System

Abstract

An object of a system according to an embodiment is to enable anyone to easily and effectively use generated AI.SOLUTION: A system includes a reception part, a prompt generation part, an evaluation part, and a provision part. The reception unit receives a task of a user. The prompt generation unit generates a prompt based on the task accepted by the acceptance unit. The evaluation unit evaluates the result of outputting the generated AI based on the prompt generated by the prompt generation unit. The providing unit provides an output result evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to create appropriate prompts to effectively use generative AI.

[0005] The system according to the embodiment aims to enable anyone to easily and effectively use generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a prompt generation unit, an evaluation unit, and a provision unit. The reception unit receives a task from a user. The prompt generation unit generates a prompt based on the task received by the reception unit. The evaluation unit evaluates an output result of the generation AI based on the prompt generated by the prompt generation unit. The provision unit provides the output result evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily and effectively use generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A generative AI support system according to an embodiment of the present invention efficiently accepts user tasks, generates appropriate prompts, and evaluates and provides output results. The generative AI support system inputs a user's desired task and automatically generates appropriate prompts based on the task. The generated prompts provide optimal instructions to the generative AI, enabling it to achieve the desired results. For example, for the task "I want to create a summary of a text," a prompt such as "Please summarize the following text" is generated. The generated prompt is input to the generative AI, which then outputs the desired results. For example, when the generative AI creates a summary of a text, it outputs a summary of the original text based on the generated prompt. This mechanism allows even users unfamiliar with generative AI to easily obtain the desired results. For example, automatically generating appropriate prompts for various tasks, such as summarizing text or generating images, makes generative AI easier to use. Furthermore, the generative AI support system also has the ability to evaluate the output results of the generative AI and modify the prompts as necessary. This improves the accuracy of the generative AI and achieves more desired results. For example, if the generated summary is insufficient, a better summary can be obtained by modifying the prompt and re-inputting it to the generative AI. This allows the generative AI usage support system to make generative AI easy for anyone to use, automatically generate appropriate prompts, and improve the accuracy of generative AI, thereby achieving the expected results.

[0029] A generation AI utilization support system according to an embodiment includes a reception unit, a prompt generation unit, an evaluation unit, and a provision unit. The reception unit receives a user's task. Examples of the user's task include, but are not limited to, work tasks, study tasks, and daily tasks. The reception unit receives the task input by the user in text format, for example. The reception unit can also receive the task using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology and receives the text data as a task. The prompt generation unit uses a generation AI to generate a prompt based on the task received by the reception unit. The prompt may be generated in a format such as a question, an instruction, or an information provision format, for example, but is not limited to these examples. For example, the prompt generation unit uses a generation AI (e.g., Transformer) to generate a prompt optimal for the user's task. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. The evaluation unit evaluates the output result of the generation AI. The evaluation is performed based on criteria such as, but not limited to, accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output result of the generative AI and modifies the prompt as necessary. The evaluation unit can also evaluate the output result of the generative AI based on user feedback and identify areas for improvement in the prompt. The provision unit provides the output result evaluated by the evaluation unit to the user. The provision can be performed in the form of, for example, email, dashboard, notification, or the like, but is not limited to, these examples. For example, the provision unit can display the output result of the generative AI to the user through a web application or mobile application. The provision unit can also print the output result of the generative AI on a printer and provide it on paper. As a result, the generative AI usage support system according to the embodiment can efficiently accept user tasks, generate appropriate prompts, and evaluate and provide the output results.

[0030] The prompt generation unit can generate prompts using a generation AI. Examples of the generation AI include, but are not limited to, Transformer. The prompt generation unit uses, for example, a generation AI to generate a prompt that is optimal for the user's task. For example, the prompt generation unit inputs an instruction to the generation AI, such as "Please summarize the following sentence," and the generation AI generates a prompt based on that instruction. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. For example, the prompt generation unit inputs an instruction to the generation AI, such as "Please collect background information related to this task," and the generation AI collects background information based on that instruction and reflects it in the prompt. In this way, using the generation AI improves the accuracy of prompt generation. Some or all of the above-mentioned processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs a user's task to the generation AI, and the generation AI generates a prompt based on that task.

[0031] The evaluation unit can evaluate the output result of the generation AI and modify the prompt as necessary. The evaluation is performed based on criteria such as, but not limited to, accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output result of the generation AI and modifies the prompt as necessary. For example, if the output result of the generation AI does not match the expected result, the evaluation unit modifies the prompt and inputs it again to the generation AI. The evaluation unit can also evaluate the output result of the generation AI based on user feedback and identify areas for improvement in the prompt. For example, the evaluation unit collects user feedback and modifies the prompt based on that feedback. This allows the prompt to be modified to improve the accuracy of the output result. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs the output result to the generation AI, and the generation AI suggests modifications to the prompt based on the output result.

[0032] The providing unit can provide the output results of the generation AI to the user. The providing unit can provide the output results of the generation AI to the user, for example, via email, dashboard, notification, etc., but is not limited to these examples. For example, the providing unit can display the output results of the generation AI to the user via a web application or mobile application. The providing unit can also print the output results of the generation AI on a printer and provide them on paper. For example, the providing unit can send the output results of the generation AI to the user via email. The providing unit can also display the output results of the generation AI on a dashboard, allowing the user to check the results in real time. This allows the user to check the results by providing the output results of the generation AI to the user. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit can input the output results to the generation AI and suggest an optimal method for the generation AI to provide the output results to the user.

[0033] The reception unit can accept tasks input by a user. Examples of user tasks include, but are not limited to, work tasks, study tasks, and daily tasks. For example, the reception unit accepts tasks input by a user in text format. The reception unit can also accept tasks using voice input or image input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology and accept it as a task. The reception unit can also analyze the user's image input using image recognition technology and accept it as a task. For example, the reception unit can analyze an image taken by the user using a smartphone camera and identify a task based on the image. In this way, the system can process the task by accepting the task input by the user. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's voice input to the generation AI, which then converts the voice input into text data.

[0034] The reception unit can analyze the user's past task history and select the optimal reception method. For example, the reception unit prioritizes receiving tasks that the user has frequently performed in the past. The reception unit can also predict tasks to be performed in a specific time period from the user's past task history and suggest the optimal reception method. The reception unit can also analyze the user's past task history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past task history. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past task history into the generation AI, and the generation AI suggests the optimal reception method based on that history.

