System

Abstract

An object of a system according to an embodiment is to facilitate understanding by adding visual information to text information.SOLUTION: A system includes a text analysis part, a flow generation part, and a visual material assignment part. The text analysis unit analyzes a text input by a user. The flow generation unit generates a flow on the basis of the content analyzed by the text analysis unit. The visual material assignment unit assigns an image or a moving image to the flow generated by the flow 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 technology has the problem that information is provided only in text, making it difficult to understand visually.

[0005] The system according to the embodiment aims to make it easier to understand by adding visual information to text information. [Means for solving the problem]

[0006] The system according to the embodiment includes a text analysis unit, a flow generation unit, and a visual material addition unit. The text analysis unit analyzes text input by a user. The flow generation unit generates a flow based on the content analyzed by the text analysis unit. The visual material addition unit adds images and videos to the flow generated by the flow generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier to understand by adding visual information to text information. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An AI system according to an embodiment of the present invention generates flows that include not only text information but also images and videos. This system creates flows from text and adds appropriate images and videos to each flow, thereby adding visual information and compensating for the disadvantages of text. This allows the AI ​​system to compensate for the disadvantages of text and deepen user understanding by adding visual information.

[0029] The AI ​​system according to the embodiment includes a text analysis unit, a flow generation unit, and a visual material attachment unit. The text analysis unit analyzes text input by a user. For example, the text analysis unit breaks down the text using morphological analysis and performs grammatical analysis. The text analysis unit can also understand the meaning of the text using semantic analysis. For example, morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of a sentence and clarifies relationships such as between subject, predicate, and object. Semantic analysis understands the meaning of the text and provides an appropriate interpretation based on the context. The flow generation unit generates a flow based on the content analyzed by the text analysis unit. For example, the flow generation unit generates a flow that shows the progress steps of a project. The flow generation unit can also generate workflows and process diagrams. For example, a flow that shows the progress steps of a project shows each step in order and clearly indicates the required resources and personnel. A workflow visually shows a business process and clarifies the relationships between each step. A process diagram illustrates the flow of business and shows the details of each step. The visual material adding unit adds images and videos to the flow generated by the flow generation unit. For example, the visual material adding unit searches for appropriate materials from an online image and video database and incorporates them into the flow. The visual material adding unit can also incorporate images and videos uploaded by users into the flow. For example, the visual material adding unit adds an image of a conference room to a step called "Preparing for a Meeting." It can also add videos showing the meeting. It can also add images and videos taken by the user showing the progress of a project to the flow. In this way, the AI ​​system according to the embodiment can compensate for the disadvantages of text and deepen user understanding by adding visual information. For example, adding images and videos to a flow showing the progress steps of a project makes it easier for all team members to understand the steps, allowing work to proceed more efficiently. In addition, in educational settings, generating flows that visually represent learning content can deepen students' understanding.

[0030] The text analysis unit can refer to a user's past input history and generate a flow optimized for an individual user. For example, the text analysis unit uses a generation AI to analyze a user's past input history and generate a flow optimized for a specific user. For example, a flow specialized for project management is generated for a user who has created many project management-related flows in the past. The text analysis unit also uses a user's past input history to allow the generation AI to learn the user's preferences and patterns and generate a flow based on that. For example, for a user who frequently uses specific terms or phrases, a flow containing those terms or phrases is generated. The text analysis unit also uses a user's past input history to generate a new flow that reflects the style and structure of a flow previously created by the user. For example, for a user who prefers a specific layout or design, a flow that reflects that layout or design is generated. In this way, flows optimized for individual users can be generated by referring to the user's past input history.

[0031] The text analysis unit uses natural language processing technology to deeply understand the context and generate a more detailed flow. For example, the generation AI uses natural language processing technology to deeply understand the context of the text and generate a detailed flow. For example, it generates a flow that shows in detail the progress steps of a complex project. The text analysis unit also uses natural language processing technology to analyze the context of the text, extract relevant information, and reflect it in the flow. For example, it automatically adds the resources and personnel required for each step of the project. The text analysis unit also allows the generation AI to deeply understand the context, accurately grasp the user's intent, and generate a flow based on that. For example, it can generate an appropriate flow even if the user enters ambiguous instructions. In this way, the use of natural language processing technology allows for a deep understanding of the context and the generation of a more detailed flow.

