system

Abstract

The system according to the embodiment aims to collect opinions from various perspectives and generate an optimal program. [Solution] A system according to an embodiment includes an opinion collection unit, an opinion analysis unit, and a program provision unit. The opinion collection unit collects user opinions. The opinion analysis unit analyzes the opinions collected by the opinion collection unit and generates an optimal program. The program provision unit provides the program generated by the opinion analysis 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 technology has faced the challenge of making it difficult to efficiently gather opinions from a variety of perspectives and generate optimal programs.

[0005] The system according to the embodiment aims to collect opinions from various perspectives and generate an optimal program. [Means for solving the problem]

[0006] The system according to the embodiment includes an opinion collection unit, an opinion analysis unit, and a program provision unit. The opinion collection unit collects user opinions. The opinion analysis unit analyzes the opinions collected by the opinion collection unit and generates an optimal program. The program provision unit provides the program generated by the opinion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can aggregate opinions from various perspectives and generate an optimal program. [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 system according to an embodiment of the present invention uses AI to aggregate opinions from various perspectives and devise an optimal program (music pieces / set list). In this system, a user inputs their opinions regarding the music pieces and set list, and AI analyzes these opinions and generates an optimal program. The generated program is provided to the user and used at a concert or event. This allows the system to easily create a program that suits the user's preferences. Furthermore, because the AI ​​analyzes the opinions, it is possible to generate a program that reflects opinions from various perspectives. For example, a user inputs their opinions regarding the music pieces and set list. For example, they input opinions such as "I like classical music" or "Up-tempo music is good." These opinions are input into the AI. The AI ​​then analyzes the input opinions. The AI ​​understands the user's opinions and generates an optimal program. For example, based on the opinion "I like classical music," the AI ​​selects the optimal classical music piece. Also, based on the opinion "Up-tempo music is good," the AI ​​selects an up-tempo piece. The generated program is provided to the user. The user uses the generated program at a concert or event. For example, a concert set list is created based on the generated program, and the performance follows that set list. This allows the system to improve satisfaction at concerts and events.

[0029] A program generation system according to an embodiment includes an opinion collection unit, an opinion analysis unit, and a program provision unit. The opinion collection unit collects user opinions. User opinions include, but are not limited to, feedback, suggestions, and complaints. The opinion collection unit collects user opinions, for example, in the form of a questionnaire or free input. The opinion collection unit can also estimate a user's emotions using AI and adjust the timing of opinion collection based on the estimated emotions. For example, if it is estimated that the user is relaxed, the timing of opinion collection can be set to nighttime. The opinion analysis unit analyzes the collected opinions and generates an optimal program. For example, the opinion analysis unit generates a program that meets the user's needs based on the collected opinions. The opinion analysis unit can also estimate a user's emotions using AI and adjust the method of analysis expression based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. The program provision unit provides the generated program to the user. The program provision unit provides the generated program to the user, for example, via a web application or a mobile application. The program providing unit can also use AI to estimate the user's emotions and adjust the display method of the program to be provided based on the estimated emotions. For example, if the user is relaxed, a detailed program can be displayed. This allows the program generation system according to the embodiment to efficiently collect, analyze, and provide user opinions.

[0030] The opinion collection unit may collect user opinions in a questionnaire format or a free-entry format. The questionnaire format may include, but is not limited to, a multiple-choice format or a descriptive format. For example, the opinion collection unit may collect user opinions using a multiple-choice questionnaire. Alternatively, the opinion collection unit may collect user opinions using a descriptive format questionnaire. Alternatively, the opinion collection unit may collect user opinions in a free-entry format. For example, the opinion collection unit may provide a free-entry field so that users can freely enter their opinions. This allows the opinion collection unit to collect user opinions in a variety of formats.

[0031] The opinion analysis unit can analyze the collected opinions and generate an appropriate program. The opinion analysis unit generates a program that meets the user's needs based on the collected opinions, for example. For example, the opinion analysis unit selects the most appropriate piece of classical music based on the opinion "I like classical music." The opinion analysis unit can also select an up-tempo piece of music based on the opinion "Up-tempo pieces are good." The opinion analysis unit can also analyze the collected opinions using AI and generate an optimal program. For example, the opinion analysis unit inputs the collected opinions into AI, which analyzes the opinions and generates an optimal program. In this way, the opinion analysis unit can generate an optimal program based on the collected opinions.

[0032] The program providing unit can provide the generated program to the user. For example, the program providing unit can provide the generated program to the user via a web application or a mobile application. The program providing unit can also send the generated program to the user by email. Furthermore, the program providing unit can also provide the generated program on paper media. For example, the program providing unit can print the generated program and mail it to the user. In this way, the program providing unit can provide the generated program to the user.

