system

Abstract

The system according to this embodiment aims to quickly select and provide appropriate materials that meet the specific needs of the user. [Solution] The system according to the embodiment comprises a collection unit, a selection unit, and a provision unit. The collection unit receives input from the user regarding the target person, destination, taste, format of the material, and details of the content of the material. The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The provision unit provides the materials selected by the selection unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly select and provide appropriate materials according to specific user requirements.

[0005] The system according to an embodiment aims to quickly select and provide appropriate materials according to specific user requirements.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a selection unit, and a provision unit. The collection unit receives inputs of a target person, a destination, a taste, a format of materials, and details of the content of materials from a user. The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The provision unit provides the materials selected by the selection unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can quickly select and provide appropriate materials that meet the user's specific needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The document selection system according to an embodiment of the present invention is a system that automatically selects and provides documents that match the user's requirements, such as who the documents are for, where they are intended, and what style of document they want to create. The document selection system takes information such as the target audience, destination, style, document format, and detailed content of the documents as input by the user. Next, the system analyzes this information and selects appropriate documents. The selected documents are then provided to the user. This system allows users to easily obtain documents that meet their needs. For example, if a user inputs "training materials for new employees, with a casual style," the document selection system analyzes this information and selects appropriate training materials. The selected documents are provided to the user, who can then use them for training. This system can be used in various situations, such as corporate training and presentations, and the creation of teaching materials in educational institutions. Because users can quickly obtain documents that meet their needs with simple input, work efficiency can be improved. Thus, the document selection system allows users to easily obtain documents that meet their needs.

[0029] The material selection system according to this embodiment comprises a collection unit, a selection unit, and a provision unit. The collection unit receives input from the user regarding the target person, destination, taste, material format, and detailed content of the material. The collection unit, for example, stores the information entered by the user in a database. The collection unit can also analyze the information entered by the user in real time. For example, the collection unit analyzes the information entered by the user and provides basic data for selecting appropriate materials. The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The selection unit, for example, refers to the database and selects materials using an algorithm. The selection unit, for example, uses a machine learning algorithm to select materials that meet the user's needs. The selection unit can also select materials using a rule-based algorithm. For example, the selection unit selects appropriate materials based on the user's input information. The provision unit provides the materials selected by the selection unit to the user. The provision unit, for example, sends the selected materials to the user by email. The provision unit can also make the selected materials available for download on a website. For example, the provisioning unit provides a link to download the materials selected by the user. This allows the material selection system according to the embodiment to easily obtain materials that meet the user's needs.

[0030] The data collection unit accepts input from users regarding target audience, destination, taste, document format, and detailed document content. Specifically, it collects information entered by users through web forms and applications. For example, target audience includes specific age groups, occupations, and interests, while destinations include specific regions, facilities, and events. Taste reflects the user's preferences and style, and document format includes file formats such as PDF, Word, and PowerPoint, as well as media formats such as video and audio. Detailed document content refers to specific topics, keywords, and the scope of necessary information. The data collection unit stores this information in a database and organizes it for each user. Furthermore, the data collection unit can analyze the information entered by users in real time. For example, it can use natural language processing technology to analyze user input and classify it into appropriate keywords and categories. This allows the data collection unit to accurately understand user needs and provide useful basic data to the selection unit. In addition, the data collection unit can refer to the user's past input history and behavioral history to collect data with higher accuracy. This enables the data collection unit to achieve flexible data collection tailored to user needs and improve the overall system performance.

[0031] The selection unit analyzes the information collected by the collection unit and selects appropriate materials. Specifically, it refers to a database and uses algorithms to select materials. For example, the selection unit can use machine learning algorithms to select materials that meet user needs. Machine learning algorithms build models that predict the optimal materials based on past data and user input. For example, they learn from data on materials previously selected and evaluated by users and propose the most suitable materials for new user needs. The selection unit can also select materials using rule-based algorithms. Rule-based algorithms select materials based on predefined rules and conditions. For example, rules can be set to prioritize materials containing specific keywords or to select materials of a specific format. Furthermore, the selection unit can combine multiple algorithms to perform more accurate material selection. For example, it can combine machine learning algorithms and rule-based algorithms to select the materials best suited to user needs. This allows the selection unit to respond to diverse user needs and quickly and accurately select the most suitable materials.

