system

Abstract

The system according to the embodiment aims to visualize the efforts of the person being evaluated and to ensure that evaluation opportunities are not missed. [Solution] A system according to an embodiment includes a providing unit, a receiving unit, and a generating unit. The providing unit provides a common report format prepared by the evaluator. The receiving unit inputs the key points and results of the evaluatione's efforts and the contents of the planned presentation. The generating unit analyzes the information input by the receiving unit and generates a report in accordance with the common report format.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately visualize the efforts of those being evaluated and do not miss evaluation opportunities, so there is room for improvement.

[0005] The system according to the embodiment aims to visualize the efforts of the person being evaluated and to ensure that evaluation opportunities are not missed. [Means for solving the problem]

[0006] The system according to the embodiment includes a providing unit, a receiving unit, and a generating unit. The providing unit provides a common report format prepared by the evaluator. The receiving unit inputs the key points and results of the evaluationee's efforts and the contents of the planned presentation. The generating unit analyzes the information input by the receiving unit and generates a report in accordance with the common report format. [Effects of the Invention]

[0007] The system according to the embodiment makes the efforts of the person being evaluated visible, and can prevent evaluation opportunities from being missed. [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) In an embodiment of the present invention, an evaluation support system provides a common report format prepared by the evaluator, inputs the key points and results of the evaluationee's efforts and the contents of the planned presentation, and a generation AI analyzes the information to automatically generate a report. In the evaluation support system, the evaluator prepares a common report format for the organization, and the evaluationee inputs the key points and results of their efforts and the contents of the planned presentation into the generation AI. The generation AI analyzes the input information and automatically generates a report in accordance with the common report format. For example, in the evaluation support system, the evaluator prepares a common report format for the organization. For example, the evaluation support system provides a format including fields for describing the key points and results of the efforts, the contents of the planned presentation, etc. Next, the evaluationee inputs the key points and results of their efforts and the contents of the planned presentation into the generation AI. For example, the evaluationee inputs content such as, "Project A is 80% complete, and the main achievement is the completion of the implementation of new functions. The next step is to conduct user testing." Next, in the evaluation support system, the generation AI analyzes the input information and automatically generates a report in accordance with the common report format. For example, the generative AI can use the input information to organize project progress, results, next steps, and other information, creating easy-to-read reports. This allows the appraisee to create reports efficiently and the evaluator to visualize more initiatives. For example, the evaluation support system can significantly reduce the time it takes to create reports for employees working reduced hours due to childcare or elderly care. It also allows evaluators to visualize more initiatives, eliminating the problem of appraisers not being mentioned during evaluations. Furthermore, the evaluation support system improves the skills of the generative AI within an organization. By allowing appraisers to utilize generative AI, more initiatives can be visualized, improving productivity throughout the organization. For example, using generative AI to create reports can free up time to focus on other tasks.

[0029] An evaluation support system according to an embodiment includes a providing unit, a receiving unit, and a generating unit. The providing unit provides a common report format prepared by the evaluator. The common report format includes, for example, key points and results of the efforts, and planned presentation content. For example, the providing unit provides a format including items for describing the project's progress, results, and challenges. The receiving unit inputs the key points and results of the efforts of the person being evaluated and the planned presentation content. The key points of the efforts of the person being evaluated include, for example, important results and problem-solving methods. For example, the receiving unit inputs content such as, "Project A is 80% complete, and the main result is the completion of the implementation of new functions. The next step is to conduct user testing." The generating unit uses a generation AI to analyze the information input by the receiving unit and generate a report in accordance with the common report format. For example, the generating unit organizes the project's progress, results, next steps, and other information based on the input information to create an easy-to-read report. For example, the generating AI uses a text generation AI (e.g., LLM) to analyze the input information and generate the report. The generation unit can also use a generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. For example, the generation AI organizes the project's progress, results, next steps, etc., based on input information, and creates easy-to-read report materials. This allows the evaluation support system according to the embodiment to enable the person being evaluated to efficiently create report materials and the evaluator to visualize more efforts. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate report materials using an AI model that generates report materials based on input information.

[0030] The provision unit can provide a common report format including items for describing the key points and results of the initiative and the contents of the planned presentation. The common report format includes, for example, the key points and results of the initiative and the contents of the planned presentation. For example, the provision unit provides a format including items for describing the progress, results, and challenges of the project. As a result, the common report format includes specific items, thereby maintaining consistency in the report. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide the format using an AI model that provides the common report format.

[0031] The reception unit can input the key points and results of the person being evaluated and the contents of the planned presentation. Key points of the person being evaluated include, for example, important achievements and methods for solving problems. For example, the reception unit inputs information such as, "Project A is 80% complete, and the main achievement is the completion of the implementation of new functions. The next step is to conduct user testing." This allows the person being evaluated to efficiently input their own efforts. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the key points and results of the person being evaluated and the contents of the planned presentation using an AI model.

[0032] The generation unit can analyze the input information and generate a report that conforms to a common report format. The generation unit can use a generation AI to analyze the information input by the reception unit and generate a report that conforms to the common report format. The generation unit can, for example, organize the project's progress, results, next steps, etc. based on the input information to create an easy-to-read report. For example, the generation AI can use a text generation AI (e.g., LLM) to analyze the input information and generate a report. The generation unit can also use the generation AI to organize the project's progress, results, next steps, etc. to create an easy-to-read report. For example, the generation AI can organize the project's progress, results, next steps, etc. based on the input information to create an easy-to-read report. This allows the report to be automatically generated based on the input information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate a report based on the input information using an AI model that generates a report.

[0033] The generation unit can organize the project's progress, results, and next steps to generate easy-to-read report materials. The generation unit uses a generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. The generation unit, for example, organizes the project's progress, results, next steps, etc., based on input information to create easy-to-read report materials. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the input information and generate report materials. The generation unit can also use the generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. For example, the generation AI organizes the project's progress, results, next steps, etc., based on input information to create easy-to-read report materials. This organizes the report materials so that they are easy to read. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate report materials based on input information using an AI model that generates report materials.