[0033] The opinion analysis unit can collect user feedback and improve the program based on the feedback. The opinion analysis unit, for example, collects user feedback and improves the program based on the feedback. Feedback includes, for example, user ratings, comments, suggestions, etc., but is not limited to these examples. The opinion analysis unit improves the program based on, for example, user ratings. The opinion analysis unit can also improve the program based on user comments. The opinion analysis unit can also improve the program based on user suggestions. In this way, the opinion analysis unit can improve the program based on user feedback.

[0034] The opinion collection unit can analyze the user's past opinion submission history and select an appropriate collection method. For example, if the user has previously submitted opinions in questionnaire format, the opinion collection unit collects opinions in a similar format. Furthermore, if the user has previously submitted opinions in free-entry format, the opinion collection unit can also collect opinions in free-entry format. Furthermore, if the user has previously submitted opinions during a specific time period, the opinion collection unit can also collect opinions during that time period. This allows the opinion collection unit to select the optimal collection method based on the user's past opinion submission history.

[0035] When collecting opinions, the opinion collection unit can filter based on the user's current areas of interest and situation. For example, if the user is currently interested in classical music, the opinion collection unit can preferentially collect opinions related to classical music. Furthermore, if the user is currently interested in up-tempo music, the opinion collection unit can preferentially collect opinions related to up-tempo music. Furthermore, if the user is currently participating in a specific event, the opinion collection unit can preferentially collect opinions related to the event. This allows the opinion collection unit to filter opinions based on the user's current areas of interest and situation.

[0036] When collecting opinions, the opinion collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the opinion collection unit can collect opinions by preferentially using voice input. Also, if the user prefers text input, the opinion collection unit can collect opinions by preferentially using text input. Also, if the user prefers image input, the opinion collection unit can collect opinions by preferentially using image input. This allows the opinion collection unit to select the optimal collection means depending on the user's input method.

[0037] When collecting opinions, the opinion collection unit can prioritize collecting highly relevant opinions based on the user's geographical location information. For example, when the user is in a specific area, the opinion collection unit prioritizes collecting opinions related to that area. Furthermore, when the user is at a specific event venue, the opinion collection unit can also prioritize collecting opinions related to the event. Furthermore, when the user is traveling, the opinion collection unit can also prioritize collecting opinions related to the travel destination. In this way, the opinion collection unit can prioritize collecting highly relevant opinions taking into account the user's geographical location information.

[0038] The opinion collection unit can analyze the user's social media activities and collect related opinions when collecting opinions. For example, if the user posts about classical music on social media, the opinion collection unit can prioritize collecting those opinions. Also, if the user posts about up-tempo music on social media, the opinion collection unit can prioritize collecting those opinions. Also, if the user posts about a specific event on social media, the opinion collection unit can prioritize collecting those opinions. In this way, the opinion collection unit can collect related opinions based on the user's social media activities.

[0039] When collecting opinions, the opinion collection unit can customize the collection method based on the user's past feedback. For example, if the user has preferred collecting opinions in a questionnaire format in the past, the opinion collection unit collects opinions in a similar format. Furthermore, if the user has preferred collecting opinions in a free-entry format in the past, the opinion collection unit can also collect opinions in a free-entry format. Furthermore, if the user has submitted opinions during a specific time period in the past, the opinion collection unit can also collect opinions during that time period. This allows the opinion collection unit to customize the collection method based on the user's past feedback.

[0040] The opinion analysis unit can adjust the accuracy of the analysis based on the importance of the opinion. For example, the opinion analysis unit performs a detailed analysis on opinions with high importance. The opinion analysis unit can also perform a concise analysis on opinions with low importance. The opinion analysis unit can also perform an analysis with an appropriate level of detail on opinions with medium importance. This allows the opinion analysis unit to adjust the level of detail of the analysis based on the importance of the opinion.

[0041] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion. For example, the opinion analysis unit can apply an analysis algorithm dedicated to classical music to opinions about classical music. Furthermore, the opinion analysis unit can also apply an analysis algorithm dedicated to up-tempo music to opinions about up-tempo music. Furthermore, the opinion analysis unit can also apply an analysis algorithm dedicated to a specific event to opinions about that event. This allows the opinion analysis unit to apply an appropriate analysis algorithm depending on the category of the opinion.

[0042] The opinion analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the opinion analysis unit analyzes current opinions based on the analysis results of opinions submitted by the user in the past. The opinion analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The opinion analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. This allows the opinion analysis unit to improve the accuracy of the analysis based on the user's past analysis results.

