system

Abstract

The system according to this embodiment aims to optimize communication strategies based on personality assessment results. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a proposal unit. The acquisition unit acquires personality diagnosis results. The analysis unit analyzes the personality diagnosis results acquired by the acquisition unit. The generation unit generates a communication strategy based on the analysis results obtained by the analysis unit. The proposal unit proposes specific actions based on the strategy generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the optimization of communication strategies based on personality diagnosis results has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize a communication strategy based on a personality diagnosis result.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a proposal unit. The acquisition unit acquires personality assessment results. The analysis unit analyzes the personality assessment results acquired by the acquisition unit. The generation unit generates a communication strategy based on the analysis results obtained by the analysis unit. The proposal unit proposes specific actions based on the strategy generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize communication strategies based on personality assessment results. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The generative AI system according to an embodiment of the present invention is a system that generates strategies to optimize communication and cooperation within a team based on the results of employee personality assessments. This generative AI system acquires the results of employee personality assessments, the generative AI provides insights into each employee's personality type, and proposes specific actions based on the generated communication strategy. This optimizes communication and cooperation within the team and improves work efficiency. For example, the generative AI system targets employees belonging to a company and solves the problem of difficulty in communication and cooperation among a large number of employees with different personality types. The generative AI system acquires the results of employee personality assessments. For example, an employee takes an online personality assessment test and inputs the results into the system. Next, the generative AI system provides insights into each employee's personality type based on the acquired personality assessment results. For example, the generative AI provides insights such as, "This employee is introverted, and written communication is more effective than direct dialogue in team communication." Furthermore, the generative AI system proposes specific actions based on the generated communication strategy. For example, the generative AI suggests specific actions such as, "It is recommended to provide this employee with regular feedback via email." This allows the AI-generated system to optimize communication and collaboration within teams and improve work efficiency. Based on employee personality assessments, the AI-generated system can generate strategies to optimize team communication and collaboration and propose specific actions.

[0029] The generation AI system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a proposal unit. The acquisition unit acquires the results of employee personality assessments. For example, the acquisition unit can input the results of an online personality assessment test taken by an employee into the system. The acquisition unit can also scan the results of a paper-based personality assessment test taken by an employee and convert them into digital data. Furthermore, the acquisition unit can acquire past personality assessment results of employees from a database. For example, the acquisition unit searches the database for and acquires the results of personality assessment tests taken by employees in the past. The analysis unit analyzes the personality assessment results acquired by the acquisition unit. The analysis unit provides insights into each employee's personality type, for example, using data analysis techniques. The analysis unit classifies employees' personality types, for example, using clustering algorithms. The analysis unit can also analyze the text data of the personality assessment results using natural language processing techniques. For example, the analysis unit extracts keywords from the text data of the personality assessment results to identify each employee's personality type. The generation unit generates a communication strategy based on the analysis results obtained by the analysis unit. The generation unit generates communication strategies tailored to each employee's personality type, for example, using a generation AI. The generation unit generates specific communication strategies for each employee, for example, using a text generation AI (e.g., LLM). Furthermore, the generation unit can generate communication strategies that include not only text but also images and audio, using a multimodal generation AI. For example, the generation unit generates specific communication strategies for each employee using a text generation AI. The suggestion unit proposes specific actions based on the strategies generated by the generation unit. The suggestion unit provides specific steps for implementing the actions proposed by the generation AI. For example, the suggestion unit proposes providing employees with regular feedback via email. The suggestion unit can also propose increasing opportunities for employees to speak in team meetings. For example, the suggestion unit proposes providing employees with regular feedback via email.As a result, the AI ​​generation system according to this embodiment can generate strategies to optimize communication and cooperation within a team based on the results of employee personality assessments, and propose specific actions.

[0030] The data acquisition unit acquires the results of employee personality assessments. For example, the unit can input the results of an online personality assessment test taken by an employee into the system. The online personality assessment test is provided through a web-based interface, and employees can take the test using a PC or smartphone. The test results are transmitted to the system in real time and stored in the database. The data acquisition unit can also have employees take a paper-based personality assessment test, and the results can be scanned and converted into digital data. Paper-based test results are scanned at high resolution using a dedicated scanner and converted into text data using OCR (optical character recognition) technology. Furthermore, the data acquisition unit can also acquire past personality assessment results of employees from the database. For example, the data acquisition unit can search the database for and acquire the results of personality assessment tests that an employee has taken in the past. This allows for long-term tracking of changes and trends in employee personality. The data acquisition unit centrally manages this data and makes it accessible to the analysis and generation units. The frequency and method of data acquisition can be flexibly adjusted according to the system settings and employee needs. For example, more detailed data can be collected by conducting personality assessment tests regularly or before and after specific events or projects. This allows the data acquisition unit to efficiently and accurately acquire employee personality assessment results, thereby improving the overall system performance.

[0031] The analysis unit analyzes the personality assessment results obtained by the acquisition unit. For example, the analysis unit provides insights into each employee's personality type using data analysis techniques. Specifically, it classifies employees' personality types using clustering algorithms. The clustering algorithm classifies employees into groups with similar personality traits based on their personality assessment results. This allows for an understanding of the characteristics and tendencies of each group. The analysis unit can also analyze the text data of the personality assessment results using natural language processing technology. For example, it extracts keywords from the text data of the personality assessment results to identify each employee's personality type. Natural language processing technology can understand the meaning and context of text data and extract important information. Furthermore, the analysis unit can analyze the relationship between personality assessment results and employee performance and behavior using machine learning algorithms. This allows for the identification of which tasks and roles are suitable for specific personality types. Based on these analysis results, the analysis unit provides a foundation for proposing optimal communication strategies tailored to each employee's personality traits. The analysis unit is required to process data in real time and provide results quickly. This allows the analysis unit to quickly and accurately analyze employee personality assessment results, maximizing the overall effectiveness of the system.

[0032] The generation unit generates communication strategies based on the analysis results obtained by the analysis unit. For example, the generation unit uses a generation AI to generate communication strategies tailored to each employee's personality type. The generation AI proposes the optimal communication method based on the employee's personality traits and past behavioral data. For example, it uses a text generation AI (e.g., LLM) to generate specific communication strategies for each employee. The text generation AI automatically generates messages and approaches tailored to the employee's personality traits and provides them to supervisors and colleagues. Furthermore, the generation unit can use a multimodal generation AI to generate communication strategies that include not only text but also images and audio. For example, the generation unit uses a text generation AI to generate specific communication strategies for each employee. The multimodal generation AI generates visual content and audio messages tailored to the employee's personality traits, enabling more effective communication. The generation unit provides these generation results to the proposal unit, which serves as the basis for specific action proposals. The generation unit is required to process data in real time and generate communication strategies quickly. This allows the generation unit to quickly and accurately generate optimal communication strategies tailored to each employee's personality traits, maximizing the overall system effectiveness.

[0033] The proposal department proposes specific actions based on the strategies generated by the generation department. For example, the proposal department provides specific steps for implementing the actions proposed by the generation AI. For example, the proposal department proposes providing employees with regular feedback via email. The emails include specific feedback and advice tailored to the employee's personality traits, supporting their growth. The proposal department can also propose increasing opportunities for employees to speak up in team meetings. For example, the proposal department proposes providing employees with regular feedback via email. Furthermore, the proposal department can propose training programs and workshops tailored to the employee's personality traits. This allows employees to acquire skills and knowledge suited to their personality traits and perform more effectively in their work. The proposal department provides specific steps and resources to implement these proposals, supporting employees in taking action. The proposal department is required to process data in real time and provide specific action proposals quickly. This allows the proposal department to provide optimal action proposals quickly and accurately tailored to the employee's personality traits, maximizing the overall effectiveness of the system.

[0034] The proposal unit comprises an execution unit that executes actions proposed by the generative AI. The proposal unit provides, for example, specific steps for executing the actions proposed by the generative AI. For example, the proposal unit proposes providing regular feedback to employees via email. The proposal unit may also propose increasing opportunities for employees to speak in team meetings. For example, the proposal unit proposes providing regular feedback to employees via email. This allows for the implementation of specific improvement measures by executing the proposed actions. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit provides specific steps for executing the actions proposed by the generative AI.

