System

Abstract

An object of the system according to the embodiment is to extract a story and a sense of values of life of a user and embody the story and the sense of values.SOLUTION: A system includes an interaction unit, a generation unit, and an output unit. The dialoguer elicits stories or values of the user's life through the dialogue with the user. The generation unit generates a story or a painting based on the information extracted by the interaction unit. The output unit provides the user with the story or the painting generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately elicit users' life stories and values ​​and embody them, so there is room for improvement.

[0005] The system according to the embodiment aims to extract the user's life story and values ​​and embody them. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a generation unit, and an output unit. The dialogue unit elicits a user's life story or values ​​through dialogue with the user. The generation unit generates a story or a picture based on the information elicited by the dialogue unit. The output unit provides the user with the story or picture generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract the user's life story and values ​​and embody them. [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 self-exploration support system according to an embodiment of the present invention utilizes an interactive generative AI to support users in embarking on a journey of self-exploration. Through dialogue with the user, the generative AI elicits the user's life story and values, and then materializes them in the form of a story or painting. This allows the self-exploration support system to deepen the user's self-understanding and communicate their values ​​and story to others.

[0029] A self-exploration support system according to an embodiment includes a dialogue unit, a generation unit, and an output unit. The dialogue unit elicits the user's life story and values ​​through dialogue with the user. For example, the dialogue unit asks the user questions such as, "What values ​​do you hold most dear?" or "What is the most memorable event in your life so far?" The dialogue unit also delves deeper into the user's inner self by understanding what the user is saying and asking appropriate questions. The generation unit generates a story or a picture based on the information elicited by the dialogue unit. For example, the generation unit creates a story based on the user's experiences and values ​​and outputs it as text. The generation unit also generates a picture based on the user's story and outputs it as a visual. The output unit provides the user with the story or picture generated by the generation unit. For example, the output unit displays the generated story or picture on the user's device. The output unit can also print and provide the generated story or picture. This allows the self-exploration support system according to an embodiment to deepen the user's self-understanding and communicate their values ​​and story to others.

[0030] The dialogue unit can refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper. For example, the generation AI in the dialogue unit refers to the user's past dialogue history and finds the relationship between past and current statements. For example, it asks the user again about values ​​that they previously discussed. The dialogue unit also refers to the user's past dialogue history, finds the relationship between past and current statements, and digs deeper into the user's inner self. This makes it possible to dig deeper into the dialogue based on the user's past statements.

[0031] The dialogue unit can analyze the user's body movements or facial expressions and proceed with the dialogue taking into consideration non-verbal information. For example, the dialogue unit uses a generation AI to analyze the user's body movements and facial expressions and proceed with the dialogue taking into consideration non-verbal information. For example, if the user is smiling, the generation AI will continue with a positive topic. The dialogue unit can also analyze the user's body movements and facial expressions, and if the user is nervous, the generation AI will provide a topic that will help the user relax. This allows the dialogue to proceed with consideration of non-verbal information.

[0032] The dialogue unit can generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, the generation AI generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, a user who is knowledgeable about Japanese culture might be asked about the tea ceremony or cherry blossom viewing. The dialogue unit also generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories, digging deeper into the user's inner self. This makes it possible to generate customized questions for users with different cultural backgrounds.

[0033] The generation unit can generate stories from multiple different perspectives based on the user's story and provide the user with options. For example, the generation unit generates stories from multiple different perspectives based on the user's story using a generation AI. For example, it can provide stories that depict the same event from the perspectives of different characters. The generation unit can also generate stories from multiple different perspectives based on the user's story and provide the user with options. This makes it possible to provide the user with stories from multiple perspectives.

[0034] The generation unit can generate a story that incorporates a historical background or a future scenario based on the user's story. For example, the generation unit generates a story that incorporates a historical background based on the user's story using a generation AI. For example, it creates a story that replaces the user's story with a medieval European background. The generation unit also generates a story that incorporates a future scenario based on the user's story. For example, it creates a story that replaces the user's story with a scenario that incorporates future technology and social changes. In this way, it is possible to generate a story that incorporates a historical background or a future scenario.

[0035] The generation unit can also embody the user's story in other artistic forms such as music or poetry. In the generation unit, for example, the generative AI creates music based on the user's story. For example, it generates a melody or lyrics based on the user's story and outputs it as music. The generation unit also creates poetry based on the user's story. For example, it generates poetry based on the user's story and outputs it as text. This allows the user's story to be embodied in other artistic forms.