[0043] The opinion analysis unit can determine the order of analysis based on the time of submission of the opinions. For example, the opinion analysis unit prioritizes analysis of the most recent opinions. The opinion analysis unit can also postpone analysis of opinions that were submitted earlier. The opinion analysis unit can also analyze opinions that were submitted at a moderate time with a moderate priority. In this way, the opinion analysis unit can determine the order of analysis priority based on the time of submission of the opinions.

[0044] The opinion analysis unit can adjust the order of analysis based on the relevance of the opinions. For example, the opinion analysis unit prioritizes the analysis of highly relevant opinions. The opinion analysis unit can also postpone the analysis of less relevant opinions. The opinion analysis unit can also analyze opinions with moderate relevance at a moderate priority. This allows the opinion analysis unit to adjust the order of analysis based on the relevance of the opinions.

[0045] The opinion analysis unit can optimize the use of technical terms in the analysis according to the user's level of expertise. For example, the opinion analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The opinion analysis unit can also provide analysis results that avoid technical terms to a user with low level of expertise. The opinion analysis unit can also provide analysis results that use technical terms appropriately to a user with medium level of expertise. This allows the opinion analysis unit to provide appropriate analysis results according to the user's level of expertise.

[0046] When providing a program, the program providing unit can select an appropriate delivery method based on the user's past feedback. The program providing unit provides the program, for example, based on a display method that the user has preferred in the past. The program providing unit can also adjust the program display method by referring to the user's past feedback. The program providing unit can also select the optimal delivery method based on the user's past feedback. This allows the program providing unit to select the optimal delivery method based on the user's past feedback.

[0047] When providing a program, the program providing unit can customize the content to be provided according to the user's current task. For example, if the user is preparing for a concert, the program providing unit can provide the most suitable program for the concert. Furthermore, if the user is preparing for an event, the program providing unit can also provide the most suitable program for the event. Furthermore, if the user is rehearsing, the program providing unit can also provide the most suitable program for the rehearsal. In this way, the program providing unit can provide an appropriate program according to the user's current task.

[0048] The program providing unit can improve the program providing method by reflecting user feedback when providing the program. For example, the program providing unit can improve the program display method based on user feedback. The program providing unit can also improve the program content by referring to user feedback. The program providing unit can also adjust the program providing timing based on user feedback. This allows the program providing unit to improve the program providing method based on user feedback.

[0049] When providing a program, the program providing unit can select an appropriate delivery method based on the user's geographical location information. For example, if the user is in a specific area, the program providing unit can provide a program related to that area. Furthermore, if the user is at a specific event venue, the program providing unit can also provide a program related to that event. Furthermore, if the user is traveling, the program providing unit can also provide a program related to the user's travel destination. This allows the program providing unit to select the optimal delivery method based on the user's geographical location information.

[0050] The program providing unit can analyze the user's social media activity when providing a program and provide a related program. For example, if the user posts about classical music on social media, the program providing unit can provide a program that reflects that opinion. Furthermore, if the user posts about up-tempo music on social media, the program providing unit can also provide a program that reflects that opinion. Furthermore, if the user posts about a specific event on social media, the program providing unit can also provide a program that reflects that opinion. In this way, the program providing unit can provide a related program based on the user's social media activity.

[0051] When providing a program, the program providing unit can customize the method of providing the program based on the user's past feedback. For example, the program providing unit provides the program based on a display method that the user has preferred in the past. The program providing unit can also adjust the method of displaying the program by referring to the user's past feedback. The program providing unit can also select the optimal method of providing the program based on the user's past feedback. This allows the program providing unit to customize the method of providing the program based on the user's past feedback.

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

[0053] When collecting user opinions, the opinion collection unit can analyze the user's past behavioral history and select the optimal collection method. For example, if the user previously submitted opinions in questionnaire format, the opinions can be collected in a similar format. Also, if the user previously submitted opinions in free-entry format, the opinions can be collected in free-entry format. Furthermore, if the user previously submitted opinions during a specific time period, the opinions can be collected during that time period. This allows the opinion collection unit to select the optimal collection method based on the user's past behavioral history.

[0054] When analyzing collected opinions, the opinion analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, detailed analysis results can be provided to users with high levels of expertise. Concise analysis results can also be provided to users with low levels of expertise. Furthermore, analysis results with an appropriate level of detail can also be provided to users with medium levels of expertise. This allows the opinion analysis unit to provide appropriate analysis results according to the user's level of expertise.

