system

Abstract

The system according to this embodiment aims to optimize communication within a company. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a planning unit. The collection unit collects internal conversation data and communication history. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal information sharing strategy based on the analysis results obtained by the analysis unit. The planning unit implements the information sharing strategy proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, communication tools within an enterprise cannot be effectively utilized unidirectionally, and there is room for improvement.

[0005] The system according to an embodiment aims to optimize communication within an enterprise.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a planning unit. The collection unit collects in-house conversation data and communication history. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal information sharing strategy based on the analysis result obtained by the analysis unit. The planning unit executes the information sharing strategy proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize communication within a company. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​agent system according to an embodiment of the present invention is a system for improving communication within a company. This AI agent system solves the problem that conventional corporate communication tools are one-way and not effectively utilized. The AI ​​agent system analyzes employees' communication patterns and clearly proposes what improvement measures should be taken. For example, the AI ​​agent system collects and analyzes internal conversation data and communication history. For example, it analyzes the content of emails and chats and identifies important topics. In this process, generative AI is used to specifically suggest the optimal communication method. Next, based on the analysis results, the AI ​​agent system proposes an effective information sharing strategy. For example, it suggests how to share information and when to communicate. This eliminates inconsistencies caused by person-dependent information transmission and realizes smooth information sharing. Furthermore, the AI ​​agent system provides a concrete plan for implementing the proposed communication method. For example, it plans project categories and service / commercialization plans. By adding the AI ​​agent to the existing system based on this plan, efficient workflows and smooth organizational operations can be realized. Through this mechanism, companies can enjoy benefits such as time savings, improved operational efficiency, and increased accuracy. For example, an AI agent system can reduce misunderstandings and improve work efficiency by suggesting the optimal communication method. Furthermore, timely communication ensures that the entire organization is aligned, leading to improved results. In this way, utilizing an AI agent system can revolutionize internal communication and propel businesses forward. For business leaders, now is the time to revolutionize communication; implementing an AI agent system enables strategic communication and improves overall organizational performance. Thus, an AI agent system can effectively improve internal communication.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a planning unit. The collection unit collects internal conversation data and communication history. Internal conversation data and communication history include, but are not limited to, emails, chats, and phone call records. The collection unit collects, for example, the content of emails and chats and identifies important topics. The collection unit analyzes the content of emails and extracts frequently occurring keywords. The collection unit can also analyze the content of chats and identify topics related to specific themes. Furthermore, the collection unit can analyze phone call records and extract important conversation content. For example, the collection unit uses speech recognition technology to convert phone call content into text data and identify important topics. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and specifically suggests the optimal communication method. The analysis unit analyzes the data using, for example, natural language processing technology and proposes the optimal communication method. The analysis unit can also analyze the data using machine learning algorithms and learn the optimal communication method. Furthermore, the analysis department can analyze data using data mining techniques and extract important patterns. For example, the analysis department can use data mining techniques to analyze employee communication patterns and propose optimal communication methods. The proposal department proposes optimal information sharing strategies based on the analysis results obtained by the analysis department. For example, the proposal department proposes how information should be shared. For example, the proposal department proposes information sharing methods using email. The proposal department can also propose information sharing methods using internal social networking services. Furthermore, the proposal department can also propose information sharing methods using meetings. For example, the proposal department proposes the optimal information sharing method according to the progress of the project. The planning department plans to implement the information sharing strategies proposed by the proposal department. For example, the planning department plans project categories and service / commercialization plans. For example, the planning department plans development projects. The planning department can also plan marketing projects.Furthermore, the planning department can also plan the service delivery method and the steps for commercialization. For example, the planning department can plan the service delivery method and propose specific implementation procedures. This allows the AI ​​agent system according to the embodiment to effectively improve communication within the company.

