System

Abstract

A system according to an embodiment aims to efficiently analyze tasks provided on a crowdsourcing platform and assign optimal human resources.SOLUTION: A system according to an embodiment includes a task analysis unit, a user evaluation unit, and a task allocation unit. The task analyzer analyzes tasks provided on the crowdsourcing platform. The user evaluation unit evaluates the content, difficulty level, and required skill of the task analyzed by the task analysis unit. The task allocation unit automatically allocates an optimal personnel to the task evaluated by the user 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] Conventional technologies have had the problem of not being able to efficiently analyze tasks offered on crowdsourcing platforms or allocate optimal human resources.

[0005] The system according to the embodiment aims to efficiently analyze tasks provided on a crowdsourcing platform and assign optimal human resources to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a task analysis unit, a user evaluation unit, and a task allocation unit. The task analysis unit analyzes tasks provided on a crowdsourcing platform. The user evaluation unit evaluates the content, difficulty, and required skills of the tasks analyzed by the task analysis unit. The task allocation unit automatically assigns the most suitable personnel to the tasks evaluated by the user evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently analyze tasks provided on a crowdsourcing platform and assign optimal human resources. [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 system according to the embodiment of the present invention utilizes AI technology to improve the efficiency of crowdsourcing mechanisms and create "jobs of the future" in a fun way, with the feeling of earning points or playing a game. This system allows AI and humans to work together to share work and efficiently complete tasks. This allows the system to improve the quality and efficiency of work.

[0029] The system according to the embodiment includes a task analysis unit, a user evaluation unit, and a task allocation unit. The task analysis unit analyzes tasks provided on a crowdsourcing platform. For example, the task analysis unit analyzes tasks such as data entry, image recognition, and text creation. The task analysis unit can also evaluate the content and difficulty of the tasks. For example, the task analysis unit performs analysis based on detailed task information. The user evaluation unit evaluates the content, difficulty, and required skills of the tasks analyzed by the task analysis unit. For example, the user evaluation unit evaluates the skill set of the tasks. The user evaluation unit can also evaluate the difficulty of the tasks. For example, the user evaluation unit evaluates the time required for the tasks. The task allocation unit automatically assigns the most suitable personnel to the tasks evaluated by the user evaluation unit. For example, the task allocation unit assigns the most suitable tasks based on the user's skill set. The task allocation unit can also assign tasks based on the user's past performance data. For example, the task allocation unit analyzes the user's past task completion data and assigns the most suitable tasks. This allows the system to evaluate the content, difficulty, and required skills of a task, and automatically assign the most suitable personnel, thereby improving the efficiency of crowdsourcing.

[0030] The task allocation unit can analyze a user's past performance data and dynamically reallocate the most appropriate task. For example, the task allocation unit uses a generation AI to analyze a user's past task completion data and identify tasks for which performance was high. For example, a user who showed high accuracy in a data entry task can be preferentially assigned similar tasks. The task allocation unit can also adjust the difficulty of tasks based on the user's past performance data. For example, a user who has shown high performance in the past can be assigned a more difficult task. In this way, task efficiency and accuracy can be improved by analyzing a user's past performance data and dynamically reallocating the most appropriate task.

[0031] The task allocation unit can assign tasks that promote growth by taking into consideration not only the user's skill set, but also their motivation to learn and growth potential. For example, the task allocation unit uses a generation AI to analyze the user's skill set and motivation to learn and assign tasks that promote growth. For example, if a user wishes to acquire a new skill, the task allocation unit assigns tasks that will allow them to utilize that skill. The task allocation unit can also evaluate the user's growth potential and assign tasks that promote growth. For example, challenging tasks can be assigned to users with high growth potential based on past performance data. In this way, by taking into consideration the user's skill set, motivation to learn, and growth potential and assigning tasks that promote growth, it is possible to support the user in improving their skills.

[0032] The task allocation unit can prioritize region-specific tasks by taking into account the user's geographical location information. For example, the task allocation unit uses a generation AI to analyze the user's geographical location information and assign region-specific tasks. For example, a field research task in a specific region can be assigned to a user who lives in that region. The task allocation unit can also assign tasks according to the characteristics of the region based on the user's geographical location information. For example, tasks related to the region's culture or language can be assigned to a user who lives in that region. In this way, by taking into account the user's geographical location information and prioritizing region-specific tasks, tasks closely related to the region can be efficiently completed.