[0055] When providing the generated program, the program providing unit can customize the content to be provided in accordance with the user's current task. For example, if the user is preparing for a concert, the program best suited to the concert can be provided. Also, if the user is preparing for an event, the program best suited to the event can be provided. Furthermore, if the user is rehearsing, the program best suited to the rehearsal can be provided. In this way, the program providing unit can provide an appropriate program in accordance with the user's current task.

[0056] The opinion collection unit can prioritize collecting highly relevant opinions based on the user's geographical location information. For example, if the user is in a specific area, opinions related to that area can be prioritized. Also, if the user is at a specific event venue, opinions related to the event can be prioritized. Furthermore, if the user is traveling, opinions related to the travel destination can be prioritized. In this way, the opinion collection unit can prioritize collecting highly relevant opinions taking into account the user's geographical location information.

[0057] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion. For example, for opinions about classical music, an analysis algorithm dedicated to classical music can be applied. For opinions about up-tempo music, an analysis algorithm dedicated to up-tempo music can be applied. Furthermore, for opinions about a specific event, an analysis algorithm dedicated to that event can be applied. This allows the opinion analysis unit to apply an appropriate analysis algorithm depending on the category of the opinion.

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

[0059] Step 1: The opinion collection unit collects user opinions. User opinions include feedback, suggestions, complaints, etc. The opinion collection unit collects user opinions in the form of a questionnaire or free-form input. The opinion collection unit can also use AI to estimate the user's emotions and adjust the timing of opinion collection based on the estimated emotions. For example, if the user is estimated to be relaxed, the timing of opinion collection can be set to nighttime. Step 2: The opinion analysis unit analyzes the collected opinions and generates an optimal program. Based on the collected opinions, the opinion analysis unit generates a program that meets the user's needs. The opinion analysis unit can also use AI to estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. Step 3: The program provider provides the generated program to the user. The program provider provides the generated program to the user through a web application or a mobile application. The program provider can also use AI to estimate the user's emotions and adjust the display method of the program to be provided based on the estimated emotions. For example, if the user is relaxed, a detailed program can be displayed.

[0060] (Example 2) A system according to an embodiment of the present invention uses AI to aggregate opinions from various perspectives and devise an optimal program (music pieces / set list). In this system, a user inputs their opinions regarding the music pieces and set list, and AI analyzes these opinions and generates an optimal program. The generated program is provided to the user and used at a concert or event. This allows the system to easily create a program that suits the user's preferences. Furthermore, because the AI ​​analyzes the opinions, it is possible to generate a program that reflects opinions from various perspectives. For example, a user inputs their opinions regarding the music pieces and set list. For example, they input opinions such as "I like classical music" or "Up-tempo music is good." These opinions are input into the AI. The AI ​​then analyzes the input opinions. The AI ​​understands the user's opinions and generates an optimal program. For example, based on the opinion "I like classical music," the AI ​​selects the optimal classical music piece. Also, based on the opinion "Up-tempo music is good," the AI ​​selects an up-tempo piece. The generated program is provided to the user. The user uses the generated program at a concert or event. For example, a concert set list is created based on the generated program, and the performance follows that set list. This allows the system to improve satisfaction at concerts and events.

[0061] A program generation system according to an embodiment includes an opinion collection unit, an opinion analysis unit, and a program provision unit. The opinion collection unit collects user opinions. User opinions include, but are not limited to, feedback, suggestions, and complaints. The opinion collection unit collects user opinions, for example, in the form of a questionnaire or free input. The opinion collection unit can also estimate a user's emotions using AI and adjust the timing of opinion collection based on the estimated emotions. For example, if it is estimated that the user is relaxed, the timing of opinion collection can be set to nighttime. The opinion analysis unit analyzes the collected opinions and generates an optimal program. For example, the opinion analysis unit generates a program that meets the user's needs based on the collected opinions. The opinion analysis unit can also estimate a user's emotions using AI and adjust the method of analysis expression based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. The program provision unit provides the generated program to the user. The program provision unit provides the generated program to the user, for example, via a web application or a mobile application. The program providing unit can also use AI to estimate the user's emotions and adjust the display method of the program to be provided based on the estimated emotions. For example, if the user is relaxed, a detailed program can be displayed. This allows the program generation system according to the embodiment to efficiently collect, analyze, and provide user opinions.

[0062] The opinion collection unit may collect user opinions in a questionnaire format or a free-entry format. The questionnaire format may include, but is not limited to, a multiple-choice format or a descriptive format. For example, the opinion collection unit may collect user opinions using a multiple-choice questionnaire. Alternatively, the opinion collection unit may collect user opinions using a descriptive format questionnaire. Alternatively, the opinion collection unit may collect user opinions in a free-entry format. For example, the opinion collection unit may provide a free-entry field so that users can freely enter their opinions. This allows the opinion collection unit to collect user opinions in a variety of formats.