[0030] The data collection department collects internal conversation data and communication history. This includes, but is not limited to, emails, chats, and phone call records. For example, the department collects the content of emails and chats and identifies important topics. Specifically, it analyzes the content of sent and received emails and extracts frequently occurring keywords and phrases. This allows for the identification of themes and issues that are frequently discussed within the company. When analyzing chat content, it uses natural language processing techniques to identify topics related to specific themes. For example, it can extract chat content related to project progress or technical challenges and organize this information. Furthermore, the data collection department can also analyze phone call records and extract important conversation content. Specifically, it uses speech recognition technology to convert phone call content into text data and identify important topics from that text data. For example, it can analyze the content of conference calls with customers and extract customer requests and feedback. This allows the data collection department to collect a wide range of data from various internal communication methods and efficiently extract important information. In addition, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes the collected data and specifically proposes the optimal communication methods. Specifically, the Analysis Department uses natural language processing technology to analyze the data and propose the optimal communication methods. For example, it can analyze the content of emails to identify which expressions and phrases are effective. It can also analyze the content of chats and extract important topics related to a specific theme. Furthermore, the Analysis Department can use machine learning algorithms to analyze the data and learn the optimal communication methods. For example, it can learn successful communication patterns based on past communication data and utilize them in future communication. Furthermore, the Analysis Department can use data mining technology to analyze the data and extract important patterns. For example, it can analyze employee communication patterns to identify which methods are most effective. This allows the Analysis Department to quickly and accurately analyze the collected data and propose the optimal communication methods. In addition, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk for a specific theme or problem based on past communication data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The proposal department proposes the optimal information sharing strategy based on the analysis results obtained by the analysis department. For example, the proposal department proposes how information should be shared. Specifically, the proposal department proposes an information sharing method using email. For example, it creates email templates for quickly sharing important information and provides them to employees. The proposal department can also propose an information sharing method using internal social networking services (SNS). For example, it creates a dedicated channel for each project and centrally manages related information. The proposal department can also propose an information sharing method using meetings. For example, it holds regular meetings to share project progress and challenges. Furthermore, the proposal department proposes the optimal information sharing method according to the progress of the project. For example, in the initial stages of a project, information is shared through frequent meetings and chat, and as the project progresses, email and internal SNS are utilized. In this way, the proposal department can propose the optimal information sharing strategy for each project and situation, and effectively improve communication within the company. Furthermore, the proposal department can evaluate the effectiveness of the proposed information sharing strategy and make improvements as needed. For example, it collects feedback from employees and continuously improves the accuracy and effectiveness of the proposal. Furthermore, the proposal department can combine multiple information-sharing methods to build the optimal strategy. This allows the proposal department to share information within the company efficiently and effectively, improving the quality of communication.

[0033] The Planning Department plans to implement the information sharing strategy proposed by the Proposal Department. For example, the Planning Department plans project categories and service / commercialization plans. Specifically, the Planning Department plans development projects. For example, it creates detailed plans for the development of new products or services, clarifying the execution procedures for each step. The Planning Department can also plan marketing projects. For example, it develops marketing strategies for launching new products and plans advertising and promotional activities. Furthermore, the Planning Department can plan service delivery methods and commercialization steps. For example, it plans how to deliver new services and proposes specific execution procedures. This allows the Planning Department to translate the information sharing strategy proposed by the Proposal Department into concrete plans and put them into action. In addition, the Planning Department can monitor the progress of the plans and revise them as needed. For example, it can regularly check the project progress and respond quickly if problems arise. The Planning Department also allocates resources and adjusts schedules to support the smooth progress of projects. This allows the Planning Department to effectively implement the proposed information sharing strategy and improve internal communication. Furthermore, the planning department can continuously improve its plans by evaluating their execution results and incorporating those improvements into future plans. This allows the planning department to support the company's growth and development and achieve effective information sharing.

[0034] The data collection unit can collect email and chat content and identify important topics. For example, the data collection unit can analyze email content and extract frequently occurring keywords. The data collection unit can also analyze chat content and identify topics related to specific themes. The data collection unit can also analyze phone call recordings and extract important conversation content. For example, the data collection unit can use speech recognition technology to convert phone call content into text data and identify important topics. This enables effective communication improvement by collecting email and chat content and identifying important topics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input email and chat content into a generating AI and have the generating AI perform the identification of important topics.