[0035] When receiving a task, the reception unit can filter the tasks based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives tasks related to the user's current project. The reception unit can also filter and receive related tasks based on the user's area of ​​interest. The reception unit can also filter and receive related tasks by referring to the user's past project history. In this way, by filtering tasks based on the user's current project or area of ​​interest, highly relevant tasks can be preferentially received. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's project information to the generation AI, and the generation AI filters related tasks based on that information.

[0036] When accepting a task, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the task using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the task using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the task using image recognition technology. This allows the acceptance of tasks to be carried out smoothly by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's input data to the generation AI, and the generation AI selects the optimal acceptance means based on that data.

[0037] When accepting a task, the reception unit can prioritize accepting highly relevant tasks by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting tasks related to that area. The reception unit can also prioritize accepting tasks related to locations close to the user's current location. The reception unit can also suggest and accept optimal tasks based on the user's geographical location information. This makes it possible to prioritize accepting highly relevant tasks by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI, and the generation AI suggests related tasks based on that information.

[0038] When accepting a task, the acceptance unit can analyze the user's social media activity and accept related tasks. For example, the acceptance unit preferentially accepts tasks mentioned by the user on social media. The acceptance unit can also analyze the user's social media activity and suggest and accept related tasks. The acceptance unit can also accept related tasks by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related tasks can be preferentially accepted. Some or all of the above-mentioned processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's social media data into the generation AI, and the generation AI suggests related tasks based on that data.

[0039] When accepting a task, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's feedback and perform optimal task reception. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's feedback data to the generation AI, and the generation AI suggests the optimal reception method based on that data.

[0040] The prompt generation unit can adjust the level of detail of the prompt based on the importance of the task when generating the prompt. For example, the prompt generation unit generates a detailed prompt for a task with high importance. The prompt generation unit can also generate a concise prompt for a task with low importance. The prompt generation unit can also dynamically adjust the level of detail of the prompt according to the importance of the task. In this way, an appropriate prompt can be generated by adjusting the level of detail of the prompt according to the importance of the task. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task importance data to the generation AI, and the generation AI adjusts the level of detail of the prompt based on that data.

[0041] When generating a prompt, the prompt generation unit can apply different prompt generation algorithms depending on the task category. For example, the prompt generation unit applies a natural language processing algorithm to a sentence generation task. The prompt generation unit can also apply an image recognition algorithm to an image generation task. The prompt generation unit can also apply a voice recognition algorithm to a voice generation task. This improves the accuracy of the prompt by applying an appropriate prompt generation algorithm depending on the task category. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task category data to the generation AI, and the generation AI applies an appropriate prompt generation algorithm based on that data.

[0042] When generating a prompt, the prompt generation unit can improve the accuracy of the prompt by referring to the user's past prompt results. For example, the prompt generation unit analyzes prompts used by the user in the past and generates an optimal prompt. The prompt generation unit can also extract and reuse effective prompts from the user's past prompt results. The prompt generation unit can also build a feedback loop to improve the accuracy of the prompt based on the user's past prompt results. In this way, the accuracy of the prompt is improved by referring to the user's past prompt results. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs the user's past prompt results into the generation AI, and the generation AI generates an optimal prompt based on that data.

[0043] When generating a prompt, the prompt generation unit can determine the priority of the prompt based on the submission time of the task. For example, the prompt generation unit prioritizes generating prompts for tasks with an approaching deadline. The prompt generation unit can also postpone generating prompts for tasks with a more distant submission time. The prompt generation unit can also dynamically adjust the priority of the prompt based on the submission time of the task. This makes it possible to generate appropriate prompts by determining the priority of the prompt based on the submission time of the task. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task submission time data to the generation AI, and the generation AI determines the priority of the prompt based on that data.

[0044] The prompt generation unit can adjust the order of prompts based on the relevance of the tasks when generating prompts. For example, the prompt generation unit prioritizes generating prompts for highly relevant tasks. The prompt generation unit can also postpone generating prompts for less relevant tasks. The prompt generation unit can also dynamically adjust the order of prompts based on the relevance of the tasks. This makes it possible to generate appropriate prompts by adjusting the order of prompts based on the relevance of the tasks. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task relevance data to the generation AI, and the generation AI adjusts the order of prompts based on that data.

[0045] When generating a prompt, the prompt generation unit can adjust the use of technical terminology in the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the prompt generation unit generates a prompt that uses a lot of technical terminology. Furthermore, if the user is a beginner, the prompt generation unit can also generate a simple prompt that avoids technical terminology. The prompt generation unit can also dynamically adjust the use of technical terminology in the prompt according to the user's level of expertise. This allows appropriate prompts to be generated by adjusting the use of technical terminology in the prompt according to the user's level of expertise. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs user's expertise level data into the generation AI, and the generation AI adjusts the use of technical terminology in the prompt based on that data.

[0046] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on the interrelationships between output results. For example, the evaluation unit analyzes the relevance of multiple output results and performs a comprehensive evaluation. The evaluation unit can also adjust the weighting of the evaluation by taking into account the interrelationships between the output results. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the interrelationships between the output results. This improves the accuracy of the evaluation by taking into account the interrelationships between the output results. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs interrelationship data between the output results to the generation AI, and the generation AI adjusts the weighting of the evaluation based on that data.

[0047] During evaluation, the evaluation unit can perform evaluation based on the attribute information of the submitter of the output result. The evaluation unit adjusts the evaluation criteria, for example, according to the submitter's level of expertise. The evaluation unit can also adjust the evaluation criteria by referring to the submitter's past evaluation history. The evaluation unit can also apply optimal evaluation criteria based on the submitter's attribute information. This allows for appropriate evaluation by taking the submitter's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs the submitter's attribute information data into the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0048] During evaluation, the evaluation unit can weight the evaluation based on the frequency of submission of the output result. For example, the evaluation unit applies stricter evaluation criteria to output results that are submitted frequently. The evaluation unit can also apply lenient evaluation criteria to output results that are submitted infrequently. The evaluation unit can also dynamically adjust the evaluation weighting according to the submission frequency. In this way, appropriate evaluation can be performed by weighting the evaluation according to the submission frequency. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs submission frequency data to the generation AI, and the generation AI adjusts the evaluation weighting based on that data.