[0034] The generation unit uses the generation AI to create reports, thereby securing time to concentrate on other tasks. The generation unit uses the generation AI to create reports. The generation unit, for example, uses the generation AI to generate reports based on input information. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the input information and generate reports. The generation unit can also use the generation AI to organize project progress, results, next steps, etc., to generate easy-to-read reports. For example, the generation AI organizes project progress, results, next steps, etc., based on the input information, to generate easy-to-read reports. This secures time to concentrate on other tasks. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate reports based on input information using an AI model that generates reports.

[0035] The providing unit can analyze past evaluation data and automatically generate an appropriate format. The providing unit analyzes past evaluation data and automatically generates an optimal format. For example, the providing unit prioritizes displaying the most frequently used items based on the past evaluation data. The providing unit can also analyze the past evaluation data and emphasize items that the evaluator highly rated. The providing unit can also automatically add items that the evaluator considers important based on the past evaluation data. In this way, an optimal format is provided based on the past evaluation data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate a format using an AI model that analyzes past evaluation data and automatically generates an optimal format.

[0036] The providing unit can continuously improve the format by reflecting the evaluator's feedback. The providing unit continuously improves the format by reflecting the evaluator's feedback. For example, the providing unit adds or deletes items from the format based on the feedback provided by the evaluator. The providing unit can also improve the layout of the format by reflecting the evaluator's feedback. The providing unit can also adjust the order of items in the format based on the evaluator's feedback. This improves the format based on the evaluator's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve the format using an AI model that continuously improves the format by reflecting the evaluator's feedback.

[0037] The providing unit can provide formats customized for different departments or projects. The providing unit provides formats customized for different departments or projects. For example, the providing unit can provide different formats for each department, enabling reports tailored to the characteristics of each department. The providing unit can also provide formats customized for each project, enabling detailed reports on the progress of each project. The providing unit can also dynamically adjust format items according to the characteristics of each department or project. This enables customization according to each department or project. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide formats using an AI model that provides formats customized for each department or project.

[0038] The providing unit can provide the optimal format taking into account the geographical location information of the rater. The providing unit provides the optimal format taking into account the geographical location information of the rater. For example, if the rater is in a different region, the providing unit provides a format tailored to the characteristics of that region. The providing unit can also provide a format that reflects the characteristics of each region based on the geographical location information of the rater. The providing unit can also provide a format tailored to the characteristics of that country if the rater is overseas. In this way, the optimal format is provided based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the format using an AI model that provides the optimal format taking into account the geographical location information of the rater.

[0039] The providing unit can customize the format by referring to the evaluator's past evaluation history. The providing unit customizes the format by referring to the evaluator's past evaluation history. For example, the providing unit prioritizes displaying items that the evaluator has previously rated highly. The providing unit can also emphasize items that the evaluator considers important based on the evaluator's past evaluation history. The providing unit can also provide a format that matches the evaluator's preferences by referring to the evaluator's past evaluation history. In this way, the format is customized based on the past evaluation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can customize the format using an AI model that customizes the format by referring to the evaluator's past evaluation history.

[0040] The providing unit can adjust the level of detail of the format according to the evaluator's level of expertise. The providing unit adjusts the level of detail of the format according to the evaluator's level of expertise. For example, if the evaluator has specialized knowledge, the providing unit can display detailed items to allow the evaluator to input specialized information. Alternatively, if the evaluator does not have specialized knowledge, the providing unit can display concise items to allow the evaluator to input basic information. The providing unit can also dynamically adjust the level of detail of the format items according to the evaluator's level of expertise. This adjusts the level of detail of the format according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the format using an AI model that adjusts the level of detail of the format according to the evaluator's level of expertise.

[0041] The reception unit can analyze the past input data of the person being evaluated and suggest the optimal input method. The reception unit can analyze the past input data of the person being evaluated and suggest the optimal input method. For example, the reception unit can automatically display items that the person being evaluated has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the person being evaluated has used in the past. The reception unit can also predict and suggest items to be used during a specific time period based on the person being evaluated's past input data. This allows the optimal input method to be suggested based on the past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that analyzes the person being evaluated's past input data and suggests the optimal input method.

[0042] The reception unit can filter the input content based on the assessee's current project or areas of interest. The reception unit filters the input content based on the assessee's current project or areas of interest. For example, the reception unit displays only items related to the project the assessee is currently working on. The reception unit can also prioritize displaying related items based on the assessee's areas of interest. The reception unit can also automatically filter the input content based on the assessee's current project or areas of interest. This filters the input content based on the current project or areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter the input content using an AI model that filters the input content based on the assessee's current project or areas of interest.

[0043] The reception unit can select the optimal input means depending on the input method (voice, text, image, etc.) of the person being evaluated. The reception unit selects the appropriate input means depending on the input method of the person being evaluated. For example, if the person being evaluated uses voice input, the reception unit automatically converts the input content into text using voice recognition technology. Furthermore, if the person being evaluated uses text input, the reception unit can automatically analyze the input content and classify it into appropriate items. Furthermore, if the person being evaluated uses image input, the reception unit can automatically analyze the input content using image recognition technology. In this way, the optimal input means is selected depending on the input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the input means using an AI model that selects the appropriate input means depending on the person being evaluated's input method.

[0044] The reception unit can prioritize acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated. The reception unit prioritizes acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated. For example, if the person being evaluated is in a different region, the reception unit can prioritize displaying items related to that region. The reception unit can also prioritize displaying items that reflect the characteristics of each region based on the geographical location information of the person being evaluated. The reception unit can also prioritize displaying items related to the country in question in consideration of the person being evaluated. In this way, highly relevant input content is acquired based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire input content using an AI model that prioritizes acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated.