[0035] The analysis department can analyze the collected data and specifically suggest the optimal communication methods. For example, the analysis department can analyze the data using natural language processing technology and propose the optimal communication methods. The analysis department can also analyze the data using machine learning algorithms and learn the optimal communication methods. The analysis department can also analyze the data using data mining technology and extract important patterns. For example, the analysis department can use data mining technology to analyze employees' communication patterns and propose the optimal communication methods. In this way, the quality of communication can be improved by analyzing the collected data and specifically suggesting the optimal communication methods. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the task of suggesting the optimal communication methods.

[0036] The proposal department can suggest how information should be shared. For example, the proposal department might suggest using email for information sharing. The proposal department could also suggest using an internal social networking service (SNS) for information sharing. The proposal department could also suggest using meetings for information sharing. For example, the proposal department could suggest the most suitable information sharing method depending on the project's progress. By suggesting how information should be shared, the efficiency of information sharing can be improved. 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 could have a generation AI generate suggestions for the most suitable information sharing method.

[0037] The proposal department can suggest when communication should take place. For example, the proposal department can suggest the optimal timing for communication based on the progress of the project. For example, the proposal department can suggest the optimal timing for communication considering both inside and outside of business hours. For example, the proposal department can suggest the optimal timing for communication before and after a specific event or meeting. For example, the proposal department can suggest the optimal timing for communication in line with important milestones in the project. In this way, the timing of communication can be optimized by suggesting when communication should take place. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI generate suggestions for the optimal timing of communication.

[0038] The planning department can plan project categories and service / commercialization plans. For example, the planning department can plan development projects. For example, the planning department can also plan marketing projects. For example, the planning department can plan service delivery methods and commercialization steps. For example, the planning department can plan service delivery methods and propose specific implementation procedures. For example, the planning department can plan commercialization steps and propose specific implementation procedures. In this way, by planning project categories and service / commercialization plans, a concrete action plan can be provided. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can have a generating AI perform the creation of project categories and service / commercialization plans.