[0049] During evaluation, the evaluation unit can perform evaluation based on the geographic distribution of the output results. For example, the evaluation unit analyzes the geographic distribution of the output results and performs evaluation taking into account the characteristics of each region. The evaluation unit can also adjust the evaluation criteria based on the geographic distribution. The evaluation unit can also establish a feedback loop to improve the accuracy of the evaluation based on the geographic distribution of the output results. This allows for appropriate evaluation by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs geographic distribution data of the output results to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0050] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the output result. For example, the evaluation unit automatically searches for literature related to the output result and reflects it in the evaluation. The evaluation unit can also adjust the evaluation criteria based on the content of the related literature. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the literature related to the output result. In this way, the accuracy of the evaluation is improved by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs related literature data to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0051] The evaluation unit can perform the evaluation based on the market value of the output result during the evaluation. For example, the evaluation unit reflects the market value of the output result in the evaluation criteria. The evaluation unit can also adjust the weighting of the evaluation based on the market value. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the market value of the output result. This allows for appropriate evaluation by taking market value into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs market value data of the output result to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0052] When providing an output result, the providing unit can select a delivery method based on the user's past feedback. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. The providing unit can also customize the delivery method by reflecting the user's feedback and provide an optimal output result. This makes it possible to select an optimal delivery method by referring to past feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests an optimal delivery method based on that data.

[0053] When providing an output result, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit prioritizes providing output results related to the task the user is currently performing. The providing unit can also customize and provide related information based on the user's current task. The providing unit can also select optimal content to be provided, taking into account the user's current task. In this way, appropriate information can be provided by customizing the content to be provided according to the current task. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's task data to the generation AI, and the generation AI selects optimal content to be provided based on that data.

[0054] The providing unit can improve the providing method by reflecting user feedback when providing an output result. For example, the providing unit improves the providing method based on feedback provided by the user regarding the providing method. The providing unit can also customize the providing method by reflecting user feedback and provide optimal output results. The providing unit can also identify and implement improvements to the providing method based on user feedback. In this way, the providing method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests improvements to the providing method based on that data.

[0055] When providing the output results, the providing unit can select a delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is simple and highly visible. This allows the optimal delivery method to be selected by taking device information into consideration. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's device information to the generation AI, and the generation AI suggests the optimal delivery method based on that information.

[0056] When providing the output results, the providing unit can make the provided content multilingual according to the user's language setting. For example, the providing unit automatically sets the language of the output results based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the output results in that language. This makes it possible to provide appropriate information by providing multilingual support according to the language setting. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's language setting data to the generation AI, and the generation AI makes the provided content multilingual based on that data.

[0057] When providing the output results, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit provides information related to content mentioned by the user on social media. The providing unit can also analyze the user's social media activity and provide related information. The providing unit can also provide related information by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's social media data into the generation AI, and the generation AI suggests related information based on that data.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When accepting a task from a user, the acceptance unit can analyze the user's past task history and select the optimal acceptance method. For example, it can prioritize accepting tasks that the user has performed frequently in the past. The acceptance unit can also predict tasks to be performed during a specific time period from the user's past task history and suggest the optimal acceptance method. Furthermore, the acceptance unit can analyze the user's past task history and select the most efficient acceptance method. In this way, the optimal acceptance method can be selected by analyzing the past task history. Some or all of the above-mentioned processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's past task history into the generation AI, and the generation AI suggests the optimal acceptance method based on that history.

[0060] When generating a prompt using the generation AI, the prompt generation unit can adjust the use of technical terminology in the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the prompt generation unit can generate a prompt that uses a lot of technical terminology. On the other hand, if the user is a beginner, the prompt generation unit can generate a simple prompt that avoids technical terminology. Furthermore, the prompt generation unit can dynamically adjust the use of technical terminology in the prompt according to the user's level of expertise. This makes it possible to generate appropriate prompts by adjusting the use of technical terminology in the prompt according to the user's level of expertise. Some or all of the above-mentioned processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs user's expertise level data to the generation AI, and the generation AI adjusts the use of technical terminology in the prompt based on that data.

[0061] The evaluation unit can improve the accuracy of the evaluation based on the interrelationships between the output results when evaluating the output results of the generation AI and correcting the prompts as necessary. For example, the evaluation unit analyzes the relevance of multiple output results and performs a comprehensive evaluation. The evaluation unit can also adjust the weighting of the evaluation by taking into account the interrelationships between the output results. Furthermore, the evaluation unit can also establish a feedback loop to improve the accuracy of the evaluation based on the interrelationships between the output results. This improves the accuracy of the evaluation by taking into account the interrelationships between the output results. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs interrelationship data between the output results to the generation AI, and the generation AI adjusts the weighting of the evaluation based on that data.

[0062] When providing the output results of the generation AI to a user, the providing unit can select a delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit can select a delivery method that matches the screen size. Also, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is simple and highly visible. This allows the optimal delivery method to be selected by taking device information into consideration. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's device information to the generation AI, and the generation AI suggests the optimal delivery method based on that information.

[0063] The prompt generation unit can adjust the level of detail of the prompt based on the importance of the task when generating the prompt. For example, a detailed prompt can be generated for a task with high importance. A concise prompt can also be generated for a task with low importance. Furthermore, the prompt generation unit can dynamically adjust the level of detail of the prompt according to the importance of the task. This allows an appropriate prompt to be generated by adjusting the level of detail of the prompt according to the importance of the task. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task importance data to the generation AI, and the generation AI adjusts the level of detail of the prompt based on that data.

[0064] When generating a prompt, the prompt generation unit can apply different prompt generation algorithms depending on the task category. For example, a natural language processing algorithm is applied to a sentence generation task. The prompt generation unit can also apply an image recognition algorithm to an image generation task. Furthermore, the prompt generation unit can also apply a voice recognition algorithm to a voice generation task. This improves the accuracy of the prompt by applying an appropriate prompt generation algorithm depending on the task category. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task category data to the generation AI, and the generation AI applies an appropriate prompt generation algorithm based on that data.

[0065] During evaluation, the evaluation unit can perform evaluation based on the attribute information of the submitter of the output result. For example, the evaluation unit adjusts the evaluation criteria according to the submitter's level of expertise. The evaluation unit can also adjust the evaluation criteria by referring to the submitter's past evaluation history. Furthermore, the evaluation unit can apply optimal evaluation criteria based on the submitter's attribute information. This allows for appropriate evaluation by taking the submitter's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs the submitter's attribute information data into the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0066] During evaluation, the evaluation unit can weight the evaluation based on the frequency of submission of the output results. For example, stricter evaluation criteria can be applied to output results that are submitted frequently. The evaluation unit can also apply looser evaluation criteria to output results that are submitted infrequently. Furthermore, the evaluation unit can dynamically adjust the evaluation weighting according to the submission frequency. This allows for appropriate evaluation by weighting the evaluation according to the submission frequency. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs submission frequency data to the generation AI, and the generation AI adjusts the evaluation weighting based on that data.

