system

Abstract

The system according to the embodiment aims to provide an appropriate manual according to the level of the user. [Solution] A system according to an embodiment includes a collection unit, a determination unit, a generation unit, and a provision unit. The collection unit collects user information. The determination unit determines the user's level based on the information collected by the collection unit. The generation unit generates a manual according to the user level determined by the determination unit. The provision unit provides the manual generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide users with appropriate manuals according to their level.

[0005] The system according to the embodiment aims to provide an appropriate manual according to the level of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, a generation unit, and a provision unit. The collection unit collects user information. The determination unit determines the user's level based on the information collected by the collection unit. The generation unit generates a manual according to the user level determined by the determination unit. The provision unit provides the manual generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate manual according to the level of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A manual optimization system according to an embodiment of the present invention optimizes a manual according to a user's level. This manual optimization system collects user information, determines the user's level based on that information, and generates and provides an appropriate manual according to the determined user level. For example, a manual for beginners, a manual for intermediate users, and a manual for advanced users are generated. This system allows users to receive a manual tailored to their level, allowing them to efficiently progress with their studies and work. First, information for determining the user's level is collected. This involves collecting data such as the user's past operation history and survey results. For example, information is collected about the user's past operations and their level of knowledge. Next, the user's level is determined based on the collected information. For example, the system determines whether the user is a beginner, intermediate user, or advanced user based on the past operation history. The system can also determine the user's knowledge level based on survey results. An appropriate manual is generated according to the determined user level. For example, a manual for beginners includes basic operation methods and explanations of terminology. On the other hand, a manual for intermediate users includes more advanced operation methods and troubleshooting information. A manual for advanced users includes specialized knowledge and customization methods. This system allows users to receive a manual that is suited to their level. For example, beginner users can easily learn basic operation methods, while intermediate users can easily master more advanced operation methods. Advanced users can deepen their specialized knowledge. This allows users to study and work efficiently. By providing a manual that is suited to the user's level, the manual optimization system enables efficient study and work.

[0029] A manual optimization system according to an embodiment includes a collection unit, a determination unit, a generation unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, operation history, survey results, age, and occupation, for example. The collection unit collects, for example, user operation history. The operation history includes click history and browsing history. The collection unit collects, for example, survey results. The survey results include, for example, question items and answer formats. The determination unit determines the user's level based on the information collected by the collection unit. The user's level includes, but is not limited to, beginner, intermediate, and advanced, for example. The determination unit determines the user's level based on, for example, the collected operation history. The determination unit determines the user's level based on, for example, the collected survey results. The generation unit generates a manual according to the user level determined by the determination unit. The generated manual includes, but is not limited to, manuals for beginners, intermediate, and advanced, for example. The generation unit generates a manual for beginners, for example. A manual for beginners includes basic operating methods and explanations of terminology. The generation unit, for example, generates a manual for intermediate users. The manual for intermediate users includes more advanced operating methods and troubleshooting information. The generation unit, for example, generates a manual for advanced users. The manual for advanced users includes specialized knowledge and customization methods. The provision unit provides the manual generated by the generation unit. For example, the provision unit makes the generated manual available for download on a website. For example, the provision unit sends the generated manual by email. In this way, the manual optimization system according to the embodiment provides a manual according to the user's level, enabling efficient learning and work.

[0030] The collection unit can collect data including the user's operation history and survey results. The collection unit, for example, collects the user's operation history. The operation history includes click history, browsing history, etc. The collection unit, for example, collects survey results. The survey results include question items, answer formats, etc. By collecting the user's operation history and survey results, the user's level can be accurately determined. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's operation history into AI and have the AI ​​analyze the operation history.

[0031] The determination unit can determine the user's level based on the collected data. The determination unit determines the user's level based on, for example, the collected operation history. The determination unit determines the user's level based on, for example, the collected questionnaire results. This allows the user's level to be determined based on the collected data, making it possible to provide an appropriate manual. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the collected data into AI and have the AI ​​determine the user's level.

[0032] The generation unit can generate manuals for beginners, intermediate users, and advanced users. The generation unit, for example, generates a manual for beginners. The manual for beginners includes basic operation methods and explanations of terms. The generation unit, for example, generates a manual for intermediate users. The manual for intermediate users includes more advanced operation methods and troubleshooting information. The generation unit, for example, generates a manual for advanced users. The manual for advanced users includes specialized knowledge and customization methods. This enables efficient learning and work by generating manuals according to the user's level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a manual according to the user's level.

[0033] The providing unit can make the generated manual available for download on a website or send it by email. For example, the providing unit makes the generated manual available for download on a website. For example, the providing unit sends the generated manual by email. This diversifies the methods for providing the generated manual to the user, thereby improving convenience. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the generated manual into AI and have the AI ​​execute the optimal method of providing the manual.

[0034] The manual optimization system further includes a feedback collection unit that collects feedback from users. The feedback collection unit collects feedback from users. The feedback includes, for example, user opinions, evaluations, and areas for improvement. The feedback collection unit collects feedback, for example, in the form of a questionnaire. The questionnaire includes question items, answer formats, and the like. The feedback collection unit collects feedback, for example, using an online form. The online form includes text boxes, options, and the like. This allows the collected feedback from users to be used to improve the manual. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the collected feedback into AI and have the AI ​​analyze the feedback.

[0035] The manual optimization system further includes an improvement unit that improves the content of the manual based on the collected feedback. The improvement unit improves the content of the manual based on the collected feedback. The improvements include, for example, corrections based on the feedback and improvement procedures. The improvement unit, for example, corrects the content of the manual based on the feedback. The corrections include correcting text, adding images, updating videos, and the like. The improvement unit, for example, improves the procedures of the manual based on the feedback. Procedure improvements include simplifying procedures, adding details to procedures, and the like. By improving the content of the manual based on the feedback, user satisfaction is thereby increased. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the collected feedback into AI and have the AI ​​execute the improvement method.

