System

Abstract

An object of the system according to the embodiment is to hold a AI contest on a nationwide scale, to aggregate excellent ideas, and to improve AI literacy.SOLUTION: A system includes a contest holding part, an idea evaluation part, and a feedback providing part. The contest hosting section hosts a nationwide AI contest. An idea evaluation part evaluates the idea of the participant by using the generation AI. The feedback providing unit provides feedback based on a result evaluated by the idea evaluation 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] With conventional technology, the scale of AI contests was limited, which limited the ability to gather excellent ideas and improve AI literacy.

[0005] The system of the embodiment aims to hold a nationwide AI contest, collect excellent ideas, and improve AI literacy. [Means for solving the problem]

[0006] The system according to the embodiment includes a contest hosting unit, an idea evaluation unit, and a feedback providing unit. The contest hosting unit holds a nationwide AI contest. The idea evaluation unit evaluates participants' ideas using a generative AI. The feedback providing unit provides feedback based on the results of the evaluation by the idea evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can hold AI contests on a nationwide scale, consolidating excellent ideas and improving AI literacy. [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 AI ​​contest system according to an embodiment of the present invention is a system that holds nationwide AI contests, evaluates participants' ideas, and provides feedback. This enables the AI ​​contest system to aggregate excellent AI ideas and accelerate AI solutions.

[0029] An AI contest system according to an embodiment includes a contest hosting unit, an idea evaluation unit, and a feedback providing unit. The contest hosting unit hosts a nationwide AI contest. For example, it invites participants from all over the country and holds the contest both online and offline. The contest hosting unit can also set themes based on the characteristics and needs of each region, with each region addressing different challenges. For example, in agriculturally active regions, it can set challenges related to agricultural technology. The idea evaluation unit uses a generation AI to evaluate participants' ideas. For example, the generation AI can analyze the content of the ideas using a text generation AI (e.g., GPT-3) and assign scores based on evaluation criteria. The generation AI can also use an emotion estimation function to monitor participants' motivation and excitement levels in real time and provide appropriate support. For example, it can analyze facial expressions and voice to calculate an emotion score. The feedback providing unit provides feedback based on the results of the evaluation by the idea evaluation unit. For example, it can provide text comments, scoring, or advice. The feedback providing unit can also use the emotion estimation function to provide personalized engagement based on participants' emotions, thereby increasing their motivation to participate. For example, it can send a message based on the emotion score. As a result, the AI ​​contest system according to the embodiment can collect excellent AI ideas and accelerate AI solutions. For example, ideas that achieve excellent results in the contest can be adopted as actual projects and put into practical use in collaboration with companies and research institutions. In addition, based on the results of the contest, funds can be invested in research and development of AI technology, promoting further technological innovation.

[0030] The contest organizers can set themes that correspond to the characteristics and needs of each region, and tackle different challenges in each region. For example, the contest organizers can set different themes for each region, taking into account the characteristics and needs of each region. For example, in an area where agriculture is thriving, they could set challenges related to agricultural technology. The contest organizers also need to clarify how to define the characteristics of each region. For example, they can consider industrial structure, culture, demographics, etc. Furthermore, they need to clarify the specific content of the needs and the research method. For example, they can understand the needs of each region through questionnaire surveys and interviews. In this way, by setting themes that correspond to the characteristics and needs of each region, it is possible to promote AI solutions that are closely tied to the region.

[0031] The contest organizer can make the contest format a hybrid of online and offline, facilitating participation from remote locations. For example, the contest organizer could make the contest format a hybrid of online and offline and build a system that facilitates participation from remote locations. For example, it could allow for online idea submission and evaluation. The contest organizer also needs to clarify how to combine online and offline hybrid formats. For example, it could combine online presentations with offline workshops. Furthermore, it needs to clarify the specific scope and definition of remote locations. For example, it could define remote locations as areas more than 100 km away from urban areas. By adopting a hybrid online and offline format, it would be possible to facilitate participation from remote locations and increase the diversity of participants.