[0035] The execution unit includes an evaluation unit that evaluates the results of the actions performed. The execution unit provides, for example, specific procedures for evaluating the results of the actions performed. For example, the execution unit proposes to provide employees with regular feedback via email and evaluates the results. The execution unit can also propose to employees to increase their opportunities to speak in team meetings and evaluate the results. For example, the execution unit proposes to provide employees with regular feedback via email and evaluates the results. By evaluating the results of the actions performed, the effectiveness of improvement measures can be confirmed and reflected in the next steps. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit provides specific procedures for evaluating the results of the actions performed.

[0036] The data acquisition unit analyzes the employee's past personality assessment results and selects the optimal acquisition method. For example, the data acquisition unit prioritizes suggesting personality assessment methods the employee has used in the past. For example, the data acquisition unit selects the most reliable assessment method from the employee's past assessment results. The data acquisition unit can also analyze trends in the employee's past assessment results and propose the optimal acquisition method. For example, the data acquisition unit prioritizes suggesting personality assessment methods the employee has used in the past. This improves the accuracy of the assessment by selecting the optimal acquisition method based on past assessment results. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit analyzes the employee's past personality assessment results and selects the optimal acquisition method.

[0037] The acquisition unit filters the personality assessment results based on the employee's current work situation and areas of interest when acquiring them. For example, the acquisition unit prioritizes acquiring personality assessment results related to the project the employee is currently working on. For example, the acquisition unit acquires highly relevant personality assessment results based on the employee's areas of interest. The acquisition unit can also acquire personality assessment results at an appropriate time depending on the employee's work situation. For example, the acquisition unit prioritizes acquiring personality assessment results related to the project the employee is currently working on. This allows for the provision of more relevant information by acquiring assessment results that match the employee's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit filters the personality assessment results based on the employee's current work situation and areas of interest when acquiring them.

[0038] The data acquisition unit prioritizes obtaining highly relevant results when acquiring personality assessment results, taking into account the employee's geographical location information. For example, if an employee is on a business trip abroad, the data acquisition unit prioritizes obtaining personality assessment results that will help them adapt to the culture of that region. For example, if an employee is working remotely, the data acquisition unit prioritizes obtaining personality assessment results that will help them improve their work efficiency at home. The data acquisition unit can also prioritize obtaining personality assessment results that are suitable for the office environment if the employee is in the office. For example, if an employee is on a business trip abroad, the data acquisition unit prioritizes obtaining personality assessment results that will help them adapt to the culture of that region. This allows for the provision of more appropriate information by acquiring highly relevant assessment results based on the employee's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit prioritizes obtaining highly relevant results when acquiring personality assessment results, taking into account the employee's geographical location information.

[0039] The acquisition unit analyzes the employee's social media activity and obtains relevant results when acquiring personality assessment results. For example, the acquisition unit acquires personality assessment results related to topics that the employee frequently mentions on social media. For example, the acquisition unit analyzes the employee's current interests from their social media activity and acquires relevant personality assessment results. The acquisition unit can also acquire personality assessment results at the optimal time, taking into account the employee's social media activity times. For example, the acquisition unit acquires personality assessment results related to topics that the employee frequently mentions on social media. This allows for the provision of more appropriate information by acquiring highly relevant assessment results based on the employee's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit analyzes the employee's social media activity and obtains relevant results when acquiring personality assessment results.

[0040] The analysis unit adjusts the level of detail of the analysis based on the importance of the personality assessment results during the analysis. For example, the analysis unit performs a detailed analysis for personality assessment results of high importance. For example, the analysis unit performs a simplified analysis for personality assessment results of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the personality assessment results. For example, the analysis unit performs a detailed analysis for personality assessment results of high importance. By adjusting the level of detail of the analysis according to the importance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the personality assessment results during the analysis.

[0041] The analysis unit applies different analysis algorithms depending on the category of the personality assessment result during analysis. For example, the analysis unit applies a communication-specific analysis algorithm to personality assessment results related to communication. For example, the analysis unit applies a leadership-specific analysis algorithm to personality assessment results related to leadership. The analysis unit can also apply a teamwork-specific analysis algorithm to personality assessment results related to teamwork. For example, the analysis unit applies a communication-specific analysis algorithm to personality assessment results related to communication. By applying an analysis algorithm according to the category of the personality assessment result, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit applies different analysis algorithms depending on the category of the personality assessment result during analysis.

[0042] The analysis unit determines the priority of analysis based on the submission date of the personality assessment results. For example, the analysis unit prioritizes the analysis of recently submitted personality assessment results. For example, the analysis unit postpones the analysis of older personality assessment results. The analysis unit can also adjust the analysis schedule based on the submission date. For example, the analysis unit prioritizes the analysis of recently submitted personality assessment results. This allows for the provision of more appropriate information by determining the priority of analysis based on the submission date of the personality assessment results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit determines the priority of analysis based on the submission date of the personality assessment results during analysis.

[0043] The analysis unit adjusts the order of analysis based on the relevance of the personality assessment results during analysis. For example, the analysis unit prioritizes analyzing personality assessment results that are highly relevant to the current work. For example, the analysis unit postpones analyzing personality assessment results that are less relevant. The analysis unit can also adjust the order of analysis based on the relevance of the personality assessment results. For example, the analysis unit prioritizes analyzing personality assessment results that are highly relevant to the current work. By adjusting the order of analysis based on the relevance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit adjusts the order of analysis based on the relevance of the personality assessment results during analysis.

[0044] The generation unit adjusts the level of detail of the strategies based on the importance of the personality assessment results during generation. For example, the generation unit generates a detailed communication strategy for high-importance personality assessment results. For example, the generation unit generates a simplified communication strategy for low-importance personality assessment results. The generation unit can also determine the priority of strategies according to the importance of the personality assessment results. For example, the generation unit generates a detailed communication strategy for high-importance personality assessment results. This allows for the provision of more appropriate information by adjusting the level of detail of the strategies according to the importance of the personality assessment results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit adjusts the level of detail of the strategies based on the importance of the personality assessment results during generation.

[0045] The generation unit applies different generation algorithms depending on the category of the personality assessment result during generation. For example, the generation unit applies a communication-specific generation algorithm to personality assessment results related to communication. For example, the generation unit applies a leadership-specific generation algorithm to personality assessment results related to leadership. The generation unit can also apply a teamwork-specific generation algorithm to personality assessment results related to teamwork. For example, the generation unit applies a communication-specific generation algorithm to personality assessment results related to communication. By applying a generation algorithm according to the category of the personality assessment result, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit applies different generation algorithms depending on the category of the personality assessment result during generation.

[0046] The generation unit determines strategy priorities based on the submission timing of personality assessment results during generation. For example, the generation unit prioritizes incorporating recently submitted personality assessment results into the strategy. For example, the generation unit postpones older personality assessment results. The generation unit can also adjust the strategy schedule based on the submission timing. For example, the generation unit prioritizes incorporating recently submitted personality assessment results into the strategy. This allows for the provision of more appropriate information by prioritizing strategies based on the submission timing of personality assessment results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit determines strategy priorities based on the submission timing of personality assessment results during generation.

[0047] The generation unit adjusts the order of strategies based on the relevance of the personality assessment results during generation. For example, the generation unit prioritizes reflecting personality assessment results that are highly relevant to current work in the strategies. For example, the generation unit postpones reflecting personality assessment results that are less relevant. The generation unit can also adjust the order of strategies based on the relevance of the personality assessment results. For example, the generation unit prioritizes reflecting personality assessment results that are highly relevant to current work in the strategies. By adjusting the order of strategies based on the relevance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit adjusts the order of strategies based on the relevance of the personality assessment results during generation.

[0048] The proposal department adjusts the level of detail in its proposals based on the importance of the communication strategies. For example, it provides detailed proposals for highly important communication strategies and simplified proposals for less important ones. The proposal department can also prioritize proposals according to the importance of the communication strategies. For example, it provides detailed proposals for highly important communication strategies. By adjusting the level of detail in proposals according to the importance of the communication strategies, it can provide more appropriate information. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department adjusts the level of detail in its proposals based on the importance of the communication strategies.