[0036] The manual optimization system further includes an additional information collection unit that collects additional information such as the user's age and occupation. The additional information collection unit collects additional information such as the user's age and occupation. The additional information includes, for example, age, occupation, and interests. The additional information collection unit, for example, collects the user's age. The age can be obtained from the user's profile information. The additional information collection unit, for example, collects the user's occupation. The occupation can be obtained from the user's survey results. The additional information collection unit, for example, collects the user's interests. The interests can be obtained from the user's operation history or survey results. By collecting the additional information, it is possible to determine the user level more accurately. Some or all of the above-mentioned processing in the additional information collection unit may be performed using, or without, AI. For example, the additional information collection unit may input the collected additional information into AI and have the AI ​​analyze the additional information.

[0037] The collection unit can analyze the user's past operation history and select the optimal information collection method. The collection unit, for example, prioritizes collecting related information based on operations frequently performed by the user in the past. The collection unit selects the optimal information collection method based on devices and applications used by the user in the past. The collection unit, for example, collects information during a specific time period from the user's past operation history. This allows the optimal information collection method to be selected by analyzing the past operation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's operation history data into a generation AI and have the generation AI select the optimal information collection method.

[0038] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to a project the user is currently working on. For example, the collection unit filters and collects related information based on the user's area of ​​interest. For example, the collection unit collects information based on topics in which the user has shown interest in the past. In this way, highly relevant information can be collected by filtering the information based on the current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's project information and area of ​​interest data into a generation AI and have the generation AI perform information filtering.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. For example, the collection unit collects area-specific information based on the user's geographical location information. For example, the collection unit collects information related to places the user has visited in the past. In this way, highly relevant information can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect information.

[0040] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit collects related information by analyzing the content of posts from accounts the user follows. For example, the collection unit collects information based on topics in which the user has shown interest on social media. In this way, related information can be collected by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect information.

[0041] The determination unit can improve the accuracy of level determination by taking into account the user's past learning history when making the determination. The determination unit improves the accuracy of level determination, for example, based on the content the user has learned in the past. The determination unit determines the knowledge level in a specific field, for example, from the user's past learning history. The determination unit improves the accuracy of level determination, for example, by referring to test results the user has taken in the past. This improves the accuracy of level determination by taking into account the past learning history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's learning history data into the generation AI and cause the generation AI to improve the accuracy of level determination.

[0042] The determination unit can determine the level by taking into account the user's attribute information when making the determination. The determination unit determines the level based on attribute information such as the user's age and occupation, for example. The determination unit determines the level by taking into account the user's educational background and work history, for example. The determination unit determines the level based on the user's interests and areas of interest, for example. By taking the attribute information into account, more accurate level determination becomes possible. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's attribute information data into the generation AI and have the generation AI perform the level determination.

[0043] The determination unit can determine the level taking into account the geographical distribution of the user when making the determination. The determination unit, for example, determines the level taking into account the educational level of the area where the user lives. The determination unit, for example, determines the level taking into account region-specific knowledge based on the user's geographical distribution. The determination unit, for example, determines the level based on information about the area where the user has previously lived. This makes it possible to determine the level by taking into account the geographical distribution, thereby reflecting region-specific knowledge. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the level determination.

[0044] The judgment unit can improve the accuracy of the level judgment by referring to the user's related literature during judgment. The judgment unit improves the accuracy of the level judgment, for example, based on related literature that the user has read in the past. The judgment unit makes the level judgment by referring to literature cited by the user, for example. The judgment unit improves the accuracy of the level judgment, for example, based on papers and articles written by the user. In this way, the accuracy of the level judgment is improved by referring to the related literature. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of the level judgment.

[0045] When generating a manual, the generation unit can adjust the level of detail of the manual according to the user's level. For example, for beginners, the generation unit generates a manual that includes basic operation methods and explanations of terms. For intermediate users, the generation unit generates a manual that includes more advanced operation methods and troubleshooting information. For advanced users, the generation unit generates a manual that includes specialized knowledge and customization methods. This makes it possible to provide appropriate information by adjusting the level of detail of the manual according to the user's level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user level data into the generation AI and cause the generation AI to adjust the level of detail of the manual.

[0046] When generating a manual, the generation unit can apply different generation algorithms depending on the user category. For example, for students, the generation unit applies an algorithm that generates a manual including information useful for learning. For business users, the generation unit applies an algorithm that generates a manual including information useful for work. For general users, the generation unit applies an algorithm that generates a manual including information useful for everyday life. In this way, by applying a generation algorithm according to the user category, a more appropriate manual can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0047] When generating manuals, the generation unit can determine the priority of the manuals based on the user's submission time. For example, the generation unit preferentially provides a quickly generated manual to a user whose deadline is approaching. For example, the generation unit provides a detailed manual to a user who has ample time for submission. For example, the generation unit adjusts the order in which the manuals are generated based on the submission time. This allows manuals that meet the user's needs to be provided by determining the priority of the manuals based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user's submission time data into the generation AI and have the generation AI determine the priority of the manuals.

[0048] When generating a manual, the generation unit can adjust the order of the manual based on the user's relevance. For example, the generation unit prioritizes providing related information based on the content of a manual that the user has previously referenced. For example, the generation unit prioritizes providing related manuals based on the user's field of interest. For example, the generation unit prioritizes providing related manuals based on content that the user has previously studied. In this way, by adjusting the order of the manual based on relevance, optimal information can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of the manual.