[0032] The contest organizer can pair participants of different age groups and professional backgrounds to fuse ideas from different perspectives. For example, the contest organizer could pair participants of different age groups and professional backgrounds and build a system to fuse ideas from different perspectives. For example, pairing students with corporate researchers. The contest organizer also needs to clarify how different age groups and professional backgrounds are defined. For example, students, engineers, and managers could be targeted. Furthermore, the specific pairing methods and criteria need to be clarified. For example, random pairing or pairing based on common interests could be used. By pairing participants of different age groups and professional backgrounds, ideas from different perspectives can be fused to create more diverse solutions.

[0033] The idea evaluation department can use generative AI to automatically classify ideas collected in a contest and identify the most promising ideas. The idea evaluation department could, for example, build a system that uses generative AI to automatically classify ideas collected in a contest and identify the most promising ideas. For example, the classification could be based on technological innovativeness or market demand. The idea evaluation department also needs to clarify specific evaluation criteria and selection methods for promising ideas. For example, technical feasibility and market demand could be taken into consideration. This allows the generative AI to automatically classify ideas and identify the most promising ideas, making it possible to efficiently select excellent ideas.

[0034] The idea evaluation department can provide a simulation environment and virtually implement an idea to evaluate its feasibility. For example, the idea evaluation department may build a system that provides a simulation environment to evaluate the feasibility of an idea. For example, the idea may be virtually implemented and technical issues verified. The idea evaluation department must also clarify the specific content and format of the simulation environment. For example, virtual reality or simulation software may be used. Furthermore, the specific methods and procedures for virtual implementation must be clarified. For example, a prototype may be created and the simulation results analyzed. In this way, by providing a simulation environment, the feasibility of an idea can be virtually evaluated and technical issues verified in advance.

[0035] The contest organizing team can hold workshops to apply the ideas gathered in the contest to different industries and applications. For example, the contest organizing team could regularly hold workshops to apply the ideas gathered in the contest to different industries and applications. For example, technical, design, and marketing experts could participate. The contest organizing team also needs to clarify the specific content and format of the different industries and applications. For example, targeting industries such as healthcare, education, and manufacturing. Furthermore, the specific format and content of the workshops need to be clarified. For example, hands-on sessions and discussions could be held. In this way, holding workshops can apply ideas to different industries and applications and promote AI solutions in a wide range of fields.

[0036] The contest organizing department can use the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans. For example, the contest organizing department builds a system that uses the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans. For example, it generates plans based on the learning content and progress status. The contest organizing department also needs to clarify the specific evaluation criteria and measurement methods for learning progress. For example, evaluation can be based on test results and assignment completion status. Furthermore, the contest organizing department needs to clarify the specific content and format of the individually optimized learning plan. For example, customization can be based on learning history and individual goals. In this way, the learning effect can be maximized by using the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans.

[0037] The competition organizer can provide online courses covering everything from the basics to applications of AI technology, allowing participants to learn at their own pace. For example, the competition organizer can provide online courses covering everything from the basics to applications of AI technology and build a system that allows participants to learn at their own pace. For example, it can provide video lectures and interactive exercises. The competition organizer must also clarify the specific content and format of the online course. For example, it can provide video lectures and interactive assignments. Furthermore, it must clarify the specific methods and support that will allow participants to learn at their own pace. For example, it can provide on-demand videos and self-assessment tests. In this way, by providing online courses covering everything from the basics to applications of AI technology, participants can progress through their learning at their own pace.

[0038] The contest organizer can develop AI literacy improvement programs targeted at people of different age groups and occupational backgrounds. For example, the contest organizer could provide programs targeted at people of all age groups and occupational backgrounds. The contest organizer should also clarify the specific content and format of the AI ​​literacy improvement program. For example, the program could include lectures on basic knowledge and practical workshops. By developing AI literacy improvement programs targeted at people of different age groups and occupational backgrounds, the contest organizer can improve AI literacy across a wide range of people.

