system

Abstract

An object of the system according to the embodiment is to provide a new added value in order to improve the ARPU of mobile business.SOLUTION: A system includes a plan provision part, a contest holding part, and a privilege provision part. The plan providing part is a high price zone brand and provides a large capacity plan. A contest holding part holds an idea collecting contest by utilizing the generated AI. The privilege providing unit provides a contest participation right as a privilege to a user who has joined the plan provided by the plan providing 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 existing technology, ARPU in the mobile business is declining, and there is room for improvement in providing new added value to increase revenue.

[0005] The system according to the embodiment aims to provide new added value to improve ARPU in the mobile business. [Means for solving the problem]

[0006] The system according to the embodiment includes a plan providing unit, a contest hosting unit, and a reward providing unit. The plan providing unit provides high-end brand, large-capacity plans. The contest hosting unit uses a generation AI to host an idea contest. The reward providing unit provides a right to participate in the contest as a reward to users who subscribe to the plan provided by the plan providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide new added value to improve the ARPU of the mobile business. [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 idea collection system according to the embodiment of the present invention is a system that increases the ARPU of the mobile business by providing a special privilege of the right to participate in an idea collection contest that utilizes a generation AI. As a result, the idea collection system can increase the added value of users and increase the profitability of the mobile business.

[0029] An idea solicitation system according to an embodiment includes a plan providing unit, a contest hosting unit, and a reward providing unit. The plan providing unit provides high-price brand, high-capacity plans. For example, users can not only use more data but also enjoy premium services. The plan providing unit allows users to comfortably use data-intensive services such as video streaming and online games. The contest hosting unit uses a generation AI to host an idea solicitation contest. For example, the generation AI analyzes user input and evaluates and classifies submitted ideas. The contest hosting unit solicits ideas for new applications and proposals for improving mobile services. The reward providing unit provides users who subscribe to a plan provided by the plan providing unit with the right to participate in a contest as a reward. For example, if an excellent idea is selected, a prize or reward is awarded. The reward providing unit, for example, provides users with an opportunity to present their ideas. As a result, the idea solicitation system according to an embodiment can improve the added value of users and increase the profits of mobile businesses.

[0030] The plan providing unit can analyze the user's data usage pattern using generation AI and automatically propose the optimal plan. The plan providing unit, for example, builds a system that analyzes the user's data usage pattern using generation AI and automatically proposes the optimal plan. For example, it proposes the plan that the user can use most efficiently based on past data usage history. For example, the plan providing unit analyzes the user's data usage pattern and proposes a plan that allows comfortable use of services that consume a lot of data. In this way, it is possible to improve user satisfaction by proposing the optimal plan to the user.

[0031] The plan providing unit can provide dedicated customer support to users who subscribe to high-price plans, thereby accelerating problem resolution. The plan providing unit, for example, builds a system that provides dedicated customer support to users who subscribe to high-price plans. For example, it sets up a dedicated support team to provide prompt responses. The plan providing unit, for example, sets up a dedicated support desk to quickly resolve users' problems. In this way, by providing dedicated customer support, users' problems can be quickly resolved.

[0032] The privilege provision unit can provide users who subscribe to a high-price plan with the right to participate in exclusive events and webinars. For example, the privilege provision unit builds a system that provides users who subscribe to a high-price plan with the right to participate in exclusive events and webinars. For example, an expert lecture or workshop on a specific topic is held. The privilege provision unit provides, for example, events and webinars based on topics that interest the user. In this way, by providing the right to participate in exclusive events and webinars, user satisfaction can be improved.

[0033] The benefit providing unit can add a sharing function that allows data from a large-capacity plan to be shared with family and friends. The benefit providing unit, for example, builds a system that adds a function that allows data from a large-capacity plan to be shared with family and friends. For example, the benefit providing unit allows a user to distribute their data to other accounts. For example, the benefit providing unit provides a data sharing function that allows a user to share data with family and friends. In this way, adding the data sharing function can increase the attractiveness of the plan.

[0034] The contest organizing department can use generative AI to automatically evaluate the originality and feasibility of submitted ideas. For example, the contest organizing department uses generative AI to build a system that automatically evaluates the originality and feasibility of submitted ideas. For example, the contest organizing department evaluates the originality by comparing with a database of past ideas. For example, the contest organizing department sets technical standards for evaluating the feasibility of ideas. This enables efficient idea selection by automatically evaluating the originality and feasibility of submitted ideas.