[0036] The generation unit generates stories or paintings in different languages ​​and can obtain feedback from an international perspective. For example, the generation unit generates stories in different languages ​​based on the user's story using a generation AI. For example, stories are created in multiple languages, such as English, French, and Chinese, and feedback is obtained from an international perspective. The generation unit also generates paintings in different languages ​​based on the user's story and can obtain feedback from an international perspective. This makes it possible to generate stories or paintings in different languages ​​and obtain feedback from an international perspective.

[0037] The generation unit can analyze the user's story or painting, extract the user's values ​​or life themes, and provide feedback. For example, the generation unit uses a generation AI to analyze the user's story and extract the user's values ​​and life themes. For example, values ​​can be identified based on keywords and phrases that appear repeatedly in the story. The generation unit can also analyze the user's painting and extract the user's values ​​and life themes. For example, values ​​can be identified based on motifs that appear repeatedly in the painting. This allows the user's values ​​and life themes to be extracted and provided feedback.

[0038] The generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit can generate new questions to deepen self-understanding. For example, the generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit generates new questions. For example, if the user places importance on "self-growth" in their self-evaluation, the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to self-evaluate, and based on that evaluation, the generation unit generates new questions to deepen self-understanding. This allows new questions to be generated based on the user's self-evaluation.

[0039] The generation unit can compare the user's story or painting with that of other users and provide feedback on similarities and differences. For example, the generation AI compares the user's story with that of other users and provides feedback on similarities and differences. For example, it compares stories between users who share the same values. The generation unit also compares the user's painting with that of other users and provides feedback on similarities and differences. For example, it compares paintings that depict the same theme. This allows the user's story or painting to be compared with that of other users and provides feedback on similarities and differences.

[0040] The generation unit allows the user to set a goal for the generation AI to deepen self-understanding, and the generation unit can conduct a dialogue based on that goal. For example, the generation unit allows the user to set a goal for the generation AI to deepen self-understanding, and the generation unit can conduct a dialogue based on that goal. For example, if the goal is set to "self-growth," the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to set a goal for deepening self-understanding, and the generation unit can conduct a dialogue based on that goal. This allows the user to conduct a dialogue based on the goal for deepening self-understanding.

[0041] The generation unit can analyze the user's story or painting and suggest the optimal way to express it when communicating it to others. For example, the generation unit can analyze the user's story using a generation AI and suggest the optimal way to express it when communicating it to others. For example, it can suggest a presentation that succinctly summarizes the main points of the story. The generation unit can also analyze the user's painting and suggest the optimal way to express it when communicating it to others. For example, it can indicate the points that should be emphasized in the painting. This makes it possible to suggest the optimal way to express the user's story or painting when communicating it to others.

[0042] The generation unit can collect feedback after a user shares a story or a painting with others, and generate a new story or painting based on that feedback. For example, the generation unit collects feedback after a user shares a story with others, and generates a new story based on that feedback. For example, it creates a story that reflects the impressions and opinions of others. The generation unit also collects feedback after a user shares a painting with others, and generates a new painting based on that feedback. For example, it creates a painting that reflects the opinions of others. In this way, a new story or painting can be generated based on feedback after a user shares a story or a painting with others.

[0043] The generation unit can analyze the user's story or painting and suggest ways to share it in different media. For example, the generation unit uses a generative AI to analyze the user's story and suggest ways to share it in video format. For example, it can suggest ways to explain the main points of the story in a video. The generation unit can also analyze the user's painting and suggest ways to share it in podcast format. For example, it can provide an explanation of the background of the painting in a podcast. This makes it possible to suggest ways to share it in different media.

[0044] The generation unit allows a user to create and share stories or paintings collaboratively with others through the generation AI. The generation unit, for example, allows a user to create a story collaboratively with others through the generation AI. For example, multiple users write stories from their own perspectives, and the generation AI integrates them. The generation unit also allows a user to create and share paintings collaboratively with others. For example, multiple users paint paintings in their own styles, and the generation AI integrates them. This allows a user to create and share stories or paintings collaboratively with others.

[0045] The generation unit can analyze the user's story or painting, extract the user's values ​​or life themes, and provide feedback. For example, the generation unit uses a generation AI to analyze the user's story and extract the user's values ​​and life themes. For example, values ​​can be identified based on keywords and phrases that appear repeatedly in the story. The generation unit can also analyze the user's painting and extract the user's values ​​and life themes. For example, values ​​can be identified based on motifs that appear repeatedly in the painting. This allows the user's values ​​and life themes to be extracted and provided feedback.

[0046] The generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit can generate new questions to deepen self-understanding. For example, the generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit generates new questions. For example, if the user places importance on "self-growth" in their self-evaluation, the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to self-evaluate, and based on that evaluation, the generation unit generates new questions to deepen self-understanding. This allows new questions to be generated based on the user's self-evaluation.