[0049] The proposal department applies different proposal algorithms depending on the category of the communication strategy when making a proposal. For example, the proposal department applies a communication-specific proposal algorithm to strategies related to communication. For example, the proposal department applies a leadership-specific proposal algorithm to strategies related to leadership. The proposal department can also apply a teamwork-specific proposal algorithm to strategies related to teamwork. For example, the proposal department applies a communication-specific proposal algorithm to strategies related to communication. This allows for the provision of more appropriate information by applying a proposal algorithm according to the category of the communication strategy. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department applies different proposal algorithms depending on the category of the communication strategy when making a proposal.

[0050] The proposal department prioritizes proposals based on the timing of their communication strategy submissions. For example, the proposal department will prioritize incorporating recently submitted communication strategies into proposals. For example, the proposal department will postpone older communication strategies. The proposal department can also adjust the proposal schedule based on the submission timing. For example, the proposal department will prioritize incorporating recently submitted communication strategies into proposals. This allows for the provision of more relevant information by prioritizing proposals based on the timing of their communication strategy submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department prioritizes proposals based on the timing of their communication strategy submissions when submitting proposals.

[0051] The proposal department adjusts the order of proposals based on the relevance of the communication strategies when making proposals. For example, the proposal department prioritizes incorporating communication strategies that are highly relevant to current operations into its proposals. For example, it postpones less relevant communication strategies. The proposal department can also adjust the order of proposals based on the relevance of the communication strategies. For example, the proposal department prioritizes incorporating communication strategies that are highly relevant to current operations into its proposals. By adjusting the order of proposals based on the relevance of the communication strategies, more appropriate information can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department adjusts the order of proposals based on the relevance of the communication strategies when making proposals.

[0052] The execution unit adjusts the level of detail of the proposed actions based on their importance during execution. For example, the execution unit performs detailed execution for highly important proposed actions. For example, the execution unit performs simplified execution for less important proposed actions. The execution unit can also determine the priority of execution according to the importance of the proposed actions. For example, the execution unit performs detailed execution for highly important proposed actions. By adjusting the level of detail of execution according to the importance of the proposed actions, more appropriate information can be provided. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit adjusts the level of detail of the proposed actions based on their importance during execution.

[0053] The execution unit applies different execution algorithms at runtime depending on the category of the proposed action. For example, the execution unit applies a communication-specific execution algorithm to proposed actions related to communication. For example, the execution unit applies a leadership-specific execution algorithm to proposed actions related to leadership. The execution unit can also apply a teamwork-specific execution algorithm to proposed actions related to teamwork. For example, the execution unit applies a communication-specific execution algorithm to proposed actions related to communication. This allows for the provision of more appropriate information by applying an execution algorithm according to the category of the proposed action. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit applies different execution algorithms at runtime depending on the category of the proposed action.

[0054] The execution unit determines the priority of execution based on the submission timing of proposed actions during execution. For example, the execution unit prioritizes recently proposed actions. For example, the execution unit postpones older proposed actions. The execution unit can also adjust the execution schedule based on the submission timing. For example, the execution unit prioritizes recently proposed actions. This allows for the provision of more relevant information by determining the priority of execution based on the submission timing of proposed actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit determines the priority of execution based on the submission timing of proposed actions during execution.

[0055] The execution unit adjusts the order of execution based on the relevance of the proposed actions during execution. For example, the execution unit prioritizes executing proposed actions that are highly relevant to the current task. For example, the execution unit postpones executing proposed actions that are less relevant. The execution unit can also adjust the order of execution based on the relevance of the proposed actions. For example, the execution unit prioritizes executing proposed actions that are highly relevant to the current task. This allows for the provision of more appropriate information by adjusting the order of execution based on the relevance of the proposed actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit adjusts the order of execution based on the relevance of the proposed actions during execution.

[0056] The evaluation unit adjusts the level of detail in its evaluation based on the importance of the actions performed. For example, the evaluation unit performs a detailed evaluation for high-importance actions. For example, the evaluation unit performs a simplified evaluation for low-importance actions. The evaluation unit can also determine the priority of the evaluation according to the importance of the actions performed. For example, the evaluation unit performs a detailed evaluation for high-importance actions. By adjusting the level of detail in the evaluation according to the importance of the actions performed, more appropriate information can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit adjusts the level of detail in its evaluation based on the importance of the actions performed during the evaluation.

[0057] The evaluation unit applies different evaluation algorithms depending on the category of the action performed during evaluation. For example, the evaluation unit applies a communication-specific evaluation algorithm to actions related to communication. For example, the evaluation unit applies a leadership-specific evaluation algorithm to actions related to leadership. The evaluation unit can also apply a teamwork-specific evaluation algorithm to actions related to teamwork. For example, the evaluation unit applies a communication-specific evaluation algorithm to actions related to communication. This allows for the provision of more appropriate information by applying evaluation algorithms according to the category of the action performed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit applies different evaluation algorithms depending on the category of the action performed during evaluation.

[0058] The evaluation unit determines the priority of evaluations based on the submission timing of the actions performed during the evaluation process. For example, the evaluation unit prioritizes evaluating recently performed actions. For example, the evaluation unit postpones evaluating older actions. The evaluation unit can also adjust the evaluation schedule based on the submission timing. For example, the evaluation unit prioritizes evaluating recently performed actions. This allows for the provision of more relevant information by determining the priority of evaluations based on the submission timing of the actions performed. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit determines the priority of evaluations based on the submission timing of the actions performed during the evaluation process.

[0059] The evaluation unit adjusts the order of evaluation based on the relevance of the actions performed during the evaluation process. For example, the evaluation unit prioritizes evaluating actions that are highly relevant to the current work. For example, the evaluation unit postpones evaluating actions that are less relevant. The evaluation unit can also adjust the order of evaluation based on the relevance of the actions performed. For example, the evaluation unit prioritizes evaluating actions that are highly relevant to the current work. By adjusting the order of evaluation based on the relevance of the actions performed, more appropriate information can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the actions performed during the evaluation process.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The acquisition unit can retrieve past work performance data from employees and analyze it in combination with personality assessment results. For example, the acquisition unit can retrieve the success rate of past projects and the employee's role within a team from a database. Next, the analysis unit integrates the acquired work performance data and personality assessment results to analyze the impact of the employee's personality type on work performance. Based on this, the generation unit can generate more effective communication strategies based on the relationship between the employee's personality type and work performance. For example, it can propose specific strategies such as recommending that introverted employees work in a quiet environment to achieve high performance.

[0062] The generation unit can acquire past feedback data from employees and combine it with personality assessment results to generate communication strategies. For example, the acquisition unit retrieves the content and frequency of feedback employees have received in the past from a database. Next, the analysis unit integrates the acquired feedback data with the personality assessment results and analyzes the influence of the employee's personality type on how they receive feedback. This allows the generation unit to generate more effective feedback strategies based on the relationship between the employee's personality type and the feedback they receive. For example, it can suggest specific strategies such as suggesting that written feedback is effective for introverted employees.

[0063] The acquisition unit can acquire current health status data from employees and analyze it in combination with personality assessment results. For example, the acquisition unit can acquire data on employees' health status (e.g., heart rate and sleep patterns) from wearable devices. Next, the analysis unit integrates the acquired health status data and personality assessment results to analyze the impact of employees' personality types on their health status. Based on this, the generation unit can generate more effective communication strategies based on the relationship between employees' personality types and their health status. For example, it can propose specific strategies such as recommending that employees who are prone to stress be provided with a relaxing environment.

[0064] The proposal department can acquire past employee behavioral data and combine it with personality assessment results to make suggestions. For example, the acquisition department retrieves past employee behavioral data (e.g., attendance times and vacation status) from a database. Next, the analysis department integrates the acquired behavioral data with the personality assessment results to analyze the influence of employee personality types on their behavior. This allows the proposal department to make more effective suggestions based on the relationship between employee personality types and behavior. For example, it can make specific suggestions such as recommending the introduction of a flexible working hours system for introverted employees.

