System

Abstract

An object of a system according to an embodiment is to provide various interaction methods and to perform interaction with a AI more smoothly.SOLUTION: A system according to an embodiment includes a dialogue method providing section, a schedule adjusting section, and a task detailing section. The conversation method provider may provide various conversation methods. The schedule adjustment section adjusts the schedule using the dialogue method provided by the dialogue method providing section. The task detailing part details the task based on the schedule adjusted by the schedule adjusting part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have only offered limited interaction methods, which has the drawback of restricting interaction with AI.

[0005] The system according to the embodiment aims to provide various dialogue methods and facilitate smoother dialogue with AI. [Means for solving the problem]

[0006] A system according to an embodiment includes a dialogue method providing unit, a schedule adjustment unit, and a task detailing unit. The dialogue method providing unit provides a variety of dialogue methods. The schedule adjustment unit adjusts the schedule using the dialogue methods provided by the dialogue method providing unit. The task detailing unit details the task based on the schedule adjusted by the schedule adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment provides various dialogue methods, enabling smoother dialogue with AI. [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) An AI dialogue system according to an embodiment of the present invention provides a variety of dialogue methods in addition to the limited dialogue methods available in the past, such as text, voice, and photos. This system allows users to interact with the AI ​​and adjust schedules and refine tasks using a user interface (UI) that incorporates the controls of popular games. This allows the AI ​​dialogue system to efficiently adjust schedules and refine tasks through a UI that users can intuitively operate.

[0029] An AI dialogue system according to an embodiment includes a dialogue method providing unit, a schedule adjustment unit, and a task detailing unit. The dialogue method providing unit provides a variety of dialogue methods. For example, the dialogue method providing unit allows a user to issue instructions to an AI by tapping and releasing an icon on the screen. The dialogue method providing unit provides, for example, voice dialogue, text dialogue, gesture dialogue, and the like. The schedule adjustment unit adjusts a schedule using the dialogue method provided by the dialogue method providing unit. For example, the schedule adjustment unit allows a user to tap a calendar icon and release a desired date and time, causing the AI ​​to add an appointment for that date and time. The schedule adjustment unit performs, for example, calendar synchronization and priority setting. The task detailing unit details a task based on the schedule adjusted by the schedule adjustment unit. For example, the task detailing unit allows a user to tap a task icon and input detailed information, causing the AI ​​to manage the task. The task detailing unit performs, for example, breaking down a task and clarifying its steps. This allows the AI ​​dialogue system according to an embodiment to provide a variety of dialogue methods and efficiently adjust schedules and detail tasks.

[0030] The dialogue method providing unit can analyze the user's past dialogue history and propose a dialogue method optimized for each individual user. For example, the dialogue method providing unit analyzes the user's past dialogue history and identifies frequently used phrases and topics. This allows the unit to learn the user's preferred dialogue style and generate optimal responses. The dialogue method providing unit also proposes a dialogue method optimized for each individual user based on the user's dialogue history. For example, it prioritizes suggesting dialogue methods that the user has frequently used in the past. This allows for more personalized dialogue by proposing an optimized dialogue method based on the user's past dialogue history.

[0031] The interaction method providing unit can provide an interface that allows the user to interact with the AI ​​by manipulating physical objects. For example, the interaction method providing unit allows the user to give instructions to the AI ​​by combining blocks of a specific shape. For example, when a red block and a blue block are combined, the AI ​​plays music. The interaction method providing unit also provides an interface that allows the user to interact with the AI ​​by manipulating physical objects. For example, when the user presses a button, the AI ​​performs a specific action. In this way, by providing an interface that allows the user to interact with the AI ​​by manipulating physical objects, more intuitive interaction becomes possible.

[0032] The dialogue method providing unit can provide a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, the dialogue method providing unit allows a user to give instructions by voice while making a specific gesture, causing the AI ​​to recognize the instruction and generate an appropriate response. For example, if a user says "play music" and waves their hand, the AI ​​will play music. The dialogue method providing unit also provides a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, if a user gives instructions by voice while making a specific gesture, the AI ​​will recognize the instruction and generate an appropriate response. This allows a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures, enabling more natural dialogue.