[0047] The generation unit can compare the user's story or painting with that of other users and provide feedback on similarities and differences. For example, the generation AI compares the user's story with that of other users and provides feedback on similarities and differences. For example, it compares stories between users who share the same values. The generation unit also compares the user's painting with that of other users and provides feedback on similarities and differences. For example, it compares paintings that depict the same theme. This allows the user's story or painting to be compared with that of other users and provides feedback on similarities and differences.

[0048] The generation unit can analyze the user's story or painting and suggest the optimal way to express it when communicating it to others. For example, the generation unit can analyze the user's story using a generation AI and suggest the optimal way to express it when communicating it to others. For example, it can suggest a presentation that succinctly summarizes the main points of the story. The generation unit can also analyze the user's painting and suggest the optimal way to express it when communicating it to others. For example, it can indicate the points that should be emphasized in the painting. This makes it possible to suggest the optimal way to express the user's story or painting when communicating it to others.

[0049] The generation unit can collect feedback after a user shares a story or a painting with others, and generate a new story or painting based on that feedback. For example, the generation unit collects feedback after a user shares a story with others, and generates a new story based on that feedback. For example, it creates a story that reflects the impressions and opinions of others. The generation unit also collects feedback after a user shares a painting with others, and generates a new painting based on that feedback. For example, it creates a painting that reflects the opinions of others. In this way, a new story or painting can be generated based on feedback after a user shares a story or a painting with others.

[0050] The generation unit can analyze the user's story or painting and suggest ways to share it in different media. For example, the generation unit uses a generative AI to analyze the user's story and suggest ways to share it in video format. For example, it can suggest ways to explain the main points of the story in a video. The generation unit can also analyze the user's painting and suggest ways to share it in podcast format. For example, it can provide an explanation of the background of the painting in a podcast. This makes it possible to suggest ways to share it in different media.

[0051] The generation unit allows a user to create and share stories or paintings collaboratively with others through the generation AI. The generation unit, for example, allows a user to create a story collaboratively with others through the generation AI. For example, multiple users write stories from their own perspectives, and the generation AI integrates them. The generation unit also allows a user to create and share paintings collaboratively with others. For example, multiple users paint paintings in their own styles, and the generation AI integrates them. This allows a user to create and share stories or paintings collaboratively with others.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The dialogue unit can refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper. For example, the dialogue unit can ask the user again about values ​​that the user previously discussed. The dialogue unit can also refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper into the user's inner thoughts. This allows the dialogue to be dig deeper based on the user's past statements.

[0054] The dialogue unit can generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, the generation AI generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, a user who is knowledgeable about Japanese culture could be asked about the tea ceremony or cherry blossom viewing. The dialogue unit can also generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories, digging deeper into the user's inner self. This makes it possible to generate questions customized for users with different cultural backgrounds.

[0055] The generation unit can generate stories from multiple different perspectives based on the user's story and provide the user with options. For example, the generation AI can generate stories from multiple different perspectives based on the user's story. For example, it can provide a story that depicts the same event from the perspective of different characters. The generation unit can also generate stories from multiple different perspectives based on the user's story and provide the user with options. This allows the user to be provided with stories from multiple perspectives.

[0056] The generation unit can generate a story that incorporates a historical background or a future scenario based on the user's story. For example, the generation AI generates a story that incorporates a historical background based on the user's story. For example, it creates a story that replaces the user's story with a medieval European background. The generation unit also generates a story that incorporates a future scenario based on the user's story. For example, it creates a story that replaces the user's story with a scenario that incorporates future technology and social changes. This makes it possible to generate a story that incorporates a historical background or a future scenario.

[0057] The generation unit can also embody the user's story in other artistic forms, such as music or poetry. For example, the generative AI creates music based on the user's story. For example, it generates a melody or lyrics based on the user's story and outputs it as music. The generation unit can also create poetry based on the user's story. For example, it can generate poetry based on the user's story and output it as text. This allows the user's story to be embodied in other artistic forms.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The dialogue unit draws out the user's life story and values ​​through dialogue with them. For example, the dialogue unit asks the user questions such as, "What values ​​do you hold most dear?" or "What is the most memorable event in your life so far?" The dialogue unit also understands what the user is saying and asks appropriate questions to delve deeper into the user's inner self. Step 2: The generator generates a story or a picture based on the information extracted by the dialogue unit. For example, the generator creates a story based on the experiences and values ​​expressed by the user and outputs it as text. The generator also generates a picture based on the user's story and outputs it as a visual. Step 3: The output unit provides the story or picture generated by the generation unit to the user. For example, the output unit displays the generated story or picture on the user's device. The output unit can also print and provide the generated story or picture.