[0065] The proposal department can acquire current project progress data from employees and combine it with personality assessment results to make suggestions. For example, the acquisition department retrieves the progress status of projects that employees are currently working on from a database. Next, the analysis department integrates the acquired project progress data with the personality assessment results and analyzes the impact of the employee's personality type on project progress. This allows the proposal department to make more effective suggestions based on the relationship between the employee's personality type and project progress. For example, it can make specific suggestions such as recommending that introverted employees be given priority in assigning individual tasks.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit retrieves the employee's personality assessment results. For example, the acquisition unit can input the results of an online personality assessment test taken by an employee into the system. Alternatively, the acquisition unit can also convert the results of a paper-based personality assessment test, scanning them and converting them into digital data. Furthermore, the acquisition unit can retrieve the employee's past personality assessment results from a database. Step 2: The analysis unit analyzes the personality assessment results obtained by the acquisition unit. The analysis unit provides insights into each employee's personality type, for example, by using data analysis techniques. The analysis unit classifies employees' personality types, for example, by using clustering algorithms. The analysis unit can also analyze the text data of the personality assessment results using natural language processing techniques. For example, the analysis unit extracts keywords from the text data of the personality assessment results to identify each employee's personality type. Step 3: The generation unit generates communication strategies based on the analysis results obtained by the analysis unit. The generation unit generates communication strategies tailored to each employee's personality type, for example, using a generation AI. The generation unit generates specific communication strategies for each employee, for example, using a text generation AI (e.g., LLM). The generation unit can also generate communication strategies that include not only text but also images and audio, using a multimodal generation AI. Step 4: The proposal unit proposes specific actions based on the strategy generated by the generation unit. For example, the proposal unit provides specific steps for carrying out the actions proposed by the generation AI. For example, the proposal unit might suggest providing regular feedback to employees via email. Alternatively, the proposal unit could suggest increasing opportunities for employees to speak up in team meetings.

[0068] (Example of form 2) The generative AI system according to an embodiment of the present invention is a system that generates strategies to optimize communication and cooperation within a team based on the results of employee personality assessments. This generative AI system acquires the results of employee personality assessments, the generative AI provides insights into each employee's personality type, and proposes specific actions based on the generated communication strategy. This optimizes communication and cooperation within the team and improves work efficiency. For example, the generative AI system targets employees belonging to a company and solves the problem of difficulty in communication and cooperation among a large number of employees with different personality types. The generative AI system acquires the results of employee personality assessments. For example, an employee takes an online personality assessment test and inputs the results into the system. Next, the generative AI system provides insights into each employee's personality type based on the acquired personality assessment results. For example, the generative AI provides insights such as, "This employee is introverted, and written communication is more effective than direct dialogue in team communication." Furthermore, the generative AI system proposes specific actions based on the generated communication strategy. For example, the generative AI suggests specific actions such as, "It is recommended to provide this employee with regular feedback via email." This allows the AI-generated system to optimize communication and collaboration within teams and improve work efficiency. Based on employee personality assessments, the AI-generated system can generate strategies to optimize team communication and collaboration and propose specific actions.

[0069] The generation AI system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a proposal unit. The acquisition unit acquires the results of employee personality assessments. For example, the acquisition unit can input the results of an online personality assessment test taken by an employee into the system. The acquisition unit can also scan the results of a paper-based personality assessment test taken by an employee and convert them into digital data. Furthermore, the acquisition unit can acquire past personality assessment results of employees from a database. For example, the acquisition unit searches the database for and acquires the results of personality assessment tests taken by employees in the past. The analysis unit analyzes the personality assessment results acquired by the acquisition unit. The analysis unit provides insights into each employee's personality type, for example, using data analysis techniques. The analysis unit classifies employees' personality types, for example, using clustering algorithms. The analysis unit can also analyze the text data of the personality assessment results using natural language processing techniques. For example, the analysis unit extracts keywords from the text data of the personality assessment results to identify each employee's personality type. The generation unit generates a communication strategy based on the analysis results obtained by the analysis unit. The generation unit generates communication strategies tailored to each employee's personality type, for example, using a generation AI. The generation unit generates specific communication strategies for each employee, for example, using a text generation AI (e.g., LLM). Furthermore, the generation unit can generate communication strategies that include not only text but also images and audio, using a multimodal generation AI. For example, the generation unit generates specific communication strategies for each employee using a text generation AI. The suggestion unit proposes specific actions based on the strategies generated by the generation unit. The suggestion unit provides specific steps for implementing the actions proposed by the generation AI. For example, the suggestion unit proposes providing employees with regular feedback via email. The suggestion unit can also propose increasing opportunities for employees to speak in team meetings. For example, the suggestion unit proposes providing employees with regular feedback via email.As a result, the AI ​​generation system according to this embodiment can generate strategies to optimize communication and cooperation within a team based on the results of employee personality assessments, and propose specific actions.

[0070] The data acquisition unit acquires the results of employee personality assessments. For example, the unit can input the results of an online personality assessment test taken by an employee into the system. The online personality assessment test is provided through a web-based interface, and employees can take the test using a PC or smartphone. The test results are transmitted to the system in real time and stored in the database. The data acquisition unit can also have employees take a paper-based personality assessment test, and the results can be scanned and converted into digital data. Paper-based test results are scanned at high resolution using a dedicated scanner and converted into text data using OCR (optical character recognition) technology. Furthermore, the data acquisition unit can also acquire past personality assessment results of employees from the database. For example, the data acquisition unit can search the database for and acquire the results of personality assessment tests that an employee has taken in the past. This allows for long-term tracking of changes and trends in employee personality. The data acquisition unit centrally manages this data and makes it accessible to the analysis and generation units. The frequency and method of data acquisition can be flexibly adjusted according to the system settings and employee needs. For example, more detailed data can be collected by conducting personality assessment tests regularly or before and after specific events or projects. This allows the data acquisition unit to efficiently and accurately acquire employee personality assessment results, thereby improving the overall system performance.

[0071] The analysis unit analyzes the personality assessment results obtained by the acquisition unit. For example, the analysis unit provides insights into each employee's personality type using data analysis techniques. Specifically, it classifies employees' personality types using clustering algorithms. The clustering algorithm classifies employees into groups with similar personality traits based on their personality assessment results. This allows for an understanding of the characteristics and tendencies of each group. The analysis unit can also analyze the text data of the personality assessment results using natural language processing technology. For example, it extracts keywords from the text data of the personality assessment results to identify each employee's personality type. Natural language processing technology can understand the meaning and context of text data and extract important information. Furthermore, the analysis unit can analyze the relationship between personality assessment results and employee performance and behavior using machine learning algorithms. This allows for the identification of which tasks and roles are suitable for specific personality types. Based on these analysis results, the analysis unit provides a foundation for proposing optimal communication strategies tailored to each employee's personality traits. The analysis unit is required to process data in real time and provide results quickly. This allows the analysis unit to quickly and accurately analyze employee personality assessment results, maximizing the overall effectiveness of the system.

[0072] The generation unit generates communication strategies based on the analysis results obtained by the analysis unit. For example, the generation unit uses a generation AI to generate communication strategies tailored to each employee's personality type. The generation AI proposes the optimal communication method based on the employee's personality traits and past behavioral data. For example, it uses a text generation AI (e.g., LLM) to generate specific communication strategies for each employee. The text generation AI automatically generates messages and approaches tailored to the employee's personality traits and provides them to supervisors and colleagues. Furthermore, the generation unit can use a multimodal generation AI to generate communication strategies that include not only text but also images and audio. For example, the generation unit uses a text generation AI to generate specific communication strategies for each employee. The multimodal generation AI generates visual content and audio messages tailored to the employee's personality traits, enabling more effective communication. The generation unit provides these generation results to the proposal unit, which serves as the basis for specific action proposals. The generation unit is required to process data in real time and generate communication strategies quickly. This allows the generation unit to quickly and accurately generate optimal communication strategies tailored to each employee's personality traits, maximizing the overall system effectiveness.