[0033] The schedule adjustment unit can analyze the user's past schedule data and propose an optimal schedule pattern. For example, the schedule adjustment unit analyzes the user's past schedule data and identifies frequently occurring events and activities. Based on this, the optimal schedule pattern is proposed. The schedule adjustment unit also analyzes the user's past schedule data and proposes an optimal schedule pattern. For example, the optimal schedule pattern is proposed based on events and activities that the user frequently performed in the past. This enables efficient schedule management by proposing an optimal schedule pattern based on the user's past schedule data.

[0034] The schedule adjustment unit can adjust the schedule taking travel time into consideration by using the user's real-time location information. The schedule adjustment unit, for example, acquires the user's real-time location information and proposes a schedule taking travel time into consideration. For example, the schedule adjustment unit calculates the travel time the user will take from their current location to their next scheduled location and proposes an appropriate departure time. The schedule adjustment unit also adjusts the schedule taking travel time into consideration by using the user's real-time location information. For example, the schedule adjustment unit calculates the travel time the user will take from their current location to their next scheduled location and proposes an appropriate departure time. This enables efficient schedule management by adjusting the schedule taking travel time into consideration by using the user's real-time location information.

[0035] The schedule adjustment unit can provide a function that allows a user to share a schedule with other users and adjust it collaboratively. The schedule adjustment unit provides, for example, a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or coworkers and adjust overlapping appointments. The schedule adjustment unit also provides a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or coworkers and adjust overlapping appointments. By providing a function that allows a user to share a schedule with other users and adjust it collaboratively, efficient schedule management is possible.

[0036] The task detailing unit can analyze the user's past task history and automatically suggest similar tasks. For example, the task detailing unit analyzes the user's past task history and identifies frequently performed tasks. Based on this, similar tasks are automatically suggested. For example, a task for preparing for a weekly meeting is suggested. The task detailing unit also analyzes the user's past task history and automatically suggests similar tasks. For example, similar tasks are automatically suggested based on tasks that the user frequently performed in the past. This enables efficient task management by automatically suggesting similar tasks based on the user's past task history.

[0037] The task detailing unit can provide a function that allows a user to input a task by voice and convert the voice into text for detailing. The task detailing unit provides, for example, a function that allows a user to input a task by voice and convert the voice into text. For example, if a user says, "Prepare for tomorrow's meeting," an AI converts the content into text and registers it as a task. The task detailing unit also provides a function that allows a user to input a task by voice and converts the voice into text for detailing. For example, if a user says, "Prepare for tomorrow's meeting," an AI converts the content into text and registers it as a task. This enables efficient task management by providing a function that allows a user to input a task by voice and converts the voice into text for detailing.

[0038] The task detailing unit can provide an interactive task board that allows a user to visually manage tasks. The task detailing unit provides, for example, an interactive task board that allows a user to visually manage tasks. For example, a task can be moved by drag and drop. The task detailing unit also provides an interactive task board that allows a user to visually manage tasks. For example, a task can be moved by drag and drop. In this way, efficient task management is possible by providing an interactive task board that allows a user to visually manage tasks.

[0039] The task detailing unit can provide a function that allows a user to share tasks with other users and manage them collaboratively. The task detailing unit provides, for example, a function that allows a user to share tasks with other users and manage them collaboratively. For example, all members of a project team share a task board and manage their own tasks. The task detailing unit also provides a function that allows a user to share tasks with other users and manage them collaboratively. For example, all members of a project team share a task board and manage their own tasks. This provides a function that allows a user to share tasks with other users and manage them collaboratively, enabling efficient task management.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The dialogue method providing unit can analyze the user's past dialogue history and learn the user's preferred dialogue style. For example, it can identify phrases and topics that the user has frequently used in the past and generate optimal responses based on them. It can also preferentially suggest dialogue methods that the user has preferred in the past. This allows for more personalized dialogue by providing the optimal dialogue method based on the user's past dialogue history.