[0032] The visual material attachment unit can learn a user's past preferences and provide visual material optimized for each individual user. For example, the generation AI of the visual material attachment unit learns a user's past preferences and provides visual material optimized for each individual user. For example, the generation AI analyzes patterns of images and videos selected by the user in the past and selects visual material suitable for a new flow based on the patterns. Furthermore, the visual material attachment unit uses the generation AI to suggest optimal visual material based on the user's past preferences. For example, it provides images and videos that reflect the user's preferred colors and designs. Furthermore, the generation AI of the visual material attachment unit learns a user's past preferences and provides the most attractive visual material for the user. For example, if a user prefers a particular theme or style, it selects visual material that matches that theme or style. This allows the generation AI to learn a user's past preferences and provide visual material optimized for each individual user.

[0033] The visual material annotation unit can select the most relevant visual material for each step of the flow using image recognition technology. For example, the generation AI in the visual material annotation unit uses image recognition technology to select the most relevant visual material for each step of the flow. For example, for the step "Preparing for the meeting," it selects an image of a conference room or a video showing the meeting. The visual material annotation unit also uses image recognition technology to automatically search for and select visual material related to each step of the flow. For example, it provides images and videos related to specific work procedures. The generation AI in the visual material annotation unit also uses image recognition technology to select the most appropriate visual material for each step of the flow. For example, it selects relevant images and videos based on the content of text entered by the user. In this way, the use of image recognition technology makes it possible to select the most relevant visual material for each step of the flow.

[0034] The visual material adding unit can provide a function that allows users to upload images and videos they have taken themselves and incorporate them into a flow. For example, the visual material adding unit provides a function that allows the generation AI to upload images and videos that users have taken themselves and incorporate them into a flow. For example, images and videos taken by the user showing the progress of a project are added to a flow. The visual material adding unit also allows users to upload images and videos that they have taken themselves, and the generation AI incorporates them into a flow. For example, a video taken by the user showing the state of a meeting is added to a flow. The visual material adding unit also provides a function that allows the generation AI to incorporate images and videos taken by users themselves into a flow, allowing users to create more personalized flows. For example, images and videos taken by the user showing specific work procedures are added to a flow. This allows users to create more personalized flows by incorporating images and videos they have taken themselves into a flow.

[0035] The flow generation unit can automatically provide related reference materials and links based on the user's input. For example, the generation AI in the flow generation unit analyzes the user's input and automatically provides related reference materials and links. For example, when generating a project management flow, it provides links to related guidelines and tools. Furthermore, the flow generation unit automatically searches for reference materials based on the user's input and incorporates them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documents and tutorials. Furthermore, the flow generation unit analyzes the user's input and provides related reference materials and links, making it easier for the user to understand the flow. For example, it provides links to materials related to each step of the flow. In this way, providing related reference materials and links based on the user's input makes it easier for the user to understand the flow.

[0036] The flow generation unit can promote interaction with the user by automatically generating relevant questions and feedback based on the user's input. In the flow generation unit, for example, the generation AI analyzes the user's input and automatically generates relevant questions and feedback. For example, it asks for additional information or confirmation items for the flow entered by the user. In addition, the flow generation unit promotes interaction with the user by automatically providing feedback based on the user's input. For example, it provides improvements and suggestions for each step of the flow. In addition, the flow generation unit makes it easier for the user to understand the flow by analyzing the user's input and generating relevant questions and feedback. For example, if the user enters something they are unsure about, it provides related information. In this way, generating relevant questions and feedback based on the user's input promotes interaction with the user.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The text analysis unit can automatically provide related reference materials and links based on the user's input. For example, when generating a project management flow, it provides links to related guidelines and tools. The text analysis unit can also enable the generation AI to automatically search for reference materials based on the user's input and incorporate them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documentation and tutorials. The text analysis unit can also analyze the user's input and provide related reference materials and links to make it easier for the user to understand the flow. For example, it can provide links to materials related to each step in the flow. This makes it easier for the user to understand the flow by providing related reference materials and links based on the user's input.

[0039] The flow generation unit can automatically generate relevant questions and feedback based on the user's input, promoting interaction with the user. For example, it can ask for additional information or confirmation regarding the flow entered by the user. The flow generation unit also promotes interaction with the user by having the generation AI automatically provide feedback based on the user's input. For example, it can provide improvements and suggestions for each step of the flow. The flow generation unit also makes it easier for the user to understand the flow by having the generation AI analyze the user's input and generate relevant questions and feedback. For example, if the user enters something they are unsure about, it can provide related information. This makes it possible to promote interaction with the user by generating relevant questions and feedback based on the user's input.