[0067] When providing an output result, the providing unit can select a delivery method based on the user's past feedback. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery method by reflecting the user's feedback and provide an optimal output result. This makes it possible to select an optimal delivery method by referring to past feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests an optimal delivery method based on that data.

[0068] When providing output results, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit can prioritize providing output results related to the task the user is currently performing. The providing unit can also customize and provide related information based on the user's current task. Furthermore, the providing unit can select the optimal content to be provided by taking the user's current task into consideration. In this way, appropriate information can be provided by customizing the content to be provided according to the current task. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's task data into the generation AI, and the generation AI selects the optimal content to be provided based on that data.

[0069] The providing unit can improve the delivery method by reflecting user feedback when providing an output result. For example, the delivery method is improved based on feedback provided by the user regarding the delivery method. The providing unit can also customize the delivery method by reflecting user feedback and provide optimal output results. Furthermore, the providing unit can identify and implement improvements to the delivery method based on user feedback. In this way, the delivery method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests improvements to the delivery method based on that data.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives the user's tasks. User tasks include work tasks, study tasks, daily tasks, etc. The reception unit receives tasks entered by the user in text format, and can also receive tasks using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology and receives it as a task. Step 2: The prompt generation unit uses a generation AI to generate a prompt based on the task accepted by the acceptance unit. The prompt is generated in a format such as a question, instruction, or information provision format. For example, the prompt generation unit uses a generation AI such as Transformer to generate a prompt that is optimal for the user's task. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. Step 3: The evaluation unit evaluates the output of the generative AI. The evaluation is based on criteria such as accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output of the generative AI and modifies the prompts as necessary. The evaluation unit can also evaluate the output of the generative AI based on user feedback and identify areas for improvement in the prompts. Step 4: The providing unit provides the output results evaluated by the evaluation unit to the user. The output results are provided in the form of email, dashboard, notification, etc. For example, the providing unit may display the output results of the generation AI to the user through a web application or mobile application. The providing unit may also print out the output results of the generation AI on a printer and provide them on paper.

[0072] (Example 2) A generative AI support system according to an embodiment of the present invention efficiently accepts user tasks, generates appropriate prompts, and evaluates and provides output results. The generative AI support system inputs a user's desired task and automatically generates appropriate prompts based on the task. The generated prompts provide optimal instructions to the generative AI, enabling it to achieve the desired results. For example, for the task "I want to create a summary of a text," a prompt such as "Please summarize the following text" is generated. The generated prompt is input to the generative AI, which then outputs the desired results. For example, when the generative AI creates a summary of a text, it outputs a summary of the original text based on the generated prompt. This mechanism allows even users unfamiliar with generative AI to easily obtain the desired results. For example, automatically generating appropriate prompts for various tasks, such as summarizing text or generating images, makes generative AI easier to use. Furthermore, the generative AI support system also has the ability to evaluate the output results of the generative AI and modify the prompts as necessary. This improves the accuracy of the generative AI and achieves more desired results. For example, if the generated summary is insufficient, a better summary can be obtained by modifying the prompt and re-inputting it to the generative AI. This allows the generative AI usage support system to make generative AI easy for anyone to use, automatically generate appropriate prompts, and improve the accuracy of generative AI, thereby achieving the expected results.

[0073] A generation AI utilization support system according to an embodiment includes a reception unit, a prompt generation unit, an evaluation unit, and a provision unit. The reception unit receives a user's task. Examples of the user's task include, but are not limited to, work tasks, study tasks, and daily tasks. The reception unit receives the task input by the user in text format, for example. The reception unit can also receive the task using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology and receives the text data as a task. The prompt generation unit uses a generation AI to generate a prompt based on the task received by the reception unit. The prompt may be generated in a format such as a question, an instruction, or an information provision format, for example, but is not limited to these examples. For example, the prompt generation unit uses a generation AI (e.g., Transformer) to generate a prompt optimal for the user's task. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. The evaluation unit evaluates the output result of the generation AI. The evaluation is performed based on criteria such as, but not limited to, accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output result of the generative AI and modifies the prompt as necessary. The evaluation unit can also evaluate the output result of the generative AI based on user feedback and identify areas for improvement in the prompt. The provision unit provides the output result evaluated by the evaluation unit to the user. The provision can be performed in the form of, for example, email, dashboard, notification, or the like, but is not limited to, these examples. For example, the provision unit can display the output result of the generative AI to the user through a web application or mobile application. The provision unit can also print the output result of the generative AI on a printer and provide it on paper. As a result, the generative AI usage support system according to the embodiment can efficiently accept user tasks, generate appropriate prompts, and evaluate and provide the output results.

[0074] The prompt generation unit can generate prompts using a generation AI. Examples of the generation AI include, but are not limited to, Transformer. The prompt generation unit uses, for example, a generation AI to generate a prompt that is optimal for the user's task. For example, the prompt generation unit inputs an instruction to the generation AI, such as "Please summarize the following sentence," and the generation AI generates a prompt based on that instruction. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. For example, the prompt generation unit inputs an instruction to the generation AI, such as "Please collect background information related to this task," and the generation AI collects background information based on that instruction and reflects it in the prompt. In this way, using the generation AI improves the accuracy of prompt generation. Some or all of the above-mentioned processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs a user's task to the generation AI, and the generation AI generates a prompt based on that task.

[0075] The evaluation unit can evaluate the output result of the generation AI and modify the prompt as necessary. The evaluation is performed based on criteria such as, but not limited to, accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output result of the generation AI and modifies the prompt as necessary. For example, if the output result of the generation AI does not match the expected result, the evaluation unit modifies the prompt and inputs it again to the generation AI. The evaluation unit can also evaluate the output result of the generation AI based on user feedback and identify areas for improvement in the prompt. For example, the evaluation unit collects user feedback and modifies the prompt based on that feedback. This allows the prompt to be modified to improve the accuracy of the output result. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs the output result to the generation AI, and the generation AI suggests modifications to the prompt based on the output result.