[0049] When providing a manual, the providing unit can select the optimal delivery method by referring to the user's past operation history. The providing unit selects the optimal delivery method, for example, based on delivery methods (website, email, etc.) used by the user in the past. The providing unit provides the manual for a specific time period, for example, based on the user's past operation history. The providing unit, for example, preferentially selects delivery methods that the user has preferred in the past. This makes it possible to select the optimal delivery method by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into the generation AI and cause the generation AI to select the delivery method.

[0050] When providing a manual, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit selects a mobile-friendly delivery method. For example, if the user is using a tablet, the providing unit selects a delivery method optimized for a large screen. For example, if the user is using a desktop, the providing unit selects a delivery method that includes detailed information. This allows the manual to be provided in the optimal way for the user by taking into account the device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and have the generation AI select the delivery method.

[0051] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, the feedback collection unit preferentially collects related questions based on feedback provided by the user in the past. For example, the feedback collection unit collects feedback during a specific time period from the user's past feedback history. For example, the feedback collection unit preferentially selects a collection method that the user has used favorably in the past. This makes it possible to select the optimal collection method by referring to the past feedback history. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, AI, for example. For example, the feedback collection unit can input the user's feedback history data into a generation AI and cause the generation AI to select a collection method.

[0052] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, the feedback collection unit prioritizes collecting feedback related to the user's current location. For example, the feedback collection unit collects region-specific feedback based on the user's geographical location information. For example, the feedback collection unit collects feedback related to places the user has visited in the past. In this way, by taking the geographical location information into account, highly relevant feedback can be collected preferentially. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect feedback.

[0053] When making an improvement, the improvement unit can select the optimal improvement method by analyzing the user's past feedback. For example, the improvement unit preferentially selects a related improvement method based on feedback provided by the user in the past. For example, the improvement unit performs improvements during a specific time period based on the user's past feedback history. For example, the improvement unit preferentially selects an improvement method that the user has used favorably in the past. This makes it possible to select the optimal improvement method by analyzing past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's feedback history data into the generation AI and have the generation AI select an improvement method.

[0054] When making an improvement, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, the improvement unit preferentially selects an improvement method related to the user's current location. For example, the improvement unit selects a region-specific improvement method based on the user's geographical location information. For example, the improvement unit selects an improvement method related to a place the user has visited in the past. In this way, the optimal improvement method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the user's geographical location data into the generation AI and have the generation AI select an improvement method.

[0055] When collecting additional information, the additional information collection unit can select the optimal collection method by referring to the user's past operation history. The additional information collection unit, for example, prioritizes collecting related additional information based on operations frequently performed by the user in the past. The additional information collection unit selects the optimal additional information collection method based on, for example, devices and applications used by the user in the past. The additional information collection unit, for example, collects additional information during a specific time period from the user's past operation history. This makes it possible to select the optimal additional information collection method by referring to the past operation history. Some or all of the above-mentioned processing in the additional information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the additional information collection unit can input the user's operation history data into a generation AI and have the generation AI select a collection method.

[0056] When collecting additional information, the additional information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The additional information collection unit, for example, prioritizes collecting additional information related to the user's current location. The additional information collection unit, for example, collects region-specific additional information based on the user's geographical location information. The additional information collection unit, for example, collects additional information related to places the user has visited in the past. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-mentioned processing in the additional information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the additional information collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] In addition to the user's operation history and survey results, the collection unit can also analyze the user's social media activity and collect related information. For example, related information can be collected based on information shared by the user on social media. Related information can be collected by analyzing the content posted by accounts the user follows. Information can also be collected based on topics the user shows interest in on social media. This makes it possible to analyze social media activity and determine the user level more accurately.

[0059] The assessment unit can also improve the accuracy of level assessment by taking into account the user's past learning history. For example, the accuracy of level assessment can be improved based on what the user has learned in the past. The knowledge level in a specific field can be determined from the user's past learning history. The accuracy of level assessment can be improved by referring to test results the user has taken in the past. In this way, the accuracy of level assessment can be improved by taking into account the user's past learning history.

[0060] The collection unit can also prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information related to the user's current location. It can collect area-specific information based on the user's geographical location information. It can collect information related to places the user has visited in the past. In this way, it is possible to prioritize collecting highly relevant information by taking into account the geographical location information.

[0061] The assessment unit can also improve the accuracy of level assessment by referring to the user's related literature. For example, the accuracy of level assessment can be improved based on related literature that the user has read in the past. The level assessment can be performed by referring to literature cited by the user. The accuracy of level assessment can be improved based on papers and articles written by the user. In this way, the accuracy of level assessment can be improved by referring to related literature.

[0062] When generating a manual, the generation unit can also apply different generation algorithms depending on the user category. For example, for students, an algorithm is applied that generates a manual containing information useful for learning. For business users, an algorithm is applied that generates a manual containing information useful for work. For general users, an algorithm is applied that generates a manual containing information useful for everyday life. In this way, by applying a generation algorithm according to the user category, it is possible to provide a more appropriate manual.

[0063] When providing a manual, the providing unit can also select the optimal method of providing the manual by taking into consideration the user's device information. For example, if the user is using a smartphone, a mobile-friendly method of providing the manual is selected. If the user is using a tablet, a method of providing the manual that is optimized for a large screen is selected. If the user is using a desktop computer, a method of providing the manual that includes detailed information is selected. In this way, by taking into consideration the device information, the manual can be provided in the optimal method for the user.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects user information. User information includes operation history, survey results, age, occupation, etc. For example, the collection unit collects the user's operation history (click history, browsing history, etc.) and survey results (question items, response format, etc.). Step 2: The determination unit determines the user's level based on the information collected by the collection unit. User levels include beginner, intermediate, advanced, etc. For example, the determination unit determines the user's level based on the collected operation history and survey results. Step 3: The generator generates a manual according to the user level determined by the determiner. The generated manuals include those for beginners, intermediate users, and advanced users. For example, the generator generates a manual for beginners (basic operation methods and explanations of terms), a manual for intermediate users (advanced operation methods and troubleshooting information), and a manual for advanced users (specialized knowledge and customization methods). Step 4: The providing unit provides the manual generated by the generating unit. For example, the providing unit makes the generated manual available for download on a website or sends it by email.

