System

Abstract

An object of a system according to an embodiment is to effectively promote the exchange and growth of participants in the operation of a contest.SOLUTION: A system includes a work collection part, an examination part, a presentation part, an interaction promotion part, and a growth promotion part. A work collection part collects works by using the generation AI. The examination unit examines the works recruited by the work recruiting unit. A presentation part presents the examination result of the work examined by the examination part. The interaction promotion unit promotes interaction between the participants on the basis of the examination result presented by the presentation unit. The growth promotion unit promotes the growth of the participant based on the interaction promoted by the interaction promotion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that they have not done enough to effectively promote interaction and growth among participants in contest management.

[0005] The system according to the embodiment aims to effectively promote interaction and growth among participants in running a contest. [Means for solving the problem]

[0006] The system according to the embodiment includes a work collection section, a judging section, a presentation section, an exchange promotion section, and a growth promotion section. The work collection section collects works using a generation AI. The judging section judges the works collected by the work collection section. The presentation section announces the results of the evaluation of the works judged by the judging section. The exchange promotion section promotes exchanges between participants based on the evaluation results announced by the presentation section. The growth promotion section promotes the growth of participants based on the exchanges promoted by the exchange promotion section. [Effects of the Invention]

[0007] The system according to the embodiment can effectively promote interaction and growth among participants in contest management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The contest hosting platform according to an embodiment of the present invention is a system that utilizes Gemini's AI technology to host innovative contests that anyone can easily participate in. This system has the functionality to solicit, judge, and publish works on various themes. It also provides a mechanism to encourage interaction and growth among participants. This allows participants to freely propose works and grow while having fun competing with their peers.

[0029] A contest hosting platform according to an embodiment includes a work collection unit, a judging unit, a presentation unit, a communication promotion unit, and a growth promotion unit. The work collection unit uses a generation AI to collect works. For example, the generation AI generates a submission guideline based on prompts containing instructions regarding the theme and format of the works submitted by participants. The judging unit uses the generation AI to judge the works. For example, the generation AI evaluates elements such as color, composition, and creativity of artworks, and melody, rhythm, and originality of musical pieces. The presentation unit uses the generation AI to announce the judging results. For example, the generation AI analyzes the judging results and specifically identifies merits and areas for improvement in the works, encouraging participants' growth. The communication promotion unit uses the generation AI to promote interaction between participants. For example, the generation AI matches participants with common interests based on their profiles and works. The growth promotion unit uses the generation AI to promote participants' growth. For example, the generation AI analyzes participants' past works and feedback, and provides advice and training plans for the next contest. This allows the contest hosting platform to host innovative contests that anyone can easily participate in.

[0030] The Artwork Collection Division can use generative AI to analyze participants' past artwork or interests and suggest individually customized themes. For example, the generative AI analyzes participants' past artwork data to identify their interests and areas of expertise. For example, it analyzes the styles and themes of artworks submitted in the past and suggests new themes that are best suited to the participants. The generative AI suggests individually customized themes based on participants' interests. For example, if a participant is interested in music, it suggests a music-related theme. By analyzing participants' past artwork and interests and suggesting individually customized themes, the generative AI can increase participants' motivation to create.

[0031] The Works Call Division uses generative AI to provide feedback on participants' works in real time, and can point out areas for improvement before submission. For example, the Works Call Division uses generative AI to analyze participants' works in real time before they are submitted, and point out specific areas for improvement. For example, it may suggest areas for improvement in the color balance or composition of an artwork. The generative AI provides feedback on participants' works in real time. For example, it may point out areas for improvement in the melody or rhythm of a musical piece. The generative AI provides feedback on participants' works in real time, and can point out areas for improvement before submission, allowing participants to improve their works.

[0032] The review department can use the generation AI to learn from past review results and dynamically optimize the review criteria. For example, the review department uses the generation AI to learn from past review results and dynamically optimize the review criteria. For example, the review criteria can be adjusted based on past evaluation data to provide a fairer evaluation. The generation AI learns from past review results and optimizes the review criteria. For example, the weighting of the evaluation criteria can be adjusted to maintain consistency in the evaluation. The generation AI can learn from past review results and dynamically optimize the review criteria to provide a fairer evaluation.