[0032] The provision department provides users with materials selected by the selection department. Specifically, it sends the selected materials to users via email. The provision department can also make the selected materials available for download on a website. For example, it can provide a link that allows users to download the selected materials. Furthermore, the provision department can diversify the methods of providing materials. For example, it can upload materials to cloud storage and make them accessible to users. It can also provide a service to print and mail the materials. This allows users to obtain materials in the most suitable way according to their needs and circumstances. In addition, the provision department can monitor the status of material delivery in real time and provide appropriate feedback to users. For example, it can track the download and viewing status of materials and send reminders to users. The provision department can also collect feedback from users and use it to improve the quality and delivery methods of the materials. This allows the provision department to provide materials to users quickly and reliably, and improve user satisfaction.

[0033] The selection unit can select materials by referring to a database and using algorithms. For example, the selection unit can search for materials stored in the database and select materials that meet the user's needs. The selection unit can also select materials using machine learning algorithms. For example, the selection unit can select appropriate materials based on user input information. Furthermore, the selection unit can also select materials using rule-based algorithms. For example, the selection unit can select appropriate materials based on user input information. In this way, appropriate materials can be selected by using both a database and algorithms. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input materials stored in the database into an AI, and the AI ​​can select materials.

[0034] The data collection unit can analyze the user's past input history and select the optimal input method. For example, the data collection unit can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The data collection unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the data collection unit can simplify the input procedure based on information the user has previously entered. This allows the optimal input method to be suggested by analyzing past input history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0035] The data collection unit can filter input based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting information related to the user's current projects. The data collection unit can also filter highly relevant information based on the user's areas of interest. For example, the data collection unit filters input based on areas the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location information during input. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist and transportation information based on their current location. For example, if the user is participating in a specific event, the data collection unit will prioritize collecting information related to that event. This allows for the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0037] The data collection unit can analyze the user's social media activity and collect relevant information during input. For example, the data collection unit can collect relevant materials based on information shared by the user on social media. The data collection unit can also analyze the content of posts from accounts that the user follows and collect relevant information. For example, the data collection unit can collect relevant information based on groups and events that the user participates in. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0038] The selection unit can optimize its selection algorithm by referring to past selection history when selecting materials. For example, the selection unit can analyze trends in materials previously selected by the user and select the most suitable materials. The selection unit can also prioritize materials on specific themes or formats based on the user's past selection history. For example, the selection unit can select similar materials based on materials that the user has previously given high ratings to. This allows the selection algorithm to be optimized by referring to past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0039] The selection unit can apply different selection algorithms depending on the user's input when selecting materials. For example, if the user provides detailed input, the selection unit will apply a detailed algorithm to select materials. Alternatively, if the user provides simple input, the selection unit can apply a simple algorithm to select materials. For example, if the user enters a specific keyword, the selection unit will select materials based on that keyword. This allows for the selection of more appropriate materials by applying a selection algorithm according to the user's input. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user input data into a generating AI and have the generating AI execute the application of different selection algorithms.

[0040] The selection unit can select the most suitable materials by considering the user's geographical location information during the material selection process. For example, if the user is in a specific region, the selection unit will prioritize selecting materials related to that region. Furthermore, if the user is traveling, the selection unit can select materials including tourist information and transportation information based on their current location. For example, if the user is participating in a specific event, the selection unit will select materials related to that event. This allows for the selection of the most suitable materials by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable materials.

[0041] The selection unit can analyze a user's social media activity and select relevant materials when selecting materials. For example, the selection unit can select relevant materials based on information shared by the user on social media. The selection unit can also analyze the content of posts from accounts that the user follows and select relevant materials. For example, the selection unit can select relevant materials based on groups and events that the user participates in. In this way, by analyzing social media activity, highly relevant materials can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant materials.

[0042] The service provider can select the optimal delivery method by referring to the user's past usage history when providing materials. For example, the service provider can select the optimal delivery method based on the delivery methods used by the user in the past. The service provider can also prioritize the provision of specific types of materials based on the user's past usage history. For example, the service provider can provide similar materials based on materials that the user has given high ratings to in the past. This allows the service provider to select the optimal delivery method by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0043] The service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. For example, the service provider may prioritize providing materials related to the user's current projects. The service provider can also provide highly relevant materials based on the user's areas of interest. For example, the service provider may customize the content based on areas the user has shown interest in in the past. This allows the service provider to provide highly relevant materials by customizing the content based on the user's projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's project and area of ​​interest data into a generating AI and have the generating AI perform the customization of the content.