[0042] The interaction method providing unit can provide an interface that allows a user to interact with an AI by manipulating physical objects. For example, a user can give instructions to an AI by combining blocks of a specific shape. For example, when a user combines a red block with a blue block, the AI ​​plays music. Also, when a user presses a button, the AI ​​performs a specific action. This allows for more intuitive interaction by providing an interface that allows a user to interact with an AI by manipulating physical objects.

[0043] The dialogue method providing unit can provide a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, when a user gives a voice instruction while making a specific gesture, the AI ​​recognizes the instruction and generates an appropriate response. For example, when a user says "play music" and waves their hand, the AI ​​plays music. Also, when a user gives a voice instruction while making a specific gesture, the AI ​​recognizes the instruction and generates an appropriate response. This allows for more natural dialogue by providing a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures.

[0044] The schedule adjustment unit can adjust the schedule taking travel time into consideration by using the user's real-time location information. For example, it calculates the travel time from the user's current location to the next scheduled location and suggests an appropriate departure time. It also adjusts the schedule taking travel time into consideration by using the user's real-time location information. This enables efficient schedule management by adjusting the schedule taking travel time into consideration by using the user's real-time location information.

[0045] The schedule adjustment unit can provide a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or colleagues and adjust overlapping appointments. The schedule adjustment unit also provides a function that allows a user to share a schedule with other users and adjust it collaboratively. This allows a user to share a schedule with other users and adjust it collaboratively, thereby enabling efficient schedule management.

[0046] The task detailing unit can analyze the user's past task history and automatically suggest similar tasks. For example, it analyzes the user's past task history and identifies frequently performed tasks. Based on this, it automatically suggests similar tasks. For example, it suggests a task for preparing for a weekly meeting that the user holds. It also analyzes the user's past task history and automatically suggests similar tasks. This enables efficient task management by automatically suggesting similar tasks based on the user's past task history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The dialogue method provider provides various dialogue methods. For example, the user can issue commands to the AI ​​by tapping and releasing an icon on the screen. The dialogue method provider provides voice dialogue, text dialogue, gesture dialogue, etc. Step 2: The schedule adjustment unit adjusts the schedule using the interaction method provided by the interaction method provision unit. For example, the user taps the calendar icon and releases the desired date and time, and the AI ​​adds an event for that date and time. The schedule adjustment unit synchronizes the calendar and sets priorities. Step 3: The task detailing unit refines the task based on the schedule adjusted by the schedule adjustment unit. For example, the user taps the task icon and enters detailed information, and the AI ​​manages the task. The task detailing unit breaks down the task and clarifies the steps.

[0049] (Example 2) An AI dialogue system according to an embodiment of the present invention provides a variety of dialogue methods in addition to the limited dialogue methods available in the past, such as text, voice, and photos. This system allows users to interact with the AI ​​and adjust schedules and refine tasks using a user interface (UI) that incorporates the controls of popular games. This allows the AI ​​dialogue system to efficiently adjust schedules and refine tasks through a UI that users can intuitively operate.

[0050] An AI dialogue system according to an embodiment includes a dialogue method providing unit, a schedule adjustment unit, and a task detailing unit. The dialogue method providing unit provides a variety of dialogue methods. For example, the dialogue method providing unit allows a user to issue instructions to an AI by tapping and releasing an icon on the screen. The dialogue method providing unit provides, for example, voice dialogue, text dialogue, gesture dialogue, and the like. The schedule adjustment unit adjusts a schedule using the dialogue method provided by the dialogue method providing unit. For example, the schedule adjustment unit allows a user to tap a calendar icon and release a desired date and time, causing the AI ​​to add an appointment for that date and time. The schedule adjustment unit performs, for example, calendar synchronization and priority setting. The task detailing unit details a task based on the schedule adjusted by the schedule adjustment unit. For example, the task detailing unit allows a user to tap a task icon and input detailed information, causing the AI ​​to manage the task. The task detailing unit performs, for example, breaking down a task and clarifying its steps. This allows the AI ​​dialogue system according to an embodiment to provide a variety of dialogue methods and efficiently adjust schedules and detail tasks.