[0045] The reception unit can analyze the social media activity of the person being evaluated and acquire related input content. The reception unit can analyze the social media activity of the person being evaluated and acquire related input content. For example, the reception unit can prioritize displaying items related to places where the person being evaluated has checked in on social media. The reception unit can also analyze the content of the person being evaluated's social media posts and prioritize displaying related items. The reception unit can also prioritize displaying related items with reference to the activities of the person being evaluated's friends on social media. In this way, related input content is acquired based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire input content using an AI model that analyzes the social media activity of the person being evaluated and acquires related input content.

[0046] The reception unit can customize the input method by reflecting the assessee's past feedback. The reception unit customizes the input method by reflecting the assessee's past feedback. For example, the reception unit customizes the input method based on feedback provided by the assessee in the past. The reception unit can also improve the input interface by reflecting the assessee's past feedback. The reception unit can also adjust the priority of input content based on the assessee's past feedback. In this way, the input method is customized based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that customizes the input method by reflecting the assessee's past feedback.

[0047] The generation unit can adjust the level of detail of the report material based on the importance of the input information. The generation unit adjusts the level of detail of the report material based on the importance of the input information. For example, the generation unit prioritizes describing important information in detail and describes other information briefly. The generation unit can also emphasize information of high importance to increase visibility. The generation unit can also adjust the order of items in the report material according to the importance. In this way, the level of detail of the report material is adjusted according to the importance of the information. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using an AI model that adjusts the level of detail of the report material based on the importance of the input information.

[0048] The generation unit can apply different generation algorithms depending on the category of the input information. The generation unit applies different generation algorithms depending on the category of the input information. For example, the generation unit applies an algorithm that visually indicates progress to information about the progress of a project. The generation unit can also apply an algorithm that emphasizes the results to information about outcomes. The generation unit can also apply an algorithm that clearly indicates the next step to information about the next step. In this way, the optimal generation algorithm is applied depending on the category of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can apply an algorithm using an AI model that applies different generation algorithms depending on the category of the input information.

[0049] The generation unit can improve the accuracy of generation by referring to the past report materials of the person being evaluated. The generation unit improves the accuracy of generation by referring to the past report materials of the person being evaluated. For example, the generation unit optimizes the format of the report materials based on the past report materials of the person being evaluated. The generation unit can also analyze the past report materials of the person being evaluated and improve the generation algorithm. The generation unit can also customize the content of the report materials by referring to the past report materials of the person being evaluated. This improves the accuracy of generation based on the past report materials. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can improve accuracy by using an AI model that improves the accuracy of generation by referring to the past report materials of the person being evaluated.

[0050] The generation unit can determine the priority of the report materials based on the submission time of the input information. The generation unit determines the priority of the report materials based on the submission time of the input information. For example, the generation unit preferentially reflects information with an upcoming deadline in the report materials. The generation unit can also preferentially reflect information with an earlier submission time in the report materials. The generation unit can also adjust the order of items in the report materials based on the submission time. In this way, the priority of the report materials is determined based on the submission time. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority using an AI model that determines the priority of the report materials based on the submission time of the input information.

[0051] The generation unit can adjust the order of the report materials based on the relevance of the input information. The generation unit adjusts the order of the report materials based on the relevance of the input information. For example, the generation unit prioritizes placing highly relevant information in the first half of the report materials. The generation unit can also place less relevant information in the second half of the report materials. The generation unit can also dynamically adjust the order of items in the report materials based on the relevance of the information. In this way, the order of the report materials is adjusted based on the relevance of the information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using an AI model that adjusts the order of the report materials based on the relevance of the input information.

[0052] The generation unit can adjust the use of technical terms in the report materials according to the expertise level of the person being evaluated. The generation unit adjusts the use of technical terms in the report materials according to the expertise level of the person being evaluated. For example, if the person being evaluated has expertise, the generation unit generates report materials that use a lot of technical terms. Alternatively, if the person being evaluated does not have expertise, the generation unit can generate concise and easy-to-understand report materials. The generation unit can also dynamically adjust the use of technical terms in the report materials according to the expertise level of the person being evaluated. This adjusts the use of technical terms in the report materials according to the expertise level. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the use of technical terms using an AI model that adjusts the use of technical terms in the report materials according to the expertise level of the person being evaluated.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The evaluation support system can further include a real-time feedback unit. The real-time feedback unit provides immediate feedback on the information entered by the person being evaluated. For example, when the person being evaluated enters the progress of a project, the real-time feedback unit provides advice on the clarity and specificity of the input content. In addition, when the person being evaluated enters results, the real-time feedback unit can also indicate how likely the results are to be evaluated. Furthermore, when the person being evaluated enters the next step, the real-time feedback unit can also suggest an optimal action plan based on past data. This allows the person being evaluated to improve the input content and create more effective reporting materials.

[0055] The evaluation support system can further include a data visualization section. The data visualization section visually displays the information entered by the person being evaluated. For example, the progress of a project can be displayed in graphs and charts to make it easier to understand visually. It can also display results and issues in diagrams to help organize information. It can also display next steps in a flowchart to visually show a specific action plan. This allows the person being evaluated to have a clearer understanding of their efforts and allows the evaluator to quickly understand the information.

[0056] The evaluation support system can further include a voice input unit. The voice input unit allows the person being evaluated to input information by voice. For example, when the person being evaluated explains the progress and results of a project by voice, the voice input unit converts that content into text and reflects it in the report. The voice input unit can also respond when the person being evaluated gives voice instructions for the next step, allowing for quick input of information. Furthermore, the voice input unit can infer the person being evaluated's emotions from the tone and speed of their voice and determine the appropriateness of the input content. This allows the person being evaluated to easily input information and efficiently create report materials.