[0039] The contest organizing department can issue badges or certificates to evaluate learning outcomes and increase the motivation of participants. The contest organizing department can, for example, build a system to issue badges or certificates to evaluate learning outcomes and increase the motivation of participants. For example, a badge can be issued when a specific task is completed. The contest organizing department also needs to clarify the specific criteria and issuing method for badges or certificates. For example, the criteria can be the completion of a specific task or passing a test. In this way, the issuance of badges or certificates to evaluate learning outcomes can increase the motivation of participants.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The competition organizers can change the theme of the AI ​​competition each season to provide participants with new challenges. For example, a spring theme could be environmental protection, a summer theme on revitalizing the tourism industry, a fall theme on innovations in educational technology, and a winter theme on improving medical technology. They can also hold special events and workshops tailored to each seasonal theme to attract participants' attention. This allows the competition to offer new challenges and increase diversity by setting different themes for each season.

[0042] The idea evaluation department can incorporate expert opinions when evaluating participants' ideas. For example, experts from various fields can be invited to form an evaluation committee, and a comprehensive evaluation can be made by combining the evaluation results from the generative AI with the expert opinions. Expert feedback can also be provided to participants, specifically indicating areas for improvement and strengths of the ideas. By incorporating expert opinions, this makes it possible to conduct more reliable evaluations and promote the growth of participants.

[0043] The feedback providing unit can provide feedback on participants' ideas in video format. For example, a video in which the evaluator directly comments can be created and sent to the participants. In addition, video-format feedback can visually show specific areas for improvement and success stories. Furthermore, an interactive function can be added that allows participants to post questions and comments on the feedback video. In this way, providing feedback in video format can improve the quality of feedback to participants and deepen their understanding.

[0044] The contest organizers can provide team-building opportunities for participants in the AI ​​contest. For example, they can create a system that allows participants to form teams through an online platform. They can also hold team-building workshops and networking events to create an environment where participants can cooperate with each other. By providing team-building opportunities, they can promote cooperation among participants and generate better ideas.

[0045] The competition organizers can provide mentoring programs for participants in AI competitions. For example, they can invite experienced mentors to provide individual advice and support to participants. They can also clarify the content and format of the mentoring program and conduct it both online and offline. They can also create a matching system between mentors and participants to select the most suitable mentor. By providing a mentoring program, they can support participants' growth and generate better ideas.

[0046] Contest organizers can provide opportunities for intercultural exchange for AI contest participants. For example, they can invite international participants to collaborate with people from different cultural backgrounds to develop ideas. They can also hold intercultural workshops and discussion sessions to create an environment where participants can understand each other's cultures. By providing opportunities for intercultural exchange, participants can broaden their perspectives and generate more diverse ideas.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The Contest Organizers will hold a nationwide AI contest. For example, participants could be invited from all over the country and the contest could be held both online and offline. It is also possible to set themes based on the characteristics and needs of each region, with each region addressing different challenges. For example, in areas where agriculture is thriving, a challenge related to agricultural technology could be set. Step 2: The idea evaluation unit uses a generation AI to evaluate the participants' ideas. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the idea and assign a score based on the evaluation criteria. The generation AI can also use an emotion estimation function to monitor participants' motivation and excitement in real time and provide appropriate support. For example, it can analyze facial expressions and voice to calculate an emotion score. Step 3: The feedback provider provides feedback based on the results of the idea evaluation. For example, it provides text comments, scores, or advice. It can also use the emotion estimation function to provide personalized engagement based on participants' emotions, increasing their motivation to participate. For example, it can send a message based on the emotion score.

[0049] (Example 2) The AI ​​contest system according to an embodiment of the present invention is a system that holds nationwide AI contests, evaluates participants' ideas, and provides feedback. This enables the AI ​​contest system to aggregate excellent AI ideas and accelerate AI solutions.