[0040] The visual material adding unit can provide a function that allows users to upload images and videos they have taken themselves and incorporate them into a flow. For example, a user can add images and videos taken by the user showing the progress of a project to a flow. The visual material adding unit also allows users to upload images and videos they have taken themselves, and the generation AI can incorporate them into a flow. For example, a user can add a video taken by the user showing the state of a meeting to a flow. The visual material adding unit also allows users to create more personalized flows by providing a function that allows the generation AI to incorporate images and videos they have taken themselves into a flow. For example, a user can add images and videos taken by the user showing a specific work procedure to a flow. This allows users to create more personalized flows by incorporating images and videos they have taken themselves into a flow.

[0041] The visual material annotation unit can use image recognition technology to select the most relevant visual material for each step of the flow. For example, for the step "Preparing for the meeting," it selects an image of a conference room or a video showing the meeting. The visual material annotation unit also uses image recognition technology to automatically search for and select visual material related to each step of the flow. For example, it provides images and videos related to specific work procedures. The visual material annotation unit also uses image recognition technology to select the most appropriate visual material for each step of the flow. For example, it selects relevant images and videos based on the content of text entered by the user. In this way, the use of image recognition technology makes it possible to select the most relevant visual material for each step of the flow.

[0042] The visual material annotation unit can learn a user's past preferences and provide visual material optimized for each individual user. For example, it analyzes patterns of images and videos selected by the user in the past and selects visual material suitable for a new flow based on that. The visual material annotation unit also uses the generation AI to suggest optimal visual material based on the user's past preferences. For example, it provides images and videos that reflect the user's preferred colors and designs. The visual material annotation unit also provides the most attractive visual material for the user by learning the user's past preferences. For example, if a user prefers a particular theme or style, it selects visual material that matches that theme or style. In this way, by learning the user's past preferences, it is possible to provide visual material optimized for each individual user.

[0043] The flow generation unit can automatically provide related reference materials and links based on the user's input. For example, when generating a project management flow, it provides links to related guidelines and tools. The flow generation unit can also have the generation AI automatically search for reference materials based on the user's input and incorporate them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documents and tutorials. The flow generation unit can also analyze the user's input and provide related reference materials and links to make it easier for the user to understand the flow. For example, it provides links to materials related to each step of the flow. This makes it easier for the user to understand the flow by providing related reference materials and links based on the user's input.

[0044] The flow generation unit can automatically generate relevant questions and feedback based on the user's input, promoting interaction with the user. For example, it can ask for additional information or confirmation regarding the flow entered by the user. The flow generation unit also promotes interaction with the user by having the generation AI automatically provide feedback based on the user's input. For example, it can provide improvements and suggestions for each step of the flow. The flow generation unit also makes it easier for the user to understand the flow by having the generation AI analyze the user's input and generate relevant questions and feedback. For example, if the user enters something they are unsure about, it can provide related information. This makes it possible to promote interaction with the user by generating relevant questions and feedback based on the user's input.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The text analysis unit analyzes the text entered by the user. For example, it uses morphological analysis to break down the text and perform grammatical analysis. It can also use semantic analysis to understand the meaning of the text. Morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies the relationships between subjects, predicates, objects, etc. Semantic analysis understands the meaning of the text and makes an appropriate interpretation based on the context. Step 2: The flow generation unit generates a flow based on the content analyzed by the text analysis unit. For example, it can generate a flow that shows the steps of a project, a workflow, or a process diagram. A flow that shows the steps of a project shows each step in order and clearly indicates the required resources and personnel. A workflow visually shows the business process and clarifies the relationships between each step. A process diagram illustrates the flow of work and shows the details of each step. Step 3: The visual material attachment unit adds images and videos to the flow generated by the flow generation unit. For example, it searches for appropriate materials from an image or video database on the Internet and incorporates them into the flow. It is also possible to incorporate images and videos uploaded by the user into the flow. For example, an image of a conference room can be attached to a step called "Preparing for a Meeting," or a video showing the meeting can be attached. Images and videos taken by the user showing the progress of a project can also be added to the flow.