[0033] The judging committee can use generative AI to analyze the background information and production process of a work and make an evaluation based on a deeper understanding. For example, the judging committee can use generative AI to analyze the background information of a work and make an evaluation based on a deeper understanding. For example, the evaluation can take into account the creative intention and background of the work. The generative AI can analyze the creative process of a work and reflect this in its evaluation. For example, the evaluation can take into account the production steps and the techniques used. By analyzing the background information and production process of a work and making an evaluation based on a deeper understanding, the generative AI can accurately evaluate the value of the work.

[0034] The presentation section can use the generation AI to translate the presentation content into multiple languages ​​and provide it in a format that is easy for international participants to understand. For example, the presentation section can use the generation AI to translate the presentation content into multiple languages ​​and provide it in a format that is easy for international participants to understand. For example, the presentation content is provided in multiple languages ​​such as English, French, and Chinese. The generation AI can translate the presentation content into multiple languages ​​and provide it to participants. For example, the generation AI can automatically translate the presentation content and display it to participants. By translating the presentation content into multiple languages, the generation AI can provide it in a format that is easy for international participants to understand.

[0035] The presentation unit can use the generation AI to visualize the content of the presentation and provide it in a form that is visually easy to understand. For example, the generation AI can visualize the content of the presentation and provide it in a form that is visually easy to understand. For example, the generation AI can visually display data using graphs and charts. The generation AI can visualize the content of the presentation and provide it to the participants. For example, the generation AI can automatically visualize the content of the presentation and display it to the participants. By visualizing the content of the presentation, the generation AI can provide it in a form that is visually easy to understand.

[0036] The interaction promotion unit can use the generation AI to analyze the participants' past interaction history and propose optimal matching. For example, the generation AI analyzes the participants' past interaction history and proposes optimal matching. For example, it matches participants with common interests. The generation AI proposes optimal matching based on the participants' interaction history. For example, it matches participants who have worked on a joint project in the past again. The generation AI can promote interaction between participants by analyzing the participants' past interaction history and proposing optimal matching.

[0037] The interaction promotion unit can use the generation AI to monitor the progress of the interaction in real time and provide support at the appropriate time. For example, the generation AI can monitor the progress of the interaction in real time and provide support at the appropriate time. For example, it can provide advice if the interaction is stagnating. The generation AI can monitor the progress of the interaction and provide support at the appropriate time. For example, it can provide support if a participant is facing difficulties. The generation AI can monitor the progress of the interaction in real time and provide support at the appropriate time, thereby allowing interactions between participants to proceed smoothly.

[0038] The growth promotion unit can use the generative AI to learn from the participant's past works and feedback, and provide an individually customized training plan. For example, the generative AI analyzes the participant's past work data and provides an individually customized training plan. For example, it suggests practice methods to improve the skills of past artworks. The generative AI customizes the training plan based on the participant's feedback. For example, it provides a training plan to improve composition skills based on feedback on musical works. The generative AI can promote the participant's growth by learning from the participant's past works and feedback, and providing an individually customized training plan.

[0039] The growth promotion unit can use the generative AI to monitor the participants' progress in real time and provide advice at the appropriate time. For example, the generative AI can monitor the participants' progress in real time and provide advice at the appropriate time. For example, it can analyze the progress of an artwork and provide specific advice. The generative AI can monitor the participants' progress and provide advice at the appropriate time. For example, it can analyze the progress of a music piece and provide advice on improving composition skills. The generative AI can monitor the participants' progress in real time and provide advice at the appropriate time, thereby promoting the participants' growth.

[0040] The growth promotion department can use the generative AI to propose training plans in different genres to encourage the multifaceted growth of participants. For example, the generative AI can propose training plans in different genres to encourage the multifaceted growth of participants. For example, training plans for both art and music can be provided. The generative AI can propose training plans in different genres based on the participants' interests and skills. For example, training plans for programming and essay writing can be provided. By proposing training plans in different genres, the generative AI can encourage the multifaceted growth of participants.