[0044] The service provider can select the optimal delivery method when providing materials, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider will prioritize providing materials related to that region. Furthermore, if the user is traveling, the service provider can provide materials including tourist information and transportation information based on their current location. For example, if the service provider is participating in a specific event, the service provider will provide materials related to that event. This allows the service provider to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0045] The service provider can analyze a user's social media activity and provide relevant materials when providing materials. For example, the service provider can provide relevant materials based on information shared by the user on social media. The service provider can also analyze the content of posts from accounts the user follows and provide relevant materials. For example, the service provider can provide relevant materials based on groups and events the user participates in. This allows for the provision of highly relevant materials by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI provide relevant materials.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The material selection system may also include an eye-tracking unit that collects user eye-tracking data. The eye-tracking unit detects which parts of the screen the user is fixated on and provides this data to the data collection unit. For example, if a user is fixated on a particular part of a material for an extended period, the system prioritizes collecting information related to that part. The eye-tracking unit can also estimate areas of interest from the user's eye-tracking data and provide this information to the data collection unit. This allows for the selection of more appropriate materials by utilizing the user's eye-tracking data.

[0048] The material selection system can also include a search history analysis unit that analyzes the user's past search history. The search history analysis unit analyzes keywords and materials the user has previously searched for and provides this information to the collection unit. For example, it can prioritize the collection of materials related to keywords the user has frequently searched for in the past. Furthermore, the search history analysis unit can estimate the user's areas of interest from their search history and provide this information to the collection unit. This allows for the selection of more appropriate materials by leveraging the user's search history.

[0049] The material selection system may also include a calendar integration unit that collects the user's calendar information. The calendar integration unit collects the user's schedule information and provides it to the collection unit. For example, if a user has a specific meeting or event scheduled, the system prioritizes collecting materials related to that event. The calendar integration unit can also adjust the timing of material delivery based on the user's schedule. This allows the system to provide more appropriate materials by utilizing the user's schedule information.

[0050] The material selection system may also include a device integration unit that collects user device information. This unit collects information about the type and settings of the device the user is using and provides it to the collection unit. For example, if the user is using a smartphone, the system provides materials optimized for smartphones. The device integration unit can also adjust the optimal display method of the materials based on the user's device settings. This allows the system to provide more appropriate materials by utilizing the user's device information.

[0051] The material selection system can also include a feedback collection unit to gather user feedback. The feedback collection unit allows users to input ratings and comments on the provided materials and provides this information to the collection unit. For example, if a user gives a high rating to a provided material, similar materials can be prioritized based on that rating. Furthermore, the feedback collection unit can analyze user comments and optimize the material selection algorithm. This allows for the provision of more appropriate materials by utilizing user feedback.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The data collection unit accepts input from users regarding target audience, destination, taste, document format, and detailed document content. The data collection unit can also store the user-entered information in a database and analyze it in real time. This provides the basic data needed to select appropriate materials. Step 2: The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The selection unit refers to the database and uses algorithms to select materials. For example, machine learning algorithms or rule-based algorithms are used to select materials that meet the user's needs. Step 3: The provisioning department provides the users with the materials selected by the selection department. The provisioning department either sends the selected materials to the users via email or makes them available for download on a website. For example, they provide a link that allows users to download the selected materials.

[0054] (Example of form 2) The document selection system according to an embodiment of the present invention is a system that automatically selects and provides documents that match the user's requirements, such as who the documents are for, where they are intended, and what style of document they want to create. The document selection system takes information such as the target audience, destination, style, document format, and detailed content of the documents as input by the user. Next, the system analyzes this information and selects appropriate documents. The selected documents are then provided to the user. This system allows users to easily obtain documents that meet their needs. For example, if a user inputs "training materials for new employees, with a casual style," the document selection system analyzes this information and selects appropriate training materials. The selected documents are provided to the user, who can then use them for training. This system can be used in various situations, such as corporate training and presentations, and the creation of teaching materials in educational institutions. Because users can quickly obtain documents that meet their needs with simple input, work efficiency can be improved. Thus, the document selection system allows users to easily obtain documents that meet their needs.