[0047] (Example 2) An AI system according to an embodiment of the present invention generates flows that include not only text information but also images and videos. This system creates flows from text and adds appropriate images and videos to each flow, thereby adding visual information and compensating for the disadvantages of text. This allows the AI ​​system to compensate for the disadvantages of text and deepen user understanding by adding visual information.

[0048] The AI ​​system according to the embodiment includes a text analysis unit, a flow generation unit, and a visual material attachment unit. The text analysis unit analyzes text input by a user. For example, the text analysis unit breaks down the text using morphological analysis and performs grammatical analysis. The text analysis unit can also understand the meaning of the text using semantic analysis. For example, morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of a sentence and clarifies relationships such as between subject, predicate, and object. Semantic analysis understands the meaning of the text and provides an appropriate interpretation based on the context. The flow generation unit generates a flow based on the content analyzed by the text analysis unit. For example, the flow generation unit generates a flow that shows the progress steps of a project. The flow generation unit can also generate workflows and process diagrams. For example, a flow that shows the progress steps of a project shows each step in order and clearly indicates the required resources and personnel. A workflow visually shows a business process and clarifies the relationships between each step. A process diagram illustrates the flow of business and shows the details of each step. The visual material adding unit adds images and videos to the flow generated by the flow generation unit. For example, the visual material adding unit searches for appropriate materials from an online image and video database and incorporates them into the flow. The visual material adding unit can also incorporate images and videos uploaded by users into the flow. For example, the visual material adding unit adds an image of a conference room to a step called "Preparing for a Meeting." It can also add videos showing the meeting. It can also add images and videos taken by the user showing the progress of a project to the flow. In this way, the AI ​​system according to the embodiment can compensate for the disadvantages of text and deepen user understanding by adding visual information. For example, adding images and videos to a flow showing the progress steps of a project makes it easier for all team members to understand the steps, allowing work to proceed more efficiently. In addition, in educational settings, generating flows that visually represent learning content can deepen students' understanding.

[0049] The text analysis unit can refer to a user's past input history and generate a flow optimized for an individual user. For example, the text analysis unit uses a generation AI to analyze a user's past input history and generate a flow optimized for a specific user. For example, a flow specialized for project management is generated for a user who has created many project management-related flows in the past. The text analysis unit also uses a user's past input history to allow the generation AI to learn the user's preferences and patterns and generate a flow based on that. For example, for a user who frequently uses specific terms or phrases, a flow containing those terms or phrases is generated. The text analysis unit also uses a user's past input history to generate a new flow that reflects the style and structure of a flow previously created by the user. For example, for a user who prefers a specific layout or design, a flow that reflects that layout or design is generated. In this way, flows optimized for individual users can be generated by referring to the user's past input history.

[0050] The text analysis unit uses natural language processing technology to deeply understand the context and generate a more detailed flow. For example, the generation AI uses natural language processing technology to deeply understand the context of the text and generate a detailed flow. For example, it generates a flow that shows in detail the progress steps of a complex project. The text analysis unit also uses natural language processing technology to analyze the context of the text, extract relevant information, and reflect it in the flow. For example, it automatically adds the resources and personnel required for each step of the project. The text analysis unit also allows the generation AI to deeply understand the context, accurately grasp the user's intent, and generate a flow based on that. For example, it can generate an appropriate flow even if the user enters ambiguous instructions. In this way, the use of natural language processing technology allows for a deep understanding of the context and the generation of a more detailed flow.

[0051] The text analysis unit can use the emotion estimation function to analyze the user's emotional state and generate a flow that will not cause stress to the user. For example, the text analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and generate a flow that will not cause stress. For example, a flow that is suitable for when the user is relaxed can be generated. The text analysis unit can also analyze the user's emotional state and incorporate elements to reduce stress into the flow. For example, it can add images or videos that have a relaxing effect to the flow. The text analysis unit can also use the emotion estimation function to generate a simple, easy-to-understand flow when the user is feeling stressed. For example, it can provide a flow that simplifies complex steps. In this way, the emotion estimation function can be used to generate a flow that will not cause stress to the user.

[0052] The visual material attachment unit can learn a user's past preferences and provide visual material optimized for each individual user. For example, the generation AI of the visual material attachment unit learns a user's past preferences and provides visual material optimized for each individual user. For example, the generation AI analyzes patterns of images and videos selected by the user in the past and selects visual material suitable for a new flow based on the patterns. Furthermore, the visual material attachment unit uses the generation AI to suggest optimal visual material based on the user's past preferences. For example, it provides images and videos that reflect the user's preferred colors and designs. Furthermore, the generation AI of the visual material attachment unit learns a user's past preferences and provides the most attractive visual material for the user. For example, if a user prefers a particular theme or style, it selects visual material that matches that theme or style. This allows the generation AI to learn a user's past preferences and provide visual material optimized for each individual user.