[0066] (Example 2) A manual optimization system according to an embodiment of the present invention optimizes a manual according to a user's level. This manual optimization system collects user information, determines the user's level based on that information, and generates and provides an appropriate manual according to the determined user level. For example, a manual for beginners, a manual for intermediate users, and a manual for advanced users are generated. This system allows users to receive a manual tailored to their level, allowing them to efficiently progress with their studies and work. First, information for determining the user's level is collected. This involves collecting data such as the user's past operation history and survey results. For example, information is collected about the user's past operations and their level of knowledge. Next, the user's level is determined based on the collected information. For example, the system determines whether the user is a beginner, intermediate user, or advanced user based on the past operation history. The system can also determine the user's knowledge level based on survey results. An appropriate manual is generated according to the determined user level. For example, a manual for beginners includes basic operation methods and explanations of terminology. On the other hand, a manual for intermediate users includes more advanced operation methods and troubleshooting information. A manual for advanced users includes specialized knowledge and customization methods. This system allows users to receive a manual that is suited to their level. For example, beginner users can easily learn basic operation methods, while intermediate users can easily master more advanced operation methods. Advanced users can deepen their specialized knowledge. This allows users to study and work efficiently. By providing a manual that is suited to the user's level, the manual optimization system enables efficient study and work.

[0067] A manual optimization system according to an embodiment includes a collection unit, a determination unit, a generation unit, and a provision unit. The collection unit collects user information. The user information includes, but is not limited to, operation history, survey results, age, and occupation, for example. The collection unit collects, for example, user operation history. The operation history includes click history and browsing history. The collection unit collects, for example, survey results. The survey results include, for example, question items and answer formats. The determination unit determines the user's level based on the information collected by the collection unit. The user's level includes, but is not limited to, beginner, intermediate, and advanced, for example. The determination unit determines the user's level based on, for example, the collected operation history. The determination unit determines the user's level based on, for example, the collected survey results. The generation unit generates a manual according to the user level determined by the determination unit. The generated manual includes, but is not limited to, manuals for beginners, intermediate, and advanced, for example. The generation unit generates a manual for beginners, for example. A manual for beginners includes basic operating methods and explanations of terminology. The generation unit, for example, generates a manual for intermediate users. The manual for intermediate users includes more advanced operating methods and troubleshooting information. The generation unit, for example, generates a manual for advanced users. The manual for advanced users includes specialized knowledge and customization methods. The provision unit provides the manual generated by the generation unit. For example, the provision unit makes the generated manual available for download on a website. For example, the provision unit sends the generated manual by email. In this way, the manual optimization system according to the embodiment provides a manual according to the user's level, enabling efficient learning and work.

[0068] The collection unit can collect data including the user's operation history and survey results. The collection unit, for example, collects the user's operation history. The operation history includes click history, browsing history, etc. The collection unit, for example, collects survey results. The survey results include question items, answer formats, etc. By collecting the user's operation history and survey results, the user's level can be accurately determined. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's operation history into AI and have the AI ​​analyze the operation history.

[0069] The determination unit can determine the user's level based on the collected data. The determination unit determines the user's level based on, for example, the collected operation history. The determination unit determines the user's level based on, for example, the collected questionnaire results. This allows the user's level to be determined based on the collected data, making it possible to provide an appropriate manual. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the collected data into AI and have the AI ​​determine the user's level.

[0070] The generation unit can generate manuals for beginners, intermediate users, and advanced users. The generation unit, for example, generates a manual for beginners. The manual for beginners includes basic operation methods and explanations of terms. The generation unit, for example, generates a manual for intermediate users. The manual for intermediate users includes more advanced operation methods and troubleshooting information. The generation unit, for example, generates a manual for advanced users. The manual for advanced users includes specialized knowledge and customization methods. This enables efficient learning and work by generating manuals according to the user's level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can cause AI to generate a manual according to the user's level.

[0071] The providing unit can make the generated manual available for download on a website or send it by email. For example, the providing unit makes the generated manual available for download on a website. For example, the providing unit sends the generated manual by email. This diversifies the methods for providing the generated manual to the user, thereby improving convenience. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the generated manual into AI and have the AI ​​execute the optimal method of providing the manual.

[0072] The manual optimization system further includes a feedback collection unit that collects feedback from users. The feedback collection unit collects feedback from users. The feedback includes, for example, user opinions, evaluations, and areas for improvement. The feedback collection unit collects feedback, for example, in the form of a questionnaire. The questionnaire includes question items, answer formats, and the like. The feedback collection unit collects feedback, for example, using an online form. The online form includes text boxes, options, and the like. This allows the collected feedback from users to be used to improve the manual. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the collected feedback into AI and have the AI ​​analyze the feedback.

[0073] The manual optimization system further includes an improvement unit that improves the content of the manual based on the collected feedback. The improvement unit improves the content of the manual based on the collected feedback. The improvements include, for example, corrections based on the feedback and improvement procedures. The improvement unit, for example, corrects the content of the manual based on the feedback. The corrections include correcting text, adding images, updating videos, and the like. The improvement unit, for example, improves the procedures of the manual based on the feedback. Procedure improvements include simplifying procedures, adding details to procedures, and the like. By improving the content of the manual based on the feedback, user satisfaction is thereby increased. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the collected feedback into AI and have the AI ​​execute the improvement method.