[0060] (Example 2) A self-exploration support system according to an embodiment of the present invention utilizes an interactive generative AI to support users in embarking on a journey of self-exploration. Through dialogue with the user, the generative AI elicits the user's life story and values, and then materializes them in the form of a story or painting. This allows the self-exploration support system to deepen the user's self-understanding and communicate their values ​​and story to others.

[0061] A self-exploration support system according to an embodiment includes a dialogue unit, a generation unit, and an output unit. The dialogue unit elicits the user's life story and values ​​through dialogue with the user. For example, the dialogue unit asks the user questions such as, "What values ​​do you hold most dear?" or "What is the most memorable event in your life so far?" The dialogue unit also delves deeper into the user's inner self by understanding what the user is saying and asking appropriate questions. The generation unit generates a story or a picture based on the information elicited by the dialogue unit. For example, the generation unit creates a story based on the user's experiences and values ​​and outputs it as text. The generation unit also generates a picture based on the user's story and outputs it as a visual. The output unit provides the user with the story or picture generated by the generation unit. For example, the output unit displays the generated story or picture on the user's device. The output unit can also print and provide the generated story or picture. This allows the self-exploration support system according to an embodiment to deepen the user's self-understanding and communicate their values ​​and story to others.

[0062] The dialogue unit can analyze the user's tone of voice or speaking style, detect changes in emotion in real time, and adjust the dialogue accordingly. For example, the dialogue unit's generation AI analyzes the user's tone of voice and speaking style to detect changes in emotion in real time. For example, if the user is excited, the generation AI adjusts the tone of the questions to be calmer. The dialogue unit also analyzes the user's tone of voice and speaking style, and if the user is relaxed, the generation AI adjusts to continue the dialogue. This makes it possible to have a dialogue that corresponds to the user's emotions.

[0063] The dialogue unit can refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper. For example, the generation AI in the dialogue unit refers to the user's past dialogue history and finds the relationship between past and current statements. For example, it asks the user again about values ​​that they previously discussed. The dialogue unit also refers to the user's past dialogue history, finds the relationship between past and current statements, and digs deeper into the user's inner self. This makes it possible to dig deeper into the dialogue based on the user's past statements.

[0064] The dialogue unit uses the emotion estimation function to generate questions based on the user's emotions, thereby enabling a deeper look into the user's inner thoughts. The dialogue unit, for example, uses the emotion estimation function to generate questions based on the user's emotions. For example, if the user is happy, the dialogue unit asks a question asking the reason for that joy. Also, if the user is sad, the dialogue unit uses the emotion estimation function to ask a question asking the cause of the sadness. This makes it possible to dig deeper into the user's inner thoughts with questions based on the user's emotions.

[0065] The dialogue unit can analyze the user's body movements or facial expressions and proceed with the dialogue taking into consideration non-verbal information. For example, the dialogue unit uses a generation AI to analyze the user's body movements and facial expressions and proceed with the dialogue taking into consideration non-verbal information. For example, if the user is smiling, the generation AI will continue with a positive topic. The dialogue unit can also analyze the user's body movements and facial expressions, and if the user is nervous, the generation AI will provide a topic that will help the user relax. This allows the dialogue to proceed with consideration of non-verbal information.

[0066] The dialogue unit can generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, the generation AI generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, a user who is knowledgeable about Japanese culture might be asked about the tea ceremony or cherry blossom viewing. The dialogue unit also generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories, digging deeper into the user's inner self. This makes it possible to generate customized questions for users with different cultural backgrounds.

[0067] The dialogue unit uses the emotion estimation function to suggest an environment in which the user can be most relaxed, and the dialogue can take place in that environment. The dialogue unit, for example, uses the emotion estimation function to suggest an environment in which the user can be most relaxed. For example, if the user prefers to relax in nature, the generation AI will conduct the dialogue with the sounds of nature in the background. The dialogue unit also uses the emotion estimation function to suggest an environment in which the user can be most relaxed, and the dialogue can take place in that environment. This allows the dialogue to take place in an environment in which the user can be most relaxed.

[0068] The generation unit can generate stories from multiple different perspectives based on the user's story and provide the user with options. For example, the generation unit generates stories from multiple different perspectives based on the user's story using a generation AI. For example, it can provide stories that depict the same event from the perspectives of different characters. The generation unit can also generate stories from multiple different perspectives based on the user's story and provide the user with options. This makes it possible to provide the user with stories from multiple perspectives.

[0069] The generation unit can generate a story that incorporates a historical background or a future scenario based on the user's story. For example, the generation unit generates a story that incorporates a historical background based on the user's story using a generation AI. For example, it creates a story that replaces the user's story with a medieval European background. The generation unit also generates a story that incorporates a future scenario based on the user's story. For example, it creates a story that replaces the user's story with a scenario that incorporates future technology and social changes. In this way, it is possible to generate a story that incorporates a historical background or a future scenario.