[0053] The visual material annotation unit can select the most relevant visual material for each step of the flow using image recognition technology. For example, the generation AI in the visual material annotation unit uses image recognition technology to select the most relevant visual material for each step of the flow. For example, for the step "Preparing for the meeting," it selects an image of a conference room or a video showing the meeting. The visual material annotation unit also uses image recognition technology to automatically search for and select visual material related to each step of the flow. For example, it provides images and videos related to specific work procedures. The generation AI in the visual material annotation unit also uses image recognition technology to select the most appropriate visual material for each step of the flow. For example, it selects relevant images and videos based on the content of text entered by the user. In this way, the use of image recognition technology makes it possible to select the most relevant visual material for each step of the flow.

[0054] The visual material providing unit can use the emotion estimation function to analyze the user's emotional state and provide visual material that evokes the most positive emotion for the user. The visual material providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide visual material that evokes the most positive emotion. For example, it provides images and videos that are suitable for when the user is relaxed. The visual material providing unit also analyzes the user's emotional state and selects visual material that elicits positive emotions. For example, it provides images and videos that make the user feel joy. The visual material providing unit also uses the emotion estimation function to provide visual material that evokes the most positive emotion for the user. For example, if the user is feeling positive emotion, it provides visual material that further enhances that emotion. In this way, by using the emotion estimation function, it is possible to provide visual material that evokes the most positive emotion for the user.

[0055] The visual material adding unit can provide a function that allows users to upload images and videos they have taken themselves and incorporate them into a flow. For example, the visual material adding unit provides a function that allows the generation AI to upload images and videos that users have taken themselves and incorporate them into a flow. For example, images and videos taken by the user showing the progress of a project are added to a flow. The visual material adding unit also allows users to upload images and videos that they have taken themselves, and the generation AI incorporates them into a flow. For example, a video taken by the user showing the state of a meeting is added to a flow. The visual material adding unit also provides a function that allows the generation AI to incorporate images and videos taken by users themselves into a flow, allowing users to create more personalized flows. For example, images and videos taken by the user showing specific work procedures are added to a flow. This allows users to create more personalized flows by incorporating images and videos they have taken themselves into a flow.

[0056] The visual material adding unit uses the emotion estimation function to analyze the emotion of the user when selecting visual material in real time and can suggest optimal visual material. The visual material adding unit, for example, uses the emotion estimation function to analyze the emotion of the user when selecting visual material in real time and suggest optimal visual material. For example, visual material suitable for when the user is relaxed is suggested. The visual material adding unit also analyzes the emotional state of the user and suggests optimal visual material. For example, if the user has positive emotions, visual material that further enhances those emotions is suggested. The visual material adding unit also uses the emotion estimation function to make optimal suggestions when the user selects visual material. For example, if the user is feeling stressed, visual material that has a relaxing effect is suggested. In this way, by using the emotion estimation function, optimal suggestions can be made when the user selects visual material.

[0057] The flow generation unit can automatically provide related reference materials and links based on the user's input. For example, the generation AI in the flow generation unit analyzes the user's input and automatically provides related reference materials and links. For example, when generating a project management flow, it provides links to related guidelines and tools. Furthermore, the flow generation unit automatically searches for reference materials based on the user's input and incorporates them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documents and tutorials. Furthermore, the flow generation unit analyzes the user's input and provides related reference materials and links, making it easier for the user to understand the flow. For example, it provides links to materials related to each step of the flow. In this way, providing related reference materials and links based on the user's input makes it easier for the user to understand the flow.

[0058] The flow generation unit can promote interaction with the user by automatically generating relevant questions and feedback based on the user's input. In the flow generation unit, for example, the generation AI analyzes the user's input and automatically generates relevant questions and feedback. For example, it asks for additional information or confirmation items for the flow entered by the user. In addition, the flow generation unit promotes interaction with the user by automatically providing feedback based on the user's input. For example, it provides improvements and suggestions for each step of the flow. In addition, the flow generation unit makes it easier for the user to understand the flow by analyzing the user's input and generating relevant questions and feedback. For example, if the user enters something they are unsure about, it provides related information. In this way, generating relevant questions and feedback based on the user's input promotes interaction with the user.