[0063] The opinion analysis unit can analyze the collected opinions and generate an appropriate program. The opinion analysis unit generates a program that meets the user's needs based on the collected opinions, for example. For example, the opinion analysis unit selects the most appropriate piece of classical music based on the opinion "I like classical music." The opinion analysis unit can also select an up-tempo piece of music based on the opinion "Up-tempo pieces are good." The opinion analysis unit can also analyze the collected opinions using AI and generate an optimal program. For example, the opinion analysis unit inputs the collected opinions into AI, which analyzes the opinions and generates an optimal program. In this way, the opinion analysis unit can generate an optimal program based on the collected opinions.

[0064] The program providing unit can provide the generated program to the user. For example, the program providing unit can provide the generated program to the user via a web application or a mobile application. The program providing unit can also send the generated program to the user by email. Furthermore, the program providing unit can also provide the generated program on paper media. For example, the program providing unit can print the generated program and mail it to the user. In this way, the program providing unit can provide the generated program to the user.

[0065] The opinion analysis unit can collect user feedback and improve the program based on the feedback. The opinion analysis unit, for example, collects user feedback and improves the program based on the feedback. Feedback includes, for example, user ratings, comments, suggestions, etc., but is not limited to these examples. The opinion analysis unit improves the program based on, for example, user ratings. The opinion analysis unit can also improve the program based on user comments. The opinion analysis unit can also improve the program based on user suggestions. In this way, the opinion analysis unit can improve the program based on user feedback.

[0066] The opinion collection unit can estimate the user's emotions and adjust the timing of opinion collection based on the estimated user's emotions. For example, if the opinion collection unit estimates that the user is relaxed, it can set the timing of opinion collection to nighttime. Furthermore, if the opinion collection unit estimates that the user is feeling stressed, it can avoid the timing of opinion collection and set it to a relaxed time period. Furthermore, if the opinion collection unit estimates that the user is excited, it can set the timing of opinion collection to immediately after an event. This allows the opinion collection unit to adjust the timing of opinion collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The opinion collection unit can analyze the user's past opinion submission history and select an appropriate collection method. For example, if the user has previously submitted opinions in questionnaire format, the opinion collection unit collects opinions in a similar format. Furthermore, if the user has previously submitted opinions in free-entry format, the opinion collection unit can also collect opinions in free-entry format. Furthermore, if the user has previously submitted opinions during a specific time period, the opinion collection unit can also collect opinions during that time period. This allows the opinion collection unit to select the optimal collection method based on the user's past opinion submission history.

[0068] When collecting opinions, the opinion collection unit can filter based on the user's current areas of interest and situation. For example, if the user is currently interested in classical music, the opinion collection unit can preferentially collect opinions related to classical music. Furthermore, if the user is currently interested in up-tempo music, the opinion collection unit can preferentially collect opinions related to up-tempo music. Furthermore, if the user is currently participating in a specific event, the opinion collection unit can preferentially collect opinions related to the event. This allows the opinion collection unit to filter opinions based on the user's current areas of interest and situation.

[0069] When collecting opinions, the opinion collection unit can select an appropriate collection means depending on the user's input method. For example, if the user prefers voice input, the opinion collection unit can collect opinions by preferentially using voice input. Also, if the user prefers text input, the opinion collection unit can collect opinions by preferentially using text input. Also, if the user prefers image input, the opinion collection unit can collect opinions by preferentially using image input. This allows the opinion collection unit to select the optimal collection means depending on the user's input method.

[0070] The opinion collection unit can estimate the user's emotions and determine the priority of opinions to be collected based on the estimated user emotions. For example, if the user is excited, the opinion collection unit can collect the user's opinions as a priority. Furthermore, if the user is relaxed, the opinion collection unit can collect the user's opinions as a second priority. Furthermore, if the user is feeling stressed, the opinion collection unit can collect the user's opinions at a later date. This allows the opinion collection unit to determine the priority of opinions based on 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 can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] When collecting opinions, the opinion collection unit can prioritize collecting highly relevant opinions based on the user's geographical location information. For example, when the user is in a specific area, the opinion collection unit prioritizes collecting opinions related to that area. Furthermore, when the user is at a specific event venue, the opinion collection unit can also prioritize collecting opinions related to the event. Furthermore, when the user is traveling, the opinion collection unit can also prioritize collecting opinions related to the travel destination. In this way, the opinion collection unit can prioritize collecting highly relevant opinions taking into account the user's geographical location information.