[0055] The material selection system according to this embodiment comprises a collection unit, a selection unit, and a provision unit. The collection unit receives input from the user regarding the target person, destination, taste, material format, and detailed content of the material. The collection unit, for example, stores the information entered by the user in a database. The collection unit can also analyze the information entered by the user in real time. For example, the collection unit analyzes the information entered by the user and provides basic data for selecting appropriate materials. The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The selection unit, for example, refers to the database and selects materials using an algorithm. The selection unit, for example, uses a machine learning algorithm to select materials that meet the user's needs. The selection unit can also select materials using a rule-based algorithm. For example, the selection unit selects appropriate materials based on the user's input information. The provision unit provides the materials selected by the selection unit to the user. The provision unit, for example, sends the selected materials to the user by email. The provision unit can also make the selected materials available for download on a website. For example, the provisioning unit provides a link to download the materials selected by the user. This allows the material selection system according to the embodiment to easily obtain materials that meet the user's needs.

[0056] The data collection unit accepts input from users regarding target audience, destination, taste, document format, and detailed document content. Specifically, it collects information entered by users through web forms and applications. For example, target audience includes specific age groups, occupations, and interests, while destinations include specific regions, facilities, and events. Taste reflects the user's preferences and style, and document format includes file formats such as PDF, Word, and PowerPoint, as well as media formats such as video and audio. Detailed document content refers to specific topics, keywords, and the scope of necessary information. The data collection unit stores this information in a database and organizes it for each user. Furthermore, the data collection unit can analyze the information entered by users in real time. For example, it can use natural language processing technology to analyze user input and classify it into appropriate keywords and categories. This allows the data collection unit to accurately understand user needs and provide useful basic data to the selection unit. In addition, the data collection unit can refer to the user's past input history and behavioral history to collect data with higher accuracy. This enables the data collection unit to achieve flexible data collection tailored to user needs and improve the overall system performance.

[0057] The selection unit analyzes the information collected by the collection unit and selects appropriate materials. Specifically, it refers to a database and uses algorithms to select materials. For example, the selection unit can use machine learning algorithms to select materials that meet user needs. Machine learning algorithms build models that predict the optimal materials based on past data and user input. For example, they learn from data on materials previously selected and evaluated by users and propose the most suitable materials for new user needs. The selection unit can also select materials using rule-based algorithms. Rule-based algorithms select materials based on predefined rules and conditions. For example, rules can be set to prioritize materials containing specific keywords or to select materials of a specific format. Furthermore, the selection unit can combine multiple algorithms to perform more accurate material selection. For example, it can combine machine learning algorithms and rule-based algorithms to select the materials best suited to user needs. This allows the selection unit to respond to diverse user needs and quickly and accurately select the most suitable materials.

[0058] The provision department provides users with materials selected by the selection department. Specifically, it sends the selected materials to users via email. The provision department can also make the selected materials available for download on a website. For example, it can provide a link that allows users to download the selected materials. Furthermore, the provision department can diversify the methods of providing materials. For example, it can upload materials to cloud storage and make them accessible to users. It can also provide a service to print and mail the materials. This allows users to obtain materials in the most suitable way according to their needs and circumstances. In addition, the provision department can monitor the status of material delivery in real time and provide appropriate feedback to users. For example, it can track the download and viewing status of materials and send reminders to users. The provision department can also collect feedback from users and use it to improve the quality and delivery methods of the materials. This allows the provision department to provide materials to users quickly and reliably, and improve user satisfaction.

[0059] The selection unit can select materials by referring to a database and using algorithms. For example, the selection unit can search for materials stored in the database and select materials that meet the user's needs. The selection unit can also select materials using machine learning algorithms. For example, the selection unit can select appropriate materials based on user input information. Furthermore, the selection unit can also select materials using rule-based algorithms. For example, the selection unit can select appropriate materials based on user input information. In this way, appropriate materials can be selected by using both a database and algorithms. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input materials stored in the database into an AI, and the AI ​​can select materials.