[0041] The growth promotion unit can use the generative AI to suggest joint training between participants and provide an environment where they can learn from each other. For example, the generative AI can suggest joint training between participants and provide an environment where they can learn from each other. For example, it can suggest training sessions in pairs or groups. The generative AI can suggest joint training based on the skills and interests of the participants. For example, it can suggest the joint creation of an artwork. The generative AI can suggest joint training between participants and provide an environment where they can learn from each other.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The contest platform may further include a gallery section for displaying participants' works. The gallery section displays participants' works online and allows other participants and the general public to freely view them. For example, an online gallery for artworks may be set up, displaying detailed information about the works and comments from the artists. In the case of musical works, a function for streaming the music may be provided. Furthermore, the gallery section may include a comment function for the works, allowing viewers to post their impressions and feedback. This allows participants to widely publicize their work and receive feedback from others, thereby facilitating further growth.

[0044] The contest hosting platform can further include a training section tailored to the skill level of each participant. The training section assesses the participant's skill level and provides an appropriate training plan. For example, it can provide basic training for beginners, applied training for intermediate participants, and advanced training for advanced participants. The training section can also monitor participants' progress and adjust the training plan as needed. This allows participants to receive training tailored to their skill level and improve their skills efficiently.

[0045] The contest hosting platform can further include an evaluation visualization section that visually displays the evaluation of participants' works. The evaluation visualization section uses generative AI to visually display the judging results in graphs and charts, allowing participants to understand the details of the evaluation at a glance. For example, the evaluation of an artwork could be displayed as a score for color balance or composition. For a musical work, the evaluation of melody and rhythm could be shown in a graph. Furthermore, the evaluation visualization section can also display the fluctuations in the evaluation over time, allowing participants to visually confirm their own growth. This makes it easier for participants to understand the details of the evaluation and clearly identify areas for improvement for the next contest.

[0046] The contest hosting platform may further include an audio feedback unit that provides audio feedback on participants' works. The audio feedback unit uses generative AI to generate audio judging results and provides them to participants. For example, the audio may explain the evaluation of an artwork and point out specific areas for improvement. In the case of a musical work, the audio may explain the evaluation and point out areas for improvement in melody or rhythm. Furthermore, the audio feedback unit may provide real-time audio feedback so that participants can improve their works while listening to the feedback. This makes it easier for participants to understand the feedback and use it to improve their works.

[0047] The contest hosting platform may further include a categorizer that automatically categorizes participants' works. The categorizer uses generative AI to automatically categorize participants' works based on theme and format and assign them to appropriate categories. For example, artworks may be categorized as paintings, sculptures, digital art, etc., while musical works may be categorized based on genre and style. The categorizer may also provide a search function to enable participants to easily find their works. This allows participants to categorize their works into appropriate categories, making them easier for other participants and viewers to find.

[0048] The contest hosting platform can further include a recommendation unit that recommends participants' works to other contests and exhibitions. The recommendation unit uses generative AI to evaluate participants' works and recommend them to other contests and exhibitions. For example, it can recommend outstanding artworks to international art contests, or in the case of musical works, it can recommend outstanding songs to music festivals. Furthermore, the recommendation unit can notify participants when their works have been recommended and support them so that they can participate in the recommended contests and exhibitions. This allows participants to have opportunities to widely showcase their works.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The submission section uses a generative AI to solicit submissions. For example, the generative AI generates submission guidelines based on prompts containing instructions on the theme and format of the submissions participants will make. Step 2: The judging panel will use the generative AI to evaluate the works. For example, the generative AI will evaluate elements such as color, composition, and creativity for artworks, and melody, rhythm, and originality for music pieces. Step 3: The presentation team will use the generative AI to announce the results of the judging. For example, the generative AI will analyze the results and specifically indicate the merits and areas for improvement of the participants' works, encouraging them to grow. Step 4: The interaction promotion department uses the generation AI to promote interaction between participants. For example, the generation AI may match participants with common interests based on their profiles and works. Step 5: The Growth Promotion Department uses generative AI to promote the participants' growth. For example, generative AI analyzes the participants' past work and feedback, and provides advice and training plans for the next competition.

[0051] (Example 2) The contest hosting platform according to an embodiment of the present invention is a system that utilizes Gemini's AI technology to host innovative contests that anyone can easily participate in. This system has the functionality to solicit, judge, and publish works on various themes. It also provides a mechanism to encourage interaction and growth among participants. This allows participants to freely propose works and grow while having fun competing with their peers.