[0057] The evaluation support system can further include an automatic translation unit. The automatic translation unit translates information entered by the person being evaluated into multiple languages. For example, information entered in Japanese by the person being evaluated can be automatically translated into English, Chinese, etc., and report materials can be provided in multiple languages. The automatic translation unit can also accommodate cases where the evaluator speaks a different language, and can provide report materials in a language that the evaluator can easily understand. Furthermore, the automatic translation unit can appropriately translate technical terms and industry jargon to maintain the accuracy of the information. This allows evaluations to be carried out smoothly in international organizations and multilingual environments.

[0058] The evaluation support system can further include a data integration unit. The data integration unit integrates the appraisee's past evaluation data with data from other systems to conduct a comprehensive evaluation. For example, it can integrate the appraisee's past project results and progress to evaluate overall performance. It can also integrate data obtained from other systems to comprehensively evaluate the appraisee's skills and experience. Furthermore, the data integration unit can analyze the appraisee's evaluation history and provide insights that will be useful for future evaluations. This allows the evaluator to grasp a comprehensive picture of the appraisee and conduct more accurate evaluations.

[0059] The evaluation support system can further include a customized notification unit. The customized notification unit sends notifications to the evaluatee and the evaluator at appropriate times. For example, when the evaluatee has completed entering report materials, a notification is sent to the evaluator to inform them that the evaluation is ready. Also, when the evaluator provides feedback, a notification can be sent to the evaluatee to prompt them to check the feedback. Furthermore, the customized notification unit sends reminders about important deadlines and events, making it easier for the evaluatee and the evaluator to manage their schedules. This helps the evaluation process proceed smoothly and improves communication.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The provision department provides the common report format prepared by the evaluators. The common report format includes the key points and results of the initiative, the contents of the planned presentation, etc. For example, a format is provided that includes items for recording the project's progress, results, challenges, etc. Step 2: The receptionist enters the key points and results of the person being evaluated, as well as the content of the presentation they are planning to give. For example, the person being evaluated might enter something like, "Project A is 80% complete, with the main achievement being the completion of the implementation of new functions. The next step is to conduct user testing." Step 3: The generation unit uses AI to analyze the information entered by the reception unit and generate reports in accordance with a common report format. For example, based on the entered information, the unit organizes the project's progress, results, next steps, etc., to create easy-to-read reports.

[0062] (Example 2) In an embodiment of the present invention, an evaluation support system provides a common report format prepared by the evaluator, inputs the key points and results of the evaluationee's efforts and the contents of the planned presentation, and a generation AI analyzes the information to automatically generate a report. In the evaluation support system, the evaluator prepares a common report format for the organization, and the evaluationee inputs the key points and results of their efforts and the contents of the planned presentation into the generation AI. The generation AI analyzes the input information and automatically generates a report in accordance with the common report format. For example, in the evaluation support system, the evaluator prepares a common report format for the organization. For example, the evaluation support system provides a format including fields for describing the key points and results of the efforts, the contents of the planned presentation, etc. Next, the evaluationee inputs the key points and results of their efforts and the contents of the planned presentation into the generation AI. For example, the evaluationee inputs content such as, "Project A is 80% complete, and the main achievement is the completion of the implementation of new functions. The next step is to conduct user testing." Next, in the evaluation support system, the generation AI analyzes the input information and automatically generates a report in accordance with the common report format. For example, the generative AI can use the input information to organize project progress, results, next steps, and other information, creating easy-to-read reports. This allows the appraisee to create reports efficiently and the evaluator to visualize more initiatives. For example, the evaluation support system can significantly reduce the time it takes to create reports for employees working reduced hours due to childcare or elderly care. It also allows evaluators to visualize more initiatives, eliminating the problem of appraisers not being mentioned during evaluations. Furthermore, the evaluation support system improves the skills of the generative AI within an organization. By allowing appraisers to utilize generative AI, more initiatives can be visualized, improving productivity throughout the organization. For example, using generative AI to create reports can free up time to focus on other tasks.

[0063] An evaluation support system according to an embodiment includes a providing unit, a receiving unit, and a generating unit. The providing unit provides a common report format prepared by the evaluator. The common report format includes, for example, key points and results of the efforts, and planned presentation content. For example, the providing unit provides a format including items for describing the project's progress, results, and challenges. The receiving unit inputs the key points and results of the efforts of the person being evaluated and the planned presentation content. The key points of the efforts of the person being evaluated include, for example, important results and problem-solving methods. For example, the receiving unit inputs content such as, "Project A is 80% complete, and the main result is the completion of the implementation of new functions. The next step is to conduct user testing." The generating unit uses a generation AI to analyze the information input by the receiving unit and generate a report in accordance with the common report format. For example, the generating unit organizes the project's progress, results, next steps, and other information based on the input information to create an easy-to-read report. For example, the generating AI uses a text generation AI (e.g., LLM) to analyze the input information and generate the report. The generation unit can also use a generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. For example, the generation AI organizes the project's progress, results, next steps, etc., based on input information, and creates easy-to-read report materials. This allows the evaluation support system according to the embodiment to enable the person being evaluated to efficiently create report materials and the evaluator to visualize more efforts. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate report materials using an AI model that generates report materials based on input information.

[0064] The provision unit can provide a common report format including items for describing the key points and results of the initiative and the contents of the planned presentation. The common report format includes, for example, the key points and results of the initiative and the contents of the planned presentation. For example, the provision unit provides a format including items for describing the progress, results, and challenges of the project. As a result, the common report format includes specific items, thereby maintaining consistency in the report. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can provide the format using an AI model that provides the common report format.