[0060] The data collection unit can estimate the user's emotions and adjust the timing of input based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the input timing to allow the user to input in a relaxed state. The data collection unit can also speed up the input timing if the user is in a hurry to collect information quickly. For example, if the data collection unit is focused, it can adjust the input timing to allow the user to input information while maintaining their concentration. By adjusting the input timing according to the user's emotions, more appropriate input becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0061] The data collection unit can analyze the user's past input history and select the optimal input method. For example, the data collection unit can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The data collection unit can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the data collection unit can simplify the input procedure based on information the user has previously entered. This allows the optimal input method to be suggested by analyzing past input history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0062] The data collection unit can filter input based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting information related to the user's current projects. The data collection unit can also filter highly relevant information based on the user's areas of interest. For example, the data collection unit filters input based on areas the user has shown interest in in the past. This allows for the collection of highly relevant information by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0063] The data collection unit can estimate the user's emotions and prioritize input fields based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize displaying important input fields and postpone other fields. If the user is relaxed, the data collection unit may prioritize displaying detailed input fields and suggest customizable input methods. For example, if the user is in a hurry, the data collection unit may prioritize displaying the most important input fields to allow for quick input. This allows for the priority of inputting important information by prioritizing input fields according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0064] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location information during input. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist and transportation information based on their current location. For example, if the user is participating in a specific event, the data collection unit will prioritize collecting information related to that event. This allows for the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0065] The data collection unit can analyze the user's social media activity and collect relevant information during input. For example, the data collection unit can collect relevant materials based on information shared by the user on social media. The data collection unit can also analyze the content of posts from accounts that the user follows and collect relevant information. For example, the data collection unit can collect relevant information based on groups and events that the user participates in. This allows for the collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0066] The selection unit can estimate the user's emotions and adjust the material selection criteria based on the estimated emotions. For example, if the user is stressed, the selection unit will prioritize selecting simple and easy-to-understand materials. Conversely, if the user is relaxed, the selection unit can prioritize selecting detailed and in-depth materials. For example, if the user is in a hurry, the selection unit will prioritize selecting materials that are concise and can be read quickly. By adjusting the material selection criteria according to the user's emotions, more appropriate materials can be selected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The selection unit can optimize its selection algorithm by referring to past selection history when selecting materials. For example, the selection unit can analyze trends in materials previously selected by the user and select the most suitable materials. The selection unit can also prioritize materials on specific themes or formats based on the user's past selection history. For example, the selection unit can select similar materials based on materials that the user has previously given high ratings to. This allows the selection algorithm to be optimized by referring to past selection history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0068] The selection unit can apply different selection algorithms depending on the user's input when selecting materials. For example, if the user provides detailed input, the selection unit will apply a detailed algorithm to select materials. Alternatively, if the user provides simple input, the selection unit can apply a simple algorithm to select materials. For example, if the user enters a specific keyword, the selection unit will select materials based on that keyword. This allows for the selection of more appropriate materials by applying a selection algorithm according to the user's input. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user input data into a generating AI and have the generating AI execute the application of different selection algorithms.

[0069] The selection unit can estimate the user's emotions and adjust the display method of the selected materials based on the estimated user emotions. For example, if the user is nervous, the selection unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the selection unit can provide a concise display method. This allows for more appropriate display by adjusting the display method of materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0070] The selection unit can select the most suitable materials by considering the user's geographical location information during the material selection process. For example, if the user is in a specific region, the selection unit will prioritize selecting materials related to that region. Furthermore, if the user is traveling, the selection unit can select materials including tourist information and transportation information based on their current location. For example, if the user is participating in a specific event, the selection unit will select materials related to that event. This allows for the selection of the most suitable materials by considering the user's geographical location information. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable materials.

[0071] The selection unit can analyze a user's social media activity and select relevant materials when selecting materials. For example, the selection unit can select relevant materials based on information shared by the user on social media. The selection unit can also analyze the content of posts from accounts that the user follows and select relevant materials. For example, the selection unit can select relevant materials based on groups and events that the user participates in. In this way, by analyzing social media activity, highly relevant materials can be selected. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity data into a generating AI and have the generating AI perform the selection of relevant materials.

[0072] The service provider can estimate the user's emotions and adjust the way materials are presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the way materials are presented according to the user's emotions, more appropriate presentation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The service provider can select the optimal delivery method by referring to the user's past usage history when providing materials. For example, the service provider can select the optimal delivery method based on the delivery methods used by the user in the past. The service provider can also prioritize the provision of specific types of materials based on the user's past usage history. For example, the service provider can provide similar materials based on materials that the user has given high ratings to in the past. This allows the service provider to select the optimal delivery method by referring to past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0074] The service provider can customize the content provided based on the user's current projects and areas of interest when providing materials. For example, the service provider may prioritize providing materials related to the user's current projects. The service provider can also provide highly relevant materials based on the user's areas of interest. For example, the service provider may customize the content based on areas the user has shown interest in in the past. This allows the service provider to provide highly relevant materials by customizing the content based on the user's projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's project and area of ​​interest data into a generating AI and have the generating AI perform the customization of the content.