[0052] A contest hosting platform according to an embodiment includes a work collection unit, a judging unit, a presentation unit, a communication promotion unit, and a growth promotion unit. The work collection unit uses a generation AI to collect works. For example, the generation AI generates a submission guideline based on prompts containing instructions regarding the theme and format of the works submitted by participants. The judging unit uses the generation AI to judge the works. For example, the generation AI evaluates elements such as color, composition, and creativity of artworks, and melody, rhythm, and originality of musical pieces. The presentation unit uses the generation AI to announce the judging results. For example, the generation AI analyzes the judging results and specifically identifies merits and areas for improvement in the works, encouraging participants' growth. The communication promotion unit uses the generation AI to promote interaction between participants. For example, the generation AI matches participants with common interests based on their profiles and works. The growth promotion unit uses the generation AI to promote participants' growth. For example, the generation AI analyzes participants' past works and feedback, and provides advice and training plans for the next contest. This allows the contest hosting platform to host innovative contests that anyone can easily participate in.

[0053] The Artwork Collection Division can use generative AI to analyze participants' past artwork or interests and suggest individually customized themes. For example, the generative AI analyzes participants' past artwork data to identify their interests and areas of expertise. For example, it analyzes the styles and themes of artworks submitted in the past and suggests new themes that are best suited to the participants. The generative AI suggests individually customized themes based on participants' interests. For example, if a participant is interested in music, it suggests a music-related theme. By analyzing participants' past artwork and interests and suggesting individually customized themes, the generative AI can increase participants' motivation to create.

[0054] The Works Call Division uses generative AI to provide feedback on participants' works in real time, and can point out areas for improvement before submission. For example, the Works Call Division uses generative AI to analyze participants' works in real time before they are submitted, and point out specific areas for improvement. For example, it may suggest areas for improvement in the color balance or composition of an artwork. The generative AI provides feedback on participants' works in real time. For example, it may point out areas for improvement in the melody or rhythm of a musical piece. The generative AI provides feedback on participants' works in real time, and can point out areas for improvement before submission, allowing participants to improve their works.

[0055] The work collection section can use the emotion estimation function to suggest themes that correspond to the emotional state of the participants, thereby increasing their motivation to create. For example, the work collection section can use the emotion estimation function to analyze the emotional state of the participants in real time and suggest themes that elicit positive emotions. For example, it can suggest themes that increase creative motivation when the participants are relaxed. The emotion estimation function analyzes the emotional state of the participants and suggest appropriate themes. For example, it can suggest a challenging theme when the participants are excited. The emotion estimation function can increase the participants' motivation to create by suggesting themes that correspond to the emotional state of the participants.

[0056] The review department can use the generation AI to learn from past review results and dynamically optimize the review criteria. For example, the review department uses the generation AI to learn from past review results and dynamically optimize the review criteria. For example, the review criteria can be adjusted based on past evaluation data to provide a fairer evaluation. The generation AI learns from past review results and optimizes the review criteria. For example, the weighting of the evaluation criteria can be adjusted to maintain consistency in the evaluation. The generation AI can learn from past review results and dynamically optimize the review criteria to provide a fairer evaluation.

[0057] The judging committee can use generative AI to analyze the background information and production process of a work and make an evaluation based on a deeper understanding. For example, the judging committee can use generative AI to analyze the background information of a work and make an evaluation based on a deeper understanding. For example, the evaluation can take into account the creative intention and background of the work. The generative AI can analyze the creative process of a work and reflect this in its evaluation. For example, the evaluation can take into account the production steps and the techniques used. By analyzing the background information and production process of a work and making an evaluation based on a deeper understanding, the generative AI can accurately evaluate the value of the work.

[0058] The judging department can use the emotion estimation function to consider the emotional state of the judges and provide a fair evaluation. For example, the judging department can use the emotion estimation function to analyze the emotional state of the judges in real time and provide a fair evaluation. For example, if a judge is tired, the evaluation is adjusted. The emotion estimation function analyzes the emotional state of the judges and reflects it in the evaluation. For example, if a judge is excited, the function encourages a calm evaluation. The emotion estimation function can provide a fair evaluation by considering the emotional state of the judges.

[0059] The presentation section can use the generation AI to translate the presentation content into multiple languages ​​and provide it in a format that is easy for international participants to understand. For example, the presentation section can use the generation AI to translate the presentation content into multiple languages ​​and provide it in a format that is easy for international participants to understand. For example, the presentation content is provided in multiple languages ​​such as English, French, and Chinese. The generation AI can translate the presentation content into multiple languages ​​and provide it to participants. For example, the generation AI can automatically translate the presentation content and display it to participants. By translating the presentation content into multiple languages, the generation AI can provide it in a format that is easy for international participants to understand.