[0033] The task allocation unit can select tasks that can be completed while having fun, taking into consideration the user's hobbies and interests. For example, the task allocation unit uses a generation AI to analyze the user's hobbies and interests and assign tasks that can be completed while having fun. For example, an image recognition task can be assigned to a user whose hobby is photography. The task allocation unit can also assign tasks that stimulate the user's interest based on the user's interests. For example, a programming task can be assigned to a user with an interest in technology. In this way, by taking into consideration the user's hobbies and interests and selecting tasks that can be completed while having fun, the user's motivation can be maintained and tasks can be completed efficiently.

[0034] The generative AI can evaluate not only the difficulty of the task but also the user's effort and creativity and award points. The generative AI can, for example, evaluate the difficulty of the task and the user's effort and award points. For example, high points are awarded to users who complete difficult tasks. The generative AI can also evaluate the user's creativity and award points. For example, special points are awarded to users who submit original ideas. In this way, by evaluating and awarding points not only based on the difficulty of the task but also based on the user's effort and creativity, it is possible to increase user motivation.

[0035] The criteria for awarding points can be dynamically changed, and rewards can be increased according to the user's growth and skill improvement. The generation AI can, for example, analyze the user's growth and skill improvement and dynamically change the criteria for awarding points. For example, high points can be awarded to users whose skills have improved. The generation AI can also evaluate the user's level of growth and increase rewards. For example, special rewards can be provided to users with high growth potential. In this way, the criteria for awarding points can be dynamically changed and rewards can be increased according to the user's growth and skill improvement, thereby maintaining user motivation and promoting growth.

[0036] The point system can be linked with other crowdsourcing platforms and services, enabling the mutual use of points. The point system can, for example, be linked with other crowdsourcing platforms, enabling the mutual use of points. For example, it can be made possible to use common points across multiple platforms. The point system can also be linked with other services, enabling the mutual use of points. For example, it can be made possible to use points across other services. In this way, by linking the point system with other crowdsourcing platforms and services and enabling the mutual use of points, it is possible to improve user convenience.

[0037] The generation AI can analyze the user's gameplay data and dynamically add optimal game elements. For example, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements. For example, it can add new game elements based on the user's preferred game genre or play style. The generation AI can also adjust the difficulty of the game based on the user's play data. For example, it can provide easy game elements to beginner users and more difficult game elements to advanced users. In this way, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements, thereby improving the user's game experience.

[0038] Game elements can be customized and individually optimized according to the user's skill level and progress. The generation AI, for example, analyzes the user's skill level and progress and customizes game elements. For example, it provides basic tasks to novice users and advanced tasks to advanced users. The generation AI can also adjust the difficulty of the game based on the user's progress. For example, it provides difficult tasks to users who progress quickly and easy tasks to users who progress slowly. In this way, the user's gaming experience can be improved by customizing and individually optimizing game elements according to the user's skill level and progress.

[0039] Game elements can be linked with other entertainment content (e.g., movies, music) to pique the user's interest. Game elements can be linked with, for example, movies or music to create a system that piques the user's interest. For example, tasks that recreate movie scenes or challenges that match the music can be provided. Game elements can also be linked with other entertainment content to pique the user's interest. For example, tasks based on the content of a television program can be provided. In this way, by linking game elements with other entertainment content and pique the user's interest, the user's entertainment experience can be improved.

[0040] Game elements can be combined with educational content and utilized as edutainment elements to promote learning. Game elements can be combined with educational content and utilized as edutainment elements to promote learning, for example, by providing a game in which users can earn points by solving math problems. Game elements can also be linked to educational content to promote learning, for example, by providing a game in which users can earn rewards by solving history quizzes. In this way, by combining game elements with educational content and utilizing them as edutainment elements to promote learning, the user's learning experience can be improved.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The task analysis unit can not only evaluate the content and difficulty of a task, but also consider the urgency and deadline of the task. For example, the task analysis unit analyzes the deadline for submitting a task and prioritizes the analysis of tasks with high urgency. The task analysis unit can also dynamically change the analysis priority according to the urgency of the task. For example, it prioritizes the analysis of tasks with approaching deadlines. This allows for the efficiency of task analysis to be improved by considering not only the content and difficulty of a task, but also the urgency and deadline.

[0043] The task allocation unit can not only analyze the user's past performance data, but also take into account the user's health condition and physical condition. For example, the task allocation unit can analyze the user's health data and assign more difficult tasks to users who are in good physical condition. The task allocation unit can also adjust task allocation according to the user's health condition. For example, it can assign easier tasks to users who are not in good physical condition. In this way, the optimal tasks can be assigned taking into account the user's health condition and physical condition, reducing the user's burden and enabling them to complete tasks efficiently.