[0051] The dialogue method providing unit recognizes the user's gestures and facial expressions, and the AI ​​can generate an appropriate response based on them. For example, when a user makes a specific gesture toward the camera, the dialogue method providing unit has the AI ​​recognize the gesture and generate an appropriate response. For example, when the user waves, the AI ​​responds with a greeting. The dialogue method providing unit also recognizes the user's facial expressions, and the AI ​​generates an appropriate response based on them. For example, when the user smiles, the AI ​​responds with a positive response. This enables more natural dialogue by generating appropriate responses based on the user's gestures and facial expressions.

[0052] The dialogue method providing unit can analyze the user's past dialogue history and propose a dialogue method optimized for each individual user. For example, the dialogue method providing unit analyzes the user's past dialogue history and identifies frequently used phrases and topics. This allows the unit to learn the user's preferred dialogue style and generate optimal responses. The dialogue method providing unit also proposes a dialogue method optimized for each individual user based on the user's dialogue history. For example, it prioritizes suggesting dialogue methods that the user has frequently used in the past. This allows for more personalized dialogue by proposing an optimized dialogue method based on the user's past dialogue history.

[0053] The dialogue method providing unit uses the emotion estimation function to automatically select a dialogue method according to the user's emotional state, thereby enabling dialogue that is more sensitive to the user's emotions. The dialogue method providing unit, for example, analyzes the user's voice tone and facial expressions to estimate the user's emotional state. For example, if the user feels tired, it provides topics that will help the user relax. The dialogue method providing unit also uses the emotion estimation function to automatically select a dialogue method according to the user's emotional state. For example, if the user is angry, it returns a calm response. In this way, by automatically selecting a dialogue method according to the user's emotional state, it becomes possible to have a dialogue that is more sensitive to the user's emotions.

[0054] The interaction method providing unit can provide an interface that allows the user to interact with the AI ​​by manipulating physical objects. For example, the interaction method providing unit allows the user to give instructions to the AI ​​by combining blocks of a specific shape. For example, when a red block and a blue block are combined, the AI ​​plays music. The interaction method providing unit also provides an interface that allows the user to interact with the AI ​​by manipulating physical objects. For example, when the user presses a button, the AI ​​performs a specific action. In this way, by providing an interface that allows the user to interact with the AI ​​by manipulating physical objects, more intuitive interaction becomes possible.

[0055] The dialogue method providing unit can provide a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, the dialogue method providing unit allows a user to give instructions by voice while making a specific gesture, causing the AI ​​to recognize the instruction and generate an appropriate response. For example, if a user says "play music" and waves their hand, the AI ​​will play music. The dialogue method providing unit also provides a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, if a user gives instructions by voice while making a specific gesture, the AI ​​will recognize the instruction and generate an appropriate response. This allows a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures, enabling more natural dialogue.

[0056] The dialogue method providing unit can use the emotion estimation function to provide a relaxation mode for reducing stress and anxiety felt by the user during a dialogue. The dialogue method providing unit, for example, uses the emotion estimation function to detect stress and anxiety felt by the user during a dialogue and automatically activates the relaxation mode. For example, if the user is nervous, calm music is played. The dialogue method providing unit also uses the emotion estimation function to provide a relaxation mode for reducing stress and anxiety felt by the user during a dialogue. For example, the relaxation mode is detected and automatically activated. This allows for a more comfortable dialogue by providing a relaxation mode for reducing stress and anxiety felt by the user during a dialogue.