[0073] The proposal department proposes specific actions based on the strategies generated by the generation department. For example, the proposal department provides specific steps for implementing the actions proposed by the generation AI. For example, the proposal department proposes providing employees with regular feedback via email. The emails include specific feedback and advice tailored to the employee's personality traits, supporting their growth. The proposal department can also propose increasing opportunities for employees to speak up in team meetings. For example, the proposal department proposes providing employees with regular feedback via email. Furthermore, the proposal department can propose training programs and workshops tailored to the employee's personality traits. This allows employees to acquire skills and knowledge suited to their personality traits and perform more effectively in their work. The proposal department provides specific steps and resources to implement these proposals, supporting employees in taking action. The proposal department is required to process data in real time and provide specific action proposals quickly. This allows the proposal department to provide optimal action proposals quickly and accurately tailored to the employee's personality traits, maximizing the overall effectiveness of the system.

[0074] The proposal unit comprises an execution unit that executes actions proposed by the generative AI. The proposal unit provides, for example, specific steps for executing the actions proposed by the generative AI. For example, the proposal unit proposes providing regular feedback to employees via email. The proposal unit may also propose increasing opportunities for employees to speak in team meetings. For example, the proposal unit proposes providing regular feedback to employees via email. This allows for the implementation of specific improvement measures by executing the proposed actions. Some or all of the above processing in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit provides specific steps for executing the actions proposed by the generative AI.

[0075] The execution unit includes an evaluation unit that evaluates the results of the actions performed. The execution unit provides, for example, specific procedures for evaluating the results of the actions performed. For example, the execution unit proposes to provide employees with regular feedback via email and evaluates the results. The execution unit can also propose to employees to increase their opportunities to speak in team meetings and evaluate the results. For example, the execution unit proposes to provide employees with regular feedback via email and evaluates the results. By evaluating the results of the actions performed, the effectiveness of improvement measures can be confirmed and reflected in the next steps. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit provides specific procedures for evaluating the results of the actions performed.

[0076] The acquisition unit estimates the employee's emotions and adjusts the timing of acquiring the personality assessment results based on the estimated emotions. For example, if the employee is stressed, the acquisition unit delays acquiring the personality assessment results until the employee is relaxed. For example, if the employee is relaxed, the acquisition unit acquires the personality assessment results immediately. The acquisition unit can also acquire the personality assessment results when the employee is busy and their work has calmed down. For example, if the acquisition unit is stressed, it delays acquiring the personality assessment results until the employee is relaxed. This allows for more accurate assessment results by acquiring the personality assessment results at the appropriate time according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit estimates the employee's emotions and adjusts the timing of acquiring the personality assessment results based on the estimated emotions.

[0077] The data acquisition unit analyzes the employee's past personality assessment results and selects the optimal acquisition method. For example, the data acquisition unit prioritizes suggesting personality assessment methods the employee has used in the past. For example, the data acquisition unit selects the most reliable assessment method from the employee's past assessment results. The data acquisition unit can also analyze trends in the employee's past assessment results and propose the optimal acquisition method. For example, the data acquisition unit prioritizes suggesting personality assessment methods the employee has used in the past. This improves the accuracy of the assessment by selecting the optimal acquisition method based on past assessment results. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit analyzes the employee's past personality assessment results and selects the optimal acquisition method.

[0078] The acquisition unit filters the personality assessment results based on the employee's current work situation and areas of interest when acquiring them. For example, the acquisition unit prioritizes acquiring personality assessment results related to the project the employee is currently working on. For example, the acquisition unit acquires highly relevant personality assessment results based on the employee's areas of interest. The acquisition unit can also acquire personality assessment results at an appropriate time depending on the employee's work situation. For example, the acquisition unit prioritizes acquiring personality assessment results related to the project the employee is currently working on. This allows for the provision of more relevant information by acquiring assessment results that match the employee's work situation and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit filters the personality assessment results based on the employee's current work situation and areas of interest when acquiring them.

[0079] The acquisition unit estimates the employee's emotions and determines the priority of personality assessment results to acquire based on the estimated emotions. For example, if an employee is stressed, the acquisition unit prioritizes acquiring personality assessment results that are helpful in reducing stress. For example, if an employee is relaxed, the acquisition unit prioritizes acquiring personality assessment results that are helpful in long-term career planning. The acquisition unit can also prioritize acquiring personality assessment results that can be acquired quickly if an employee is in a hurry. For example, if an employee is stressed, the acquisition unit prioritizes acquiring personality assessment results that are helpful in reducing stress. This allows for the provision of more appropriate information by prioritizing assessment results according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit estimates the employee's emotions and determines the priority of personality assessment results to acquire based on the estimated emotions.

[0080] The data acquisition unit prioritizes obtaining highly relevant results when acquiring personality assessment results, taking into account the employee's geographical location information. For example, if an employee is on a business trip abroad, the data acquisition unit prioritizes obtaining personality assessment results that will help them adapt to the culture of that region. For example, if an employee is working remotely, the data acquisition unit prioritizes obtaining personality assessment results that will help them improve their work efficiency at home. The data acquisition unit can also prioritize obtaining personality assessment results that are suitable for the office environment if the employee is in the office. For example, if an employee is on a business trip abroad, the data acquisition unit prioritizes obtaining personality assessment results that will help them adapt to the culture of that region. This allows for the provision of more appropriate information by acquiring highly relevant assessment results based on the employee's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit prioritizes obtaining highly relevant results when acquiring personality assessment results, taking into account the employee's geographical location information.

[0081] The acquisition unit analyzes the employee's social media activity and obtains relevant results when acquiring personality assessment results. For example, the acquisition unit acquires personality assessment results related to topics that the employee frequently mentions on social media. For example, the acquisition unit analyzes the employee's current interests from their social media activity and acquires relevant personality assessment results. The acquisition unit can also acquire personality assessment results at the optimal time, taking into account the employee's social media activity times. For example, the acquisition unit acquires personality assessment results related to topics that the employee frequently mentions on social media. This allows for the provision of more appropriate information by acquiring highly relevant assessment results based on the employee's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit analyzes the employee's social media activity and obtains relevant results when acquiring personality assessment results.

[0082] The analysis unit estimates the employee's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the employee is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise analysis result that gets to the point if the employee is in a hurry. For example, if the analysis unit is stressed, it provides a simple and easy-to-understand analysis result. This allows for more easily understandable information to be provided by adjusting the presentation of the analysis result according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit estimates the employee's emotions and adjusts the presentation of the analysis based on the estimated emotions.

[0083] The analysis unit adjusts the level of detail of the analysis based on the importance of the personality assessment results during the analysis. For example, the analysis unit performs a detailed analysis for personality assessment results of high importance. For example, the analysis unit performs a simplified analysis for personality assessment results of low importance. The analysis unit can also determine the priority of the analysis according to the importance of the personality assessment results. For example, the analysis unit performs a detailed analysis for personality assessment results of high importance. By adjusting the level of detail of the analysis according to the importance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the personality assessment results during the analysis.

[0084] The analysis unit applies different analysis algorithms depending on the category of the personality assessment result during analysis. For example, the analysis unit applies a communication-specific analysis algorithm to personality assessment results related to communication. For example, the analysis unit applies a leadership-specific analysis algorithm to personality assessment results related to leadership. The analysis unit can also apply a teamwork-specific analysis algorithm to personality assessment results related to teamwork. For example, the analysis unit applies a communication-specific analysis algorithm to personality assessment results related to communication. By applying an analysis algorithm according to the category of the personality assessment result, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit applies different analysis algorithms depending on the category of the personality assessment result during analysis.

[0085] The analysis unit estimates the employee's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the employee is stressed, the analysis unit provides a short, concise analysis. For example, if the employee is relaxed, the analysis unit provides a detailed analysis. The analysis unit can also provide a brief analysis if the employee is in a hurry. For example, if the analysis unit is stressed, it provides a short, concise analysis. By adjusting the length of the analysis according to the employee's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the employee's emotions and adjusts the length of the analysis based on the estimated emotions.

[0086] The analysis unit determines the priority of analysis based on the submission date of the personality assessment results. For example, the analysis unit prioritizes the analysis of recently submitted personality assessment results. For example, the analysis unit postpones the analysis of older personality assessment results. The analysis unit can also adjust the analysis schedule based on the submission date. For example, the analysis unit prioritizes the analysis of recently submitted personality assessment results. This allows for the provision of more appropriate information by determining the priority of analysis based on the submission date of the personality assessment results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit determines the priority of analysis based on the submission date of the personality assessment results during analysis.