[0075] The delivery unit can estimate the user's emotions and adjust the timing of material delivery based on the estimated emotions. For example, if the user is nervous, the delivery unit can delay the delivery of materials so that the user can receive them in a relaxed state. Conversely, if the user is relaxed, the delivery unit can advance the delivery of materials to provide information quickly. For example, if the user is in a hurry, the delivery unit can adjust the delivery timing to provide information quickly. In this way, by adjusting the timing of material delivery according to the user's emotions, materials can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The service provider can select the optimal delivery method when providing materials, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider will prioritize providing materials related to that region. Furthermore, if the user is traveling, the service provider can provide materials including tourist information and transportation information based on their current location. For example, if the service provider is participating in a specific event, the service provider will provide materials related to that event. This allows the service provider to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal delivery method.

[0077] The service provider can analyze a user's social media activity and provide relevant materials when providing materials. For example, the service provider can provide relevant materials based on information shared by the user on social media. The service provider can also analyze the content of posts from accounts the user follows and provide relevant materials. For example, the service provider can provide relevant materials based on groups and events the user participates in. This allows for the provision of highly relevant materials by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI provide relevant materials.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The document selection system can also include a voice analysis unit that analyzes the user's voice input. The voice analysis unit converts the information entered by the user via voice into text and provides it to the collection unit. For example, if a user voice-inputs "training materials for new employees, with a casual tone," the voice analysis unit converts the voice into text and sends it to the collection unit. The voice analysis unit can also estimate the user's emotions from their voice tone and speed and provide this information to the collection unit. This allows users to easily obtain documents that meet their needs even when using voice input.

[0080] The material selection system may also include an eye-tracking unit that collects user eye-tracking data. The eye-tracking unit detects which parts of the screen the user is fixated on and provides this data to the data collection unit. For example, if a user is fixated on a particular part of a material for an extended period, the system prioritizes collecting information related to that part. The eye-tracking unit can also estimate areas of interest from the user's eye-tracking data and provide this information to the data collection unit. This allows for the selection of more appropriate materials by utilizing the user's eye-tracking data.

[0081] The material selection system can also be equipped with a biometric information collection unit that collects the user's biometric information. The biometric information collection unit collects biometric information such as the user's heart rate and skin electrical activity and provides it to the collection unit. For example, if the user is stressed, the system can detect an increase in heart rate and adjust the timing of input based on that information. The biometric information collection unit can also estimate the stress level from the user's biometric information and provide it to the collection unit. This allows for the provision of a more appropriate input environment by utilizing the user's biometric information.

[0082] The material selection system can also include a search history analysis unit that analyzes the user's past search history. The search history analysis unit analyzes keywords and materials the user has previously searched for and provides this information to the collection unit. For example, it can prioritize the collection of materials related to keywords the user has frequently searched for in the past. Furthermore, the search history analysis unit can estimate the user's areas of interest from their search history and provide this information to the collection unit. This allows for the selection of more appropriate materials by leveraging the user's search history.

[0083] The document selection system can further estimate the user's emotions and adjust the document format based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible format. If the user is relaxed, it can provide a format that includes detailed information. For example, if the user is in a hurry, it can provide a format that is concise and easy to read. In this way, by adjusting the document format according to the user's emotions, more appropriate documents can be provided.

[0084] The material selection system may also include a calendar integration unit that collects the user's calendar information. The calendar integration unit collects the user's schedule information and provides it to the collection unit. For example, if a user has a specific meeting or event scheduled, the system prioritizes collecting materials related to that event. The calendar integration unit can also adjust the timing of material delivery based on the user's schedule. This allows the system to provide more appropriate materials by utilizing the user's schedule information.

[0085] The material selection system can further estimate the user's emotions and customize the content of the materials based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand content. Conversely, if the user is relaxed, it can provide detailed and in-depth content. For example, if the user is in a hurry, it can provide concise content that can be read quickly. In this way, by customizing the content of materials according to the user's emotions, it can provide more appropriate materials.

[0086] The material selection system may also include a device integration unit that collects user device information. This unit collects information about the type and settings of the device the user is using and provides it to the collection unit. For example, if the user is using a smartphone, the system provides materials optimized for smartphones. The device integration unit can also adjust the optimal display method of the materials based on the user's device settings. This allows the system to provide more appropriate materials by utilizing the user's device information.

[0087] The material selection system can further estimate the user's emotions and customize how materials are presented based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. For example, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by customizing how materials are presented according to the user's emotions, more appropriate materials can be provided.