[0057] The schedule adjustment unit can analyze the user's past schedule data and propose an optimal schedule pattern. For example, the schedule adjustment unit analyzes the user's past schedule data and identifies frequently occurring events and activities. Based on this, the optimal schedule pattern is proposed. The schedule adjustment unit also analyzes the user's past schedule data and proposes an optimal schedule pattern. For example, the optimal schedule pattern is proposed based on events and activities that the user frequently performed in the past. This enables efficient schedule management by proposing an optimal schedule pattern based on the user's past schedule data.

[0058] The schedule adjustment unit can adjust the schedule taking travel time into consideration by using the user's real-time location information. The schedule adjustment unit, for example, acquires the user's real-time location information and proposes a schedule taking travel time into consideration. For example, the schedule adjustment unit calculates the travel time the user will take from their current location to their next scheduled location and proposes an appropriate departure time. The schedule adjustment unit also adjusts the schedule taking travel time into consideration by using the user's real-time location information. For example, the schedule adjustment unit calculates the travel time the user will take from their current location to their next scheduled location and proposes an appropriate departure time. This enables efficient schedule management by adjusting the schedule taking travel time into consideration by using the user's real-time location information.

[0059] The schedule adjustment unit uses the emotion estimation function to propose a schedule based on the user's emotional state, thereby reducing stress. The schedule adjustment unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and propose a schedule that reduces stress. For example, if the user is tired, the schedule adjustment unit sets longer break times. The schedule adjustment unit also uses the emotion estimation function to propose a schedule based on the user's emotional state. For example, if the user is feeling stressed, the schedule adjustment unit suggests a time period when the user can relax. In this way, by proposing a schedule based on the user's emotional state, stress can be reduced and efficient schedule management becomes possible.

[0060] The schedule adjustment unit can provide a function that allows a user to share a schedule with other users and adjust it collaboratively. The schedule adjustment unit provides, for example, a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or coworkers and adjust overlapping appointments. The schedule adjustment unit also provides a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or coworkers and adjust overlapping appointments. By providing a function that allows a user to share a schedule with other users and adjust it collaboratively, efficient schedule management is possible.

[0061] The schedule adjustment unit can use the emotion estimation function to identify a time period when the user can be most relaxed and suggest a relaxing activity for that time period. The schedule adjustment unit, for example, can use the emotion estimation function to identify a time period when the user can be most relaxed and suggest a relaxing activity for that time period. For example, it can suggest relaxing music for the evening time period. The schedule adjustment unit can also use the emotion estimation function to identify a time period when the user can be most relaxed and suggest a relaxing activity for that time period. For example, it can suggest relaxing music for the evening time period. In this way, by identifying a time period when the user can be most relaxed and suggesting a relaxing activity for that time period, efficient schedule management is possible.

[0062] The task detailing unit can analyze the user's past task history and automatically suggest similar tasks. For example, the task detailing unit analyzes the user's past task history and identifies frequently performed tasks. Based on this, similar tasks are automatically suggested. For example, a task for preparing for a weekly meeting is suggested. The task detailing unit also analyzes the user's past task history and automatically suggests similar tasks. For example, similar tasks are automatically suggested based on tasks that the user frequently performed in the past. This enables efficient task management by automatically suggesting similar tasks based on the user's past task history.

[0063] The task detailing unit can provide a function that allows a user to input a task by voice and convert the voice into text for detailing. The task detailing unit provides, for example, a function that allows a user to input a task by voice and convert the voice into text. For example, if a user says, "Prepare for tomorrow's meeting," an AI converts the content into text and registers it as a task. The task detailing unit also provides a function that allows a user to input a task by voice and converts the voice into text for detailing. For example, if a user says, "Prepare for tomorrow's meeting," an AI converts the content into text and registers it as a task. This enables efficient task management by providing a function that allows a user to input a task by voice and converts the voice into text for detailing.