[0070] The generation unit can use the emotion estimation function to generate a painting using a color or style that most resonates with the user's emotion. For example, the generation unit uses the emotion estimation function to generate a painting using a color or style that most resonates with the user's emotion. For example, if the user is feeling happy, the generation unit generates a painting using bright colors. Furthermore, the generation unit uses the emotion estimation function to generate a painting using a color or style that most resonates with the user's emotion and provides it to the user. In this way, a painting that resonates with the user's emotion can be generated.

[0071] The generation unit can also embody the user's story in other artistic forms such as music or poetry. In the generation unit, for example, the generative AI creates music based on the user's story. For example, it generates a melody or lyrics based on the user's story and outputs it as music. The generation unit also creates poetry based on the user's story. For example, it generates poetry based on the user's story and outputs it as text. This allows the user's story to be embodied in other artistic forms.

[0072] The generation unit generates stories or paintings in different languages ​​and can obtain feedback from an international perspective. For example, the generation unit generates stories in different languages ​​based on the user's story using a generation AI. For example, stories are created in multiple languages, such as English, French, and Chinese, and feedback is obtained from an international perspective. The generation unit also generates paintings in different languages ​​based on the user's story and can obtain feedback from an international perspective. This makes it possible to generate stories or paintings in different languages ​​and obtain feedback from an international perspective.

[0073] The generation unit can use the emotion estimation function to identify the scene that moves the user the most and construct a story or a painting centered on that scene. The generation unit, for example, uses the emotion estimation function to identify the scene that moves the user the most. For example, the generation unit constructs a story or a painting based on a scene that made the user cry. The generation unit can also use the emotion estimation function to identify the scene that moves the user the most and construct a story or a painting centered on that scene. This allows the generation unit to construct a story or a painting centered on the scene that moves the user the most.

[0074] The generation unit can analyze the user's story or painting, extract the user's values ​​or life themes, and provide feedback. For example, the generation unit uses a generation AI to analyze the user's story and extract the user's values ​​and life themes. For example, values ​​can be identified based on keywords and phrases that appear repeatedly in the story. The generation unit can also analyze the user's painting and extract the user's values ​​and life themes. For example, values ​​can be identified based on motifs that appear repeatedly in the painting. This allows the user's values ​​and life themes to be extracted and provided feedback.

[0075] The generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit can generate new questions to deepen self-understanding. For example, the generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit generates new questions. For example, if the user places importance on "self-growth" in their self-evaluation, the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to self-evaluate, and based on that evaluation, the generation unit generates new questions to deepen self-understanding. This allows new questions to be generated based on the user's self-evaluation.

[0076] The generation unit can use the emotion estimation function to identify the part that moved the user the most and conduct a dialogue to further explore that part. The generation unit, for example, uses the emotion estimation function to identify the part that moved the user the most. For example, the dialogue can be further explored based on a scene in which the user shed tears. The generation unit can also use the emotion estimation function to identify the part that moved the user the most and conduct a dialogue to further explore that part. This makes it possible to conduct a dialogue to further explore the part that moved the user the most.

[0077] The generation unit can compare the user's story or painting with that of other users and provide feedback on similarities and differences. For example, the generation AI compares the user's story with that of other users and provides feedback on similarities and differences. For example, it compares stories between users who share the same values. The generation unit also compares the user's painting with that of other users and provides feedback on similarities and differences. For example, it compares paintings that depict the same theme. This allows the user's story or painting to be compared with that of other users and provides feedback on similarities and differences.

[0078] The generation unit allows the user to set a goal for the generation AI to deepen self-understanding, and the generation unit can conduct a dialogue based on that goal. For example, the generation unit allows the user to set a goal for the generation AI to deepen self-understanding, and the generation unit can conduct a dialogue based on that goal. For example, if the goal is set to "self-growth," the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to set a goal for deepening self-understanding, and the generation unit can conduct a dialogue based on that goal. This allows the user to conduct a dialogue based on the goal for deepening self-understanding.

[0079] The generation unit uses the emotion estimation function to have a dialogue during a time period when the user is most relaxed, thereby deepening self-understanding. The generation unit, for example, uses the emotion estimation function to identify a time period when the user is most relaxed and have a dialogue during that time period. For example, if the user prefers to relax at night, the dialogue is held at night. The generation unit also uses the emotion estimation function to have a dialogue during a time period when the user is most relaxed, thereby deepening self-understanding. This allows the user to have a dialogue during a time period when they are most relaxed, thereby deepening self-understanding.