[0035] The contest hosting unit can periodically change the theme of the contest to keep users interested. The contest hosting unit, for example, periodically changes the theme of the contest to build a system that keeps users interested. For example, a different theme can be set every month to provide users with a new challenge. The contest hosting unit, for example, selects a theme based on users' interests and holds the contest. In this way, by periodically changing the theme of the contest, users' interest can be kept.

[0036] The contest hosting unit can provide contest participants with idea brush-up sessions using generative AI. The contest hosting unit, for example, builds a system that provides contest participants with idea brush-up sessions using generative AI. For example, the contest hosting unit presents specific improvement proposals for submitted ideas. For example, the contest hosting unit provides specific advice for improving participants' ideas. In this way, by providing idea brush-up sessions using generative AI, the quality of participants' ideas can be improved.

[0037] The contest organizing unit can hold a panel discussion inviting experts from different industries and evaluate the ideas. The contest organizing unit, for example, holds a panel discussion inviting experts from different industries and builds a system for evaluating submitted ideas. For example, experts in technology, design, and marketing participate. The contest organizing unit, for example, sets criteria for evaluating ideas based on the opinions of the experts. In this way, by holding a panel discussion inviting experts from different industries, ideas can be evaluated from multiple perspectives.

[0038] The reward provider can provide a dedicated online community to users who have obtained the right to participate in the contest, thereby promoting information exchange. The reward provider, for example, builds a system that provides a dedicated online community to users who have obtained the right to participate in the contest. For example, a forum or chat room can be set up to promote information exchange between users. The reward provider, for example, provides a platform for users to share ideas and receive feedback. In this way, by providing a dedicated online community, it is possible to promote information exchange between users and improve the quality of ideas.

[0039] The reward providing unit can provide contest participants with real-time feedback on their ideas using the generative AI. The reward providing unit, for example, builds a system that provides contest participants with real-time feedback on their ideas using the generative AI. For example, it immediately evaluates and presents improvement suggestions for submitted ideas. For example, when a user submits an idea, the reward providing unit has the generative AI analyze the idea and provide feedback in real time. In this way, by providing real-time feedback using the generative AI, the quality of the participants' ideas can be improved.

[0040] The reward provider can provide limited digital content and tools to users who have obtained the right to participate in a contest. The reward provider, for example, builds a system that provides limited digital content and tools to users who have obtained the right to participate in a contest. For example, the reward provider provides special guidebooks and templates. For example, the reward provider provides digital tools and resources for users to materialize their ideas. In this way, by providing limited digital content and tools, it is possible to improve user convenience.

[0041] The reward provision unit can provide support for contest participants in presenting their ideas using generative AI. The reward provision unit, for example, builds a system that provides support for contest participants in presenting their ideas using generative AI. For example, the reward provision unit automatically suggests the structure and design of the presentation. For example, the reward provision unit provides a support tool that enables users to effectively present their ideas. In this way, by providing presentation support using generative AI, it is possible to support participants in effectively presenting their ideas.

[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 plan provider analyzes the user's data usage patterns and proposes the optimal plan, as well as providing customized plans tailored to the user's lifestyle. For example, for a user who travels frequently, a plan that allows for inexpensive data roaming overseas can be proposed. Furthermore, for students, a data plan specialized for online learning can be offered. Furthermore, for users who work remotely, a plan that allows for comfortable use of video conferencing and cloud services can be proposed. In this way, by providing customized plans tailored to the user's lifestyle, user satisfaction can be improved.

[0044] The reward provision unit can monitor the user's health status and provide health-related rewards. For example, by linking with a fitness tracker, it can provide a data bonus to a user who has achieved a certain amount of exercise. Furthermore, it can provide the right to participate in a health improvement program based on the results of a health check. It can also monitor stress levels and provide free access to a relaxation app. In this way, by monitoring the user's health status and providing health-related rewards, it is possible to increase the user's health awareness.

[0045] The contest organizer can provide a platform where users can share the ideas they have submitted with other users and receive feedback. For example, a dedicated forum can be set up for users to post ideas and receive comments and ratings from other users. It can also hold online workshops where users can work together to refine their ideas. Furthermore, it can introduce a system whereby prize money or other benefits are awarded to outstanding ideas based on votes from other users. This can promote interaction between users and improve the quality of ideas.