[0072] The opinion collection unit can analyze the user's social media activities and collect related opinions when collecting opinions. For example, if the user posts about classical music on social media, the opinion collection unit can prioritize collecting those opinions. Also, if the user posts about up-tempo music on social media, the opinion collection unit can prioritize collecting those opinions. Also, if the user posts about a specific event on social media, the opinion collection unit can prioritize collecting those opinions. In this way, the opinion collection unit can collect related opinions based on the user's social media activities.

[0073] When collecting opinions, the opinion collection unit can customize the collection method based on the user's past feedback. For example, if the user has preferred collecting opinions in a questionnaire format in the past, the opinion collection unit collects opinions in a similar format. Furthermore, if the user has preferred collecting opinions in a free-entry format in the past, the opinion collection unit can also collect opinions in a free-entry format. Furthermore, if the user has submitted opinions during a specific time period in the past, the opinion collection unit can also collect opinions during that time period. This allows the opinion collection unit to customize the collection method based on the user's past feedback.

[0074] The opinion analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the opinion analysis unit can provide detailed analysis results when the user is relaxed. The opinion analysis unit can also provide concise analysis results when the user is in a hurry. The opinion analysis unit can also provide visually stimulating analysis results when the user is excited. This allows the opinion analysis unit to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The opinion analysis unit can adjust the accuracy of the analysis based on the importance of the opinion. For example, the opinion analysis unit performs a detailed analysis on opinions with high importance. The opinion analysis unit can also perform a concise analysis on opinions with low importance. The opinion analysis unit can also perform an analysis with an appropriate level of detail on opinions with medium importance. This allows the opinion analysis unit to adjust the level of detail of the analysis based on the importance of the opinion.

[0076] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion. For example, the opinion analysis unit can apply an analysis algorithm dedicated to classical music to opinions about classical music. Furthermore, the opinion analysis unit can also apply an analysis algorithm dedicated to up-tempo music to opinions about up-tempo music. Furthermore, the opinion analysis unit can also apply an analysis algorithm dedicated to a specific event to opinions about that event. This allows the opinion analysis unit to apply an appropriate analysis algorithm depending on the category of the opinion.

[0077] The opinion analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the opinion analysis unit analyzes current opinions based on the analysis results of opinions submitted by the user in the past. The opinion analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. The opinion analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. This allows the opinion analysis unit to improve the accuracy of the analysis based on the user's past analysis results.

[0078] The opinion analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the opinion analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the opinion analysis unit can also provide a detailed analysis result. If the user is excited, the opinion analysis unit can also provide a visually stimulating analysis result. This allows the opinion analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The opinion analysis unit can determine the order of analysis based on the time of submission of the opinions. For example, the opinion analysis unit prioritizes analysis of the most recent opinions. The opinion analysis unit can also postpone analysis of opinions that were submitted earlier. The opinion analysis unit can also analyze opinions that were submitted at a moderate time with a moderate priority. In this way, the opinion analysis unit can determine the order of analysis priority based on the time of submission of the opinions.

[0080] The opinion analysis unit can adjust the order of analysis based on the relevance of the opinions. For example, the opinion analysis unit prioritizes the analysis of highly relevant opinions. The opinion analysis unit can also postpone the analysis of less relevant opinions. The opinion analysis unit can also analyze opinions with moderate relevance at a moderate priority. This allows the opinion analysis unit to adjust the order of analysis based on the relevance of the opinions.

[0081] The opinion analysis unit can optimize the use of technical terms in the analysis according to the user's level of expertise. For example, the opinion analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The opinion analysis unit can also provide analysis results that avoid technical terms to a user with low level of expertise. The opinion analysis unit can also provide analysis results that use technical terms appropriately to a user with medium level of expertise. This allows the opinion analysis unit to provide appropriate analysis results according to the user's level of expertise.

[0082] The program providing unit can estimate the user's emotions and adjust the display method of the program to be provided based on the estimated user's emotions. For example, if the user is relaxed, the program providing unit can display a detailed program. Also, if the user is in a hurry, the program providing unit can display a concise program. Also, if the user is excited, the program providing unit can display a visually stimulating program. This allows the program providing unit to adjust the display method of the program based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] When providing a program, the program providing unit can select an appropriate delivery method based on the user's past feedback. The program providing unit provides the program, for example, based on a display method that the user has preferred in the past. The program providing unit can also adjust the program display method by referring to the user's past feedback. The program providing unit can also select the optimal delivery method based on the user's past feedback. This allows the program providing unit to select the optimal delivery method based on the user's past feedback.