[0074] The manual optimization system further includes an additional information collection unit that collects additional information such as the user's age and occupation. The additional information collection unit collects additional information such as the user's age and occupation. The additional information includes, for example, age, occupation, and interests. The additional information collection unit, for example, collects the user's age. The age can be obtained from the user's profile information. The additional information collection unit, for example, collects the user's occupation. The occupation can be obtained from the user's survey results. The additional information collection unit, for example, collects the user's interests. The interests can be obtained from the user's operation history or survey results. By collecting the additional information, it is possible to determine the user level more accurately. Some or all of the above-mentioned processing in the additional information collection unit may be performed using, or without, AI. For example, the additional information collection unit may input the collected additional information into AI and have the AI ​​analyze the additional information.

[0075] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. For example, when the user is relaxed, the collection unit increases the frequency of information collection and collects detailed data. For example, when the user is in a hurry, the collection unit quickly collects the minimum amount of information necessary. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0076] The collection unit can analyze the user's past operation history and select the optimal information collection method. The collection unit, for example, prioritizes collecting related information based on operations frequently performed by the user in the past. The collection unit selects the optimal information collection method based on devices and applications used by the user in the past. The collection unit, for example, collects information during a specific time period from the user's past operation history. This allows the optimal information collection method to be selected by analyzing the past operation history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's operation history data into a generation AI and have the generation AI select the optimal information collection method.

[0077] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to a project the user is currently working on. For example, the collection unit filters and collects related information based on the user's area of ​​interest. For example, the collection unit collects information based on topics in which the user has shown interest in the past. In this way, highly relevant information can be collected by filtering the information based on the current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's project information and area of ​​interest data into a generation AI and have the generation AI perform information filtering.

[0078] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting information of high importance. For example, when the user is relaxed, the collection unit prioritizes collecting detailed information. For example, when the user is in a hurry, the collection unit prioritizes collecting information that can be obtained quickly. This allows important information to be collected preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0079] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. For example, the collection unit collects area-specific information based on the user's geographical location information. For example, the collection unit collects information related to places the user has visited in the past. In this way, highly relevant information can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect information.

[0080] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit collects related information by analyzing the content of posts from accounts the user follows. For example, the collection unit collects information based on topics in which the user has shown interest on social media. In this way, related information can be collected by analyzing social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect information.

[0081] The determination unit can estimate the user's emotions and adjust the level determination criteria based on the estimated user emotions. For example, the determination unit relaxes the level determination criteria when the user is stressed. For example, the determination unit tightens the level determination criteria when the user is relaxed. For example, the determination unit sets criteria for rapid level determination when the user is in a hurry. This allows for more appropriate level determination by adjusting the level determination criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed using an AI, for example, or without an AI. For example, the determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the level determination criteria.

[0082] The determination unit can improve the accuracy of level determination by taking into account the user's past learning history when making the determination. The determination unit improves the accuracy of level determination, for example, based on the content the user has learned in the past. The determination unit determines the knowledge level in a specific field, for example, from the user's past learning history. The determination unit improves the accuracy of level determination, for example, by referring to test results the user has taken in the past. This improves the accuracy of level determination by taking into account the past learning history. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's learning history data into the generation AI and cause the generation AI to improve the accuracy of level determination.

[0083] The determination unit can determine the level by taking into account the user's attribute information when making the determination. The determination unit determines the level based on attribute information such as the user's age and occupation, for example. The determination unit determines the level by taking into account the user's educational background and work history, for example. The determination unit determines the level based on the user's interests and areas of interest, for example. By taking the attribute information into account, more accurate level determination becomes possible. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's attribute information data into the generation AI and have the generation AI perform the level determination.

[0084] The determination unit can estimate the user's emotions and adjust the order in which the level determination results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit displays important results first. For example, if the user is relaxed, the determination unit displays detailed results in an orderly manner. For example, if the user is in a hurry, the determination unit displays results that highlight the main points first. This allows the display order of results to be adjusted according to the user's emotions, making it possible to display results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit may be performed using an AI, for example, or without an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI adjust the order in which the results are displayed.

[0085] The determination unit can determine the level taking into account the geographical distribution of the user when making the determination. The determination unit, for example, determines the level taking into account the educational level of the area where the user lives. The determination unit, for example, determines the level taking into account region-specific knowledge based on the user's geographical distribution. The determination unit, for example, determines the level based on information about the area where the user has previously lived. This makes it possible to determine the level by taking into account the geographical distribution, thereby reflecting region-specific knowledge. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the level determination.

[0086] The judgment unit can improve the accuracy of the level judgment by referring to the user's related literature during judgment. The judgment unit improves the accuracy of the level judgment, for example, based on related literature that the user has read in the past. The judgment unit makes the level judgment by referring to literature cited by the user, for example. The judgment unit improves the accuracy of the level judgment, for example, based on papers and articles written by the user. In this way, the accuracy of the level judgment is improved by referring to the related literature. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of the level judgment.

[0087] The generation unit can estimate the user's emotions and adjust the way the manual is presented based on the estimated user's emotions. For example, if the user is stressed, the generation unit employs a simple and easy-to-understand presentation. For example, if the user is relaxed, the generation unit employs a presentation that includes detailed explanations. For example, if the user is in a hurry, the generation unit employs a concise presentation that focuses on the main points. This allows the manual's presentation to be adjusted according to the user's emotions, thereby providing a manual that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the manual is presented.