[0065] The reception unit can input the key points and results of the person being evaluated and the contents of the planned presentation. Key points of the person being evaluated include, for example, important achievements and methods for solving problems. For example, the reception unit inputs information such as, "Project A is 80% complete, and the main achievement is the completion of the implementation of new functions. The next step is to conduct user testing." This allows the person being evaluated to efficiently input their own efforts. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the key points and results of the person being evaluated and the contents of the planned presentation using an AI model.

[0066] The generation unit can analyze the input information and generate a report that conforms to a common report format. The generation unit can use a generation AI to analyze the information input by the reception unit and generate a report that conforms to the common report format. The generation unit can, for example, organize the project's progress, results, next steps, etc. based on the input information to create an easy-to-read report. For example, the generation AI can use a text generation AI (e.g., LLM) to analyze the input information and generate a report. The generation unit can also use the generation AI to organize the project's progress, results, next steps, etc. to create an easy-to-read report. For example, the generation AI can organize the project's progress, results, next steps, etc. based on the input information to create an easy-to-read report. This allows the report to be automatically generated based on the input information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can generate a report based on the input information using an AI model that generates a report.

[0067] The generation unit can organize the project's progress, results, and next steps to generate easy-to-read report materials. The generation unit uses a generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. The generation unit, for example, organizes the project's progress, results, next steps, etc., based on input information to create easy-to-read report materials. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the input information and generate report materials. The generation unit can also use the generation AI to organize the project's progress, results, next steps, etc., and generate easy-to-read report materials. For example, the generation AI organizes the project's progress, results, next steps, etc., based on input information to create easy-to-read report materials. This organizes the report materials so that they are easy to read. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate report materials based on input information using an AI model that generates report materials.

[0068] The generation unit uses the generation AI to create reports, thereby securing time to concentrate on other tasks. The generation unit uses the generation AI to create reports. The generation unit, for example, uses the generation AI to generate reports based on input information. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the input information and generate reports. The generation unit can also use the generation AI to organize project progress, results, next steps, etc., to generate easy-to-read reports. For example, the generation AI organizes project progress, results, next steps, etc., based on the input information, to generate easy-to-read reports. This secures time to concentrate on other tasks. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can generate reports based on input information using an AI model that generates reports.

[0069] The providing unit can estimate the user's emotions and dynamically adjust the items in the common report format based on the estimated user emotions. The providing unit estimates the user's emotions and dynamically adjusts the items in the common report format based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit displays only important items to reduce the burden of input. Alternatively, if the user is relaxed, the providing unit displays detailed items to allow the user to enter more information. Alternatively, if the user is in a hurry, the providing unit displays only the minimum number of items to allow the user to complete the input quickly. In this way, the items in the format are adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can adjust the format using an AI model that estimates a user's emotions and dynamically adjusts items in the common report document format based on the estimated user's emotions.

[0070] The providing unit can analyze past evaluation data and automatically generate an appropriate format. The providing unit analyzes past evaluation data and automatically generates an optimal format. For example, the providing unit prioritizes displaying the most frequently used items based on the past evaluation data. The providing unit can also analyze the past evaluation data and emphasize items that the evaluator highly rated. The providing unit can also automatically add items that the evaluator considers important based on the past evaluation data. In this way, an optimal format is provided based on the past evaluation data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate a format using an AI model that analyzes past evaluation data and automatically generates an optimal format.

[0071] The providing unit can continuously improve the format by reflecting the evaluator's feedback. The providing unit continuously improves the format by reflecting the evaluator's feedback. For example, the providing unit adds or deletes items from the format based on the feedback provided by the evaluator. The providing unit can also improve the layout of the format by reflecting the evaluator's feedback. The providing unit can also adjust the order of items in the format based on the evaluator's feedback. This improves the format based on the evaluator's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve the format using an AI model that continuously improves the format by reflecting the evaluator's feedback.

[0072] The providing unit can provide formats customized for different departments or projects. The providing unit provides formats customized for different departments or projects. For example, the providing unit can provide different formats for each department, enabling reports tailored to the characteristics of each department. The providing unit can also provide formats customized for each project, enabling detailed reports on the progress of each project. The providing unit can also dynamically adjust format items according to the characteristics of each department or project. This enables customization according to each department or project. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide formats using an AI model that provides formats customized for each department or project.

[0073] The providing unit can estimate the user's emotions and adjust the display order of the formats based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the display order of the formats based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can display important items first to reduce the burden of input. If the user is relaxed, the providing unit can display detailed items later to allow the user to enjoy the input task. If the user is in a hurry, the providing unit can display the minimum number of items first to allow the user to complete the input quickly. In this way, the display order of the formats is adjusted 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 providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can adjust the formats using an AI model that estimates the user's emotions and adjusts the display order of the formats based on the estimated user emotions.

[0074] The providing unit can provide the optimal format taking into account the geographical location information of the rater. The providing unit provides the optimal format taking into account the geographical location information of the rater. For example, if the rater is in a different region, the providing unit provides a format tailored to the characteristics of that region. The providing unit can also provide a format that reflects the characteristics of each region based on the geographical location information of the rater. The providing unit can also provide a format tailored to the characteristics of that country if the rater is overseas. In this way, the optimal format is provided based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the format using an AI model that provides the optimal format taking into account the geographical location information of the rater.

[0075] The providing unit can customize the format by referring to the evaluator's past evaluation history. The providing unit customizes the format by referring to the evaluator's past evaluation history. For example, the providing unit prioritizes displaying items that the evaluator has previously rated highly. The providing unit can also emphasize items that the evaluator considers important based on the evaluator's past evaluation history. The providing unit can also provide a format that matches the evaluator's preferences by referring to the evaluator's past evaluation history. In this way, the format is customized based on the past evaluation history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can customize the format using an AI model that customizes the format by referring to the evaluator's past evaluation history.