[0080] The generation unit can analyze the user's story or painting and suggest the optimal way to express it when communicating it to others. For example, the generation unit can analyze the user's story using a generation AI and suggest the optimal way to express it when communicating it to others. For example, it can suggest a presentation that succinctly summarizes the main points of the story. The generation unit can also analyze the user's painting and suggest the optimal way to express it when communicating it to others. For example, it can indicate the points that should be emphasized in the painting. This makes it possible to suggest the optimal way to express the user's story or painting when communicating it to others.

[0081] The generation unit can collect feedback after a user shares a story or a painting with others, and generate a new story or painting based on that feedback. For example, the generation unit collects feedback after a user shares a story with others, and generates a new story based on that feedback. For example, it creates a story that reflects the impressions and opinions of others. The generation unit also collects feedback after a user shares a painting with others, and generates a new painting based on that feedback. For example, it creates a painting that reflects the opinions of others. In this way, a new story or painting can be generated based on feedback after a user shares a story or a painting with others.

[0082] The generation unit can use the emotion estimation function to identify the part that moves others the most and generate a story or a painting that emphasizes that part. The generation unit, for example, uses the emotion estimation function to identify the part that moves others the most and generate a story that emphasizes that part. For example, the generation unit builds a story around a scene in which the other person sheds tears. The generation unit also uses the emotion estimation function to identify the part that moves others the most and generate a painting that emphasizes that part. For example, a painting is created that emphasizes a scene that moved the other person. In this way, a story or a painting that emphasizes the part that moves others the most can be generated.

[0083] The generation unit can analyze the user's story or painting and suggest ways to share it in different media. For example, the generation unit uses a generative AI to analyze the user's story and suggest ways to share it in video format. For example, it can suggest ways to explain the main points of the story in a video. The generation unit can also analyze the user's painting and suggest ways to share it in podcast format. For example, it can provide an explanation of the background of the painting in a podcast. This makes it possible to suggest ways to share it in different media.

[0084] The generation unit allows a user to create and share stories or paintings collaboratively with others through the generation AI. The generation unit, for example, allows a user to create a story collaboratively with others through the generation AI. For example, multiple users write stories from their own perspectives, and the generation AI integrates them. The generation unit also allows a user to create and share paintings collaboratively with others. For example, multiple users paint paintings in their own styles, and the generation AI integrates them. This allows a user to create and share stories or paintings collaboratively with others.

[0085] The generation unit can use the emotion estimation function to identify a theme that others most empathize with and generate a story or a painting based on that theme. For example, the generation unit can use the emotion estimation function to identify a theme that others most empathize with and generate a story based on that theme. For example, a story based on the theme of "friendship" that others empathize with can be created. The generation unit can also use the emotion estimation function to identify a theme that others most empathize with and generate a painting based on that theme. For example, a painting depicting a theme that others empathize with can be created. This makes it possible to generate a story or a painting based on a theme that others most empathize with.

[0086] The generation unit can analyze the user's story or painting, extract the user's values ​​or life themes, and provide feedback. For example, the generation unit uses a generation AI to analyze the user's story and extract the user's values ​​and life themes. For example, values ​​can be identified based on keywords and phrases that appear repeatedly in the story. The generation unit can also analyze the user's painting and extract the user's values ​​and life themes. For example, values ​​can be identified based on motifs that appear repeatedly in the painting. This allows the user's values ​​and life themes to be extracted and provided feedback.

[0087] The generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit can generate new questions to deepen self-understanding. For example, the generation unit allows the user to self-evaluate the generation AI, and based on that evaluation, the generation unit generates new questions. For example, if the user places importance on "self-growth" in their self-evaluation, the generation AI will ask, "What experiences have led to self-growth?" The generation unit also allows the user to self-evaluate, and based on that evaluation, the generation unit generates new questions to deepen self-understanding. This allows new questions to be generated based on the user's self-evaluation.

[0088] The generation unit can use the emotion estimation function to identify the part that moved the user the most and conduct a dialogue to further explore that part. The generation unit, for example, uses the emotion estimation function to identify the part that moved the user the most. For example, the dialogue can be further explored based on a scene in which the user shed tears. The generation unit can also use the emotion estimation function to identify the part that moved the user the most and conduct a dialogue to further explore that part. This makes it possible to conduct a dialogue to further explore the part that moved the user the most.

[0089] The generation unit can compare the user's story or painting with that of other users and provide feedback on similarities and differences. For example, the generation AI compares the user's story with that of other users and provides feedback on similarities and differences. For example, it compares stories between users who share the same values. The generation unit also compares the user's painting with that of other users and provides feedback on similarities and differences. For example, it compares paintings that depict the same theme. This allows the user's story or painting to be compared with that of other users and provides feedback on similarities and differences.