[0046] The plan providing unit can analyze a user's data usage pattern and offer discount plans for specific time periods to users who use a lot of data during specific time periods. For example, a night-time-only data discount plan can be offered to a user who uses a lot of data at night. Furthermore, a weekend-only data bonus can be offered to a user who uses a lot of data on weekends. Furthermore, a special plan for a specific event period can be offered to a user whose data usage increases during that period. In this way, user satisfaction can be improved by offering discount plans for specific time periods that match the user's data usage pattern.

[0047] The contest organizer can provide a crowdfunding platform for realizing ideas submitted by users. For example, users can post their ideas and solicit support from other users. Supporters can also be offered rewards when their ideas are realized. Furthermore, a dashboard can be provided that allows users to check the progress of crowdfunding in real time. This allows users to raise funds to realize their ideas, increasing the possibility of their ideas being realized.

[0048] The plan provider can analyze a user's data usage pattern and provide plans with unlimited data usage for specific applications. For example, a user who frequently uses video streaming apps can be offered a plan with unlimited data usage for those apps. Furthermore, a user who frequently uses social media apps can be offered a plan with unlimited data usage for those apps. Furthermore, a user who enjoys online games can be offered a plan with unlimited data usage for game apps. This allows for increased user satisfaction by providing unlimited plans tailored to the user's data usage pattern.

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

[0050] Step 1: The plan provider offers high-price brands and large-capacity plans. For example, users can not only use more data but also enjoy premium services. The plan provider allows users to comfortably use data-intensive services such as video streaming and online games. Step 2: The contest organizer uses the generative AI to hold an idea contest. For example, the generative AI analyzes user input and evaluates and categorizes the proposed ideas. The contest organizer then solicits ideas for new applications, proposals for improving mobile services, and so on. Step 3: The reward provider provides the right to participate in the contest as a reward to users who subscribe to the plan provided by the plan provider. For example, if an excellent idea is selected, a prize or reward may be awarded. The reward provider may, for example, provide users with an opportunity to present their ideas.

[0051] (Example 2) The idea collection system according to the embodiment of the present invention is a system that increases the ARPU of the mobile business by providing a special privilege of the right to participate in an idea collection contest that utilizes a generation AI. As a result, the idea collection system can increase the added value of users and increase the profitability of the mobile business.

[0052] An idea solicitation system according to an embodiment includes a plan providing unit, a contest hosting unit, and a reward providing unit. The plan providing unit provides high-price brand, high-capacity plans. For example, users can not only use more data but also enjoy premium services. The plan providing unit allows users to comfortably use data-intensive services such as video streaming and online games. The contest hosting unit uses a generation AI to host an idea solicitation contest. For example, the generation AI analyzes user input and evaluates and classifies submitted ideas. The contest hosting unit solicits ideas for new applications and proposals for improving mobile services. The reward providing unit provides users who subscribe to a plan provided by the plan providing unit with the right to participate in a contest as a reward. For example, if an excellent idea is selected, a prize or reward is awarded. The reward providing unit, for example, provides users with an opportunity to present their ideas. As a result, the idea solicitation system according to an embodiment can improve the added value of users and increase the profits of mobile businesses.

[0053] The plan providing unit can analyze the user's data usage pattern using generation AI and automatically propose the optimal plan. The plan providing unit, for example, builds a system that analyzes the user's data usage pattern using generation AI and automatically proposes the optimal plan. For example, it proposes the plan that the user can use most efficiently based on past data usage history. For example, the plan providing unit analyzes the user's data usage pattern and proposes a plan that allows comfortable use of services that consume a lot of data. In this way, it is possible to improve user satisfaction by proposing the optimal plan to the user.

[0054] The plan providing unit can provide dedicated customer support to users who subscribe to high-price plans, thereby accelerating problem resolution. The plan providing unit, for example, builds a system that provides dedicated customer support to users who subscribe to high-price plans. For example, it sets up a dedicated support team to provide prompt responses. The plan providing unit, for example, sets up a dedicated support desk to quickly resolve users' problems. In this way, by providing dedicated customer support, users' problems can be quickly resolved.