[0060] The presentation unit can use the generation AI to visualize the content of the presentation and provide it in a form that is visually easy to understand. For example, the generation AI can visualize the content of the presentation and provide it in a form that is visually easy to understand. For example, the generation AI can visually display data using graphs and charts. The generation AI can visualize the content of the presentation and provide it to the participants. For example, the generation AI can automatically visualize the content of the presentation and display it to the participants. By visualizing the content of the presentation, the generation AI can provide it in a form that is visually easy to understand.

[0061] The presentation unit can use the emotion estimation function to provide feedback according to the emotional state of the participants and encourage their growth. The presentation unit, for example, uses the emotion estimation function to analyze the emotional state of the participants in real time and provide appropriate feedback. For example, feedback that elicits positive emotions is provided. The emotion estimation function analyzes the emotional state of the participants and reflects this in the feedback. For example, if a participant is feeling down, encouraging feedback is provided. The emotion estimation function can encourage the growth of participants by providing feedback according to their emotional state.

[0062] The interaction promotion unit can use the generation AI to analyze the participants' past interaction history and propose optimal matching. For example, the generation AI analyzes the participants' past interaction history and proposes optimal matching. For example, it matches participants with common interests. The generation AI proposes optimal matching based on the participants' interaction history. For example, it matches participants who have worked on a joint project in the past again. The generation AI can promote interaction between participants by analyzing the participants' past interaction history and proposing optimal matching.

[0063] The interaction promotion unit can use the generation AI to monitor the progress of the interaction in real time and provide support at the appropriate time. For example, the generation AI can monitor the progress of the interaction in real time and provide support at the appropriate time. For example, it can provide advice if the interaction is stagnating. The generation AI can monitor the progress of the interaction and provide support at the appropriate time. For example, it can provide support if a participant is facing difficulties. The generation AI can monitor the progress of the interaction in real time and provide support at the appropriate time, thereby allowing interactions between participants to proceed smoothly.

[0064] The interaction promotion unit can use the emotion estimation function to suggest interaction methods that correspond to the emotional state of the participants, thereby promoting smooth communication. The interaction promotion unit, for example, uses the emotion estimation function to analyze the emotional state of the participants in real time and suggest appropriate interaction methods. For example, if a participant is nervous, it will suggest a method that will help them relax. The emotion estimation function analyzes the emotional state of the participants and reflects this in the interaction method. For example, if a participant is excited, it will suggest a calm discussion. The emotion estimation function can promote smooth communication by suggesting interaction methods that correspond to the emotional state of the participants.

[0065] The growth promotion unit can use the generative AI to learn from the participant's past works and feedback, and provide an individually customized training plan. For example, the generative AI analyzes the participant's past work data and provides an individually customized training plan. For example, it suggests practice methods to improve the skills of past artworks. The generative AI customizes the training plan based on the participant's feedback. For example, it provides a training plan to improve composition skills based on feedback on musical works. The generative AI can promote the participant's growth by learning from the participant's past works and feedback, and providing an individually customized training plan.

[0066] The growth promotion unit can use the generative AI to monitor the participants' progress in real time and provide advice at the appropriate time. For example, the generative AI can monitor the participants' progress in real time and provide advice at the appropriate time. For example, it can analyze the progress of an artwork and provide specific advice. The generative AI can monitor the participants' progress and provide advice at the appropriate time. For example, it can analyze the progress of a music piece and provide advice on improving composition skills. The generative AI can monitor the participants' progress in real time and provide advice at the appropriate time, thereby promoting the participants' growth.

[0067] The growth promotion unit uses the emotion estimation function to suggest a training method that corresponds to the emotional state of the participant, thereby maintaining motivation. The growth promotion unit, for example, uses the emotion estimation function to analyze the emotional state of the participant in real time and suggest an appropriate training method. For example, if the participant is tired, it suggests a training method that will help them relax. The emotion estimation function analyzes the emotional state of the participant and reflects this in the training method. For example, if the participant is excited, it suggests a training method that will improve concentration. The emotion estimation function can maintain the motivation of the participant by suggesting a training method that corresponds to the emotional state of the participant.