[0088] When generating a manual, the generation unit can adjust the level of detail of the manual according to the user's level. For example, for beginners, the generation unit generates a manual that includes basic operation methods and explanations of terms. For intermediate users, the generation unit generates a manual that includes more advanced operation methods and troubleshooting information. For advanced users, the generation unit generates a manual that includes specialized knowledge and customization methods. This makes it possible to provide appropriate information by adjusting the level of detail of the manual according to the user's level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user level data into the generation AI and cause the generation AI to adjust the level of detail of the manual.

[0089] When generating a manual, the generation unit can apply different generation algorithms depending on the user category. For example, for students, the generation unit applies an algorithm that generates a manual including information useful for learning. For business users, the generation unit applies an algorithm that generates a manual including information useful for work. For general users, the generation unit applies an algorithm that generates a manual including information useful for everyday life. In this way, by applying a generation algorithm according to the user category, a more appropriate manual can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0090] The generation unit can estimate the user's emotions and adjust the length of the manual based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a short, concise manual. For example, if the user is relaxed, the generation unit generates a longer manual with detailed explanations. For example, if the user is in a hurry, the generation unit generates a short manual that can be quickly understood. This allows the length of the manual to be adjusted according to the user's emotions, thereby providing an optimal manual for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the manual.

[0091] When generating manuals, the generation unit can determine the priority of the manuals based on the user's submission time. For example, the generation unit preferentially provides a quickly generated manual to a user whose deadline is approaching. For example, the generation unit provides a detailed manual to a user who has ample time for submission. For example, the generation unit adjusts the order in which the manuals are generated based on the submission time. This allows manuals that meet the user's needs to be provided by determining the priority of the manuals based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user's submission time data into the generation AI and have the generation AI determine the priority of the manuals.

[0092] When generating a manual, the generation unit can adjust the order of the manual based on the user's relevance. For example, the generation unit prioritizes providing related information based on the content of a manual that the user has previously referenced. For example, the generation unit prioritizes providing related manuals based on the user's field of interest. For example, the generation unit prioritizes providing related manuals based on content that the user has previously studied. In this way, by adjusting the order of the manual based on relevance, optimal information can be provided to the user. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI and cause the generation AI to adjust the order of the manual.

[0093] The providing unit can estimate the user's emotions and adjust the method of providing the manual based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides the manual with a simple interface. For example, if the user is relaxed, the providing unit provides the manual with a detailed interface. For example, if the user is in a hurry, the providing unit provides the manual in a quickly accessible manner. This allows the manual to be provided in an optimal manner for the user by adjusting the method of providing the manual according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing the manual.

[0094] When providing a manual, the providing unit can select the optimal delivery method by referring to the user's past operation history. The providing unit selects the optimal delivery method, for example, based on delivery methods (website, email, etc.) used by the user in the past. The providing unit provides the manual for a specific time period, for example, based on the user's past operation history. The providing unit, for example, preferentially selects delivery methods that the user has preferred in the past. This makes it possible to select the optimal delivery method by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data into the generation AI and cause the generation AI to select the delivery method.

[0095] The providing unit can estimate the user's emotions and adjust the manual delivery procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit adopts a simple and quick delivery procedure. For example, if the user is relaxed, the providing unit adopts a detailed delivery procedure. For example, if the user is in a hurry, the providing unit adopts the shortest delivery procedure. By adjusting the delivery procedure according to the user's emotions, the manual can be delivered in an optimal manner for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the delivery procedure.

[0096] When providing a manual, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit selects a mobile-friendly delivery method. For example, if the user is using a tablet, the providing unit selects a delivery method optimized for a large screen. For example, if the user is using a desktop, the providing unit selects a delivery method that includes detailed information. This allows the manual to be provided in the optimal way for the user by taking into account the device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and have the generation AI select the delivery method.

[0097] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated user's emotions. For example, when the user is stressed, the feedback collection unit reduces the frequency of feedback collection and collects feedback when the user is relaxed. For example, when the user is relaxed, the feedback collection unit increases the frequency of feedback collection and collects detailed feedback. For example, when the user is in a hurry, the feedback collection unit quickly collects concise feedback. This reduces the burden on the user by adjusting the timing of feedback collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of feedback collection.

[0098] When collecting feedback, the feedback collection unit can select the optimal collection method by referring to the user's past feedback history. For example, the feedback collection unit preferentially collects related questions based on feedback provided by the user in the past. For example, the feedback collection unit collects feedback during a specific time period from the user's past feedback history. For example, the feedback collection unit preferentially selects a collection method that the user has used favorably in the past. This makes it possible to select the optimal collection method by referring to the past feedback history. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, AI, for example. For example, the feedback collection unit can input the user's feedback history data into a generation AI and cause the generation AI to select a collection method.

[0099] The feedback collection unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user's emotions. For example, when the user is stressed, the feedback collection unit prioritizes collecting feedback with high importance. For example, when the user is relaxed, the feedback collection unit prioritizes collecting detailed feedback. For example, when the user is in a hurry, the feedback collection unit prioritizes collecting feedback that can be obtained quickly. In this way, by determining the priority of feedback according to the user's emotions, it is possible to prioritize collecting important feedback. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit may be performed using an AI, for example, or without an AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the feedback.

[0100] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, the feedback collection unit prioritizes collecting feedback related to the user's current location. For example, the feedback collection unit collects region-specific feedback based on the user's geographical location information. For example, the feedback collection unit collects feedback related to places the user has visited in the past. In this way, by taking the geographical location information into account, highly relevant feedback can be collected preferentially. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect feedback.