[0076] The providing unit can adjust the level of detail of the format according to the evaluator's level of expertise. The providing unit adjusts the level of detail of the format according to the evaluator's level of expertise. For example, if the evaluator has specialized knowledge, the providing unit can display detailed items to allow the evaluator to input specialized information. Alternatively, if the evaluator does not have specialized knowledge, the providing unit can display concise items to allow the evaluator to input basic information. The providing unit can also dynamically adjust the level of detail of the format items according to the evaluator's level of expertise. This adjusts the level of detail of the format according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the format using an AI model that adjusts the level of detail of the format according to the evaluator's level of expertise.

[0077] The reception unit can estimate the user's emotion and adjust the input interface based on the estimated user emotion. The reception unit can estimate the user's emotion and adjust the input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick completion of input. This adjusts the input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can estimate the user's emotion and adjust the input interface based on the estimated user emotion using an AI model.

[0078] The reception unit can analyze the past input data of the person being evaluated and suggest the optimal input method. The reception unit can analyze the past input data of the person being evaluated and suggest the optimal input method. For example, the reception unit can automatically display items that the person being evaluated has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the person being evaluated has used in the past. The reception unit can also predict and suggest items to be used during a specific time period based on the person being evaluated's past input data. This allows the optimal input method to be suggested based on the past input data. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest an input method using an AI model that analyzes the person being evaluated's past input data and suggests the optimal input method.

[0079] The reception unit can filter the input content based on the assessee's current project or areas of interest. The reception unit filters the input content based on the assessee's current project or areas of interest. For example, the reception unit displays only items related to the project the assessee is currently working on. The reception unit can also prioritize displaying related items based on the assessee's areas of interest. The reception unit can also automatically filter the input content based on the assessee's current project or areas of interest. This filters the input content based on the current project or areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter the input content using an AI model that filters the input content based on the assessee's current project or areas of interest.

[0080] The reception unit can select the optimal input means depending on the input method (voice, text, image, etc.) of the person being evaluated. The reception unit selects the appropriate input means depending on the input method of the person being evaluated. For example, if the person being evaluated uses voice input, the reception unit automatically converts the input content into text using voice recognition technology. Furthermore, if the person being evaluated uses text input, the reception unit can automatically analyze the input content and classify it into appropriate items. Furthermore, if the person being evaluated uses image input, the reception unit can automatically analyze the input content using image recognition technology. In this way, the optimal input means is selected depending on the input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the input means using an AI model that selects the appropriate input means depending on the person being evaluated's input method.

[0081] The reception unit can estimate the user's emotions and prioritize the input contents based on the estimated user emotions. The reception unit estimates the user's emotions and prioritizes the input contents based on the estimated user emotions. For example, if the user is stressed, the reception unit prompts the user to prioritize inputting important items. If the user is relaxed, the reception unit prompts the user to postpone inputting detailed items. If the user is in a hurry, the reception unit prompts the user to prioritize inputting minimal items. In this way, the priority of the input contents is determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can determine the priority using an AI model that estimates the user's emotions and prioritizes the input contents based on the estimated user emotions.

[0082] The reception unit can prioritize acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated. The reception unit prioritizes acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated. For example, if the person being evaluated is in a different region, the reception unit can prioritize displaying items related to that region. The reception unit can also prioritize displaying items that reflect the characteristics of each region based on the geographical location information of the person being evaluated. The reception unit can also prioritize displaying items related to the country in question in consideration of the person being evaluated. In this way, highly relevant input content is acquired based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire input content using an AI model that prioritizes acquiring highly relevant input content in consideration of the geographical location information of the person being evaluated.

[0083] The reception unit can analyze the social media activity of the person being evaluated and acquire related input content. The reception unit can analyze the social media activity of the person being evaluated and acquire related input content. For example, the reception unit can prioritize displaying items related to places where the person being evaluated has checked in on social media. The reception unit can also analyze the content of the person being evaluated's social media posts and prioritize displaying related items. The reception unit can also prioritize displaying related items with reference to the activities of the person being evaluated's friends on social media. In this way, related input content is acquired based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can acquire input content using an AI model that analyzes the social media activity of the person being evaluated and acquires related input content.

[0084] The reception unit can customize the input method by reflecting the assessee's past feedback. The reception unit customizes the input method by reflecting the assessee's past feedback. For example, the reception unit customizes the input method based on feedback provided by the assessee in the past. The reception unit can also improve the input interface by reflecting the assessee's past feedback. The reception unit can also adjust the priority of input content based on the assessee's past feedback. In this way, the input method is customized based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the input method using an AI model that customizes the input method by reflecting the assessee's past feedback.

[0085] The generation unit can estimate the user's emotions and adjust the presentation style of the report materials based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the presentation style of the report materials based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a simple, highly visible report material. If the user is relaxed, the generation unit generates a report material that includes detailed information. If the user is in a hurry, the generation unit generates a report material that focuses on the main points. This adjusts the presentation style of the report materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can estimate the user's emotions and adjust the presentation style of the report materials based on the estimated user emotions using an AI model.

[0086] The generation unit can adjust the level of detail of the report material based on the importance of the input information. The generation unit adjusts the level of detail of the report material based on the importance of the input information. For example, the generation unit prioritizes describing important information in detail and describes other information briefly. The generation unit can also emphasize information of high importance to increase visibility. The generation unit can also adjust the order of items in the report material according to the importance. In this way, the level of detail of the report material is adjusted according to the importance of the information. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail using an AI model that adjusts the level of detail of the report material based on the importance of the input information.

[0087] The generation unit can apply different generation algorithms depending on the category of the input information. The generation unit applies different generation algorithms depending on the category of the input information. For example, the generation unit applies an algorithm that visually indicates progress to information about the progress of a project. The generation unit can also apply an algorithm that emphasizes the results to information about outcomes. The generation unit can also apply an algorithm that clearly indicates the next step to information about the next step. In this way, the optimal generation algorithm is applied depending on the category of the information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can apply an algorithm using an AI model that applies different generation algorithms depending on the category of the input information.