[0044] The task allocating unit can assign tasks that promote growth by taking into consideration not only the user's skill set and motivation to learn, but also the user's career goals and future aspirations. For example, the task allocating unit analyzes the user's career goals and assigns tasks that match those goals. The task allocating unit can also assign tasks that promote growth based on the user's future aspirations. For example, if the user wishes to demonstrate leadership in the future, the task allocating unit can assign tasks that foster leadership. In this way, by taking into consideration the user's career goals and future aspirations and assigning tasks that promote growth, it is possible to support the user's skill development.

[0045] The task allocation unit can prioritize region-specific tasks by considering not only the user's geographical location information but also the user's cultural background and language skills. For example, the task allocation unit can analyze the user's cultural background and assign tasks related to that culture. The task allocation unit can also assign language-related tasks based on the user's language skills. For example, a user who is fluent in a particular language can be assigned a translation task in that language. In this way, by prioritizing region-specific tasks by considering the user's cultural background and language skills, tasks closely related to the region can be efficiently completed.

[0046] The task allocation unit can select tasks that can be completed while having fun, taking into consideration not only the user's hobbies and interests, but also the user's lifestyle and daily activity patterns. For example, the task allocation unit can analyze the user's daily activity patterns and assign tasks that match those patterns. The task allocation unit can also assign tasks that suit the user's lifestyle based on the user's lifestyle. For example, a user who enjoys the outdoors can be assigned a field research task. In this way, by selecting tasks that can be completed while having fun, taking into consideration the user's lifestyle and daily activity patterns, the user's motivation can be maintained and tasks can be completed efficiently.

[0047] The generative AI can not only evaluate the difficulty of the task, the user's effort, and creativity, but also evaluate the user's cooperation and teamwork and award points accordingly. For example, the generative AI can award high points if a user completes a task in cooperation with other users. The generative AI can also evaluate the user's teamwork and award points based on team performance. For example, it can award special points if the entire team demonstrates high performance. This can increase user motivation by awarding points based on the evaluation of cooperation and teamwork, as well as the difficulty of the task, the user's effort, and creativity.

[0048] In addition to dynamically changing the criteria for awarding points, the point system can also be improved based on user feedback. For example, the generation AI can analyze user feedback and revise the criteria for awarding points. The generation AI can also identify areas for improvement in the point system based on user feedback and update the system. For example, it can improve parts of the point system that users are dissatisfied with. This makes it possible to not only dynamically change the criteria for awarding points, but also improve the point system based on user feedback, thereby increasing user satisfaction.

[0049] Not only can the point system be linked to other crowdsourcing platforms and services, but the point exchange rate can also be dynamically adjusted. For example, the point system can analyze the status of linkage with other platforms and set the optimal exchange rate. The point system can also dynamically change the exchange rate based on user usage. For example, a campaign can be run to increase the point exchange rate during a specific period. This allows the point system to be linked to other crowdsourcing platforms and services, and the point exchange rate can also be dynamically adjusted, improving user convenience.

[0050] The generation AI can analyze the user's gameplay data and not only dynamically add optimal game elements, but also customize the game scenario according to the user's play style. For example, the generation AI can analyze the user's play style and provide a scenario that suits that style. The generation AI can also adjust the difficulty of the scenario based on the user's play data. For example, it can provide a scenario with many action elements to a user who is good at action games. In this way, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements, as well as customize the scenario, thereby improving the user's game experience.

[0051] The generative AI can analyze a user's skill level and progress and not only customize game elements but also adjust the game progress according to the user's learning style and pace. For example, the generative AI can analyze a user's learning style and provide game elements that suit that style. The generative AI can also adjust the speed of the game progress based on the user's learning pace. For example, it can provide fast-paced game elements to fast learners and slower game elements to slow learners. This allows the game elements to be customized and individually optimized according to the user's skill level, progress, learning style, and pace, thereby improving the user's gaming experience.