[0087] The analysis unit adjusts the order of analysis based on the relevance of the personality assessment results during analysis. For example, the analysis unit prioritizes analyzing personality assessment results that are highly relevant to the current work. For example, the analysis unit postpones analyzing personality assessment results that are less relevant. The analysis unit can also adjust the order of analysis based on the relevance of the personality assessment results. For example, the analysis unit prioritizes analyzing personality assessment results that are highly relevant to the current work. By adjusting the order of analysis based on the relevance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit adjusts the order of analysis based on the relevance of the personality assessment results during analysis.

[0088] The generation unit estimates the employee's emotions and adjusts the expression of the communication strategy it generates based on the estimated employee's emotions. For example, if the employee is stressed, the generation unit generates a simple and easy-to-understand communication strategy. For example, if the employee is relaxed, the generation unit generates a detailed communication strategy. The generation unit can also generate a concise and to-the-point communication strategy if the employee is in a hurry. For example, if the employee is stressed, the generation unit generates a simple and easy-to-understand communication strategy. This allows for the provision of more easily understandable information by adjusting the expression of the communication strategy according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit estimates the employee's emotions and adjusts the expression of the communication strategy it generates based on the estimated employee's emotions.

[0089] The generation unit adjusts the level of detail of the strategies based on the importance of the personality assessment results during generation. For example, the generation unit generates a detailed communication strategy for high-importance personality assessment results. For example, the generation unit generates a simplified communication strategy for low-importance personality assessment results. The generation unit can also determine the priority of strategies according to the importance of the personality assessment results. For example, the generation unit generates a detailed communication strategy for high-importance personality assessment results. This allows for the provision of more appropriate information by adjusting the level of detail of the strategies according to the importance of the personality assessment results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit adjusts the level of detail of the strategies based on the importance of the personality assessment results during generation.

[0090] The generation unit applies different generation algorithms depending on the category of the personality assessment result during generation. For example, the generation unit applies a communication-specific generation algorithm to personality assessment results related to communication. For example, the generation unit applies a leadership-specific generation algorithm to personality assessment results related to leadership. The generation unit can also apply a teamwork-specific generation algorithm to personality assessment results related to teamwork. For example, the generation unit applies a communication-specific generation algorithm to personality assessment results related to communication. By applying a generation algorithm according to the category of the personality assessment result, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit applies different generation algorithms depending on the category of the personality assessment result during generation.

[0091] The generation unit determines strategy priorities based on the submission timing of personality assessment results during generation. For example, the generation unit prioritizes incorporating recently submitted personality assessment results into the strategy. For example, the generation unit postpones older personality assessment results. The generation unit can also adjust the strategy schedule based on the submission timing. For example, the generation unit prioritizes incorporating recently submitted personality assessment results into the strategy. This allows for the provision of more appropriate information by prioritizing strategies based on the submission timing of personality assessment results. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit determines strategy priorities based on the submission timing of personality assessment results during generation.

[0092] The generation unit adjusts the order of strategies based on the relevance of the personality assessment results during generation. For example, the generation unit prioritizes reflecting personality assessment results that are highly relevant to current work in the strategies. For example, the generation unit postpones reflecting personality assessment results that are less relevant. The generation unit can also adjust the order of strategies based on the relevance of the personality assessment results. For example, the generation unit prioritizes reflecting personality assessment results that are highly relevant to current work in the strategies. By adjusting the order of strategies based on the relevance of the personality assessment results, more appropriate information can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit adjusts the order of strategies based on the relevance of the personality assessment results during generation.

[0093] The suggestion department estimates the employee's emotions and adjusts the way suggestions are presented based on the estimated emotions. For example, if an employee is stressed, the suggestion department will make simple and easy-to-understand suggestions. If an employee is relaxed, the suggestion department will make detailed suggestions. Furthermore, if an employee is in a hurry, the suggestion department can also make concise and to-the-point suggestions. For example, if an employee is stressed, the suggestion department will make simple and easy-to-understand suggestions. This allows for the provision of more easily understood information by adjusting the way suggestions are presented according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department estimates the employee's emotions and adjusts the way suggestions are presented based on the estimated emotions.

[0094] The proposal department adjusts the level of detail in its proposals based on the importance of the communication strategies. For example, it provides detailed proposals for highly important communication strategies and simplified proposals for less important ones. The proposal department can also prioritize proposals according to the importance of the communication strategies. For example, it provides detailed proposals for highly important communication strategies. By adjusting the level of detail in proposals according to the importance of the communication strategies, it can provide more appropriate information. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department adjusts the level of detail in its proposals based on the importance of the communication strategies.

[0095] The proposal department applies different proposal algorithms depending on the category of the communication strategy when making a proposal. For example, the proposal department applies a communication-specific proposal algorithm to strategies related to communication. For example, the proposal department applies a leadership-specific proposal algorithm to strategies related to leadership. The proposal department can also apply a teamwork-specific proposal algorithm to strategies related to teamwork. For example, the proposal department applies a communication-specific proposal algorithm to strategies related to communication. This allows for the provision of more appropriate information by applying a proposal algorithm according to the category of the communication strategy. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department applies different proposal algorithms depending on the category of the communication strategy when making a proposal.

[0096] The suggestion department estimates the employee's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the employee is stressed, the suggestion department will make a short, to-the-point suggestion. For example, if the employee is relaxed, the suggestion department will make a detailed suggestion. The suggestion department can also make a concise suggestion if the employee is in a hurry. For example, if the suggestion department is stressed, it will make a short, to-the-point suggestion. By adjusting the length of the suggestion according to the employee's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not using AI. For example, the suggestion department estimates the employee's emotions and adjusts the length of the suggestion based on the estimated emotions.

[0097] The proposal department prioritizes proposals based on the timing of their communication strategy submissions. For example, the proposal department will prioritize incorporating recently submitted communication strategies into proposals. For example, the proposal department will postpone older communication strategies. The proposal department can also adjust the proposal schedule based on the submission timing. For example, the proposal department will prioritize incorporating recently submitted communication strategies into proposals. This allows for the provision of more relevant information by prioritizing proposals based on the timing of their communication strategy submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department prioritizes proposals based on the timing of their communication strategy submissions when submitting proposals.

[0098] The proposal department adjusts the order of proposals based on the relevance of the communication strategies when making proposals. For example, the proposal department prioritizes incorporating communication strategies that are highly relevant to current operations into its proposals. For example, it postpones less relevant communication strategies. The proposal department can also adjust the order of proposals based on the relevance of the communication strategies. For example, the proposal department prioritizes incorporating communication strategies that are highly relevant to current operations into its proposals. By adjusting the order of proposals based on the relevance of the communication strategies, more appropriate information can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department adjusts the order of proposals based on the relevance of the communication strategies when making proposals.

[0099] The execution unit estimates the employee's emotions and adjusts the way it expresses the actions it takes based on the estimated emotions. For example, if the employee is stressed, the execution unit will take simple and easy-to-understand actions. For example, if the employee is relaxed, the execution unit will take detailed actions. The execution unit can also take concise and to-the-point actions if the employee is in a hurry. For example, if the employee is stressed, the execution unit will take simple and easy-to-understand actions. This allows for more easily understandable information to be provided by adjusting the way actions are expressed according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit estimates the employee's emotions and adjusts the way it expresses the actions it takes based on the estimated emotions.

[0100] The execution unit adjusts the level of detail of the proposed actions based on their importance during execution. For example, the execution unit performs detailed execution for highly important proposed actions. For example, the execution unit performs simplified execution for less important proposed actions. The execution unit can also determine the priority of execution according to the importance of the proposed actions. For example, the execution unit performs detailed execution for highly important proposed actions. By adjusting the level of detail of execution according to the importance of the proposed actions, more appropriate information can be provided. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit adjusts the level of detail of the proposed actions based on their importance during execution.