[0088] The generation unit can improve the accuracy of generation by referring to the past report materials of the person being evaluated. The generation unit improves the accuracy of generation by referring to the past report materials of the person being evaluated. For example, the generation unit optimizes the format of the report materials based on the past report materials of the person being evaluated. The generation unit can also analyze the past report materials of the person being evaluated and improve the generation algorithm. The generation unit can also customize the content of the report materials by referring to the past report materials of the person being evaluated. This improves the accuracy of generation based on the past report materials. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can improve accuracy by using an AI model that improves the accuracy of generation by referring to the past report materials of the person being evaluated.

[0089] The generation unit can estimate the user's emotions and adjust the length of the report materials based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the report materials based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point report materials. If the user is relaxed, the generation unit generates longer report materials with detailed explanations. If the user is stressed, the generation unit generates simple, highly visible report materials. In this way, the length of the report materials is adjusted 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, 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 generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can adjust the length of the report materials using an AI model that estimates the user's emotions and adjusts the length of the report materials based on the estimated user emotions.

[0090] The generation unit can determine the priority of the report materials based on the submission time of the input information. The generation unit determines the priority of the report materials based on the submission time of the input information. For example, the generation unit preferentially reflects information with an upcoming deadline in the report materials. The generation unit can also preferentially reflect information with an earlier submission time in the report materials. The generation unit can also adjust the order of items in the report materials based on the submission time. In this way, the priority of the report materials is determined based on the submission time. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority using an AI model that determines the priority of the report materials based on the submission time of the input information.

[0091] The generation unit can adjust the order of the report materials based on the relevance of the input information. The generation unit adjusts the order of the report materials based on the relevance of the input information. For example, the generation unit prioritizes placing highly relevant information in the first half of the report materials. The generation unit can also place less relevant information in the second half of the report materials. The generation unit can also dynamically adjust the order of items in the report materials based on the relevance of the information. In this way, the order of the report materials is adjusted based on the relevance of the information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order using an AI model that adjusts the order of the report materials based on the relevance of the input information.

[0092] The generation unit can adjust the use of technical terms in the report materials according to the expertise level of the person being evaluated. The generation unit adjusts the use of technical terms in the report materials according to the expertise level of the person being evaluated. For example, if the person being evaluated has expertise, the generation unit generates report materials that use a lot of technical terms. Alternatively, if the person being evaluated does not have expertise, the generation unit can generate concise and easy-to-understand report materials. The generation unit can also dynamically adjust the use of technical terms in the report materials according to the expertise level of the person being evaluated. This adjusts the use of technical terms in the report materials according to the expertise level. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the use of technical terms using an AI model that adjusts the use of technical terms in the report materials according to the expertise level of the person being evaluated. === Hard Collateral 1-1 === Each of the multiple elements, including the providing unit, receiving unit, and generating unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 and provides a common report format. The receiving unit inputs the key points and results of the evaluationee's efforts and the contents of the planned presentation using the touch panel 38A and microphone 38B of the smart device 14. The generating unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to automatically generate a report in accordance with the common report format. The providing unit can estimate the user's emotions and dynamically adjust the items in the common report format based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the providing unit, receiving unit, and generating unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 and provides a common report format. The receiving unit inputs the key points and results of the evaluationee's efforts and the planned presentation content using the microphone 238 of the smart glasses 214. The generating unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to automatically generate a report in accordance with the common report format. The providing unit can estimate the user's emotions and dynamically adjust the items in the common report format based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the providing unit, receiving unit, and generating unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset-type terminal 314 and provides a common report format. The receiving unit inputs the key points and results of the evaluationee's efforts and the planned presentation content using the microphone 238 of the headset-type terminal 314. The generating unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to automatically generate a report in accordance with the common report format. The providing unit can estimate the user's emotions and dynamically adjust the items in the common report format based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the providing unit, receiving unit, and generating unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 and provides a common report format. The receiving unit uses the microphone 238 of the robot 414 to input the key points and results of the evaluationee's efforts and the contents of the planned presentation. The generating unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to automatically generate a report in accordance with the common report format. The providing unit can estimate the user's emotions and dynamically adjust the items in the common report format based on the estimated user emotions.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The evaluation support system can further include a real-time feedback unit. The real-time feedback unit provides immediate feedback on the information entered by the person being evaluated. For example, when the person being evaluated enters the progress of a project, the real-time feedback unit provides advice on the clarity and specificity of the input content. In addition, when the person being evaluated enters results, the real-time feedback unit can also indicate how likely the results are to be evaluated. Furthermore, when the person being evaluated enters the next step, the real-time feedback unit can also suggest an optimal action plan based on past data. This allows the person being evaluated to improve the input content and create more effective reporting materials.

[0095] The evaluation support system can further include a data visualization section. The data visualization section visually displays the information entered by the person being evaluated. For example, the progress of a project can be displayed in graphs and charts to make it easier to understand visually. It can also display results and issues in diagrams to help organize information. It can also display next steps in a flowchart to visually show a specific action plan. This allows the person being evaluated to have a clearer understanding of their efforts and allows the evaluator to quickly understand the information.

[0096] The evaluation support system further includes an emotion estimation unit, which can adjust the input interface based on the emotion of the person being evaluated. The emotion estimation unit estimates the emotion of the person being evaluated when entering information and dynamically changes the interface based on the estimated emotion. For example, if the person being evaluated is feeling stressed, the emotion estimation unit provides a simple and intuitive interface to reduce the input burden. If the person being evaluated is relaxed, the emotion estimation unit provides detailed input options to allow the person being evaluated to enter more information. Furthermore, if the person being evaluated is in a hurry, the emotion estimation unit displays only the minimum number of input items to allow the person being evaluated to complete the input quickly. This provides an optimal input environment according to the person being evaluated's emotion.