[0055] The plan providing unit can use the emotion estimation function to monitor user satisfaction in real time and identify areas for improvement in the plan. The plan providing unit, for example, uses the emotion estimation function to build a system for monitoring user satisfaction in real time. For example, the plan providing unit analyzes the user's facial expressions and voice and calculates a satisfaction score. The plan providing unit, for example, monitors user satisfaction and collects data for identifying areas for improvement in the plan. This makes it possible to monitor user satisfaction in real time and identify areas for improvement in the plan.

[0056] The privilege provision unit can provide users who subscribe to a high-price plan with the right to participate in exclusive events and webinars. For example, the privilege provision unit builds a system that provides users who subscribe to a high-price plan with the right to participate in exclusive events and webinars. For example, an expert lecture or workshop on a specific topic is held. The privilege provision unit provides, for example, events and webinars based on topics that interest the user. In this way, by providing the right to participate in exclusive events and webinars, user satisfaction can be improved.

[0057] The benefit providing unit can add a sharing function that allows data from a large-capacity plan to be shared with family and friends. The benefit providing unit, for example, builds a system that adds a function that allows data from a large-capacity plan to be shared with family and friends. For example, the benefit providing unit allows a user to distribute their data to other accounts. For example, the benefit providing unit provides a data sharing function that allows a user to share data with family and friends. In this way, adding the data sharing function can increase the attractiveness of the plan.

[0058] The reward provision unit can use the emotion estimation function to identify the content in which the user is most interested and provide personalized recommended content. The reward provision unit, for example, uses the emotion estimation function to build a system that identifies the content in which the user is most interested. For example, the reward provision unit analyzes the user's facial expressions and voice and calculates an interest score. For example, the reward provision unit collects data for providing content based on the user's interests. This can improve user satisfaction by providing content that matches the user's interests.

[0059] The contest organizing department can use generative AI to automatically evaluate the originality and feasibility of submitted ideas. For example, the contest organizing department uses generative AI to build a system that automatically evaluates the originality and feasibility of submitted ideas. For example, the contest organizing department evaluates the originality by comparing with a database of past ideas. For example, the contest organizing department sets technical standards for evaluating the feasibility of ideas. This enables efficient idea selection by automatically evaluating the originality and feasibility of submitted ideas.

[0060] The contest hosting unit can periodically change the theme of the contest to keep users interested. The contest hosting unit, for example, periodically changes the theme of the contest to build a system that keeps users interested. For example, a different theme can be set every month to provide users with a new challenge. The contest hosting unit, for example, selects a theme based on users' interests and holds the contest. In this way, by periodically changing the theme of the contest, users' interest can be kept.

[0061] The contest hosting unit can use the emotion estimation function to analyze the emotions of users when they submit ideas and provide positive feedback. The contest hosting unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of users when they submit ideas. For example, the contest hosting unit analyzes the user's facial expressions and voice and calculates an emotion score. For example, the contest hosting unit collects data for providing positive feedback based on the user's emotions. This makes it possible to analyze the emotions of users when they submit ideas and provide positive feedback, thereby increasing the user's motivation.

[0062] The contest hosting unit can provide contest participants with idea brush-up sessions using generative AI. The contest hosting unit, for example, builds a system that provides contest participants with idea brush-up sessions using generative AI. For example, the contest hosting unit presents specific improvement proposals for submitted ideas. For example, the contest hosting unit provides specific advice for improving participants' ideas. In this way, by providing idea brush-up sessions using generative AI, the quality of participants' ideas can be improved.

[0063] The contest organizing unit can hold a panel discussion inviting experts from different industries and evaluate the ideas. The contest organizing unit, for example, holds a panel discussion inviting experts from different industries and builds a system for evaluating submitted ideas. For example, experts in technology, design, and marketing participate. The contest organizing unit, for example, sets criteria for evaluating ideas based on the opinions of the experts. In this way, by holding a panel discussion inviting experts from different industries, ideas can be evaluated from multiple perspectives.

[0064] The contest hosting unit can use the emotion estimation function to identify a theme in which a user is most interested and hold a contest based on that theme. The contest hosting unit, for example, uses the emotion estimation function to build a system that identifies a theme in which a user is most interested. For example, the system analyzes the user's facial expressions and voice and calculates an interest score. The contest hosting unit, for example, selects a theme based on the user's interest and holds a contest based on that theme. In this way, by holding a contest based on a theme in which a user is most interested, it is possible to increase the user's motivation to participate.

