translating a message. For example, it appropriately translates expressions that are unique to a particular culture. The message reading unit can also take emotional nuances into consideration when translating. For example, it appropriately translates expressions of gratitude. Furthermore, the message reading unit can also provide translations that are appropriate to the context. For example, it performs translations that follow the flow of the conversation. This allows it to provide a natural translation that takes cultural background and nuances into consideration.

[0079] The message reading unit can accurately convey emotional nuances between different languages ​​by using the emotion estimation function. The message reading unit can accurately convey emotional nuances between different languages ​​by using, for example, the emotion estimation function. For example, the message reading unit can appropriately translate the emotion of joy. The message reading unit can also appropriately translate the emotion of sadness. Furthermore, the message reading unit can also appropriately translate the emotion of anger. This makes it possible to accurately convey emotional nuances between different languages.

[0080] In addition to being multilingual, the message reading unit can automatically provide news and information in different languages. In addition to being multilingual, the message reading unit can automatically provide news and information in different languages. For example, English news can be translated and provided in Japanese. The message reading unit can also provide news that matches the user's interests. For example, it can provide sports news or entertainment news. Furthermore, the message reading unit can update news in real time. For example, it can automatically provide the latest news. This makes it possible to automatically provide news and information in different languages.

[0081] The message reading unit can provide feedback to correct the user's pronunciation or accent when supporting multiple languages. The message reading unit can provide feedback to correct the user's pronunciation or accent when supporting multiple languages, for example, by evaluating the accuracy of the pronunciation. The message reading unit can also provide advice to improve the user's pronunciation, for example, by providing guidance on correct pronunciation. Furthermore, the message reading unit can also provide feedback to correct the user's accent, for example, by pointing out the emphasis on the accent. This makes it possible to provide feedback to correct the user's pronunciation or accent.

[0082] The message read-out unit can use the emotion estimation function to suggest expressions for promoting emotional empathy between different languages. The message read-out unit, for example, uses the emotion estimation function to suggest expressions for promoting emotional empathy between different languages. For example, it appropriately suggests expressions of gratitude. The message read-out unit can also suggest expressions of empathy for the emotion of sadness. Furthermore, the message read-out unit can also suggest expressions of empathy for the emotion of joy. In this way, it is possible to suggest expressions for promoting emotional empathy between different languages.

[0083] The message reply unit can customize the tone and wording of the response according to the user's preferences. The message reply unit customizes the tone and wording of the response according to the user's preferences, for example. For example, a formal tone is used. The message reply unit can also use a casual tone. Furthermore, the message reply unit can use wording according to the user's preferences. For example, polite wording is used. This allows the response to be customized with a tone and wording according to the user's preferences.

[0084] The message reply unit can use the emotion estimation function to provide an optimal response according to the emotional state of the user. The message reply unit can use the emotion estimation function to provide an optimal response according to the emotional state of the user. For example, a congratulatory message can be provided for an emotion of joy. The message reply unit can also provide a comforting message for an emotion of sadness. Furthermore, the message reply unit can also provide a message suggesting a calm response for an emotion of anger. This makes it possible to provide an optimal response according to the emotional state of the user.

[0085] The message reply unit can support a user's schedule and task management in addition to customizable responses. The message reply unit can, for example, support a user's schedule and task management in addition to customizable responses. For example, it can automatically set schedule reminders. The message reply unit can also support a user's task management. For example, it can manage a task list. Furthermore, the message reply unit can provide an appropriate response based on the user's schedule. For example, it can prompt a response before a meeting. This can support a user's schedule and task management.

[0086] The message reply unit can provide music and entertainment information according to the user's preferences when providing a customizable response. For example, the message reply unit can automatically provide music according to the user's preferences when providing a customizable response. For example, it can play songs by the user's favorite artist. The message reply unit can also provide entertainment information according to the user's preferences. For example, it can provide movie information and event information. Furthermore, the message reply unit can create a playlist according to the user's preferences. For example, it can provide a playlist of relaxing music. This makes it possible to provide music and entertainment information according to the user's preferences.

[0087] The message reply unit can use the emotion estimation function to suggest relaxation methods and stress relief methods according to the user's emotional state. The message reply unit can, for example, use the emotion estimation function to suggest relaxation methods according to the user's emotional state. For example, it can provide deep breathing guidance. The message reply unit can also suggest stress relief methods according to the user's emotional state. For example, it can suggest exercise or hobby activities. Furthermore, the message reply unit can provide relaxing music according to the user's emotional state. For example, it can play relaxing music. This makes it possible to suggest relaxation methods and stress relief methods according to the user's emotional state.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The message processing system can also learn the user's driving style and provide advice to reduce stress levels while driving. For example, it can analyze the frequency of sudden braking and sudden acceleration and provide advice to encourage calm driving. It can also monitor the user's heart rate and breathing patterns while driving and provide deep breathing guidance to help them relax. It can also adjust the music selection while driving to suit the user's preferences, providing a relaxing environment.