[0050] An AI contest system according to an embodiment includes a contest hosting unit, an idea evaluation unit, and a feedback providing unit. The contest hosting unit hosts a nationwide AI contest. For example, it invites participants from all over the country and holds the contest both online and offline. The contest hosting unit can also set themes based on the characteristics and needs of each region, with each region addressing different challenges. For example, in agriculturally active regions, it can set challenges related to agricultural technology. The idea evaluation unit uses a generation AI to evaluate participants' ideas. For example, the generation AI can analyze the content of the ideas using a text generation AI (e.g., GPT-3) and assign scores based on evaluation criteria. The generation AI can also use an emotion estimation function to monitor participants' motivation and excitement levels in real time and provide appropriate support. For example, it can analyze facial expressions and voice to calculate an emotion score. The feedback providing unit provides feedback based on the results of the evaluation by the idea evaluation unit. For example, it can provide text comments, scoring, or advice. The feedback providing unit can also use the emotion estimation function to provide personalized engagement based on participants' emotions, thereby increasing their motivation to participate. For example, it can send a message based on the emotion score. As a result, the AI ​​contest system according to the embodiment can collect excellent AI ideas and accelerate AI solutions. For example, ideas that achieve excellent results in the contest can be adopted as actual projects and put into practical use in collaboration with companies and research institutions. In addition, based on the results of the contest, funds can be invested in research and development of AI technology, promoting further technological innovation.

[0051] The contest organizers can set themes that correspond to the characteristics and needs of each region, and tackle different challenges in each region. For example, the contest organizers can set different themes for each region, taking into account the characteristics and needs of each region. For example, in an area where agriculture is thriving, they could set challenges related to agricultural technology. The contest organizers also need to clarify how to define the characteristics of each region. For example, they can consider industrial structure, culture, demographics, etc. Furthermore, they need to clarify the specific content of the needs and the research method. For example, they can understand the needs of each region through questionnaire surveys and interviews. In this way, by setting themes that correspond to the characteristics and needs of each region, it is possible to promote AI solutions that are closely tied to the region.

[0052] The idea evaluation unit can use the emotion estimation function to monitor participants' motivation and excitement levels in real time and provide appropriate support. The idea evaluation unit, for example, builds a system that uses the emotion estimation function to monitor participants' motivation and excitement levels in real time. For example, it analyzes facial expressions and voice to calculate an emotion score. The idea evaluation unit also needs to clarify the specific technology and algorithm of the emotion estimation function. For example, it uses facial expression recognition and voice analysis. Furthermore, it needs to clarify the specific evaluation criteria and measurement method for motivation. For example, it evaluates motivation through questionnaires and analysis of behavioral data. In this way, by monitoring participants' motivation and excitement levels in real time, it is possible to provide appropriate support and increase their willingness to participate.

[0053] The contest organizer can make the contest format a hybrid of online and offline, facilitating participation from remote locations. For example, the contest organizer could make the contest format a hybrid of online and offline and build a system that facilitates participation from remote locations. For example, it could allow for online idea submission and evaluation. The contest organizer also needs to clarify how to combine online and offline hybrid formats. For example, it could combine online presentations with offline workshops. Furthermore, it needs to clarify the specific scope and definition of remote locations. For example, it could define remote locations as areas more than 100 km away from urban areas. By adopting a hybrid online and offline format, it would be possible to facilitate participation from remote locations and increase the diversity of participants.

[0054] The contest organizer can pair participants of different age groups and professional backgrounds to fuse ideas from different perspectives. For example, the contest organizer could pair participants of different age groups and professional backgrounds and build a system to fuse ideas from different perspectives. For example, pairing students with corporate researchers. The contest organizer also needs to clarify how different age groups and professional backgrounds are defined. For example, students, engineers, and managers could be targeted. Furthermore, the specific pairing methods and criteria need to be clarified. For example, random pairing or pairing based on common interests could be used. By pairing participants of different age groups and professional backgrounds, ideas from different perspectives can be fused to create more diverse solutions.

[0055] The idea evaluation unit can use the emotion estimation function to provide personalized engagement based on participants' emotions, thereby increasing their willingness to participate. The idea evaluation unit, for example, uses the emotion estimation function to build a system that provides personalized engagement based on participants' emotions. For example, it sends messages according to emotion scores. The idea evaluation unit also needs to clarify specific methods and criteria for personalized engagement. For example, it can customize the engagement based on individual interests and past behavioral data. Furthermore, it needs to clarify specific evaluation criteria and measurement methods for willingness to participate. For example, it can evaluate willingness to participate through a survey or an analysis of behavioral data. This makes it possible to increase willingness to participate by using the emotion estimation function to provide personalized engagement based on participants' emotions.