[0088] The material selection system can also include a feedback collection unit to gather user feedback. The feedback collection unit allows users to input ratings and comments on the provided materials and provides this information to the collection unit. For example, if a user gives a high rating to a provided material, similar materials can be prioritized based on that rating. Furthermore, the feedback collection unit can analyze user comments and optimize the material selection algorithm. This allows for the provision of more appropriate materials by utilizing user feedback.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The data collection unit accepts input from users regarding target audience, destination, taste, document format, and detailed document content. The data collection unit can also store the user-entered information in a database and analyze it in real time. This provides the basic data needed to select appropriate materials. Step 2: The selection unit analyzes the information collected by the collection unit and selects appropriate materials. The selection unit refers to the database and uses algorithms to select materials. For example, machine learning algorithms or rule-based algorithms are used to select materials that meet the user's needs. Step 3: The provisioning department provides the users with the materials selected by the selection department. The provisioning department either sends the selected materials to the users via email or makes them available for download on a website. For example, they provide a link that allows users to download the selected materials.

[0091] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0092] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0093] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0094] Each of the multiple elements described above, including the collection unit, selection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit receives input from the user using the receiving device 38 of the smart device 14 and stores the input information in the database 24 using the control unit 46A. The selection unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the information provided by the collection unit and selects appropriate materials. The provision unit provides the selected materials to the user using the output device 40 of the smart device 14. The provision unit can also send the selected materials to the user via email using the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0101] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0102] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0103] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0104] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0105] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0109] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the collection unit, selection unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit receives voice input from the user using the microphone 238 of the smart glasses 214 and stores the input information in the database 24 using the control unit 46A. The selection unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the information provided by the collection unit and selects appropriate materials. The provision unit provides the selected materials to the user using the speaker 240 of the smart glasses 214. The provision unit can also send the selected materials to the user via email through the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, selection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit receives voice input from the user using the microphone 238 of the headset terminal 314 and stores the input information in the database 24 using the control unit 46A. The selection unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the information provided by the collection unit and selects appropriate materials. The provision unit provides the selected materials to the user using the display 343 of the headset terminal 314. The provision unit can also send the selected materials to the user via email using the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0135] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, selection unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit receives voice input from the user using the microphone 238 of the robot 414 and stores the input information in the database 24 by the control unit 46A. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the information provided by the collection unit and selects appropriate materials. The provision unit provides the selected materials to the user using, for example, the speaker 240 of the robot 414. The provision unit can also send the selected materials to the user by email via the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0145] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0146] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0147] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0148] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0149] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0152] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0153] 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.

[0154] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0155] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0156] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0157] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0158] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0159] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0160] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0161] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0162] (Note 1) A collection unit that accepts input from users regarding the target audience, destination, taste, document format, and detailed content of the documents, A selection unit analyzes the information collected by the aforementioned collection unit and selects appropriate materials, The system includes a provisioning unit that provides the materials selected by the selection unit to the user. A system characterized by the following features. (Note 2) The aforementioned selection unit is The database is referenced, and the materials are selected using an algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is When inputting information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When users input data, the system prioritizes collecting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is We estimate the user's emotions and adjust the criteria for selecting materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned selection unit is When selecting materials, the selection algorithm is optimized by referring to past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is When selecting materials, different selection algorithms are applied depending on the user's input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is It estimates the user's emotions and adjusts how selected materials are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is When selecting materials, the most suitable materials are chosen by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is When selecting materials, we analyze users' social media activity and select relevant materials. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we provide materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing materials, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing materials, customize the content based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of material delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing materials, the optimal delivery method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing materials, we analyze the user's social media activity and provide relevant materials. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that accepts input from users regarding the target audience, destination, taste, document format, and detailed content of the documents, A selection unit analyzes the information collected by the aforementioned collection unit and selects appropriate materials, The system includes a provisioning unit that provides the materials selected by the selection unit to the user. A system characterized by the following features.

2. The aforementioned selection unit is The database is referenced, and materials are selected using an algorithm. The system according to feature 1.

3. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

5. The aforementioned collection unit is When inputting information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is When users input data, the system prioritizes collecting highly relevant information by considering their geographical location. The system according to feature 1.

8. The aforementioned collection unit is During input, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

9. The aforementioned selection unit is We estimate the user's emotions and adjust the criteria for selecting materials based on those estimated emotions. The system according to feature 1.

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