[0059] The flow generation unit uses the emotion estimation function to analyze the emotion of the user when generating a flow in real time, and can provide an environment in which the user can be most relaxed. The flow generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when generating a flow in real time, and can provide an environment in which the user can be most relaxed. For example, if the user is feeling stressed, music with a relaxing effect is played. The flow generation unit also analyzes the user's emotional state and incorporates elements into the flow to provide an environment in which the user can be most relaxed. For example, images and videos that help the user relax are added to the flow. The flow generation unit also uses the emotion estimation function to provide an environment in which the user can be most relaxed. For example, a flow appropriate for when the user is relaxed is generated. In this way, the emotion estimation function can be used to provide an environment in which the user can be most relaxed.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The text analysis unit can automatically provide related reference materials and links based on the user's input. For example, when generating a project management flow, it provides links to related guidelines and tools. The text analysis unit can also enable the generation AI to automatically search for reference materials based on the user's input and incorporate them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documentation and tutorials. The text analysis unit can also analyze the user's input and provide related reference materials and links to make it easier for the user to understand the flow. For example, it can provide links to materials related to each step in the flow. This makes it easier for the user to understand the flow by providing related reference materials and links based on the user's input.

[0062] The flow generation unit can automatically generate relevant questions and feedback based on the user's input, promoting interaction with the user. For example, it can ask for additional information or confirmation regarding the flow entered by the user. The flow generation unit also promotes interaction with the user by having the generation AI automatically provide feedback based on the user's input. For example, it can provide improvements and suggestions for each step of the flow. The flow generation unit also makes it easier for the user to understand the flow by having the generation AI analyze the user's input and generate relevant questions and feedback. For example, if the user enters something they are unsure about, it can provide related information. This makes it possible to promote interaction with the user by generating relevant questions and feedback based on the user's input.

[0063] The visual material adding unit can provide a function that allows users to upload images and videos they have taken themselves and incorporate them into a flow. For example, a user can add images and videos taken by the user showing the progress of a project to a flow. The visual material adding unit also allows users to upload images and videos they have taken themselves, and the generation AI can incorporate them into a flow. For example, a user can add a video taken by the user showing the state of a meeting to a flow. The visual material adding unit also allows users to create more personalized flows by providing a function that allows the generation AI to incorporate images and videos they have taken themselves into a flow. For example, a user can add images and videos taken by the user showing a specific work procedure to a flow. This allows users to create more personalized flows by incorporating images and videos they have taken themselves into a flow.

[0064] The visual material providing unit can use the emotion estimation function to analyze the user's emotional state and provide visual material that evokes the most positive emotion for the user. For example, it can provide images and videos that are suitable for when the user is relaxed. The visual material providing unit can also analyze the user's emotional state and select visual material that elicits positive emotions. For example, it can provide images and videos that make the user feel joy. The visual material providing unit can also use the emotion estimation function to provide visual material that evokes the most positive emotion for the user. For example, if the user is feeling positive emotion, it can provide visual material that further enhances that emotion. In this way, by using the emotion estimation function, it is possible to provide visual material that evokes the most positive emotion for the user.

[0065] The flow generation unit uses the emotion estimation function to analyze the user's emotions in real time when generating a flow, and can provide the user with an environment in which they can most relax. For example, if the user is feeling stressed, music with a relaxing effect is played. The flow generation unit also analyzes the user's emotional state and incorporates elements into the flow to provide a relaxing environment. For example, images and videos that help the user relax are added to the flow. The flow generation unit also uses the emotion estimation function to provide the user with an environment in which they can most relax. For example, a flow appropriate for when the user is relaxed is generated. In this way, the emotion estimation function can be used to provide the user with an environment in which they can most relax.

[0066] The visual material annotation unit can use image recognition technology to select the most relevant visual material for each step of the flow. For example, for the step "Preparing for the meeting," it selects an image of a conference room or a video showing the meeting. The visual material annotation unit also uses image recognition technology to automatically search for and select visual material related to each step of the flow. For example, it provides images and videos related to specific work procedures. The visual material annotation unit also uses image recognition technology to select the most appropriate visual material for each step of the flow. For example, it selects relevant images and videos based on the content of text entered by the user. In this way, the use of image recognition technology makes it possible to select the most relevant visual material for each step of the flow.