[0039] The data collection unit can analyze an employee's past communication history and select the optimal collection method. For example, the data collection unit can prioritize collecting communication tools that an employee has frequently used in the past. The data collection unit can also analyze an employee's past communication patterns and select the most effective collection method. For example, the data collection unit can concentrate data collection on specific time periods based on an employee's past communication history. This allows the optimal collection method to be selected by analyzing an employee's past communication history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an employee's past communication history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter conversation data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting conversation data related to a project the employee is currently working on. The data collection unit can also filter highly relevant conversation data based on an employee's areas of interest. The data collection unit can also select conversation data to collect based on an employee's current work content. This allows for the collection of highly relevant data by filtering based on an employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on an employee's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the employee's geographical location information when collecting conversation data. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of conversation data related to that region. The data collection unit can also filter highly relevant conversation data based on the employee's geographical location information. For example, if an employee is on the move, the data collection unit can select conversation data to collect based on their current location. This enables more effective data collection by prioritizing the collection of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze employees' social media activity and collect relevant data when collecting conversation data. For example, the data collection unit can collect relevant conversation data based on information shared by employees on social media. The data collection unit can also analyze employees' social media activity and prioritize the collection of highly relevant data. The data collection unit can also select conversation data to collect based on topics that employees have shown interest in on social media. This allows for the collection of highly relevant data by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on employees' social media activity into a generating AI and have the generating AI collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data. For example, the analysis unit can perform a detailed analysis on conversation data with high importance. For example, the analysis unit can also perform a concise analysis on conversation data with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the conversation data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the conversation data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the conversation data. For example, the analysis unit can apply a specialized analysis algorithm to technical conversation data. For example, the analysis unit can also apply a business analysis algorithm to business-related conversation data. For example, the analysis unit can apply a sentiment analysis algorithm to employees' personal conversation data. By applying different analysis algorithms depending on the category of the conversation data, more accurate analysis results can be provided. 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 can input the categories of conversation data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the conversation data was collected. For example, the analysis unit may prioritize the analysis of the most recent conversation data. The analysis unit may also prioritize the most recent data while referring to past conversation data. The analysis unit may also adjust the priority of analysis in stages according to when the conversation data was collected. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on when the conversation data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the conversation data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the conversation data. For example, the analysis unit may prioritize the analysis of highly relevant conversation data. For example, the analysis unit may postpone the analysis of less relevant conversation data. For example, the analysis unit may also adjust the order of analysis in stages according to the relevance of the conversation data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the conversation data. 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 can input the relevance of the conversation data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the information sharing strategies. For example, the proposal unit can provide detailed proposals for highly important information sharing strategies. For example, it can also provide concise proposals for less important information sharing strategies. The proposal unit can also adjust the level of detail of its proposals in stages according to the importance of the information sharing strategies. This allows for efficient information sharing by adjusting the level of detail of proposals based on the importance of the information sharing strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the information sharing strategies into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of information sharing strategy. For example, the proposal unit can apply a specialized proposal algorithm to a technical information sharing strategy. For example, the proposal unit can apply a business proposal algorithm to a business-related information sharing strategy. For example, the proposal unit can apply a sentiment analysis algorithm to an employee's personal information sharing strategy. By applying different proposal algorithms depending on the category of information sharing strategy, it is possible to provide more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the category of information sharing strategy into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the implementation timing of information sharing strategies. For example, the proposal department may prioritize proposals for information sharing strategies to be implemented in the immediate future. The proposal department may also postpone proposals for long-term information sharing strategies. The proposal department may also adjust the priority of proposals in stages according to the implementation timing of information sharing strategies. This enables efficient information sharing by determining the priority of proposals based on the implementation timing of information sharing strategies. 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 can input the implementation timing of information sharing strategies into a generating AI and have the generating AI determine the priority of proposals.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the information sharing strategies. For example, the proposal unit may prioritize proposing highly relevant information sharing strategies. For example, the proposal unit may postpone less relevant information sharing strategies. For example, the proposal unit may also adjust the order of proposals in stages according to the relevance of the information sharing strategies. This allows for efficient information sharing by adjusting the order of proposals based on the relevance of the information sharing strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the information sharing strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0051] The planning department can analyze an employee's past project history to select the optimal planning method. For example, the planning department can select the optimal planning method based on an employee's past successful project history. For example, the planning department can also select a planning method that reflects lessons learned from failed projects based on an employee's past project history. For example, the planning department can analyze an employee's past project history to select the most efficient planning method. In this way, the optimal planning method can be selected by analyzing an employee's past project history. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input an employee's past project history into a generating AI and have the generating AI select the optimal planning method.

[0052] The planning department can customize the planning methods based on the employee's current project status. For example, the planning department can customize the planning methods based on the progress of the project the employee is currently working on. The planning department can also suggest the optimal planning methods, taking into account the employee's current project status. The planning department can also adjust the planning methods step by step, depending on the employee's current project status. This allows for the provision of more effective plans by customizing the planning methods based on the employee's current project status. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input the employee's current project status into a generating AI and have the generating AI perform the customization of the planning methods.

[0053] The planning department can select the optimal planning method based on the geographical location information of employees. For example, if an employee is in a specific region, the planning department will select a planning method relevant to that region. The planning department can also propose the optimal planning method based on the geographical location information of employees. For example, if an employee is on the move, the planning department can select a planning method based on their current location. By selecting the optimal planning method based on the geographical location information of employees, more effective plans can be provided. Some or all of the above processes in the planning department may be performed using AI, for example, or without AI. For example, the planning department can input the geographical location information of employees into a generating AI and have the generating AI select the optimal planning method.