[0065] The reward provider can provide a dedicated online community to users who have obtained the right to participate in the contest, thereby promoting information exchange. The reward provider, for example, builds a system that provides a dedicated online community to users who have obtained the right to participate in the contest. For example, a forum or chat room can be set up to promote information exchange between users. The reward provider, for example, provides a platform for users to share ideas and receive feedback. In this way, by providing a dedicated online community, it is possible to promote information exchange between users and improve the quality of ideas.

[0066] The reward providing unit can provide contest participants with real-time feedback on their ideas using the generative AI. The reward providing unit, for example, builds a system that provides contest participants with real-time feedback on their ideas using the generative AI. For example, it immediately evaluates and presents improvement suggestions for submitted ideas. For example, when a user submits an idea, the reward providing unit has the generative AI analyze the idea and provide feedback in real time. In this way, by providing real-time feedback using the generative AI, the quality of the participants' ideas can be improved.

[0067] The reward provision unit can use the emotion estimation function to design incentives to increase user motivation. The reward provision unit, for example, uses the emotion estimation function to build a system that designs incentives to increase user motivation. For example, the reward provision unit provides appropriate incentives based on the user's emotion score. For example, the reward provision unit analyzes the user's emotion data and designs rewards and rewards to increase motivation. In this way, by designing incentives to increase user motivation, it is possible to increase the user's willingness to participate.

[0068] The reward provider can provide limited digital content and tools to users who have obtained the right to participate in a contest. The reward provider, for example, builds a system that provides limited digital content and tools to users who have obtained the right to participate in a contest. For example, the reward provider provides special guidebooks and templates. For example, the reward provider provides digital tools and resources for users to materialize their ideas. In this way, by providing limited digital content and tools, it is possible to improve user convenience.

[0069] The reward provision unit can provide support for contest participants in presenting their ideas using generative AI. The reward provision unit, for example, builds a system that provides support for contest participants in presenting their ideas using generative AI. For example, the reward provision unit automatically suggests the structure and design of the presentation. For example, the reward provision unit provides a support tool that enables users to effectively present their ideas. In this way, by providing presentation support using generative AI, it is possible to support participants in effectively presenting their ideas.

[0070] The reward provision unit can use the emotion estimation function to identify the reward that the user is most interested in and provide that reward. The reward provision unit, for example, uses the emotion estimation function to build a system that identifies the reward that the user is most interested in. For example, the reward provision unit analyzes the user's facial expressions and voice and calculates an interest score. For example, the reward provision unit collects data for providing rewards based on the user's interests. This allows the user to be provided with the reward that the user is most interested in, thereby improving user satisfaction.

[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 plan provider analyzes the user's data usage patterns and proposes the optimal plan, as well as providing customized plans tailored to the user's lifestyle. For example, for a user who travels frequently, a plan that allows for inexpensive data roaming overseas can be proposed. Furthermore, for students, a data plan specialized for online learning can be offered. Furthermore, for users who work remotely, a plan that allows for comfortable use of video conferencing and cloud services can be proposed. In this way, by providing customized plans tailored to the user's lifestyle, user satisfaction can be improved.

[0073] The reward provision unit can monitor the user's health status and provide health-related rewards. For example, by linking with a fitness tracker, it can provide a data bonus to a user who has achieved a certain amount of exercise. Furthermore, it can provide the right to participate in a health improvement program based on the results of a health check. It can also monitor stress levels and provide free access to a relaxation app. In this way, by monitoring the user's health status and providing health-related rewards, it is possible to increase the user's health awareness.

[0074] The contest organizer can provide a platform where users can share the ideas they have submitted with other users and receive feedback. For example, a dedicated forum can be set up for users to post ideas and receive comments and ratings from other users. It can also hold online workshops where users can work together to refine their ideas. Furthermore, it can introduce a system whereby prize money or other benefits are awarded to outstanding ideas based on votes from other users. This can promote interaction between users and improve the quality of ideas.

[0075] The reward provider can use the emotion estimation function to monitor the user's stress level and provide relaxation content. For example, if the user's facial expressions and voice are analyzed and a high stress level is determined, relaxation music or a meditation guide is provided. Furthermore, activities to reduce stress can be suggested. Also, depending on the user's stress level, the reward provider can provide the user with the right to participate in a specific relaxation event. This makes it possible to monitor the user's stress level and provide appropriate relaxation content to support the user's physical and mental health.