[0076] The providing unit can provide the output results of the generation AI to the user. The providing unit can provide the output results of the generation AI to the user, for example, via email, dashboard, notification, etc., but is not limited to these examples. For example, the providing unit can display the output results of the generation AI to the user via a web application or mobile application. The providing unit can also print the output results of the generation AI on a printer and provide them on paper. For example, the providing unit can send the output results of the generation AI to the user via email. The providing unit can also display the output results of the generation AI on a dashboard, allowing the user to check the results in real time. This allows the user to check the results by providing the output results of the generation AI to the user. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit can input the output results to the generation AI and suggest an optimal method for the generation AI to provide the output results to the user.

[0077] The reception unit can accept tasks input by a user. Examples of user tasks include, but are not limited to, work tasks, study tasks, and daily tasks. For example, the reception unit accepts tasks input by a user in text format. The reception unit can also accept tasks using voice input or image input. For example, the reception unit can convert the user's voice input into text data using voice recognition technology and accept it as a task. The reception unit can also analyze the user's image input using image recognition technology and accept it as a task. For example, the reception unit can analyze an image taken by the user using a smartphone camera and identify a task based on the image. In this way, the system can process the task by accepting the task input by the user. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's voice input to the generation AI, which then converts the voice input into text data.

[0078] The reception unit can estimate the user's emotions and adjust the timing of task acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit temporarily delays accepting the task and accepts it again when the user is relaxed. Furthermore, if the user is relaxed, the reception unit can immediately accept the task and quickly start processing it. Furthermore, if the user is in a hurry, the reception unit can prioritize accepting the task and quickly process it. This allows the timing of task acceptance to be adjusted according to the user's emotions, allowing the task to be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's facial expression data to the generation AI, and the generation AI estimates the user's emotions based on the facial expression data.

[0079] The reception unit can analyze the user's past task history and select the optimal reception method. For example, the reception unit prioritizes receiving tasks that the user has frequently performed in the past. The reception unit can also predict tasks to be performed in a specific time period from the user's past task history and suggest the optimal reception method. The reception unit can also analyze the user's past task history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past task history. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past task history into the generation AI, and the generation AI suggests the optimal reception method based on that history.

[0080] When receiving a task, the reception unit can filter the tasks based on the user's current project or area of ​​interest. For example, the reception unit preferentially receives tasks related to the user's current project. The reception unit can also filter and receive related tasks based on the user's area of ​​interest. The reception unit can also filter and receive related tasks by referring to the user's past project history. In this way, by filtering tasks based on the user's current project or area of ​​interest, highly relevant tasks can be preferentially received. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's project information to the generation AI, and the generation AI filters related tasks based on that information.

[0081] When accepting a task, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the task using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the task using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the task using image recognition technology. This allows the acceptance of tasks to be carried out smoothly by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's input data to the generation AI, and the generation AI selects the optimal acceptance means based on that data.

[0082] The reception unit can estimate the user's emotions and determine the priority of tasks to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize simple tasks. Furthermore, when the user is relaxed, the reception unit can also prioritize complex tasks. Furthermore, when the user is in a hurry, the reception unit can also prioritize urgent tasks. This enables more appropriate task processing by determining the priority of tasks according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's emotion data into the generation AI, and the generation AI determines the priority of tasks based on the data.

[0083] When accepting a task, the reception unit can prioritize accepting highly relevant tasks by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting tasks related to that area. The reception unit can also prioritize accepting tasks related to locations close to the user's current location. The reception unit can also suggest and accept optimal tasks based on the user's geographical location information. This makes it possible to prioritize accepting highly relevant tasks by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information to the generation AI, and the generation AI suggests related tasks based on that information.

[0084] When accepting a task, the acceptance unit can analyze the user's social media activity and accept related tasks. For example, the acceptance unit preferentially accepts tasks mentioned by the user on social media. The acceptance unit can also analyze the user's social media activity and suggest and accept related tasks. The acceptance unit can also accept related tasks by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related tasks can be preferentially accepted. Some or all of the above-mentioned processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's social media data into the generation AI, and the generation AI suggests related tasks based on that data.

[0085] When accepting a task, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method based on the user's past feedback. The reception unit can also customize the reception method by reflecting the user's feedback and perform optimal task reception. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's feedback data to the generation AI, and the generation AI suggests the optimal reception method based on that data.

[0086] The prompt generation unit can estimate the user's emotions and adjust the prompt expression method based on the estimated user emotions. For example, if the user is feeling stressed, the prompt generation unit generates a simple and clear prompt. The prompt generation unit can also generate a detailed and customizable prompt if the user is relaxed. The prompt generation unit can also generate a short prompt that can be processed quickly if the user is in a hurry. This allows for the generation of more appropriate prompts by adjusting the prompt expression method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the prompt expression method based on the data.

[0087] The prompt generation unit can adjust the level of detail of the prompt based on the importance of the task when generating the prompt. For example, the prompt generation unit generates a detailed prompt for a task with high importance. The prompt generation unit can also generate a concise prompt for a task with low importance. The prompt generation unit can also dynamically adjust the level of detail of the prompt according to the importance of the task. In this way, an appropriate prompt can be generated by adjusting the level of detail of the prompt according to the importance of the task. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task importance data to the generation AI, and the generation AI adjusts the level of detail of the prompt based on that data.

[0088] When generating a prompt, the prompt generation unit can apply different prompt generation algorithms depending on the task category. For example, the prompt generation unit applies a natural language processing algorithm to a sentence generation task. The prompt generation unit can also apply an image recognition algorithm to an image generation task. The prompt generation unit can also apply a voice recognition algorithm to a voice generation task. This improves the accuracy of the prompt by applying an appropriate prompt generation algorithm depending on the task category. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task category data to the generation AI, and the generation AI applies an appropriate prompt generation algorithm based on that data.

[0089] When generating a prompt, the prompt generation unit can improve the accuracy of the prompt by referring to the user's past prompt results. For example, the prompt generation unit analyzes prompts used by the user in the past and generates an optimal prompt. The prompt generation unit can also extract and reuse effective prompts from the user's past prompt results. The prompt generation unit can also build a feedback loop to improve the accuracy of the prompt based on the user's past prompt results. In this way, the accuracy of the prompt is improved by referring to the user's past prompt results. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs the user's past prompt results into the generation AI, and the generation AI generates an optimal prompt based on that data.

[0090] The prompt generation unit can estimate the user's emotions and adjust the length of the prompt based on the estimated user emotions. For example, if the user is feeling stressed, the prompt generation unit can generate a short and concise prompt. If the user is relaxed, the prompt generation unit can also generate a detailed and longer prompt. If the user is in a hurry, the prompt generation unit can also generate a short prompt that can be processed quickly. This allows for the generation of more appropriate prompts by adjusting the length of the prompt according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the length of the prompt based on the data.