[0090] The message processing system can also monitor the user's health status and take appropriate action if an abnormality is detected. For example, it can monitor the user's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. It can also provide advice based on the user's health status, for example, suggesting that they take a break. Furthermore, it can notify emergency contacts if an abnormality is detected, for example, contacting family members or a medical institution.

[0091] The message processing system can also monitor the user's driving status in real time and send notifications at appropriate times. For example, it can send notifications when the user is waiting at a traffic light. It can also adjust the timing of notifications depending on the driving situation. For example, it can refrain from sending notifications when driving on a highway. It can also send notifications taking driving safety into consideration. For example, it can delay notifications when the driver brakes suddenly.

[0092] The message processing system can further provide relaxing music or encouraging words according to the user's emotional state. For example, using an emotion estimation function, relaxing music according to the user's emotional state can be automatically played. For example, relaxing music can be played when the user is feeling stressed. The system can also provide encouraging words according to the user's emotional state. For example, encouraging words can be read out when the user is feeling depressed. Furthermore, the system can suggest relaxation methods according to the user's emotional state. For example, deep breathing guidance can be provided.

[0093] The message processing system can also learn the user's past message history and suggest the most appropriate reply content. For example, it can analyze the user's past reply patterns and automatically suggest the most appropriate reply content. For example, it can prioritize suggestions of frequently used phrases. It can also generate appropriate reply content based on the user's reply patterns. For example, it can suggest reply content for business emails. It can also learn the user's reply patterns and customize reply content. For example, it can suggest reply content for casual messages.

[0094] The message processing system can further suggest relaxation methods and stress relief methods according to the user's emotional state. For example, the emotion estimation function can be used to suggest relaxation methods according to the user's emotional state. For example, deep breathing guidance can be provided. The message processing system can also suggest stress relief methods according to the user's emotional state. For example, exercise or hobby activities can be suggested. Furthermore, the message processing system can provide relaxing music according to the user's emotional state. For example, relaxing music can be played.

[0095] The message processing system can further support the user's schedule and task management, for example by automatically setting schedule reminders. It can also support the user's task management, for example by managing a task list. It can also provide appropriate responses based on the user's schedule, for example by prompting a response before a meeting.

[0096] The message processing system can further provide driving advice and relaxation methods according to the user's emotional state. For example, the emotion estimation function can be used to provide driving advice according to the user's emotional state. For example, advice on how to relax when the user is feeling stressed can be provided. The message processing system can also suggest relaxation methods according to the user's emotional state. For example, deep breathing guidance can be provided. Furthermore, the message processing system can provide driving tips according to the user's emotional state. For example, advice on safe driving can be provided.

[0097] The message processing system can further select appropriate expressions according to the user's language proficiency. For example, the system can analyze the user's language proficiency and automatically select appropriate expressions. For example, the system can use simple expressions for beginners. Alternatively, the system can use complex expressions for advanced users. For example, the system can use expressions containing technical terms. Furthermore, the system can adjust the tone and wording according to the user's language proficiency. For example, the system can use formal expressions.

[0098] The message processing system can further suggest expressions to promote emotional empathy across different languages. For example, the system uses an emotion estimation function to suggest expressions to promote emotional empathy across different languages. For example, the system can appropriately suggest expressions of gratitude. It can also suggest expressions of empathy for sadness. It can also suggest expressions of empathy for joy.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The voice input unit accepts voice instructions from the user. For example, if the user says "Read the message," the voice input unit accepts that instruction. Also, if the user says "Reply," the voice input unit can accept that instruction as well. Step 2: The message reading unit reads out the message based on the instructions received by the voice input unit. For example, the generation AI reads out a message received on a smartphone. The generation AI reads out the message based on the user's instructions. Step 3: The message reply unit sends a reply message based on the instructions received by the voice input unit. For example, the generation AI analyzes the user's voice and automatically sends a reply message. The generation AI generates a reply message based on the user's instructions.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 voice input unit that accepts voice instructions from a user; a message reading unit that reads out a message based on an instruction received by the voice input unit; a message reply unit that replies to a message based on an instruction received by the voice input unit. A system characterized by:

2. The message reading unit Refer to the user's past message history and optimize the reading order according to importance 2. The system of claim 1.

3. The message reading unit In addition to reading out the message, it will also read out any related calendar events and reminders.

2. The system of claim 1.

4. The message reply unit Learn the user's past reply patterns and suggest the most appropriate reply content 2. The system of claim 1.

5. The voice input unit Incorporating noise-cancelling technology to more accurately recognize the user's voice commands during hands-free operation.

2. The system of claim 1.

6. The message reading unit Accurately convey emotional nuances across different languages ​​using emotion estimation 2. The system of claim 1.

7. The message reply unit Using emotion estimation functionality to provide optimal responses according to the user's emotional state 2. The system of claim 1.

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