[0068] The growth promotion department can use the generative AI to propose training plans in different genres to encourage the multifaceted growth of participants. For example, the generative AI can propose training plans in different genres to encourage the multifaceted growth of participants. For example, training plans for both art and music can be provided. The generative AI can propose training plans in different genres based on the participants' interests and skills. For example, training plans for programming and essay writing can be provided. By proposing training plans in different genres, the generative AI can encourage the multifaceted growth of participants.

[0069] The growth promotion unit can use the generative AI to suggest joint training between participants and provide an environment where they can learn from each other. For example, the generative AI can suggest joint training between participants and provide an environment where they can learn from each other. For example, it can suggest training sessions in pairs or groups. The generative AI can suggest joint training based on the skills and interests of the participants. For example, it can suggest the joint creation of an artwork. The generative AI can suggest joint training between participants and provide an environment where they can learn from each other.

[0070] The growth promotion unit can use the emotion estimation function to monitor the emotional responses of participants in real time and continuously suggest optimal training methods. The growth promotion unit, for example, uses the emotion estimation function to monitor the emotional responses of participants in real time and continuously suggest optimal training methods. For example, the training plan is adjusted according to the emotional state of the participant. The emotion estimation function analyzes the emotional responses of participants and reflects them in the training method. For example, if a participant is feeling down, an encouraging training method is suggested. The emotion estimation function can promote the growth of participants by monitoring the emotional responses of participants in real time and continuously suggesting optimal training methods.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The contest platform may further include a gallery section for displaying participants' works. The gallery section displays participants' works online and allows other participants and the general public to freely view them. For example, an online gallery for artworks may be set up, displaying detailed information about the works and comments from the artists. In the case of musical works, a function for streaming the music may be provided. Furthermore, the gallery section may include a comment function for the works, allowing viewers to post their impressions and feedback. This allows participants to widely publicize their work and receive feedback from others, thereby facilitating further growth.

[0073] The contest hosting platform can further include a training section tailored to the skill level of each participant. The training section assesses the participant's skill level and provides an appropriate training plan. For example, it can provide basic training for beginners, applied training for intermediate participants, and advanced training for advanced participants. The training section can also monitor participants' progress and adjust the training plan as needed. This allows participants to receive training tailored to their skill level and improve their skills efficiently.

[0074] The contest hosting platform can further include an evaluation visualization section that visually displays the evaluation of participants' works. The evaluation visualization section uses generative AI to visually display the judging results in graphs and charts, allowing participants to understand the details of the evaluation at a glance. For example, the evaluation of an artwork could be displayed as a score for color balance or composition. For a musical work, the evaluation of melody and rhythm could be shown in a graph. Furthermore, the evaluation visualization section can also display the fluctuations in the evaluation over time, allowing participants to visually confirm their own growth. This makes it easier for participants to understand the details of the evaluation and clearly identify areas for improvement for the next contest.

[0075] The contest hosting platform may further include a relaxation unit that provides a relaxing environment based on the participant's emotional state. The relaxation unit uses an emotion estimation function to analyze the participant's emotional state and provide relaxing music and videos. For example, if the participant is nervous, relaxing music is played. The emotion estimation function analyzes the participant's emotional state and provides appropriate relaxation content. For example, if the participant is feeling stressed, relaxing videos are displayed. By providing a relaxing environment based on the participant's emotional state, the relaxation unit can increase the participant's motivation to create.

[0076] The contest hosting platform may further include an audio feedback unit that provides audio feedback on participants' works. The audio feedback unit uses generative AI to generate audio judging results and provides them to participants. For example, the audio may explain the evaluation of an artwork and point out specific areas for improvement. In the case of a musical work, the audio may explain the evaluation and point out areas for improvement in melody or rhythm. Furthermore, the audio feedback unit may provide real-time audio feedback so that participants can improve their works while listening to the feedback. This makes it easier for participants to understand the feedback and use it to improve their works.

[0077] The contest hosting platform may further include a feedback adjustment unit that adjusts the content of the feedback based on the emotional state of the participant. The feedback adjustment unit uses the emotion estimation function to analyze the emotional state of the participant and provide appropriate feedback. For example, if the participant is depressed, it provides encouraging feedback. The emotion estimation function analyzes the emotional state of the participant and adjusts the content of the feedback. For example, if the participant is excited, it provides calm feedback. The feedback adjustment unit can promote the growth of the participant by adjusting the content of the feedback based on the emotional state of the participant.