[0101] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user's emotions. For example, if the user is stressed, the improvement unit adopts a simple and quick improvement method. For example, if the user is relaxed, the improvement unit adopts a detailed improvement method. For example, if the user is in a hurry, the improvement unit adopts the shortest improvement method. This allows the improvement method to be optimized for the user by adjusting the improvement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit can input the user's emotion data into the generation AI and have the generation AI adjust the improvement method.

[0102] When making an improvement, the improvement unit can select the optimal improvement method by analyzing the user's past feedback. For example, the improvement unit preferentially selects a related improvement method based on feedback provided by the user in the past. For example, the improvement unit performs improvements during a specific time period based on the user's past feedback history. For example, the improvement unit preferentially selects an improvement method that the user has used favorably in the past. This makes it possible to select the optimal improvement method by analyzing past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's feedback history data into the generation AI and have the generation AI select an improvement method.

[0103] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit prioritizes important improvements. For example, if the user is relaxed, the improvement unit prioritizes detailed improvements. For example, if the user is in a hurry, the improvement unit prioritizes improvements that can be made quickly. In this way, by determining the priority of improvements according to the user's emotions, important improvements can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of improvements.

[0104] When making an improvement, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, the improvement unit preferentially selects an improvement method related to the user's current location. For example, the improvement unit selects a region-specific improvement method based on the user's geographical location information. For example, the improvement unit selects an improvement method related to a place the user has visited in the past. In this way, the optimal improvement method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI. For example, the improvement unit can input the user's geographical location data into the generation AI and have the generation AI select an improvement method.

[0105] The additional information collection unit can estimate the user's emotions and adjust the timing of additional information collection based on the estimated user's emotions. For example, when the user is feeling stressed, the additional information collection unit reduces the frequency of additional information collection and collects additional information when the user is relaxed. For example, when the user is relaxed, the additional information collection unit increases the frequency of additional information collection and collects detailed data. For example, when the user is in a hurry, the additional information collection unit quickly collects the minimum amount of additional information necessary. This reduces the burden on the user by adjusting the timing of additional information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the additional information collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the additional information collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of additional information collection.

[0106] When collecting additional information, the additional information collection unit can select the optimal collection method by referring to the user's past operation history. The additional information collection unit, for example, prioritizes collecting related additional information based on operations frequently performed by the user in the past. The additional information collection unit selects the optimal additional information collection method based on, for example, devices and applications used by the user in the past. The additional information collection unit, for example, collects additional information during a specific time period from the user's past operation history. This makes it possible to select the optimal additional information collection method by referring to the past operation history. Some or all of the above-mentioned processing in the additional information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the additional information collection unit can input the user's operation history data into a generation AI and have the generation AI select a collection method.

[0107] The additional information collection unit can estimate the user's emotions and determine the priority of additional information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the additional information collection unit prioritizes collecting important additional information. For example, when the user is relaxed, the additional information collection unit prioritizes collecting detailed additional information. For example, when the user is in a hurry, the additional information collection unit prioritizes collecting additional information that can be obtained quickly. This allows important information to be collected preferentially by determining the priority of additional information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the additional information collection unit may be performed using an AI, for example, or without an AI. For example, the additional information collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the additional information.

[0108] When collecting additional information, the additional information collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The additional information collection unit, for example, prioritizes collecting additional information related to the user's current location. The additional information collection unit, for example, collects region-specific additional information based on the user's geographical location information. The additional information collection unit, for example, collects additional information related to places the user has visited in the past. This allows highly relevant information to be collected preferentially by taking into account the geographical location information. Some or all of the above-mentioned processing in the additional information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the additional information collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, determination unit, generation unit, provision unit, feedback collection unit, improvement unit, and additional information collection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a user's operation history and survey results using the camera 42 and microphone 38B of the smart device 14. The determination unit determines the user's level based on the information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a manual according to the user's level using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manual via the output device 40 of the smart device 14. The feedback collection unit collects feedback from the user using the reception device 38 of the smart device 14. The improvement unit improves the content of the manual based on the feedback collected by the specific processing unit 290 of the data processing device 12. The additional information collection unit collects additional information, such as the user's age and occupation, using the camera 42 and microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, determination unit, generation unit, provision unit, feedback collection unit, improvement unit, and additional information collection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a user's operation history and survey results using the camera 42 and microphone 238 of the smart glasses 214. The determination unit determines the user's level based on information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a manual according to the user's level using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manual through the speaker 240 of the smart glasses 214. The feedback collection unit collects feedback from the user using the microphone 238 of the smart glasses 214. The improvement unit improves the content of the manual based on the feedback collected by the specific processing unit 290 of the data processing device 12. The additional information collection unit collects additional information, such as the user's age and occupation, using the camera 42 and microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, determination unit, generation unit, provision unit, feedback collection unit, improvement unit, and additional information collection unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user operation histories and survey results using the camera 42 and microphone 238 of the headset type terminal 314. The determination unit determines the user's level based on information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a manual according to the user's level using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manual through the display 343 of the headset type terminal 314. The feedback collection unit collects feedback from the user using the microphone 238 of the headset type terminal 314. The improvement unit improves the content of the manual based on the feedback collected by the specific processing unit 290 of the data processing device 12. The additional information collection unit collects additional information such as the user's age and occupation using the camera 42 and microphone 238 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, determination unit, generation unit, provision unit, feedback collection unit, improvement unit, and additional information collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user operation history and questionnaire results using the camera 42 and microphone 238 of the robot 414. The determination unit determines the user's level based on information collected by the specific processing unit 290 of the data processing device 12. The generation unit generates a manual according to the user's level using the specific processing unit 290 of the data processing device 12. The provision unit provides the generated manual through the speaker 240 of the robot 414. The feedback collection unit collects feedback from the user using the microphone 238 of the robot 414. The improvement unit improves the content of the manual based on the feedback collected by the specific processing unit 290 of the data processing device 12. The additional information collection unit collects additional information such as the user's age and occupation using the camera 42 and microphone 238 of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] In addition to the user's operation history and survey results, the collection unit can also analyze the user's social media activity and collect related information. For example, related information can be collected based on information shared by the user on social media. Related information can be collected by analyzing the content posted by accounts the user follows. Information can also be collected based on topics the user shows interest in on social media. This makes it possible to analyze social media activity and determine the user level more accurately.