[0097] The evaluation support system can further include a voice input unit. The voice input unit allows the person being evaluated to input information by voice. For example, when the person being evaluated explains the progress and results of a project by voice, the voice input unit converts that content into text and reflects it in the report. The voice input unit can also respond when the person being evaluated gives voice instructions for the next step, allowing for quick input of information. Furthermore, the voice input unit can infer the person being evaluated's emotions from the tone and speed of their voice and determine the appropriateness of the input content. This allows the person being evaluated to easily input information and efficiently create report materials.

[0098] The evaluation support system further includes an emotion estimation unit, and can adjust the content of the report based on the emotion of the person being evaluated. The emotion estimation unit estimates the emotion of the person being evaluated and dynamically changes the content of the report based on the estimated emotion. For example, if the person being evaluated is feeling stressed, the emotion estimation unit generates a report that concisely summarizes important information. If the person being evaluated is relaxed, the emotion estimation unit generates a report that includes detailed information. If the person being evaluated is in a hurry, the emotion estimation unit generates a short report that focuses on the main points. This allows the optimal report to be provided according to the emotion of the person being evaluated.

[0099] The evaluation support system can further include an automatic translation unit. The automatic translation unit translates information entered by the person being evaluated into multiple languages. For example, information entered in Japanese by the person being evaluated can be automatically translated into English, Chinese, etc., and report materials can be provided in multiple languages. The automatic translation unit can also accommodate cases where the evaluator speaks a different language, and can provide report materials in a language that the evaluator can easily understand. Furthermore, the automatic translation unit can appropriately translate technical terms and industry jargon to maintain the accuracy of the information. This allows evaluations to be carried out smoothly in international organizations and multilingual environments.

[0100] The evaluation support system further includes an emotion estimation unit, which can adjust the content of the feedback based on the emotion of the person being evaluated. The emotion estimation unit estimates the emotion of the person being evaluated and dynamically changes the content of the feedback based on the estimated emotion. For example, if the person being evaluated is feeling stressed, the emotion estimation unit preferentially provides positive feedback to increase motivation. If the person being evaluated is relaxed, the emotion estimation unit provides feedback including specific areas for improvement to encourage growth. If the person being evaluated is in a hurry, the emotion estimation unit provides concise feedback that focuses on the main points. This allows optimal feedback to be provided according to the person being evaluated's emotion.

[0101] The evaluation support system can further include a data integration unit. The data integration unit integrates the appraisee's past evaluation data with data from other systems to conduct a comprehensive evaluation. For example, it can integrate the appraisee's past project results and progress to evaluate overall performance. It can also integrate data obtained from other systems to comprehensively evaluate the appraisee's skills and experience. Furthermore, the data integration unit can analyze the appraisee's evaluation history and provide insights that will be useful for future evaluations. This allows the evaluator to grasp a comprehensive picture of the appraisee and conduct more accurate evaluations.

[0102] The evaluation support system further includes an emotion estimation unit, which can adjust the layout of the report materials based on the emotions of the person being evaluated. The emotion estimation unit estimates the emotions of the person being evaluated and dynamically changes the layout of the report materials based on the estimated emotions. For example, if the person being evaluated is feeling stressed, the emotion estimation unit provides a simple, highly visible layout to help the person understand the information. If the person being evaluated is relaxed, the emotion estimation unit provides a layout including detailed information to encourage deeper digging. If the person being evaluated is in a hurry, the emotion estimation unit provides a compact layout that focuses on the main points. This allows the optimal layout of the report materials to be provided according to the person being evaluated's emotions.

[0103] The evaluation support system can further include a customized notification unit. The customized notification unit sends notifications to the evaluatee and the evaluator at appropriate times. For example, when the evaluatee has completed entering report materials, a notification is sent to the evaluator to inform them that the evaluation is ready. Also, when the evaluator provides feedback, a notification can be sent to the evaluatee to prompt them to check the feedback. Furthermore, the customized notification unit sends reminders about important deadlines and events, making it easier for the evaluatee and the evaluator to manage their schedules. This helps the evaluation process proceed smoothly and improves communication.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The provision department provides the common report format prepared by the evaluators. The common report format includes the key points and results of the initiative, the contents of the planned presentation, etc. For example, a format is provided that includes items for recording the project's progress, results, challenges, etc. Step 2: The receptionist enters the key points and results of the person being evaluated, as well as the content of the presentation they are planning to give. For example, the person being evaluated might enter something like, "Project A is 80% complete, with the main achievement being the completion of the implementation of new functions. The next step is to conduct user testing." Step 3: The generation unit uses AI to analyze the information entered by the reception unit and generate reports in accordance with a common report format. For example, based on the entered information, the unit organizes the project's progress, results, next steps, etc., to create easy-to-read reports.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 provision department that provides a common report format prepared by evaluators; A reception desk where the points and results of the evaluationee's efforts and the contents of the planned presentation are entered; a generating unit that analyzes the information input by the receiving unit and generates a report material in accordance with a common report material format. A system characterized by:

2. The providing unit Provide a common report format that includes items for describing the key points and results of the initiative and the contents of the presentations planned 2. The system of claim 1.

3. The reception unit Enter the key points and results of the evaluation recipient's efforts and the contents of the presentation they are planning to give.

2. The system of claim 1.

4. The generation unit Analyzes the input information and generates reports in accordance with the common report format 2. The system of claim 1.

5. The generation unit Organize project progress, results, and next steps to generate easy-to-read reports 2. The system of claim 1.

6. The generation unit By using generative AI to create reports, you can free up time to focus on other tasks.

2. The system of claim 1.

7. The providing unit Estimating user emotions and dynamically adjusting items in a common report format based on the estimated user emotions 2. The system of claim 1.

8. The providing unit Analyze past evaluation data and automatically generate the appropriate format 2. The system of claim 1.

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