[0084] When providing a program, the program providing unit can customize the content to be provided according to the user's current task. For example, if the user is preparing for a concert, the program providing unit can provide the most suitable program for the concert. Furthermore, if the user is preparing for an event, the program providing unit can also provide the most suitable program for the event. Furthermore, if the user is rehearsing, the program providing unit can also provide the most suitable program for the rehearsal. In this way, the program providing unit can provide an appropriate program according to the user's current task.

[0085] The program providing unit can improve the program providing method by reflecting user feedback when providing the program. For example, the program providing unit can improve the program display method based on user feedback. The program providing unit can also improve the program content by referring to user feedback. The program providing unit can also adjust the program providing timing based on user feedback. This allows the program providing unit to improve the program providing method based on user feedback.

[0086] The program providing unit can estimate the user's emotions and determine the priority of programs to provide based on the estimated user's emotions. For example, if the user is relaxed, the program providing unit can prioritize providing detailed programs. Furthermore, if the user is in a hurry, the program providing unit can prioritize providing concise programs. Furthermore, if the user is excited, the program providing unit can prioritize providing visually stimulating programs. This allows the program providing unit to determine the priority of programs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When providing a program, the program providing unit can select an appropriate delivery method based on the user's geographical location information. For example, if the user is in a specific area, the program providing unit can provide a program related to that area. Furthermore, if the user is at a specific event venue, the program providing unit can also provide a program related to that event. Furthermore, if the user is traveling, the program providing unit can also provide a program related to the user's travel destination. This allows the program providing unit to select the optimal delivery method based on the user's geographical location information.

[0088] The program providing unit can analyze the user's social media activity when providing a program and provide a related program. For example, if the user posts about classical music on social media, the program providing unit can provide a program that reflects that opinion. Furthermore, if the user posts about up-tempo music on social media, the program providing unit can also provide a program that reflects that opinion. Furthermore, if the user posts about a specific event on social media, the program providing unit can also provide a program that reflects that opinion. In this way, the program providing unit can provide a related program based on the user's social media activity.

[0089] When providing a program, the program providing unit can customize the method of providing the program based on the user's past feedback. For example, the program providing unit provides the program based on a display method that the user has preferred in the past. The program providing unit can also adjust the method of displaying the program by referring to the user's past feedback. The program providing unit can also select the optimal method of providing the program based on the user's past feedback. This allows the program providing unit to customize the method of providing the program based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the opinion collection unit, opinion analysis unit, and program provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the opinion collection unit collects user opinions using the reception device 38 of the smart device 14 and transmits the opinions to the data processing device 12 via the control unit 46A. The opinion analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected opinions to generate an optimal program. The program provision unit provides the generated program to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the opinion collection unit, opinion analysis unit, and program provision 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 opinion collection unit collects user opinions using the microphone 238 of the smart glasses 214 and transmits the opinions to the data processing device 12 via the control unit 46A. The opinion analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected opinions to generate an optimal program. The program provision unit provides the generated program to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the opinion collection unit, opinion analysis unit, and program provision 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 opinion collection unit collects user opinions using the microphone 238 of the headset type terminal 314 and transmits the opinions to the data processing device 12 by the control unit 46A. The opinion analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected opinions to generate an optimal program. The program provision unit provides the generated program to the user using, for example, the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the opinion collection unit, opinion analysis unit, and program provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the opinion collection unit collects user opinions using the microphone 238 of the robot 414 and transmits the opinions to the data processing device 12 via the control unit 46A. The opinion analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected opinions to generate an optimal program. The program provision unit provides the generated program to the user using, for example, the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When collecting user opinions, the opinion collection unit can analyze the user's past behavioral history and select the optimal collection method. For example, if the user previously submitted opinions in questionnaire format, the opinions can be collected in a similar format. Also, if the user previously submitted opinions in free-entry format, the opinions can be collected in free-entry format. Furthermore, if the user previously submitted opinions during a specific time period, the opinions can be collected during that time period. This allows the opinion collection unit to select the optimal collection method based on the user's past behavioral history.

[0092] When analyzing collected opinions, the opinion analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, detailed analysis results can be provided to users with high levels of expertise. Concise analysis results can also be provided to users with low levels of expertise. Furthermore, analysis results with an appropriate level of detail can also be provided to users with medium levels of expertise. This allows the opinion analysis unit to provide appropriate analysis results according to the user's level of expertise.