[0076] The plan providing unit can analyze a user's data usage pattern and offer discount plans for specific time periods to users who use a lot of data during specific time periods. For example, a night-time-only data discount plan can be offered to a user who uses a lot of data at night. Furthermore, a weekend-only data bonus can be offered to a user who uses a lot of data on weekends. Furthermore, a special plan for a specific event period can be offered to a user whose data usage increases during that period. In this way, user satisfaction can be improved by offering discount plans for specific time periods that match the user's data usage pattern.

[0077] The reward provider can use the emotion estimation function to identify the user's interests and provide personalized learning content. For example, it can analyze the user's facial expressions and voice to identify areas of interest. It can then suggest online courses and webinars related to those areas. It can also suggest the next topic to study based on the user's learning progress. This can increase the user's motivation to learn by providing learning content based on the user's interests.

[0078] The contest organizer can provide a crowdfunding platform for realizing ideas submitted by users. For example, users can post their ideas and solicit support from other users. Supporters can also be offered rewards when their ideas are realized. Furthermore, a dashboard can be provided that allows users to check the progress of crowdfunding in real time. This allows users to raise funds to realize their ideas, increasing the possibility of their ideas being realized.

[0079] The reward provision unit can use the emotion estimation function to introduce gamification elements to increase user motivation. For example, by analyzing the user's facial expressions and voice, if it determines that the user's motivation is declining, it can suggest challenges that allow the user to earn points or badges. Furthermore, it can also provide rewards and benefits according to the goals achieved. It can also incorporate a competitive element with other users and display rankings. In this way, by introducing gamification elements to increase user motivation, it is possible to increase the user's willingness to participate.

[0080] The plan provider can analyze a user's data usage pattern and provide plans with unlimited data usage for specific applications. For example, a user who frequently uses video streaming apps can be offered a plan with unlimited data usage for those apps. Furthermore, a user who frequently uses social media apps can be offered a plan with unlimited data usage for those apps. Furthermore, a user who enjoys online games can be offered a plan with unlimited data usage for game apps. This allows for increased user satisfaction by providing unlimited plans tailored to the user's data usage pattern.

[0081] The contest hosting unit can use the emotion estimation function to analyze the emotions of users when they submit their ideas and provide positive feedback. For example, the contest hosting unit can analyze the user's facial expressions and voice and calculate an emotion score. Furthermore, if the user is nervous when submitting their idea, the contest hosting unit can provide advice to help them relax. It can also send encouraging messages to help the user submit their idea with confidence. In this way, by analyzing the emotions of users when they submit their ideas and providing positive feedback, the contest hosting unit can increase the user's motivation.

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

[0083] Step 1: The plan provider offers high-price brands and large-capacity plans. For example, users can not only use more data but also enjoy premium services. The plan provider allows users to comfortably use data-intensive services such as video streaming and online games. Step 2: The contest organizer uses the generative AI to hold an idea contest. For example, the generative AI analyzes user input and evaluates and categorizes the proposed ideas. The contest organizer then solicits ideas for new applications, proposals for improving mobile services, and so on. Step 3: The reward provider provides the right to participate in the contest as a reward to users who subscribe to the plan provided by the plan provider. For example, if an excellent idea is selected, a prize or reward may be awarded. The reward provider may, for example, provide users with an opportunity to present their ideas.

[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, 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.

[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. 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 plan offering department that offers high-end, high-capacity plans; A contest organizing department will hold an idea collection contest using generative AI, a reward provision unit that provides a right to participate in a contest as a reward to a user who subscribes to a plan provided by the plan provision unit. A system characterized by:

2. The plan providing unit The generation AI analyzes the user's data usage patterns and automatically proposes the optimal plan.

2. The system of claim 1.

3. The benefit providing unit: Offering access to exclusive events or webinars to users who subscribe to higher-priced plans 2. The system of claim 1.

4. The contest organizing department: The generative AI is used to automatically evaluate the originality and feasibility of submitted ideas.

2. The system of claim 1.

5. The plan providing unit Monitor the user's satisfaction in real time and identify areas for improvement in the plan 2. The system of claim 1.

6. The benefit providing unit: Identify the content that is most interesting to the user and provide personalized content recommendations 2. The system of claim 1.

7. The contest organizing department: Analyze the sentiment of the user when submitting an idea and provide positive feedback 2. The system of claim 1.

8. The benefit providing unit: Design incentives to increase the motivation of the users 2. The system of claim 1.

Images