[0091] When generating a prompt, the prompt generation unit can determine the priority of the prompt based on the submission time of the task. For example, the prompt generation unit prioritizes generating prompts for tasks with an approaching deadline. The prompt generation unit can also postpone generating prompts for tasks with a more distant submission time. The prompt generation unit can also dynamically adjust the priority of the prompt based on the submission time of the task. This makes it possible to generate appropriate prompts by determining the priority of the prompt based on the submission time of the task. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task submission time data to the generation AI, and the generation AI determines the priority of the prompt based on that data.

[0092] The prompt generation unit can adjust the order of prompts based on the relevance of the tasks when generating prompts. For example, the prompt generation unit prioritizes generating prompts for highly relevant tasks. The prompt generation unit can also postpone generating prompts for less relevant tasks. The prompt generation unit can also dynamically adjust the order of prompts based on the relevance of the tasks. This makes it possible to generate appropriate prompts by adjusting the order of prompts based on the relevance of the tasks. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task relevance data to the generation AI, and the generation AI adjusts the order of prompts based on that data.

[0093] When generating a prompt, the prompt generation unit can adjust the use of technical terminology in the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the prompt generation unit generates a prompt that uses a lot of technical terminology. Furthermore, if the user is a beginner, the prompt generation unit can also generate a simple prompt that avoids technical terminology. The prompt generation unit can also dynamically adjust the use of technical terminology in the prompt according to the user's level of expertise. This allows appropriate prompts to be generated by adjusting the use of technical terminology in the prompt according to the user's level of expertise. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs user's expertise level data into the generation AI, and the generation AI adjusts the use of technical terminology in the prompt based on that data.

[0094] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the output results based on the estimated user emotions. For example, the evaluation unit relaxes strict evaluation criteria when the user is stressed. The evaluation unit can also apply detailed evaluation criteria when the user is relaxed. The evaluation unit can also apply simple evaluation criteria for quick evaluation when the user is in a hurry. This allows for appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs user emotion data into the generation AI, and the generation AI adjusts the evaluation criteria based on the data.

[0095] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on the interrelationships between output results. For example, the evaluation unit analyzes the relevance of multiple output results and performs a comprehensive evaluation. The evaluation unit can also adjust the weighting of the evaluation by taking into account the interrelationships between the output results. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the interrelationships between the output results. This improves the accuracy of the evaluation by taking into account the interrelationships between the output results. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs interrelationship data between the output results to the generation AI, and the generation AI adjusts the weighting of the evaluation based on that data.

[0096] During evaluation, the evaluation unit can perform evaluation based on the attribute information of the submitter of the output result. The evaluation unit adjusts the evaluation criteria, for example, according to the submitter's level of expertise. The evaluation unit can also adjust the evaluation criteria by referring to the submitter's past evaluation history. The evaluation unit can also apply optimal evaluation criteria based on the submitter's attribute information. This allows for appropriate evaluation by taking the submitter's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs the submitter's attribute information data into the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0097] During evaluation, the evaluation unit can weight the evaluation based on the frequency of submission of the output result. For example, the evaluation unit applies stricter evaluation criteria to output results that are submitted frequently. The evaluation unit can also apply lenient evaluation criteria to output results that are submitted infrequently. The evaluation unit can also dynamically adjust the evaluation weighting according to the submission frequency. In this way, appropriate evaluation can be performed by weighting the evaluation according to the submission frequency. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs submission frequency data to the generation AI, and the generation AI adjusts the evaluation weighting based on that data.

[0098] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows for appropriate display by adjusting the display method of the evaluation results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs user emotion data into the generation AI, and the generation AI adjusts the display method of the evaluation results based on the data.

[0099] During evaluation, the evaluation unit can perform evaluation based on the geographic distribution of the output results. For example, the evaluation unit analyzes the geographic distribution of the output results and performs evaluation taking into account the characteristics of each region. The evaluation unit can also adjust the evaluation criteria based on the geographic distribution. The evaluation unit can also establish a feedback loop to improve the accuracy of the evaluation based on the geographic distribution of the output results. This allows for appropriate evaluation by taking the geographic distribution into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs geographic distribution data of the output results to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0100] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the output result. For example, the evaluation unit automatically searches for literature related to the output result and reflects it in the evaluation. The evaluation unit can also adjust the evaluation criteria based on the content of the related literature. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the literature related to the output result. In this way, the accuracy of the evaluation is improved by referring to the related literature. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs related literature data to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0101] The evaluation unit can perform the evaluation based on the market value of the output result during the evaluation. For example, the evaluation unit reflects the market value of the output result in the evaluation criteria. The evaluation unit can also adjust the weighting of the evaluation based on the market value. The evaluation unit can also build a feedback loop to improve the accuracy of the evaluation based on the market value of the output result. This allows for appropriate evaluation by taking market value into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using a generation AI. For example, the evaluation unit inputs market value data of the output result to the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0102] The providing unit can estimate the user's emotions and adjust the method of providing the output results based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit selects a simple, highly visible method of providing the output results. Furthermore, if the user is relaxed, the providing unit can select a method of providing detailed information. Furthermore, if the user is in a hurry, the providing unit can select a simple method of providing information quickly. This allows appropriate information to be provided by adjusting the method of providing information according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the method of providing the output results based on the data.

[0103] When providing an output result, the providing unit can select a delivery method based on the user's past feedback. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. The providing unit can also customize the delivery method by reflecting the user's feedback and provide an optimal output result. This makes it possible to select an optimal delivery method by referring to past feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests an optimal delivery method based on that data.

[0104] When providing an output result, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit prioritizes providing output results related to the task the user is currently performing. The providing unit can also customize and provide related information based on the user's current task. The providing unit can also select optimal content to be provided, taking into account the user's current task. In this way, appropriate information can be provided by customizing the content to be provided according to the current task. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's task data to the generation AI, and the generation AI selects optimal content to be provided based on that data.

[0105] The providing unit can improve the providing method by reflecting user feedback when providing an output result. For example, the providing unit improves the providing method based on feedback provided by the user regarding the providing method. The providing unit can also customize the providing method by reflecting user feedback and provide optimal output results. The providing unit can also identify and implement improvements to the providing method based on user feedback. In this way, the providing method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests improvements to the providing method based on that data.