[0093] When providing the generated program, the program providing unit can customize the content to be provided in accordance with the user's current task. For example, if the user is preparing for a concert, the program best suited to the concert can be provided. Also, if the user is preparing for an event, the program best suited to the event can be provided. Furthermore, if the user is rehearsing, the program best suited to the rehearsal can be provided. In this way, the program providing unit can provide an appropriate program in accordance with the user's current task.

[0094] The opinion collection unit can prioritize collecting highly relevant opinions based on the user's geographical location information. For example, if the user is in a specific area, opinions related to that area can be prioritized. Also, if the user is at a specific event venue, opinions related to the event can be prioritized. Furthermore, if the user is traveling, opinions related to the travel destination can be prioritized. In this way, the opinion collection unit can prioritize collecting highly relevant opinions taking into account the user's geographical location information.

[0095] The opinion analysis unit can apply different analysis algorithms depending on the category of the opinion. For example, for opinions about classical music, an analysis algorithm dedicated to classical music can be applied. For opinions about up-tempo music, an analysis algorithm dedicated to up-tempo music can be applied. Furthermore, for opinions about a specific event, an analysis algorithm dedicated to that event can be applied. This allows the opinion analysis unit to apply an appropriate analysis algorithm depending on the category of the opinion.

[0096] The opinion collection unit can estimate the user's emotions and adjust the timing of opinion collection based on the estimated user's emotions. For example, if it is estimated that the user is relaxed, the timing of opinion collection can be set to nighttime. Also, if it is estimated that the user is feeling stressed, the timing of opinion collection can be avoided and set to a relaxed time period. Furthermore, if it is estimated that the user is excited, the timing of opinion collection can be set to immediately after an event. In this way, the opinion collection unit can adjust the timing of opinion collection according to the user's emotions.

[0097] The opinion analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. This allows the opinion analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0098] The program providing unit can estimate the user's emotions and adjust the display method of the programs to be provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed program can be displayed. If the user is in a hurry, a concise program can be displayed. Furthermore, if the user is excited, a visually stimulating program can be displayed. In this way, the program providing unit can adjust the display method of the programs based on the user's emotions.

[0099] The opinion collection unit can estimate the user's emotions and determine the priority of opinions to be collected based on the estimated user's emotions. For example, if the user is excited, the opinions can be collected with priority. Also, if the user is relaxed, the opinions can be collected with second priority. Furthermore, if the user is feeling stressed, the opinions can be collected later. In this way, the opinion collection unit can determine the priority of opinions based on the user's emotions.

[0100] The program providing unit can estimate the user's emotions and determine the priority of programs to be provided based on the estimated user's emotions. For example, if the user is relaxed, a detailed program can be provided preferentially. If the user is in a hurry, a concise program can be provided preferentially. Furthermore, if the user is excited, a visually stimulating program can be provided preferentially. In this way, the program providing unit can determine the priority of programs based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The opinion collection unit collects user opinions. User opinions include feedback, suggestions, complaints, etc. The opinion collection unit collects user opinions in the form of a questionnaire or free-form input. The opinion collection unit can also use AI to estimate the user's emotions and adjust the timing of opinion collection based on the estimated emotions. For example, if the user is estimated to be relaxed, the timing of opinion collection can be set to nighttime. Step 2: The opinion analysis unit analyzes the collected opinions and generates an optimal program. Based on the collected opinions, the opinion analysis unit generates a program that meets the user's needs. The opinion analysis unit can also use AI to estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. Step 3: The program provider provides the generated program to the user. The program provider provides the generated program to the user through a web application or a mobile application. The program provider can also use AI to estimate the user's emotions and adjust the display method of the program to be provided based on the estimated emotions. For example, if the user is relaxed, a detailed program can be displayed.

[0103] 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.

[0104] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. an opinion collection unit that collects user opinions; an opinion analysis unit that analyzes the opinions collected by the opinion collection unit and generates an optimal program; a program providing unit that provides the program generated by the opinion analysis unit. A system characterized by:

2. The opinion collection unit Collect user feedback in the form of a survey or free-form text 2. The system of claim 1.

3. The opinion analysis unit Analyze collected opinions and generate appropriate programs 2. The system of claim 1.

4. The program providing unit Providing the generated program to the user 2. The system of claim 1.

5. The opinion analysis unit Gather user feedback and improve the program based on that feedback 2. The system of claim 1.

6. The opinion collection unit Estimate user emotions and adjust the timing of opinion collection based on the estimated user emotions.

2. The system of claim 1.

7. The opinion collection unit Analyze users' past opinion submission history and select the appropriate collection method 2. The system of claim 1.

8. The opinion collection unit When collecting opinions, filter them based on the user's current interests and situation.

2. The system of claim 1.

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