[0078] The contest hosting platform may further include an interaction timing adjustment unit that adjusts the timing of interaction based on the emotional state of the participant. The interaction timing adjustment unit uses the emotion estimation function to analyze the emotional state of the participant and promote interaction at an appropriate time. For example, it promotes interaction when the participant is relaxed. The emotion estimation function analyzes the emotional state of the participant and adjusts the timing of interaction. For example, it refrains from interaction when the participant is tired. The interaction timing adjustment unit adjusts the timing of interaction based on the emotional state of the participant, thereby promoting smooth communication.

[0079] The contest hosting platform may further include a categorizer that automatically categorizes participants' works. The categorizer uses generative AI to automatically categorize participants' works based on theme and format and assign them to appropriate categories. For example, artworks may be categorized as paintings, sculptures, digital art, etc., while musical works may be categorized based on genre and style. The categorizer may also provide a search function to enable participants to easily find their works. This allows participants to categorize their works into appropriate categories, making them easier for other participants and viewers to find.

[0080] The contest hosting platform may further include a training plan adjustment unit that adjusts the training plan based on the emotional state of the participant. The training plan adjustment unit uses the emotion estimation function to analyze the emotional state of the participant and provide an appropriate training plan. For example, if the participant is tired, it suggests a training plan that will help them relax. The emotion estimation function analyzes the emotional state of the participant and adjusts the training plan. For example, if the participant is excited, it suggests a training plan that will help them improve their concentration. By adjusting the training plan based on the emotional state of the participant, the training plan adjustment unit can maintain the participant's motivation and promote their growth.

[0081] The contest hosting platform can further include a recommendation unit that recommends participants' works to other contests and exhibitions. The recommendation unit uses generative AI to evaluate participants' works and recommend them to other contests and exhibitions. For example, it can recommend outstanding artworks to international art contests, or in the case of musical works, it can recommend outstanding songs to music festivals. Furthermore, the recommendation unit can notify participants when their works have been recommended and support them so that they can participate in the recommended contests and exhibitions. This allows participants to have opportunities to widely showcase their works.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The submission section uses a generative AI to solicit submissions. For example, the generative AI generates submission guidelines based on prompts containing instructions on the theme and format of the submissions participants will make. Step 2: The judging panel will use the generative AI to evaluate the works. For example, the generative AI will evaluate elements such as color, composition, and creativity for artworks, and melody, rhythm, and originality for music pieces. Step 3: The presentation team will use the generative AI to announce the results of the judging. For example, the generative AI will analyze the results and specifically indicate the merits and areas for improvement of the participants' works, encouraging them to grow. Step 4: The interaction promotion department uses the generation AI to promote interaction between participants. For example, the generation AI may match participants with common interests based on their profiles and works. Step 5: The Growth Promotion Department uses generative AI to promote the participants' growth. For example, generative AI analyzes the participants' past work and feedback, and provides advice and training plans for the next competition.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A work submission department that uses generative AI to submit works; a reviewing section that reviews the works collected by the work collection section; a presentation section that presents the results of the works judged by the judging section; an interaction promotion section that promotes interaction between participants based on the judging results announced by the announcement section; a growth promotion unit that promotes the growth of participants based on the exchange promoted by the exchange promotion unit. A system characterized by:

2. The submission section will: The generative AI is used to analyze the participant's past work or interests and suggest individually customized themes.

2. The system of claim 1.

3. The Examination Division: The generation AI is used to learn from past review results and dynamically optimize review criteria.

2. The system of claim 1.

4. The presentation section: The generative AI will be used to translate the presentation content into multiple languages, making it easy for international participants to understand.

2. The system of claim 1.

5. The exchange promotion department The generating AI is used to analyze the participants' past interaction history and propose optimal matches.

2. The system of claim 1.

6. The growth promotion part is The generative AI learns from the participant's past work and feedback to provide an individually customized training plan.

2. The system of claim 1.

7. The submission section will: Propose themes that correspond to the participants' emotional state, and increase their motivation to create.

2. The system of claim 1.

Images