[0067] The visual material annotation unit can learn a user's past preferences and provide visual material optimized for each individual user. For example, it analyzes patterns of images and videos selected by the user in the past and selects visual material suitable for a new flow based on that. The visual material annotation unit also uses the generation AI to suggest optimal visual material based on the user's past preferences. For example, it provides images and videos that reflect the user's preferred colors and designs. The visual material annotation unit also provides the most attractive visual material for the user by learning the user's past preferences. For example, if a user prefers a particular theme or style, it selects visual material that matches that theme or style. In this way, by learning the user's past preferences, it is possible to provide visual material optimized for each individual user.

[0068] The flow generation unit can automatically provide related reference materials and links based on the user's input. For example, when generating a project management flow, it provides links to related guidelines and tools. The flow generation unit can also have the generation AI automatically search for reference materials based on the user's input and incorporate them into the flow. For example, when generating a flow related to a specific technology, it provides links to related technical documents and tutorials. The flow generation unit can also analyze the user's input and provide related reference materials and links to make it easier for the user to understand the flow. For example, it provides links to materials related to each step of the flow. This makes it easier for the user to understand the flow by providing related reference materials and links based on the user's input.

[0069] The flow generation unit can automatically generate relevant questions and feedback based on the user's input, promoting interaction with the user. For example, it can ask for additional information or confirmation regarding the flow entered by the user. The flow generation unit also promotes interaction with the user by having the generation AI automatically provide feedback based on the user's input. For example, it can provide improvements and suggestions for each step of the flow. The flow generation unit also makes it easier for the user to understand the flow by having the generation AI analyze the user's input and generate relevant questions and feedback. For example, if the user enters something they are unsure about, it can provide related information. This makes it possible to promote interaction with the user by generating relevant questions and feedback based on the user's input.

[0070] The visual material adding unit uses the emotion estimation function to analyze the emotions of the user when selecting visual materials in real time and can suggest optimal visual materials. For example, visual materials suitable for when the user is relaxed are suggested. The visual material adding unit also analyzes the user's emotional state and suggests optimal visual materials. For example, if the user has positive emotions, visual materials that further enhance those emotions are suggested. The visual material adding unit also uses the emotion estimation function to make optimal suggestions when the user selects visual materials. For example, if the user is feeling stressed, visual materials that have a relaxing effect are suggested. In this way, by using the emotion estimation function, optimal suggestions can be made when the user selects visual materials.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The text analysis unit analyzes the text entered by the user. For example, it uses morphological analysis to break down the text and perform grammatical analysis. It can also use semantic analysis to understand the meaning of the text. Morphological analysis breaks down the text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and clarifies the relationships between subjects, predicates, objects, etc. Semantic analysis understands the meaning of the text and makes an appropriate interpretation based on the context. Step 2: The flow generation unit generates a flow based on the content analyzed by the text analysis unit. For example, it can generate a flow that shows the steps of a project, a workflow, or a process diagram. A flow that shows the steps of a project shows each step in order and clearly indicates the required resources and personnel. A workflow visually shows the business process and clarifies the relationships between each step. A process diagram illustrates the flow of work and shows the details of each step. Step 3: The visual material attachment unit adds images and videos to the flow generated by the flow generation unit. For example, it searches for appropriate materials from an image or video database on the Internet and incorporates them into the flow. It is also possible to incorporate images and videos uploaded by the user into the flow. For example, an image of a conference room can be attached to a step called "Preparing for a Meeting," or a video showing the meeting can be attached. Images and videos taken by the user showing the progress of a project can also be added to the flow.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] 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]

[0140] 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 text analysis unit that analyzes text input by a user; a flow generation unit that generates a flow based on the content analyzed by the text analysis unit; a visual material adding unit that adds images and videos to the flow generated by the flow generating unit. A system characterized by:

2. The text analysis unit The flow optimized for each individual user is generated by referring to the user's past input history.

2. The system of claim 1.

3. The text analysis unit Natural language processing technology is used to deeply understand the context and generate more detailed flow 2. The system of claim 1.

4. The text analysis unit Analyzing the emotional state of the user and generating the flow so that the user does not feel stressed.

2. The system of claim 1.

5. The visual material providing unit Learns the user's past preferences and provides visual material optimized for the individual user 2. The system of claim 1.

Images