[0056] The idea evaluation department can use generative AI to automatically classify ideas collected in a contest and identify the most promising ideas. The idea evaluation department could, for example, build a system that uses generative AI to automatically classify ideas collected in a contest and identify the most promising ideas. For example, the classification could be based on technological innovativeness or market demand. The idea evaluation department also needs to clarify specific evaluation criteria and selection methods for promising ideas. For example, technical feasibility and market demand could be taken into consideration. This allows the generative AI to automatically classify ideas and identify the most promising ideas, making it possible to efficiently select excellent ideas.

[0057] The idea evaluation department can provide a simulation environment and virtually implement an idea to evaluate its feasibility. For example, the idea evaluation department may build a system that provides a simulation environment to evaluate the feasibility of an idea. For example, the idea may be virtually implemented and technical issues verified. The idea evaluation department must also clarify the specific content and format of the simulation environment. For example, virtual reality or simulation software may be used. Furthermore, the specific methods and procedures for virtual implementation must be clarified. For example, a prototype may be created and the simulation results analyzed. In this way, by providing a simulation environment, the feasibility of an idea can be virtually evaluated and technical issues verified in advance.

[0058] The idea evaluation unit can use the emotion estimation function to analyze the user's emotional reactions during the idea implementation process and identify areas for improvement. The idea evaluation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional reactions during the idea implementation process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotional score. The idea evaluation unit also needs to clarify specific methods for measuring emotional reactions and evaluation criteria. For example, it uses facial expression recognition and voice analysis. Furthermore, it needs to clarify specific methods for identifying areas for improvement and evaluation criteria. For example, it identifies areas for improvement based on user feedback and performance data. In this way, it is possible to provide a better solution by using the emotion estimation function to analyze the user's emotional reactions and identify specific areas for improvement.

[0059] The contest organizing team can hold workshops to apply the ideas gathered in the contest to different industries and applications. For example, the contest organizing team could regularly hold workshops to apply the ideas gathered in the contest to different industries and applications. For example, technical, design, and marketing experts could participate. The contest organizing team also needs to clarify the specific content and format of the different industries and applications. For example, targeting industries such as healthcare, education, and manufacturing. Furthermore, the specific format and content of the workshops need to be clarified. For example, hands-on sessions and discussions could be held. In this way, holding workshops can apply ideas to different industries and applications and promote AI solutions in a wide range of fields.

[0060] The idea evaluation unit can use the emotion estimation function to provide real-time feedback on the user's emotional reactions during the idea implementation process, thereby pursuing the optimal solution. The idea evaluation unit, for example, builds a system that uses the emotion estimation function to provide real-time feedback on the user's emotional reactions during the idea implementation process. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The idea evaluation unit also needs to clarify specific evaluation criteria and selection methods for the optimal solution. For example, it considers technical feasibility and cost efficiency. This allows the optimal solution to be pursued by using the emotion estimation function to provide real-time feedback on the user's emotional reactions.

[0061] The contest organizing department can use the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans. For example, the contest organizing department builds a system that uses the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans. For example, it generates plans based on the learning content and progress status. The contest organizing department also needs to clarify the specific evaluation criteria and measurement methods for learning progress. For example, evaluation can be based on test results and assignment completion status. Furthermore, the contest organizing department needs to clarify the specific content and format of the individually optimized learning plan. For example, customization can be based on learning history and individual goals. In this way, the learning effect can be maximized by using the generative AI to evaluate the learning progress of participants in real time and provide individually optimized learning plans.

[0062] The competition organizer can provide online courses covering everything from the basics to applications of AI technology, allowing participants to learn at their own pace. For example, the competition organizer can provide online courses covering everything from the basics to applications of AI technology and build a system that allows participants to learn at their own pace. For example, it can provide video lectures and interactive exercises. The competition organizer must also clarify the specific content and format of the online course. For example, it can provide video lectures and interactive assignments. Furthermore, it must clarify the specific methods and support that will allow participants to learn at their own pace. For example, it can provide on-demand videos and self-assessment tests. In this way, by providing online courses covering everything from the basics to applications of AI technology, participants can progress through their learning at their own pace.