[0064] The task detailing unit can automatically set task priorities according to the emotional state of the user using the emotion estimation function. The task detailing unit, for example, uses the emotion estimation function to analyze the emotional state of the user and automatically set task priorities. For example, if the user is feeling stressed, tasks with low importance are postponed. The task detailing unit also automatically sets task priorities according to the emotional state of the user using the emotion estimation function. For example, if the user is feeling stressed, tasks with low importance are postponed. In this way, efficient task management is possible by automatically setting task priorities according to the emotional state of the user.

[0065] The task detailing unit can provide an interactive task board that allows a user to visually manage tasks. The task detailing unit provides, for example, an interactive task board that allows a user to visually manage tasks. For example, a task can be moved by drag and drop. The task detailing unit also provides an interactive task board that allows a user to visually manage tasks. For example, a task can be moved by drag and drop. In this way, efficient task management is possible by providing an interactive task board that allows a user to visually manage tasks.

[0066] The task detailing unit can provide a function that allows a user to share tasks with other users and manage them collaboratively. The task detailing unit provides, for example, a function that allows a user to share tasks with other users and manage them collaboratively. For example, all members of a project team share a task board and manage their own tasks. The task detailing unit also provides a function that allows a user to share tasks with other users and manage them collaboratively. For example, all members of a project team share a task board and manage their own tasks. This provides a function that allows a user to share tasks with other users and manage them collaboratively, enabling efficient task management.

[0067] The task detailing unit can use the emotion estimation function to provide feedback to enhance the sense of accomplishment felt by the user when completing a task. The task detailing unit, for example, uses the emotion estimation function to provide feedback to enhance the sense of accomplishment felt by the user when completing a task. For example, a positive message is displayed when the task is completed. The task detailing unit also uses the emotion estimation function to provide feedback to enhance the sense of accomplishment felt by the user when completing a task. For example, a positive message is displayed when the task is completed. This enables efficient task management by providing feedback to enhance the sense of accomplishment felt by the user when completing a task.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The dialogue method providing unit can analyze the tone and speed of the user's voice to estimate the user's emotional state. For example, if the user speaks quickly, it can estimate that the user is feeling tense or impatient and provide a topic that will help them relax. On the other hand, if the user speaks slowly, it can estimate that the user is relaxed and encourage deeper dialogue. In this way, by estimating the user's emotional state based on the tone and speed of the user's voice and providing an appropriate dialogue method, more natural dialogue is possible.

[0070] The dialogue method providing unit can analyze the user's past dialogue history and learn the user's preferred dialogue style. For example, it can identify phrases and topics that the user has frequently used in the past and generate optimal responses based on them. It can also preferentially suggest dialogue methods that the user has preferred in the past. This allows for more personalized dialogue by providing the optimal dialogue method based on the user's past dialogue history.

[0071] The dialogue method providing unit can provide a relaxation mode to reduce stress and anxiety felt by the user during dialogue. For example, if the user feels nervous, it can play calm music. Furthermore, it can detect stress and anxiety felt by the user during dialogue and automatically activate the relaxation mode. By providing the relaxation mode to reduce stress and anxiety felt by the user during dialogue, a more comfortable dialogue can be achieved.

[0072] The interaction method providing unit can provide an interface that allows a user to interact with an AI by manipulating physical objects. For example, a user can give instructions to an AI by combining blocks of a specific shape. For example, when a user combines a red block with a blue block, the AI ​​plays music. Also, when a user presses a button, the AI ​​performs a specific action. This allows for more intuitive interaction by providing an interface that allows a user to interact with an AI by manipulating physical objects.

[0073] The dialogue method providing unit can provide a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures. For example, when a user gives a voice instruction while making a specific gesture, the AI ​​recognizes the instruction and generates an appropriate response. For example, when a user says "play music" and waves their hand, the AI ​​plays music. Also, when a user gives a voice instruction while making a specific gesture, the AI ​​recognizes the instruction and generates an appropriate response. This allows for more natural dialogue by providing a multimodal dialogue system that allows a user to give instructions to an AI by combining voice and gestures.