[0111] The assessment unit can also improve the accuracy of level assessment by taking into account the user's past learning history. For example, the accuracy of level assessment can be improved based on what the user has learned in the past. The knowledge level in a specific field can be determined from the user's past learning history. The accuracy of level assessment can be improved by referring to test results the user has taken in the past. In this way, the accuracy of level assessment can be improved by taking into account the user's past learning history.

[0112] The generation unit can also estimate the user's emotions and adjust the way the manual is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy-to-understand presentation is used. If the user is relaxed, a presentation that includes detailed explanations is used. If the user is in a hurry, a concise presentation that focuses on the main points is used. In this way, by adjusting the way the manual is presented according to the user's emotions, it is possible to provide a manual that is easy for the user to understand.

[0113] The providing unit can also estimate the user's emotions and adjust the method of providing the manual based on the estimated user's emotions. For example, if the user is feeling stressed, the manual is provided with a simple interface. If the user is relaxed, the manual is provided with a detailed interface. If the user is in a hurry, the manual is provided in a manner that allows quick access. In this way, by adjusting the method of providing the manual according to the user's emotions, the manual can be provided in an optimal manner for the user.

[0114] The feedback collection unit can also estimate the user's emotions and adjust the timing of feedback collection based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of feedback collection can be reduced and feedback can be collected when the user is relaxed. If the user is relaxed, the frequency of feedback collection can be increased and detailed feedback can be collected. If the user is in a hurry, concise feedback can be collected quickly. In this way, the burden on the user can be reduced by adjusting the timing of feedback collection according to the user's emotions.

[0115] The collection unit can also prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, it can prioritize collecting information related to the user's current location. It can collect area-specific information based on the user's geographical location information. It can collect information related to places the user has visited in the past. In this way, it is possible to prioritize collecting highly relevant information by taking into account the geographical location information.

[0116] The assessment unit can also improve the accuracy of level assessment by referring to the user's related literature. For example, the accuracy of level assessment can be improved based on related literature that the user has read in the past. The level assessment can be performed by referring to literature cited by the user. The accuracy of level assessment can be improved based on papers and articles written by the user. In this way, the accuracy of level assessment can be improved by referring to related literature.

[0117] When generating a manual, the generation unit can also apply different generation algorithms depending on the user category. For example, for students, an algorithm is applied that generates a manual containing information useful for learning. For business users, an algorithm is applied that generates a manual containing information useful for work. For general users, an algorithm is applied that generates a manual containing information useful for everyday life. In this way, by applying a generation algorithm according to the user category, it is possible to provide a more appropriate manual.

[0118] When providing a manual, the providing unit can also select the optimal method of providing the manual by taking into consideration the user's device information. For example, if the user is using a smartphone, a mobile-friendly method of providing the manual is selected. If the user is using a tablet, a method of providing the manual that is optimized for a large screen is selected. If the user is using a desktop computer, a method of providing the manual that includes detailed information is selected. In this way, by taking into consideration the device information, the manual can be provided in the optimal method for the user.

[0119] The improvement unit can also estimate the user's emotions and determine the priority of improvements based on the estimated user's emotions. For example, if the user is feeling stressed, priority is given to making improvements of high importance. If the user is relaxed, priority is given to making detailed improvements. If the user is in a hurry, priority is given to making improvements that can be made quickly. In this way, by determining the priority of improvements according to the user's emotions, important improvements can be made with priority.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects user information. User information includes operation history, survey results, age, occupation, etc. For example, the collection unit collects the user's operation history (click history, browsing history, etc.) and survey results (question items, response format, etc.). Step 2: The determination unit determines the user's level based on the information collected by the collection unit. User levels include beginner, intermediate, advanced, etc. For example, the determination unit determines the user's level based on the collected operation history and survey results. Step 3: The generator generates a manual according to the user level determined by the determiner. The generated manuals include those for beginners, intermediate users, and advanced users. For example, the generator generates a manual for beginners (basic operation methods and explanations of terms), a manual for intermediate users (advanced operation methods and troubleshooting information), and a manual for advanced users (specialized knowledge and customization methods). Step 4: The providing unit provides the manual generated by the generating unit. For example, the providing unit makes the generated manual available for download on a website or sends it by email.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] 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.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; a determination unit that determines a level of a user based on the information collected by the collection unit; a generation unit that generates a manual in accordance with the user level determined by the determination unit; a providing unit that provides the manual generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Collect data including user operation history and survey results 2. The system of claim 1.

3. The determination unit Determine the user's level based on the collected data 2. The system of claim 1.

4. The generation unit Generate beginner, intermediate, and advanced manuals 2. The system of claim 1.

5. The providing unit Make the generated manual available for download on your website or send it by email 2. The system of claim 1.

6. The system further includes a feedback collection unit that collects feedback from users.

2. The system of claim 1.

7. It also has an improvement department that will improve the content of the manual based on the feedback collected.

2. The system of claim 1.

8. The device further includes an additional information collection unit that collects additional information such as the user's age and occupation.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

10. The collecting unit Analyze the user's past operation history and select the optimal information collection method 2. The system of claim 1.

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