[0052] In addition to combining game elements with educational content, it is possible to monitor a user's learning progress and level of understanding in real time and provide appropriate educational content. For example, the generation AI can analyze a user's learning progress and provide educational content that corresponds to that progress. The generation AI can also provide educational content that corresponds to the user's level of understanding based on that user's level of understanding. For example, it can provide advanced educational content to users with a high level of understanding and basic educational content to users with a low level of understanding. In this way, it is possible to improve the user's learning experience by combining game elements with educational content, monitoring a user's learning progress and level of understanding in real time, and providing appropriate educational content.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The task analysis unit analyzes tasks provided on the crowdsourcing platform. For example, it analyzes tasks such as data entry, image recognition, and text writing, and evaluates the content and difficulty of the tasks. The task analysis unit performs its analysis based on detailed task information. Step 2: The user evaluation unit evaluates the content, difficulty, and required skills of the task analyzed by the task analysis unit. For example, the user evaluates the skill set, difficulty, and required time of the task. Step 3: The task allocation unit automatically assigns the most suitable personnel to the tasks evaluated by the user evaluation unit, for example, based on the user's skill set and past performance data.

[0055] (Example 2) The system according to the embodiment of the present invention utilizes AI technology to improve the efficiency of crowdsourcing mechanisms and create "jobs of the future" in a fun way, with the feeling of earning points or playing a game. This system allows AI and humans to work together to share work and efficiently complete tasks. This allows the system to improve the quality and efficiency of work.

[0056] The system according to the embodiment includes a task analysis unit, a user evaluation unit, and a task allocation unit. The task analysis unit analyzes tasks provided on a crowdsourcing platform. For example, the task analysis unit analyzes tasks such as data entry, image recognition, and text creation. The task analysis unit can also evaluate the content and difficulty of the tasks. For example, the task analysis unit performs analysis based on detailed task information. The user evaluation unit evaluates the content, difficulty, and required skills of the tasks analyzed by the task analysis unit. For example, the user evaluation unit evaluates the skill set of the tasks. The user evaluation unit can also evaluate the difficulty of the tasks. For example, the user evaluation unit evaluates the time required for the tasks. The task allocation unit automatically assigns the most suitable personnel to the tasks evaluated by the user evaluation unit. For example, the task allocation unit assigns the most suitable tasks based on the user's skill set. The task allocation unit can also assign tasks based on the user's past performance data. For example, the task allocation unit analyzes the user's past task completion data and assigns the most suitable tasks. This allows the system to evaluate the content, difficulty, and required skills of a task, and automatically assign the most suitable personnel, thereby improving the efficiency of crowdsourcing.

[0057] The task allocation unit can analyze a user's past performance data and dynamically reallocate the most appropriate task. For example, the task allocation unit uses a generation AI to analyze a user's past task completion data and identify tasks for which performance was high. For example, a user who showed high accuracy in a data entry task can be preferentially assigned similar tasks. The task allocation unit can also adjust the difficulty of tasks based on the user's past performance data. For example, a user who has shown high performance in the past can be assigned a more difficult task. In this way, task efficiency and accuracy can be improved by analyzing a user's past performance data and dynamically reallocating the most appropriate task.

[0058] The task allocation unit can evaluate the user's current psychological state and stress level using the emotion estimation function and select an appropriate task. The task allocation unit, for example, uses the emotion estimation function to analyze the user's current psychological state in real time and assigns a more difficult task to a user with a low stress level. For example, a complex data analysis task is assigned to a user with a low stress level. The task allocation unit can also use the emotion estimation function to evaluate the user's stress level and assign an easier task to a user with a high stress level. For example, a simple data entry task is assigned to a user with a high stress level. In this way, by evaluating the user's psychological state and stress level and selecting an appropriate task, the burden on the user can be reduced and the task can be performed efficiently.

[0059] The task allocation unit can assign tasks that promote growth by taking into consideration not only the user's skill set, but also their motivation to learn and growth potential. For example, the task allocation unit uses a generation AI to analyze the user's skill set and motivation to learn and assign tasks that promote growth. For example, if a user wishes to acquire a new skill, the task allocation unit assigns tasks that will allow them to utilize that skill. The task allocation unit can also evaluate the user's growth potential and assign tasks that promote growth. For example, challenging tasks can be assigned to users with high growth potential based on past performance data. In this way, by taking into consideration the user's skill set, motivation to learn, and growth potential and assigning tasks that promote growth, it is possible to support the user in improving their skills.

[0060] The task allocation unit can prioritize region-specific tasks by taking into account the user's geographical location information. For example, the task allocation unit uses a generation AI to analyze the user's geographical location information and assign region-specific tasks. For example, a field research task in a specific region can be assigned to a user who lives in that region. The task allocation unit can also assign tasks according to the characteristics of the region based on the user's geographical location information. For example, tasks related to the region's culture or language can be assigned to a user who lives in that region. In this way, by taking into account the user's geographical location information and prioritizing region-specific tasks, tasks closely related to the region can be efficiently completed.