[0074] The schedule adjustment unit can adjust the schedule taking travel time into consideration by using the user's real-time location information. For example, it calculates the travel time from the user's current location to the next scheduled location and suggests an appropriate departure time. It also adjusts the schedule taking travel time into consideration by using the user's real-time location information. This enables efficient schedule management by adjusting the schedule taking travel time into consideration by using the user's real-time location information.

[0075] The schedule adjustment unit can use the emotion estimation function to suggest a schedule based on the user's emotional state, thereby reducing stress. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest a schedule that reduces stress. For example, if the user is tired, longer break times can be set. Also, if the user is feeling stressed, time periods when the user can relax can be suggested. In this way, by suggesting a schedule based on the user's emotional state, stress can be reduced and efficient schedule management can be achieved.

[0076] The schedule adjustment unit can provide a function that allows a user to share a schedule with other users and adjust it collaboratively. For example, a user can share a schedule with family or colleagues and adjust overlapping appointments. The schedule adjustment unit also provides a function that allows a user to share a schedule with other users and adjust it collaboratively. This allows a user to share a schedule with other users and adjust it collaboratively, thereby enabling efficient schedule management.

[0077] The task detailing unit can analyze the user's past task history and automatically suggest similar tasks. For example, it analyzes the user's past task history and identifies frequently performed tasks. Based on this, it automatically suggests similar tasks. For example, it suggests a task for preparing for a weekly meeting that the user holds. It also analyzes the user's past task history and automatically suggests similar tasks. This enables efficient task management by automatically suggesting similar tasks based on the user's past task history.

[0078] The task detailing unit can use the emotion estimation function to provide feedback to enhance the sense of accomplishment felt by the user when completing a task. For example, the emotion estimation function can be used to provide feedback to enhance the sense of accomplishment felt by the user when completing a task. For example, a positive message can be displayed when a task is completed. Furthermore, feedback to enhance the sense of accomplishment felt by the user when completing a task can be provided. This enables efficient task management by providing feedback to enhance the sense of accomplishment felt by the user when completing a task.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The dialogue method provider provides various dialogue methods. For example, the user can issue commands to the AI ​​by tapping and releasing an icon on the screen. The dialogue method provider provides voice dialogue, text dialogue, gesture dialogue, etc. Step 2: The schedule adjustment unit adjusts the schedule using the interaction method provided by the interaction method provision unit. For example, the user taps the calendar icon and releases the desired date and time, and the AI ​​adds an event for that date and time. The schedule adjustment unit synchronizes the calendar and sets priorities. Step 3: The task detailing unit refines the task based on the schedule adjusted by the schedule adjustment unit. For example, the user taps the task icon and enters detailed information, and the AI ​​manages the task. The task detailing unit breaks down the task and clarifies the steps.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dialogue method providing unit that provides a variety of dialogue methods; a schedule adjustment unit that adjusts a schedule using the interaction method provided by the interaction method providing unit; a task detailing unit that details a task based on the schedule adjusted by the schedule adjustment unit; A system characterized by:

2. The interaction method providing unit The AI ​​recognizes the user's gestures and facial expressions and generates appropriate responses based on them.

2. The system of claim 1.

3. The interaction method providing unit Providing an interface that allows users to interact with the AI ​​by manipulating physical objects 2. The system of claim 1.

4. The schedule adjustment unit Analyzes the user's past schedule data and proposes optimal schedule patterns 2. The system of claim 1.

5. The schedule adjustment unit To provide an interactive calendar that allows users to visually manage their schedules.

2. The system of claim 1.

6. The task detailing unit Analyzes the user's past task history and automatically suggests similar tasks 2. The system of claim 1.

7. The task detailing unit Providing an interactive task board that allows users to visually manage the tasks 2. The system of claim 1.

8. The interaction method providing unit To automatically select a dialogue method according to a user's emotional state and realize a dialogue that is sensitive to the user's emotions.

2. The system of claim 1.

Images