[0054] The planning department can analyze employees' social media activity and propose relevant planning measures. For example, the planning department can propose the most suitable planning measures based on information shared by employees on social media. The planning department can also analyze employees' social media activity and propose highly relevant planning measures. The planning department can also select planning measures based on topics that employees have shown interest in on social media. This allows the planning department to propose highly relevant planning measures by analyzing employees' social media activity. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input data on employees' social media activity into a generating AI and have the generating AI propose relevant planning measures.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection department can collect data while taking employees' schedules into consideration. For example, the department can choose to collect data during times when employees are not in meetings or engaged in important work. The department can also collect data during employees' breaks or lunch breaks. Furthermore, the department can choose to collect data after employees have finished their workday. This allows the department to collect data without disrupting employees' work.

[0057] The analytics department can conduct analyses that take into account the individual communication styles of employees. For example, the analytics department can analyze employees by considering their preferred means of communication (email, chat, phone, etc.). It can also analyze the frequency and timing of communication preferred by employees. Furthermore, the analytics department can analyze employees by considering their preferred tone and style of communication. This allows the analytics department to propose optimal communication methods tailored to each employee's individual communication style.

[0058] The proposal department can customize information sharing strategies according to employees' positions and job responsibilities. For example, the proposal department can propose different information sharing strategies for managers and general employees. It can also propose different strategies for sales departments and technical departments. Furthermore, it can propose different strategies for project managers and team members. This allows the proposal department to propose the optimal information sharing strategy tailored to each employee's position and job responsibilities.

[0059] The planning department can create plans that take employees' skill sets into account. For example, the planning department can determine project roles based on employees' expertise and experience. The planning department can also plan training programs based on employees' skill sets. Furthermore, the planning department can plan career paths based on employees' skill sets. This allows the planning department to create plans that make the most of employees' skill sets.