[0061] The task allocation unit can select tasks that can be completed while having fun, taking into consideration the user's hobbies and interests. For example, the task allocation unit uses a generation AI to analyze the user's hobbies and interests and assign tasks that can be completed while having fun. For example, an image recognition task can be assigned to a user whose hobby is photography. The task allocation unit can also assign tasks that stimulate the user's interest based on the user's interests. For example, a programming task can be assigned to a user with an interest in technology. In this way, by taking into consideration the user's hobbies and interests and selecting tasks that can be completed while having fun, the user's motivation can be maintained and tasks can be completed efficiently.

[0062] The task allocation unit can increase motivation by using the emotion estimation function to assign a task that evokes the most positive emotion to the user. The task allocation unit, for example, uses the emotion estimation function to assign a task that evokes the most positive emotion to the user. For example, it reassigns a task that has previously generated high satisfaction. The task allocation unit can also use the emotion estimation function to analyze the user's emotion data and assign a task that elicits positive emotion. For example, it can assign a task that the user can enjoy completing. In this way, by using the emotion estimation function to assign a task that evokes the most positive emotion to the user, it is possible to increase the user's motivation and efficiently complete tasks.

[0063] The generative AI can evaluate not only the difficulty of the task but also the user's effort and creativity and award points. The generative AI can, for example, evaluate the difficulty of the task and the user's effort and award points. For example, high points are awarded to users who complete difficult tasks. The generative AI can also evaluate the user's creativity and award points. For example, special points are awarded to users who submit original ideas. In this way, by evaluating and awarding points not only based on the difficulty of the task but also based on the user's effort and creativity, it is possible to increase user motivation.

[0064] The emotion estimation function can be used to provide a reward that elicits positive emotions in the user. For example, the emotion estimation function can be used to identify and provide a reward that elicits positive emotions in the user. For example, a gift card or product that the user will enjoy can be provided as a reward. The emotion estimation function can also be used to analyze the user's emotion data and provide a reward that elicits positive emotions. For example, a benefit that satisfies the user can be provided. In this way, the emotion estimation function can be used to provide a reward that elicits positive emotions in the user, thereby increasing the user's motivation.

[0065] The criteria for awarding points can be dynamically changed, and rewards can be increased according to the user's growth and skill improvement. The generation AI can, for example, analyze the user's growth and skill improvement and dynamically change the criteria for awarding points. For example, high points can be awarded to users whose skills have improved. The generation AI can also evaluate the user's level of growth and increase rewards. For example, special rewards can be provided to users with high growth potential. In this way, the criteria for awarding points can be dynamically changed and rewards can be increased according to the user's growth and skill improvement, thereby maintaining user motivation and promoting growth.

[0066] The point system can be linked with other crowdsourcing platforms and services, enabling the mutual use of points. The point system can, for example, be linked with other crowdsourcing platforms, enabling the mutual use of points. For example, it can be made possible to use common points across multiple platforms. The point system can also be linked with other services, enabling the mutual use of points. For example, it can be made possible to use points across other services. In this way, by linking the point system with other crowdsourcing platforms and services and enabling the mutual use of points, it is possible to improve user convenience.

[0067] The emotion estimation function can be used to identify the reward that the user would be most pleased with, and provide it as an option for point exchange. For example, the emotion estimation function can be used to identify the reward that the user would be most pleased with, and provide it as an option for point exchange. For example, the user's emotion data can be analyzed to select a reward with a high joy score. The emotion estimation function can also be used to identify the reward that the user would be most pleased with, based on the user's past selection history. For example, a reward that has previously generated high satisfaction can be provided again. In this way, the emotion estimation function can be used to identify the reward that the user would be most pleased with, and provide it as an option for point exchange, thereby improving user satisfaction.

[0068] The generation AI can analyze the user's gameplay data and dynamically add optimal game elements. For example, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements. For example, it can add new game elements based on the user's preferred game genre or play style. The generation AI can also adjust the difficulty of the game based on the user's play data. For example, it can provide easy game elements to beginner users and more difficult game elements to advanced users. In this way, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements, thereby improving the user's game experience.