[0101] The execution unit applies different execution algorithms at runtime depending on the category of the proposed action. For example, the execution unit applies a communication-specific execution algorithm to proposed actions related to communication. For example, the execution unit applies a leadership-specific execution algorithm to proposed actions related to leadership. The execution unit can also apply a teamwork-specific execution algorithm to proposed actions related to teamwork. For example, the execution unit applies a communication-specific execution algorithm to proposed actions related to communication. This allows for the provision of more appropriate information by applying an execution algorithm according to the category of the proposed action. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit applies different execution algorithms at runtime depending on the category of the proposed action.

[0102] The execution unit estimates the employee's emotions and adjusts the length of the actions it performs based on the estimated emotions. For example, if the employee is stressed, the execution unit will perform short, concise actions. For example, if the employee is relaxed, the execution unit will perform detailed actions. The execution unit can also perform concise actions if the employee is in a hurry. For example, if the employee is stressed, the execution unit will perform short, concise actions. This allows for the provision of more appropriate information by adjusting the length of actions according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit estimates the employee's emotions and adjusts the length of the actions it performs based on the estimated emotions.

[0103] The execution unit determines the priority of execution based on the submission timing of proposed actions during execution. For example, the execution unit prioritizes recently proposed actions. For example, the execution unit postpones older proposed actions. The execution unit can also adjust the execution schedule based on the submission timing. For example, the execution unit prioritizes recently proposed actions. This allows for the provision of more relevant information by determining the priority of execution based on the submission timing of proposed actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit determines the priority of execution based on the submission timing of proposed actions during execution.

[0104] The execution unit adjusts the order of execution based on the relevance of the proposed actions during execution. For example, the execution unit prioritizes executing proposed actions that are highly relevant to the current task. For example, the execution unit postpones executing proposed actions that are less relevant. The execution unit can also adjust the order of execution based on the relevance of the proposed actions. For example, the execution unit prioritizes executing proposed actions that are highly relevant to the current task. This allows for the provision of more appropriate information by adjusting the order of execution based on the relevance of the proposed actions. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit adjusts the order of execution based on the relevance of the proposed actions during execution.

[0105] The evaluation unit estimates the employee's emotions and adjusts the evaluation's presentation based on the estimated emotions. For example, if an employee is stressed, the evaluation unit provides a simple and easy-to-understand evaluation. For example, if an employee is relaxed, the evaluation unit provides a detailed evaluation. The evaluation unit can also provide a concise and to-the-point evaluation if an employee is in a hurry. For example, if an employee is stressed, the evaluation unit provides a simple and easy-to-understand evaluation. By adjusting the evaluation's presentation according to the employee's emotions, more easily understandable information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit estimates the employee's emotions and adjusts the evaluation's presentation based on the estimated emotions.

[0106] The evaluation unit adjusts the level of detail in its evaluation based on the importance of the actions performed. For example, the evaluation unit performs a detailed evaluation for high-importance actions. For example, the evaluation unit performs a simplified evaluation for low-importance actions. The evaluation unit can also determine the priority of the evaluation according to the importance of the actions performed. For example, the evaluation unit performs a detailed evaluation for high-importance actions. By adjusting the level of detail in the evaluation according to the importance of the actions performed, more appropriate information can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit adjusts the level of detail in its evaluation based on the importance of the actions performed during the evaluation.

[0107] The evaluation unit applies different evaluation algorithms depending on the category of the action performed during evaluation. For example, the evaluation unit applies a communication-specific evaluation algorithm to actions related to communication. For example, the evaluation unit applies a leadership-specific evaluation algorithm to actions related to leadership. The evaluation unit can also apply a teamwork-specific evaluation algorithm to actions related to teamwork. For example, the evaluation unit applies a communication-specific evaluation algorithm to actions related to communication. This allows for the provision of more appropriate information by applying evaluation algorithms according to the category of the action performed. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit applies different evaluation algorithms depending on the category of the action performed during evaluation.

[0108] The evaluation unit estimates the employee's emotions and adjusts the length of the evaluation based on the estimated emotions. For example, if the employee is stressed, the evaluation unit will provide a short, concise evaluation. If the employee is relaxed, the evaluation unit will provide a detailed evaluation. The evaluation unit can also provide a brief evaluation if the employee is in a hurry. For example, if the evaluation unit is stressed, the evaluation unit will provide a short, concise evaluation. By adjusting the length of the evaluation according to the employee's emotions, more relevant information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit estimates the employee's emotions and adjusts the length of the evaluation based on the estimated emotions.

[0109] The evaluation unit determines the priority of evaluations based on the submission timing of the actions performed during the evaluation process. For example, the evaluation unit prioritizes evaluating recently performed actions. For example, the evaluation unit postpones evaluating older actions. The evaluation unit can also adjust the evaluation schedule based on the submission timing. For example, the evaluation unit prioritizes evaluating recently performed actions. This allows for the provision of more relevant information by determining the priority of evaluations based on the submission timing of the actions performed. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit determines the priority of evaluations based on the submission timing of the actions performed during the evaluation process.

[0110] The evaluation unit adjusts the order of evaluation based on the relevance of the actions performed during the evaluation process. For example, the evaluation unit prioritizes evaluating actions that are highly relevant to the current work. For example, the evaluation unit postpones evaluating actions that are less relevant. The evaluation unit can also adjust the order of evaluation based on the relevance of the actions performed. For example, the evaluation unit prioritizes evaluating actions that are highly relevant to the current work. By adjusting the order of evaluation based on the relevance of the actions performed, more appropriate information can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit adjusts the order of evaluation based on the relevance of the actions performed during the evaluation process.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The acquisition unit can retrieve past work performance data from employees and analyze it in combination with personality assessment results. For example, the acquisition unit can retrieve the success rate of past projects and the employee's role within a team from a database. Next, the analysis unit integrates the acquired work performance data and personality assessment results to analyze the impact of the employee's personality type on work performance. Based on this, the generation unit can generate more effective communication strategies based on the relationship between the employee's personality type and work performance. For example, it can propose specific strategies such as recommending that introverted employees work in a quiet environment to achieve high performance.

[0113] The analysis unit can estimate employees' emotions and adjust the timing of the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit will temporarily delay the analysis and resume it when the employee becomes relaxed. Conversely, if the employee is relaxed, the analysis can be performed immediately. By adjusting the timing of the analysis according to the employee's emotional state, more accurate analysis results can be obtained. Emotion estimation is achieved using an emotion engine or generative AI.

[0114] The generation unit can acquire past feedback data from employees and combine it with personality assessment results to generate communication strategies. For example, the acquisition unit retrieves the content and frequency of feedback employees have received in the past from a database. Next, the analysis unit integrates the acquired feedback data with the personality assessment results and analyzes the influence of the employee's personality type on how they receive feedback. This allows the generation unit to generate more effective feedback strategies based on the relationship between the employee's personality type and the feedback they receive. For example, it can suggest specific strategies such as suggesting that written feedback is effective for introverted employees.

[0115] The suggestion department can estimate employees' emotions and adjust the timing of suggestions based on those estimates. For example, if an employee is stressed, the suggestion department can temporarily delay the suggestion until the employee is relaxed. Conversely, if the employee is relaxed, the suggestion can be made immediately. This allows for more effective suggestions by adjusting the timing of suggestions according to the employee's emotional state. Emotion estimation is achieved using an emotion engine or generative AI.

[0116] The acquisition unit can acquire current health status data from employees and analyze it in combination with personality assessment results. For example, the acquisition unit can acquire data on employees' health status (e.g., heart rate and sleep patterns) from wearable devices. Next, the analysis unit integrates the acquired health status data and personality assessment results to analyze the impact of employees' personality types on their health status. Based on this, the generation unit can generate more effective communication strategies based on the relationship between employees' personality types and their health status. For example, it can propose specific strategies such as recommending that employees who are prone to stress be provided with a relaxing environment.

[0117] The analysis unit can estimate employees' emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit provides simple and easy-to-understand analysis results. Conversely, if an employee is relaxed, it can provide detailed analysis results. In this way, by adjusting the level of detail in the analysis according to the employee's emotional state, more easily understandable information can be provided. Emotion estimation is achieved using an emotion engine or generative AI.