[0063] The idea evaluation unit can use the emotion estimation function to monitor the emotions of participants during learning and provide content that increases motivation at the appropriate time. The idea evaluation unit, for example, uses the emotion estimation function to build a system that monitors the emotions of participants during learning in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. The idea evaluation unit also needs to clarify the specific content and format of the content that increases motivation. For example, it could introduce success stories or provide incentives. In this way, the learning effect can be maximized by using the emotion estimation function to monitor the emotions of participants during learning and providing content that increases motivation at the appropriate time.

[0064] The contest organizer can develop AI literacy improvement programs targeted at people of different age groups and occupational backgrounds. For example, the contest organizer could provide programs targeted at people of all age groups and occupational backgrounds. The contest organizer should also clarify the specific content and format of the AI ​​literacy improvement program. For example, the program could include lectures on basic knowledge and practical workshops. By developing AI literacy improvement programs targeted at people of different age groups and occupational backgrounds, the contest organizer can improve AI literacy across a wide range of people.

[0065] The contest organizing department can issue badges or certificates to evaluate learning outcomes and increase the motivation of participants. The contest organizing department can, for example, build a system to issue badges or certificates to evaluate learning outcomes and increase the motivation of participants. For example, a badge can be issued when a specific task is completed. The contest organizing department also needs to clarify the specific criteria and issuing method for badges or certificates. For example, the criteria can be the completion of a specific task or passing a test. In this way, the issuance of badges or certificates to evaluate learning outcomes can increase the motivation of participants.

[0066] The idea evaluation unit can use the emotion estimation function to provide feedback based on the emotions of participants during learning, thereby maximizing the learning effect. The idea evaluation unit, for example, uses the emotion estimation function to build a system that provides feedback based on the emotions of participants during learning. For example, it sends a message according to the emotion score. The idea evaluation unit also needs to clarify specific evaluation criteria and measurement methods for learning effect. For example, evaluation can be based on test results or self-assessment. In this way, the learning effect can be maximized by using the emotion estimation function to provide feedback based on the emotions of participants during learning.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The competition organizers can change the theme of the AI ​​competition each season to provide participants with new challenges. For example, a spring theme could be environmental protection, a summer theme on revitalizing the tourism industry, a fall theme on innovations in educational technology, and a winter theme on improving medical technology. They can also hold special events and workshops tailored to each seasonal theme to attract participants' attention. This allows the competition to offer new challenges and increase diversity by setting different themes for each season.

[0069] The idea evaluation department can incorporate expert opinions when evaluating participants' ideas. For example, experts from various fields can be invited to form an evaluation committee, and a comprehensive evaluation can be made by combining the evaluation results from the generative AI with the expert opinions. Expert feedback can also be provided to participants, specifically indicating areas for improvement and strengths of the ideas. By incorporating expert opinions, this makes it possible to conduct more reliable evaluations and promote the growth of participants.

[0070] The feedback providing unit can provide feedback on participants' ideas in video format. For example, a video in which the evaluator directly comments can be created and sent to the participants. In addition, video-format feedback can visually show specific areas for improvement and success stories. Furthermore, an interactive function can be added that allows participants to post questions and comments on the feedback video. In this way, providing feedback in video format can improve the quality of feedback to participants and deepen their understanding.

[0071] The contest organizers can provide team-building opportunities for participants in the AI ​​contest. For example, they can create a system that allows participants to form teams through an online platform. They can also hold team-building workshops and networking events to create an environment where participants can cooperate with each other. By providing team-building opportunities, they can promote cooperation among participants and generate better ideas.