[0090] The generation unit uses the emotion estimation function to have a dialogue during a time period when the user is most relaxed, thereby deepening self-understanding. The generation unit, for example, uses the emotion estimation function to identify a time period when the user is most relaxed and have a dialogue during that time period. For example, if the user prefers to relax at night, the dialogue is held at night. The generation unit also uses the emotion estimation function to have a dialogue during a time period when the user is most relaxed, thereby deepening self-understanding. This allows the user to have a dialogue during a time period when they are most relaxed, thereby deepening self-understanding.

[0091] The generation unit can analyze the user's story or painting and suggest the optimal way to express it when communicating it to others. For example, the generation unit can analyze the user's story using a generation AI and suggest the optimal way to express it when communicating it to others. For example, it can suggest a presentation that succinctly summarizes the main points of the story. The generation unit can also analyze the user's painting and suggest the optimal way to express it when communicating it to others. For example, it can indicate the points that should be emphasized in the painting. This makes it possible to suggest the optimal way to express the user's story or painting when communicating it to others.

[0092] The generation unit can collect feedback after a user shares a story or a painting with others, and generate a new story or painting based on that feedback. For example, the generation unit collects feedback after a user shares a story with others, and generates a new story based on that feedback. For example, it creates a story that reflects the impressions and opinions of others. The generation unit also collects feedback after a user shares a painting with others, and generates a new painting based on that feedback. For example, it creates a painting that reflects the opinions of others. In this way, a new story or painting can be generated based on feedback after a user shares a story or a painting with others.

[0093] The generation unit can use the emotion estimation function to identify the part that moves others the most and generate a story or a painting that emphasizes that part. The generation unit, for example, uses the emotion estimation function to identify the part that moves others the most and generate a story that emphasizes that part. For example, the generation unit builds a story around a scene in which the other person sheds tears. The generation unit also uses the emotion estimation function to identify the part that moves others the most and generate a painting that emphasizes that part. For example, a painting is created that emphasizes a scene that moved the other person. In this way, a story or a painting that emphasizes the part that moves others the most can be generated.

[0094] The generation unit can analyze the user's story or painting and suggest ways to share it in different media. For example, the generation unit uses a generative AI to analyze the user's story and suggest ways to share it in video format. For example, it can suggest ways to explain the main points of the story in a video. The generation unit can also analyze the user's painting and suggest ways to share it in podcast format. For example, it can provide an explanation of the background of the painting in a podcast. This makes it possible to suggest ways to share it in different media.

[0095] The generation unit allows a user to create and share stories or paintings collaboratively with others through the generation AI. The generation unit, for example, allows a user to create a story collaboratively with others through the generation AI. For example, multiple users write stories from their own perspectives, and the generation AI integrates them. The generation unit also allows a user to create and share paintings collaboratively with others. For example, multiple users paint paintings in their own styles, and the generation AI integrates them. This allows a user to create and share stories or paintings collaboratively with others.

[0096] The generation unit can use the emotion estimation function to identify a theme that others most empathize with and generate a story or a painting based on that theme. For example, the generation unit can use the emotion estimation function to identify a theme that others most empathize with and generate a story based on that theme. For example, a story based on the theme of "friendship" that others empathize with can be created. The generation unit can also use the emotion estimation function to identify a theme that others most empathize with and generate a painting based on that theme. For example, a painting depicting a theme that others empathize with can be created. This makes it possible to generate a story or a painting based on a theme that others most empathize with.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The dialogue unit analyzes the user's tone of voice or speaking style, and can detect changes in emotion in real time and adjust the dialogue accordingly. For example, if the user is excited, the generation AI will adjust the tone of its questions to be calmer. The dialogue unit also analyzes the user's tone of voice and speaking style, and if the user is relaxed, the generation AI will adjust to continue the dialogue. This makes it possible to have a dialogue that corresponds to the user's emotions.

[0099] The dialogue unit can refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper. For example, the dialogue unit can ask the user again about values ​​that the user previously discussed. The dialogue unit can also refer to the user's past dialogue history, find the relationship between past and current statements, and dig deeper into the user's inner thoughts. This allows the dialogue to be dig deeper based on the user's past statements.

[0100] The dialogue unit uses the emotion estimation function to generate questions based on the user's emotions, allowing the dialogue unit to delve deeper into the user's inner thoughts. For example, if the user is happy, the dialogue unit asks a question asking the reason for the joy. Also, if the user is sad, the dialogue unit uses the emotion estimation function to ask a question asking the cause of the sadness. This allows the dialogue unit to delve deeper into the user's inner thoughts with questions based on the user's emotions.