[0106] The providing unit can estimate the user's emotions and adjust the procedure for providing the output results based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit selects a simple and quick procedure for providing the output results. Furthermore, if the user is relaxed, the providing unit can select a procedure for providing detailed information. Furthermore, if the user is in a hurry, the providing unit can select a simple procedure for providing the output results quickly. By adjusting the procedure for providing the output results according to the user's emotions, appropriate provision can be achieved. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the procedure for providing the output results based on the data.

[0107] When providing the output results, the providing unit can select a delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit selects a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is simple and highly visible. This allows the optimal delivery method to be selected by taking device information into consideration. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's device information to the generation AI, and the generation AI suggests the optimal delivery method based on that information.

[0108] When providing the output results, the providing unit can make the provided content multilingual according to the user's language setting. For example, the providing unit automatically sets the language of the output results based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the output results in that language. This makes it possible to provide appropriate information by providing multilingual support according to the language setting. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's language setting data to the generation AI, and the generation AI makes the provided content multilingual based on that data.

[0109] When providing the output results, the providing unit can analyze the user's social media activity and provide related information. For example, the providing unit provides information related to content mentioned by the user on social media. The providing unit can also analyze the user's social media activity and provide related information. The providing unit can also provide related information by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's social media data into the generation AI, and the generation AI suggests related information based on that data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, prompt generation unit, evaluation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a user's task using the reception device 38 of the smart device 14. The prompt generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a prompt using the generation AI. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates an output result of the generation AI. The provision unit can provide the output result to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, prompt generation unit, evaluation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a user's task using the microphone 238 of the smart glasses 214. The prompt generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a prompt using the generation AI. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates an output result of the generation AI. The provision unit can provide the output result to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, prompt generation unit, evaluation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive a user's task using the microphone 238 of the headset type terminal 314. The prompt generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a prompt using the generation AI. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the output result of the generation AI. The provision unit can provide the output result to the user using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, prompt generation unit, evaluation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive a user's task using the microphone 238 of the robot 414. The prompt generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a prompt using the generation AI. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the output result of the generation AI. The provision unit can provide the output result to the user using the speaker 240 of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When accepting a task from a user, the acceptance unit can analyze the user's past task history and select the optimal acceptance method. For example, it can prioritize accepting tasks that the user has performed frequently in the past. The acceptance unit can also predict tasks to be performed during a specific time period from the user's past task history and suggest the optimal acceptance method. Furthermore, the acceptance unit can analyze the user's past task history and select the most efficient acceptance method. In this way, the optimal acceptance method can be selected by analyzing the past task history. Some or all of the above-mentioned processing in the acceptance unit is performed using a generation AI. For example, the acceptance unit inputs the user's past task history into the generation AI, and the generation AI suggests the optimal acceptance method based on that history.

[0112] When generating a prompt using the generation AI, the prompt generation unit can adjust the use of technical terminology in the prompt according to the user's level of expertise. For example, if the user has specialized knowledge, the prompt generation unit can generate a prompt that uses a lot of technical terminology. On the other hand, if the user is a beginner, the prompt generation unit can generate a simple prompt that avoids technical terminology. Furthermore, the prompt generation unit can dynamically adjust the use of technical terminology in the prompt according to the user's level of expertise. This makes it possible to generate appropriate prompts by adjusting the use of technical terminology in the prompt according to the user's level of expertise. Some or all of the above-mentioned processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs user's expertise level data to the generation AI, and the generation AI adjusts the use of technical terminology in the prompt based on that data.

[0113] The evaluation unit can improve the accuracy of the evaluation based on the interrelationships between the output results when evaluating the output results of the generation AI and correcting the prompts as necessary. For example, the evaluation unit analyzes the relevance of multiple output results and performs a comprehensive evaluation. The evaluation unit can also adjust the weighting of the evaluation by taking into account the interrelationships between the output results. Furthermore, the evaluation unit can also establish a feedback loop to improve the accuracy of the evaluation based on the interrelationships between the output results. This improves the accuracy of the evaluation by taking into account the interrelationships between the output results. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs interrelationship data between the output results to the generation AI, and the generation AI adjusts the weighting of the evaluation based on that data.

[0114] When providing the output results of the generation AI to a user, the providing unit can select a delivery method based on the user's device information. For example, if the user is using a smartphone, the providing unit can select a delivery method that matches the screen size. Also, if the user is using a tablet, the providing unit can select a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select a delivery method that is simple and highly visible. This allows the optimal delivery method to be selected by taking device information into consideration. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's device information to the generation AI, and the generation AI suggests the optimal delivery method based on that information.

[0115] The reception unit can estimate the user's emotions and adjust the timing of task acceptance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can temporarily delay the task acceptance and then accept it again when the user is relaxed. Also, if the user is relaxed, the task can be accepted immediately and processing can begin quickly. Furthermore, if the user is in a hurry, the task can be accepted with priority and processed quickly. This allows the timing of task acceptance to be adjusted according to the user's emotions, allowing tasks to be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then estimates the user's emotions based on the facial expression data.

[0116] The prompt generation unit can estimate the user's emotions and adjust the prompt expression based on the estimated user emotions. For example, if the user is feeling stressed, a simple and clear prompt can be generated. If the user is relaxed, a detailed and customizable prompt can be generated. Furthermore, if the user is in a hurry, a short prompt that can be processed quickly can be generated. This allows for the generation of more appropriate prompts by adjusting the prompt expression based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prompt generation unit is performed using the generation AI. For example, the prompt generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the prompt expression based on the data.

[0117] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the output results based on the estimated user emotions. For example, if the user is feeling stressed, strict evaluation criteria can be relaxed. Also, if the user is relaxed, detailed evaluation criteria can be applied. Furthermore, if the user is in a hurry, simple evaluation criteria can be applied for quick evaluation. This allows for appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs user emotion data into the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0118] The providing unit can estimate the user's emotions and adjust the method of providing the output results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible method of providing the output results can be selected. Alternatively, if the user is relaxed, a method of providing detailed information can be selected. Furthermore, if the user is in a hurry, a simple method of providing information that can be provided quickly can be selected. By adjusting the method of providing information according to the user's emotions, appropriate information can be provided. The estimation of emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the method of providing the output results based on the data.

[0119] The providing unit can estimate the user's emotions and adjust the procedure for providing the output results based on the estimated user emotions. For example, if the user is feeling stressed, a simple and quick procedure can be selected. Also, if the user is relaxed, a procedure that includes detailed information can be selected. Furthermore, if the user is in a hurry, a simple procedure that can be quickly provided can be selected. By adjusting the procedure for providing information according to the user's emotions, appropriate information can be provided. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the procedure for providing information based on the data.