[0072] The idea evaluation unit can use the emotion estimation function to monitor participants' stress levels and provide appropriate relaxation content. For example, it can analyze facial expressions and voice to calculate a stress score. It can also provide relaxation music or guided meditations based on the stress score. It can also send a message encouraging participants with high stress levels to take a break. In this way, by monitoring participants' stress levels using the emotion estimation function and providing appropriate relaxation content, it is possible to improve the health and performance of participants.

[0073] The feedback providing unit can use the emotion estimation function to analyze the participants' emotional reactions to the feedback and adjust the content of the feedback. For example, it can analyze the facial expressions and voices of the participants when receiving the feedback and calculate an emotion score. It can also adjust the tone and content of the feedback based on the emotion score and provide the feedback in a form that is easy for the participants to accept. In this way, by adjusting the content of the feedback using the emotion estimation function, it is possible to increase the participants' acceptability and maximize the effectiveness of the feedback.

[0074] The competition organizers can provide mentoring programs for participants in AI competitions. For example, they can invite experienced mentors to provide individual advice and support to participants. They can also clarify the content and format of the mentoring program and conduct it both online and offline. They can also create a matching system between mentors and participants to select the most suitable mentor. By providing a mentoring program, they can support participants' growth and generate better ideas.

[0075] The idea evaluation unit can use the emotion estimation function to evaluate participants' presentation skills and identify areas for improvement. For example, it can analyze facial expressions and voice during the presentation to calculate an emotion score. Based on the emotion score, it can also specifically indicate the strengths of the presentation and areas for improvement. It can also provide training programs to improve presentation skills and support participants so that they can give presentations with confidence. In this way, using the emotion estimation function to evaluate presentation skills and identify areas for improvement can promote skill improvement for participants.

[0076] The feedback providing unit can use the emotion estimation function to monitor participants' emotional reactions to feedback in real time and adjust the content of the feedback. For example, it can analyze facial expressions and voice when receiving feedback and calculate an emotion score. It can also adjust the tone and content of the feedback based on the emotion score and provide it in a form that is easy for participants to accept. In this way, by adjusting the content of the feedback using the emotion estimation function, it is possible to increase participants' acceptability and maximize the effectiveness of the feedback.

[0077] Contest organizers can provide opportunities for intercultural exchange for AI contest participants. For example, they can invite international participants to collaborate with people from different cultural backgrounds to develop ideas. They can also hold intercultural workshops and discussion sessions to create an environment where participants can understand each other's cultures. By providing opportunities for intercultural exchange, participants can broaden their perspectives and generate more diverse ideas.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The Contest Organizers will hold a nationwide AI contest. For example, participants could be invited from all over the country and the contest could be held both online and offline. It is also possible to set themes based on the characteristics and needs of each region, with each region addressing different challenges. For example, in areas where agriculture is thriving, a challenge related to agricultural technology could be set. Step 2: The idea evaluation unit uses a generation AI to evaluate the participants' ideas. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the content of the idea and assign a score based on the evaluation criteria. The generation AI can also use an emotion estimation function to monitor participants' motivation and excitement in real time and provide appropriate support. For example, it can analyze facial expressions and voice to calculate an emotion score. Step 3: The feedback provider provides feedback based on the results of the idea evaluation. For example, it provides text comments, scores, or advice. It can also use the emotion estimation function to provide personalized engagement based on participants' emotions, increasing their motivation to participate. For example, it can send a message based on the emotion score.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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. 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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. The Contest Organizing Division, which organizes nationwide AI contests, An idea evaluation section that uses generative AI to evaluate participants' ideas; a feedback providing unit that provides feedback based on the result of evaluation by the idea evaluation unit. A system characterized by:

2. The contest organizing department: The contest will be a hybrid of online and offline formats to facilitate remote participation.

2. The system of claim 1.

3. The idea evaluation unit Using the generative AI to automatically classify the ideas collected in the contest and identify the most promising ideas.

2. The system of claim 1.

4. The contest organizing department: The generative AI is used to evaluate the participant's learning progress in real time and provide an individually optimized learning plan.

2. The system of claim 1.

5. The idea evaluation unit Emotion estimation function monitors participants' motivation and excitement in real time and provides appropriate support 2. The system of claim 1.

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