[0101] The dialogue unit can analyze the user's physical movements or facial expressions and proceed with the dialogue taking into account non-verbal information. For example, the generation AI can analyze the user's physical movements and facial expressions and proceed with the dialogue taking into account non-verbal information. For example, if the user is smiling, the generation AI will continue with a positive topic. The dialogue unit can also analyze the user's physical movements and facial expressions, and if the user is nervous, the generation AI will provide a topic that will relax the user. This allows the dialogue to proceed with consideration of non-verbal information.

[0102] The dialogue unit can generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, the generation AI generates customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories. For example, a user who is knowledgeable about Japanese culture could be asked about the tea ceremony or cherry blossom viewing. The dialogue unit can also generate customized questions for users with different cultural backgrounds to draw out culturally specific values ​​and stories, digging deeper into the user's inner self. This makes it possible to generate questions customized for users with different cultural backgrounds.

[0103] The generation unit can generate stories from multiple different perspectives based on the user's story and provide the user with options. For example, the generation AI can generate stories from multiple different perspectives based on the user's story. For example, it can provide a story that depicts the same event from the perspective of different characters. The generation unit can also generate stories from multiple different perspectives based on the user's story and provide the user with options. This allows the user to be provided with stories from multiple perspectives.

[0104] The generation unit can generate a story that incorporates a historical background or a future scenario based on the user's story. For example, the generation AI generates a story that incorporates a historical background based on the user's story. For example, it creates a story that replaces the user's story with a medieval European background. The generation unit also generates a story that incorporates a future scenario based on the user's story. For example, it creates a story that replaces the user's story with a scenario that incorporates future technology and social changes. This makes it possible to generate a story that incorporates a historical background or a future scenario.

[0105] The generation unit can use the emotion estimation function to generate a painting using a color or style that most resonates with the user's emotion. For example, the emotion estimation function is used to generate a painting using a color or style that most resonates with the user's emotion. For example, if the user is feeling happy, a painting using bright colors is generated. The generation unit also uses the emotion estimation function to generate a painting using a color or style that most resonates with the user's emotion and provides it to the user. In this way, a painting that resonates with the user's emotion can be generated.

[0106] The generation unit can also embody the user's story in other artistic forms, such as music or poetry. For example, the generative AI creates music based on the user's story. For example, it generates a melody or lyrics based on the user's story and outputs it as music. The generation unit can also create poetry based on the user's story. For example, it can generate poetry based on the user's story and output it as text. This allows the user's story to be embodied in other artistic forms.

[0107] The generation unit can use the emotion estimation function to identify the scene that moves the user the most and construct a story or painting around that scene. For example, the emotion estimation function can be used to identify the scene that moves the user the most. For example, a story or painting can be constructed based on a scene that made the user cry. The generation unit can also use the emotion estimation function to identify the scene that moves the user the most and construct a story or painting around that scene. This allows a story or painting to be constructed around the scene that moves the user the most.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The dialogue unit draws out the user's life story and values ​​through dialogue with them. For example, the dialogue unit asks the user questions such as, "What values ​​do you hold most dear?" or "What is the most memorable event in your life so far?" The dialogue unit also understands what the user is saying and asks appropriate questions to delve deeper into the user's inner self. Step 2: The generator generates a story or a picture based on the information extracted by the dialogue unit. For example, the generator creates a story based on the experiences and values ​​expressed by the user and outputs it as text. The generator also generates a picture based on the user's story and outputs it as a visual. Step 3: The output unit provides the story or picture generated by the generation unit to the user. For example, the output unit displays the generated story or picture on the user's device. The output unit can also print and provide the generated story or picture.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] 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]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dialogue unit that draws out the user's life story or values ​​through dialogue with the user; a generation unit that generates a story or a picture based on the information extracted by the dialogue unit; an output unit that provides a user with the story or picture generated by the generation unit; A system characterized by:

2. The dialogue unit Analyzing the user's tone of voice or speaking style to detect emotional changes in real time and adjust the dialogue accordingly The system of claim 1 .

3. The dialogue unit Analyzing the user's body movements or facial expressions and taking non-verbal information into consideration to proceed with the dialogue. The system of claim 1 .

4. The generation unit The story is generated from a plurality of different perspectives based on the user's story, and options are provided to the user. The system of claim 1 .

5. The generation unit The story or the painting of the user is analyzed, and the values ​​or themes of the user's life are extracted and fed back. The system of claim 1 .

6. The generation unit Analyzing the user's story or painting and suggesting the optimal way to convey it to others The system of claim 1 .

7. The generation unit The story or the painting of the user is analyzed, and the values ​​or themes of the user's life are extracted and fed back. The system of claim 1 .

8. The generation unit Analyzing the user's story or painting and suggesting the optimal way to convey it to others The system of claim 1 .

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