[0120] The prompt generation unit can adjust the level of detail of the prompt based on the importance of the task when generating the prompt. For example, a detailed prompt can be generated for a task with high importance. A concise prompt can also be generated for a task with low importance. Furthermore, the prompt generation unit can dynamically adjust the level of detail of the prompt according to the importance of the task. This allows an appropriate prompt to be generated by adjusting the level of detail of the prompt according to the importance of the task. Some or all of the above-described processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task importance data to the generation AI, and the generation AI adjusts the level of detail of the prompt based on that data.

[0121] When generating a prompt, the prompt generation unit can apply different prompt generation algorithms depending on the task category. For example, a natural language processing algorithm is applied to a sentence generation task. The prompt generation unit can also apply an image recognition algorithm to an image generation task. Furthermore, the prompt generation unit can also apply a voice recognition algorithm to a voice generation task. This improves the accuracy of the prompt by applying an appropriate prompt generation algorithm depending on the task category. Some or all of the above-mentioned processing in the prompt generation unit is performed using a generation AI. For example, the prompt generation unit inputs task category data to the generation AI, and the generation AI applies an appropriate prompt generation algorithm based on that data.

[0122] During evaluation, the evaluation unit can perform evaluation based on the attribute information of the submitter of the output result. For example, the evaluation unit adjusts the evaluation criteria according to the submitter's level of expertise. The evaluation unit can also adjust the evaluation criteria by referring to the submitter's past evaluation history. Furthermore, the evaluation unit can apply optimal evaluation criteria based on the submitter's attribute information. This allows for appropriate evaluation by taking the submitter's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs the submitter's attribute information data into the generation AI, and the generation AI adjusts the evaluation criteria based on that data.

[0123] During evaluation, the evaluation unit can weight the evaluation based on the frequency of submission of the output results. For example, stricter evaluation criteria can be applied to output results that are submitted frequently. The evaluation unit can also apply looser evaluation criteria to output results that are submitted infrequently. Furthermore, the evaluation unit can dynamically adjust the evaluation weighting according to the submission frequency. This allows for appropriate evaluation by weighting the evaluation according to the submission frequency. Some or all of the above-mentioned processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs submission frequency data to the generation AI, and the generation AI adjusts the evaluation weighting based on that data.

[0124] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for appropriate display by adjusting the display method of the evaluation results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit is performed using the generation AI. For example, the evaluation unit inputs user emotion data into the generation AI, and the generation AI adjusts the display method of the evaluation results based on that data.

[0125] When providing an output result, the providing unit can select a delivery method based on the user's past feedback. For example, the providing unit can suggest an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially select a specific delivery method based on the user's past feedback. Furthermore, the providing unit can customize the delivery method by reflecting the user's feedback and provide an optimal output result. This makes it possible to select an optimal delivery method by referring to past feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests an optimal delivery method based on that data.

[0126] When providing output results, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit can prioritize providing output results related to the task the user is currently performing. The providing unit can also customize and provide related information based on the user's current task. Furthermore, the providing unit can select the optimal content to be provided by taking the user's current task into consideration. In this way, appropriate information can be provided by customizing the content to be provided according to the current task. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's task data into the generation AI, and the generation AI selects the optimal content to be provided based on that data.

[0127] The providing unit can improve the delivery method by reflecting user feedback when providing an output result. For example, the delivery method is improved based on feedback provided by the user regarding the delivery method. The providing unit can also customize the delivery method by reflecting user feedback and provide optimal output results. Furthermore, the providing unit can identify and implement improvements to the delivery method based on user feedback. In this way, the delivery method can be improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit is performed using a generation AI. For example, the providing unit inputs user feedback data to the generation AI, and the generation AI suggests improvements to the delivery method based on that data.

[0128] The providing unit can estimate the user's emotions and adjust the procedure for providing the output results based on the estimated user emotions. For example, if the user is feeling stressed, a simple and quick procedure can be selected. Also, if the user is relaxed, a procedure that includes detailed information can be selected. Furthermore, if the user is in a hurry, a simple procedure that can be quickly provided can be selected. By adjusting the procedure for providing information according to the user's emotions, appropriate information can be provided. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the procedure for providing information based on the data.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit receives the user's tasks. User tasks include work tasks, study tasks, daily tasks, etc. The reception unit receives tasks entered by the user in text format, and can also receive tasks using voice input or image input. For example, the reception unit converts the user's voice input into text data using voice recognition technology and receives it as a task. Step 2: The prompt generation unit uses a generation AI to generate a prompt based on the task accepted by the acceptance unit. The prompt is generated in a format such as a question, instruction, or information provision format. For example, the prompt generation unit uses a generation AI such as Transformer to generate a prompt that is optimal for the user's task. The prompt generation unit can also use the generation AI to collect background information related to the user's task and reflect it in the prompt. Step 3: The evaluation unit evaluates the output of the generative AI. The evaluation is based on criteria such as accuracy, reliability, and user satisfaction. For example, the evaluation unit analyzes the output of the generative AI and modifies the prompts as necessary. The evaluation unit can also evaluate the output of the generative AI based on user feedback and identify areas for improvement in the prompts. Step 4: The providing unit provides the output results evaluated by the evaluation unit to the user. The output results are provided in the form of email, dashboard, notification, etc. For example, the providing unit may display the output results of the generation AI to the user through a web application or mobile application. The providing unit may also print out the output results of the generation AI on a printer and provide them on paper.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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 reception unit that receives a task from a user; a prompt generation unit that generates a prompt based on the task accepted by the accepting unit; an evaluation unit that evaluates the output result of the generation AI based on the prompt generated by the prompt generation unit; a providing unit that provides an output result evaluated by the evaluating unit. A system characterized by:

2. The prompt generation unit Generate prompts using AI 2. The system of claim 1.

3. The evaluation unit Evaluate the output of the generated AI and modify the prompts as needed 2. The system of claim 1.

4. The providing unit Providing the output of the generative AI to the user 2. The system of claim 1.

5. The reception unit Accepts tasks entered by the user 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of task acceptance based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past task history and select the reception method 2. The system of claim 1.

8. The reception unit Filter tasks based on your current projects and interests when accepting them 2. The system of claim 1.

9. The reception unit When accepting a task, select the acceptance method according to the user's input method.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize tasks based on the estimated user emotions.

2. The system of claim 1.

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