[0069] By using the emotion estimation function to monitor the user's emotional state in real time, it is possible to provide rewards and benefits at the appropriate time. For example, by using the emotion estimation function, it is possible to monitor the user's emotional state in real time and provide rewards and benefits at the appropriate time. For example, a special reward can be provided when the user shows positive emotions. The emotion estimation function can also be used to analyze the user's emotional data and provide benefits according to the emotional state. For example, a benefit that helps the user relax can be provided when the user is feeling stressed. In this way, by using the emotion estimation function to monitor the user's emotional state in real time and provide rewards and benefits at the appropriate time, it is possible to maintain the user's motivation and improve the game experience.

[0070] Game elements can be customized and individually optimized according to the user's skill level and progress. The generation AI, for example, analyzes the user's skill level and progress and customizes game elements. For example, it provides basic tasks to novice users and advanced tasks to advanced users. The generation AI can also adjust the difficulty of the game based on the user's progress. For example, it provides difficult tasks to users who progress quickly and easy tasks to users who progress slowly. In this way, the user's gaming experience can be improved by customizing and individually optimizing game elements according to the user's skill level and progress.

[0071] Game elements can be linked with other entertainment content (e.g., movies, music) to pique the user's interest. Game elements can be linked with, for example, movies or music to create a system that piques the user's interest. For example, tasks that recreate movie scenes or challenges that match the music can be provided. Game elements can also be linked with other entertainment content to pique the user's interest. For example, tasks based on the content of a television program can be provided. In this way, by linking game elements with other entertainment content and pique the user's interest, the user's entertainment experience can be improved.

[0072] Game elements can be combined with educational content and utilized as edutainment elements to promote learning. Game elements can be combined with educational content and utilized as edutainment elements to promote learning, for example, by providing a game in which users can earn points by solving math problems. Game elements can also be linked to educational content to promote learning, for example, by providing a game in which users can earn rewards by solving history quizzes. In this way, by combining game elements with educational content and utilizing them as edutainment elements to promote learning, the user's learning experience can be improved.

[0073] The emotion estimation function can be used to identify the game elements that the user enjoys most, thereby personalizing the game experience. The emotion estimation function can be used, for example, to identify the game elements that the user enjoys most and personalize the game experience. For example, the user's emotion data can be analyzed and game elements with high enjoyment scores can be provided. The emotion estimation function can also be used to identify the game elements that the user enjoys most based on the user's past game play data. For example, game elements that have previously provided high satisfaction can be provided again. In this way, the emotion estimation function can be used to identify the game elements that the user enjoys most and personalize the game experience, thereby improving user satisfaction.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The task analysis unit can not only evaluate the content and difficulty of a task, but also consider the urgency and deadline of the task. For example, the task analysis unit analyzes the deadline for submitting a task and prioritizes the analysis of tasks with high urgency. The task analysis unit can also dynamically change the analysis priority according to the urgency of the task. For example, it prioritizes the analysis of tasks with approaching deadlines. This allows for the efficiency of task analysis to be improved by considering not only the content and difficulty of a task, but also the urgency and deadline.

[0076] The task allocation unit can not only analyze the user's past performance data, but also take into account the user's health condition and physical condition. For example, the task allocation unit can analyze the user's health data and assign more difficult tasks to users who are in good physical condition. The task allocation unit can also adjust task allocation according to the user's health condition. For example, it can assign easier tasks to users who are not in good physical condition. In this way, the optimal tasks can be assigned taking into account the user's health condition and physical condition, reducing the user's burden and enabling them to complete tasks efficiently.

[0077] The task allocation unit can not only evaluate the user's current psychological state and stress level using the emotion estimation function, but also take into account the user's motivation and willingness. For example, the task allocation unit can use the emotion estimation function to analyze the user's motivation in real time and assign challenging tasks to highly motivated users. The task allocation unit can also use the emotion estimation function to evaluate the user's motivation and assign easier tasks to less motivated users. This reduces the user's burden and enables them to complete tasks efficiently by selecting appropriate tasks based on the user's motivation and willingness.

[0078] The task allocating unit can assign tasks that promote growth by taking into consideration not only the user's skill set and motivation to learn, but also the user's career goals and future aspirations. For example, the task allocating unit analyzes the user's career goals and assigns tasks that match those goals. The task allocating unit can also assign tasks that promote growth based on the user's future aspirations. For example, if the user wishes to demonstrate leadership in the future, the task allocating unit can assign tasks that foster leadership. In this way, by taking into consideration the user's career goals and future aspirations and assigning tasks that promote growth, it is possible to support the user's skill development.