[0118] The proposal department can acquire past employee behavioral data and combine it with personality assessment results to make suggestions. For example, the acquisition department retrieves past employee behavioral data (e.g., attendance times and vacation status) from a database. Next, the analysis department integrates the acquired behavioral data with the personality assessment results to analyze the influence of employee personality types on their behavior. This allows the proposal department to make more effective suggestions based on the relationship between employee personality types and behavior. For example, it can make specific suggestions such as recommending the introduction of a flexible working hours system for introverted employees.

[0119] The generation unit can estimate employees' emotions and prioritize communication strategies based on those estimated emotions. For example, if an employee is stressed, it will prioritize generating communication strategies that help reduce stress. Conversely, if an employee is relaxed, it will prioritize generating communication strategies that help with long-term career planning. By prioritizing communication strategies according to the employee's emotional state, it is possible to provide more appropriate information. Emotion estimation is achieved using an emotion engine or generative AI.

[0120] The proposal department can acquire current project progress data from employees and combine it with personality assessment results to make suggestions. For example, the acquisition department retrieves the progress status of projects that employees are currently working on from a database. Next, the analysis department integrates the acquired project progress data with the personality assessment results and analyzes the impact of the employee's personality type on project progress. This allows the proposal department to make more effective suggestions based on the relationship between the employee's personality type and project progress. For example, it can make specific suggestions such as recommending that introverted employees be given priority in assigning individual tasks.

[0121] The evaluation unit can estimate an employee's emotions and adjust the timing of the evaluation based on those estimates. For example, if an employee is stressed, the evaluation unit can temporarily delay the evaluation until the employee is relaxed. Alternatively, if the employee is relaxed, the evaluation can be performed immediately. This allows for more accurate evaluations by adjusting the timing of the evaluation according to the employee's emotional state. Emotion estimation is achieved using an emotion engine or generative AI.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The acquisition unit retrieves the employee's personality assessment results. For example, the acquisition unit can input the results of an online personality assessment test taken by an employee into the system. Alternatively, the acquisition unit can also convert the results of a paper-based personality assessment test, scanning them and converting them into digital data. Furthermore, the acquisition unit can retrieve the employee's past personality assessment results from a database. Step 2: The analysis unit analyzes the personality assessment results obtained by the acquisition unit. The analysis unit provides insights into each employee's personality type, for example, by using data analysis techniques. The analysis unit classifies employees' personality types, for example, by using clustering algorithms. The analysis unit can also analyze the text data of the personality assessment results using natural language processing techniques. For example, the analysis unit extracts keywords from the text data of the personality assessment results to identify each employee's personality type. Step 3: The generation unit generates communication strategies based on the analysis results obtained by the analysis unit. The generation unit generates communication strategies tailored to each employee's personality type, for example, using a generation AI. The generation unit generates specific communication strategies for each employee, for example, using a text generation AI (e.g., LLM). The generation unit can also generate communication strategies that include not only text but also images and audio, using a multimodal generation AI. Step 4: The proposal unit proposes specific actions based on the strategy generated by the generation unit. For example, the proposal unit provides specific steps for carrying out the actions proposed by the generation AI. For example, the proposal unit might suggest providing regular feedback to employees via email. Alternatively, the proposal unit could suggest increasing opportunities for employees to speak up in team meetings.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, proposal unit, execution unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the personality assessment results of employees using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired personality assessment results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a communication strategy based on the analysis results. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes specific actions based on the generated strategy. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the proposed actions. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the results of the executed actions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, proposal unit, execution unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the personality assessment results of employees using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired personality assessment results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a communication strategy based on the analysis results. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes specific actions based on the generated strategy. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the proposed actions. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the results of the executed actions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, proposal unit, execution unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the personality assessment results of employees using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired personality assessment results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a communication strategy based on the analysis results. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes specific actions based on the generated strategy. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the proposed actions. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the results of the executed actions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, proposal unit, execution unit, and evaluation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the personality assessment results of employees using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired personality assessment results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a communication strategy based on the analysis results. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes specific actions based on the generated strategy. The execution unit is implemented by the control unit 46A of the robot 414 and executes the proposed actions. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the results of the executed actions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The acquisition unit obtains the personality assessment results, An analysis unit analyzes the personality diagnosis results obtained by the acquisition unit, A generation unit generates a communication strategy based on the analysis results obtained by the analysis unit, The system comprises a proposal unit that proposes specific actions based on the strategy generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It includes an execution unit that carries out actions proposed by a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The execution unit is, It includes an evaluation unit that evaluates the results of the actions performed. The system described in Appendix 2, characterized by the features described herein. (Note 4) The acquisition unit is, The system estimates the emotions of employees and adjusts the timing of obtaining personality assessment results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, We analyze past personality assessment results of employees and select the most suitable method for obtaining them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When obtaining personality assessment results, filtering is performed based on the employee's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates employees' emotions and prioritizes the personality assessment results obtained based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When obtaining personality assessment results, the system prioritizes retrieving highly relevant results by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When obtaining personality assessment results, we analyze employees' social media activity and retrieve relevant results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the personality assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the personality assessment result. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates employee sentiment and adjusts the length of the analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the personality assessment results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the personality assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate employee sentiment and adjust the way communication strategies are expressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the level of detail in the strategy based on the importance of the personality assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the category of the personality assessment result. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, strategic priorities are determined based on when the personality assessment results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the order of strategies is adjusted based on the relevance of the personality assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, We estimate the employees' emotions and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the communication strategy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the communication strategy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Estimate the employee's feelings and adjust the length of the suggestion based on those feelings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the communication strategy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, We estimate employee emotions and adjust how we express the actions we take based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The execution unit is, During execution, adjust the level of detail of the proposed actions based on their importance. The system described in Appendix 2, characterized by the features described herein. (Note 29) The execution unit is, At runtime, different execution algorithms are applied depending on the category of the proposed action. The system described in Appendix 2, characterized by the features described herein. (Note 30) The execution unit is, Estimate employee emotions and adjust the length of actions taken based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The execution unit is, During execution, the priority of actions will be determined based on when the proposed actions were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 32) The execution unit is, During execution, the order of execution is adjusted based on the relevance of the proposed actions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The evaluation unit described above, The system estimates employee sentiment and adjusts the way evaluations are presented based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The evaluation unit described above, During evaluation, adjust the level of detail based on the importance of the actions performed. The system described in Appendix 3, characterized by the features described herein. (Note 35) The evaluation unit described above, During evaluation, different evaluation algorithms are applied depending on the category of the action performed. The system described in Appendix 3, characterized by the features described herein. (Note 36) The evaluation unit described above, Estimate employee sentiment and adjust the length of the evaluation based on the estimated employee sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The evaluation unit described above, During evaluation, the priority of evaluations is determined based on when the actions performed were submitted. The system described in Appendix 3, characterized by the features described herein. (Note 38) The evaluation unit described above, During evaluation, the order of evaluations is adjusted based on the relevance of the actions performed. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit obtains the personality assessment results, An analysis unit analyzes the personality diagnosis results obtained by the acquisition unit, A generation unit generates a communication strategy based on the analysis results obtained by the analysis unit, The system comprises a proposal unit that proposes specific actions based on the strategy generated by the generation unit. A system characterized by the following features.

2. The aforementioned proposal section is, It includes an execution unit that carries out actions proposed by the generative AI. The system according to feature 1.

3. The execution unit is, It includes an evaluation unit that evaluates the results of the actions performed. The system according to feature 2.

4. The acquisition unit is, The system estimates the emotions of employees and adjusts the timing of obtaining personality assessment results based on those estimated emotions. The system according to feature 1.

5. The acquisition unit is, We analyze past personality assessment results of employees and select the most suitable method for obtaining them. The system according to feature 1.

6. The acquisition unit is, When obtaining personality assessment results, filtering is performed based on the employee's current work situation and areas of interest. The system according to feature 1.

7. The acquisition unit is, The system estimates employees' emotions and prioritizes the personality assessment results obtained based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, When obtaining personality assessment results, the system prioritizes retrieving highly relevant results by considering the employee's geographical location. The system according to feature 1.

9. The acquisition unit is, When obtaining personality assessment results, we analyze employees' social media activity and retrieve relevant results. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the emotions of employees and adjust the representation of the analysis based on the estimated emotions of the employees. The system according to feature 1.

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