[0079] The task allocation unit can prioritize region-specific tasks by considering not only the user's geographical location information but also the user's cultural background and language skills. For example, the task allocation unit can analyze the user's cultural background and assign tasks related to that culture. The task allocation unit can also assign language-related tasks based on the user's language skills. For example, a user who is fluent in a particular language can be assigned a translation task in that language. In this way, by prioritizing region-specific tasks by considering the user's cultural background and language skills, tasks closely related to the region can be efficiently completed.

[0080] The task allocation unit can select tasks that can be completed while having fun, taking into consideration not only the user's hobbies and interests, but also the user's lifestyle and daily activity patterns. For example, the task allocation unit can analyze the user's daily activity patterns and assign tasks that match those patterns. The task allocation unit can also assign tasks that suit the user's lifestyle based on the user's lifestyle. For example, a user who enjoys the outdoors can be assigned a field research task. In this way, by selecting tasks that can be completed while having fun, taking into consideration the user's lifestyle and daily activity patterns, the user's motivation can be maintained and tasks can be completed efficiently.

[0081] The task allocation unit uses the emotion estimation function to not only assign tasks that will evoke the most positive emotions in the user, but also to avoid tasks that cause stress to the user. For example, the task allocation unit uses the emotion estimation function to identify tasks that have caused stress to the user in the past and avoid such tasks. The task allocation unit can also use the emotion estimation function to analyze the user's emotion data and preemptively eliminate tasks that may cause stress. In this way, the emotion estimation function can not only assign tasks that will evoke the most positive emotions in the user, but also avoid tasks that cause stress, thereby increasing the user's motivation and enabling them to complete tasks efficiently.

[0082] The generative AI can not only evaluate the difficulty of the task, the user's effort, and creativity, but also evaluate the user's cooperation and teamwork and award points accordingly. For example, the generative AI can award high points if a user completes a task in cooperation with other users. The generative AI can also evaluate the user's teamwork and award points based on team performance. For example, it can award special points if the entire team demonstrates high performance. This can increase user motivation by awarding points based on the evaluation of cooperation and teamwork, as well as the difficulty of the task, the user's effort, and creativity.

[0083] The emotion estimation function can be used to not only provide a reward that elicits positive emotions in the user, but also to adjust the timing of the reward according to the user's emotional state. For example, the emotion estimation function can be used to provide a reward at the timing when the user is most positively emotional. The emotion estimation function can also be used to analyze the user's emotional data and provide a reward according to the emotional state. For example, when the user is feeling stressed, a reward that helps the user relax can be provided. In this way, the emotion estimation function can not only provide a reward that elicits positive emotions in the user, but also adjust the timing of the reward, thereby increasing the user's motivation.

[0084] In addition to dynamically changing the criteria for awarding points, the point system can also be improved based on user feedback. For example, the generation AI can analyze user feedback and revise the criteria for awarding points. The generation AI can also identify areas for improvement in the point system based on user feedback and update the system. For example, it can improve parts of the point system that users are dissatisfied with. This makes it possible to not only dynamically change the criteria for awarding points, but also improve the point system based on user feedback, thereby increasing user satisfaction.

[0085] Not only can the point system be linked to other crowdsourcing platforms and services, but the point exchange rate can also be dynamically adjusted. For example, the point system can analyze the status of linkage with other platforms and set the optimal exchange rate. The point system can also dynamically change the exchange rate based on user usage. For example, a campaign can be run to increase the point exchange rate during a specific period. This allows the point system to be linked to other crowdsourcing platforms and services, and the point exchange rate can also be dynamically adjusted, improving user convenience.

[0086] The emotion estimation function can be used to not only identify the reward that the user would enjoy most, but also customize the type of reward according to the user's emotional state. For example, the emotion estimation function can be used to identify the type of reward that the user would enjoy most and provide it as an option for point exchange. The emotion estimation function can also be used to analyze the user's emotional data and provide rewards according to the emotional state. For example, if the user is seeking relaxation, a reward that helps them relax can be provided. In this way, the emotion estimation function can be used to not only identify the reward that the user would enjoy most, but also customize the type of reward, thereby improving user satisfaction.

[0087] The generation AI can analyze the user's gameplay data and not only dynamically add optimal game elements, but also customize the game scenario according to the user's play style. For example, the generation AI can analyze the user's play style and provide a scenario that suits that style. The generation AI can also adjust the difficulty of the scenario based on the user's play data. For example, it can provide a scenario with many action elements to a user who is good at action games. In this way, the generation AI can analyze the user's gameplay data and dynamically add optimal game elements, as well as customize the scenario, thereby improving the user's game experience.

[0088] The emotion estimation function can be used not only to monitor the user's emotional state in real time, but also to predict the user's emotional changes and provide rewards and benefits at the appropriate time. For example, the emotion estimation function can be used to predict the user's emotional changes and provide rewards when positive emotions are increasing. The emotion estimation function can also be used to analyze the user's emotional data and provide rewards in accordance with emotional changes. For example, a reward that allows the user to relax before feeling stressed can be provided. In this way, the emotion estimation function can be used to not only monitor the user's emotional state in real time, but also to predict emotional changes and provide rewards and benefits at the appropriate time, thereby maintaining the user's motivation and improving the game experience.

[0089] The generative AI can analyze a user's skill level and progress and not only customize game elements but also adjust the game progress according to the user's learning style and pace. For example, the generative AI can analyze a user's learning style and provide game elements that suit that style. The generative AI can also adjust the speed of the game progress based on the user's learning pace. For example, it can provide fast-paced game elements to fast learners and slower game elements to slow learners. This allows the game elements to be customized and individually optimized according to the user's skill level, progress, learning style, and pace, thereby improving the user's gaming experience.

[0090] In addition to linking game elements with other entertainment content, the user's emotion estimation function can be used to identify the content that will most interest the user. For example, the emotion estimation function can be used to identify the movies or music that the user is most interested in and provide game elements related to those. The emotion estimation function can also be used to analyze the user's emotion data and dynamically change the content that will most interest the user. For example, if the user is interested in a particular genre of movies, game elements related to that genre can be added. In this way, by linking game elements with other entertainment content and using the user's emotion estimation function to identify the content that will most interest the user, the user's entertainment experience can be improved.

[0091] In addition to combining game elements with educational content, it is possible to monitor a user's learning progress and level of understanding in real time and provide appropriate educational content. For example, the generation AI can analyze a user's learning progress and provide educational content that corresponds to that progress. The generation AI can also provide educational content that corresponds to the user's level of understanding based on that user's level of understanding. For example, it can provide advanced educational content to users with a high level of understanding and basic educational content to users with a low level of understanding. In this way, it is possible to improve the user's learning experience by combining game elements with educational content, monitoring a user's learning progress and level of understanding in real time, and providing appropriate educational content.

[0092] The emotion estimation function can be used to identify the game elements that a user enjoys most, as well as to dynamically adjust the difficulty level and content of the game according to the user's emotional state. For example, the emotion estimation function can be used to identify the game elements that a user enjoys most and emphasize those elements. The emotion estimation function can also be used to analyze the user's emotional data and adjust the difficulty level and content of the game according to the user's emotional state. For example, the difficulty level can be lowered when the user is feeling stressed, and raised when the user is relaxed. In this way, the emotion estimation function can be used to identify the game elements that a user enjoys most and dynamically adjust the difficulty level and content of the game according to the user's emotional state, thereby improving user satisfaction.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The task analysis unit analyzes tasks provided on the crowdsourcing platform. For example, it analyzes tasks such as data entry, image recognition, and text writing, and evaluates the content and difficulty of the tasks. The task analysis unit performs its analysis based on detailed task information. Step 2: The user evaluation unit evaluates the content, difficulty, and required skills of the task analyzed by the task analysis unit. For example, the user evaluates the skill set, difficulty, and required time of the task. Step 3: The task allocation unit automatically assigns the most suitable personnel to the tasks evaluated by the user evaluation unit, for example, based on the user's skill set and past performance data.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 AI 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 task analysis unit that analyzes tasks provided on a crowdsourcing platform; a user evaluation unit that evaluates the content, difficulty, and required skills of the task analyzed by the task analysis unit; a task allocation unit that automatically allocates optimal human resources to the tasks evaluated by the user evaluation unit. A system characterized by:

2. The task allocation unit Analyze users' past performance data and dynamically reallocate tasks to best suit them 2. The system of claim 1.

3. The task allocation unit Considering the user's geographic location and prioritizing local tasks 2. The system of claim 1.

4. The generating AI is Points are awarded based on the user's effort and creativity, as well as the difficulty of the task.

2. The system of claim 1.

5. The generating AI is Analyzing user gameplay data and dynamically adding optimal game elements 2. The system of claim 1.

6. The task allocation unit Evaluate the user's current mental state and stress level using emotion estimation function and select appropriate tasks 2. The system of claim 1.

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