[0060] The planning department can create plans that take into account the health status of employees. For example, the planning department can set realistic schedules based on the results of employees' health checkups. The planning department can also incorporate vacations and refresh time into the plan according to the health status of employees. Furthermore, the planning department can adjust the workload while taking into account the health status of employees. In this way, the planning department can create plans that take into account the health status of employees, thereby achieving efficient work execution while maintaining employee health.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects internal conversation data and communication history. This includes emails, chats, and phone call records. The data collection unit collects the content of emails and chats and identifies important topics. For example, it analyzes the content of emails and extracts frequently occurring keywords. It can also analyze the content of chats and identify topics related to specific themes. Furthermore, the data collection unit analyzes phone call records and extracts important conversation content. For example, it uses speech recognition technology to convert phone call content into text data and identify important topics. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the collected data and specifically proposes the optimal communication method. For example, it can use natural language processing technology to analyze the data and propose the optimal communication method. It can also use machine learning algorithms to analyze the data and learn the optimal communication method. Furthermore, it can use data mining technology to analyze the data and extract important patterns. For example, it can analyze employees' communication patterns and propose the optimal communication method. Step 3: The proposal team proposes the optimal information sharing strategy based on the analysis results obtained by the analysis team. The proposal team proposes how information should be shared. For example, they may propose methods such as information sharing via email, information sharing via internal social networking services, or information sharing via meetings. They propose the most suitable information sharing method according to the progress of the project. Step 4: The Planning Department plans to implement the information sharing strategy proposed by the Proposal Department. The Planning Department plans the project categories and service / commercialization plans. For example, it plans development projects and marketing projects. Furthermore, it plans the service delivery methods and commercialization steps. It proposes specific implementation procedures to effectively improve internal communication.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for improving communication within a company. This AI agent system solves the problem that conventional corporate communication tools are one-way and not effectively utilized. The AI ​​agent system analyzes employees' communication patterns and clearly proposes what improvement measures should be taken. For example, the AI ​​agent system collects and analyzes internal conversation data and communication history. For example, it analyzes the content of emails and chats and identifies important topics. In this process, generative AI is used to specifically suggest the optimal communication method. Next, based on the analysis results, the AI ​​agent system proposes an effective information sharing strategy. For example, it suggests how to share information and when to communicate. This eliminates inconsistencies caused by person-dependent information transmission and realizes smooth information sharing. Furthermore, the AI ​​agent system provides a concrete plan for implementing the proposed communication method. For example, it plans project categories and service / commercialization plans. By adding the AI ​​agent to the existing system based on this plan, efficient workflows and smooth organizational operations can be realized. Through this mechanism, companies can enjoy benefits such as time savings, improved operational efficiency, and increased accuracy. For example, an AI agent system can reduce misunderstandings and improve work efficiency by suggesting the optimal communication method. Furthermore, timely communication ensures that the entire organization is aligned, leading to improved results. In this way, utilizing an AI agent system can revolutionize internal communication and propel businesses forward. For business leaders, now is the time to revolutionize communication; implementing an AI agent system enables strategic communication and improves overall organizational performance. Thus, an AI agent system can effectively improve internal communication.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a planning unit. The collection unit collects internal conversation data and communication history. Internal conversation data and communication history include, but are not limited to, emails, chats, and phone call records. The collection unit collects, for example, the content of emails and chats and identifies important topics. The collection unit analyzes the content of emails and extracts frequently occurring keywords. The collection unit can also analyze the content of chats and identify topics related to specific themes. Furthermore, the collection unit can analyze phone call records and extract important conversation content. For example, the collection unit uses speech recognition technology to convert phone call content into text data and identify important topics. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and specifically suggests the optimal communication method. The analysis unit analyzes the data using, for example, natural language processing technology and proposes the optimal communication method. The analysis unit can also analyze the data using machine learning algorithms and learn the optimal communication method. Furthermore, the analysis department can analyze data using data mining techniques and extract important patterns. For example, the analysis department can use data mining techniques to analyze employee communication patterns and propose optimal communication methods. The proposal department proposes optimal information sharing strategies based on the analysis results obtained by the analysis department. For example, the proposal department proposes how information should be shared. For example, the proposal department proposes information sharing methods using email. The proposal department can also propose information sharing methods using internal social networking services. Furthermore, the proposal department can also propose information sharing methods using meetings. For example, the proposal department proposes the optimal information sharing method according to the progress of the project. The planning department plans to implement the information sharing strategies proposed by the proposal department. For example, the planning department plans project categories and service / commercialization plans. For example, the planning department plans development projects. The planning department can also plan marketing projects.Furthermore, the planning department can also plan the service delivery method and the steps for commercialization. For example, the planning department can plan the service delivery method and propose specific implementation procedures. This allows the AI ​​agent system according to the embodiment to effectively improve communication within the company.

[0065] The data collection department collects internal conversation data and communication history. This includes, but is not limited to, emails, chats, and phone call records. For example, the department collects the content of emails and chats and identifies important topics. Specifically, it analyzes the content of sent and received emails and extracts frequently occurring keywords and phrases. This allows for the identification of themes and issues that are frequently discussed within the company. When analyzing chat content, it uses natural language processing techniques to identify topics related to specific themes. For example, it can extract chat content related to project progress or technical challenges and organize this information. Furthermore, the data collection department can also analyze phone call records and extract important conversation content. Specifically, it uses speech recognition technology to convert phone call content into text data and identify important topics from that text data. For example, it can analyze the content of conference calls with customers and extract customer requests and feedback. This allows the data collection department to collect a wide range of data from various internal communication methods and efficiently extract important information. In addition, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0066] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes the collected data and specifically proposes the optimal communication methods. Specifically, the Analysis Department uses natural language processing technology to analyze the data and propose the optimal communication methods. For example, it can analyze the content of emails to identify which expressions and phrases are effective. It can also analyze the content of chats and extract important topics related to a specific theme. Furthermore, the Analysis Department can use machine learning algorithms to analyze the data and learn the optimal communication methods. For example, it can learn successful communication patterns based on past communication data and utilize them in future communication. Furthermore, the Analysis Department can use data mining technology to analyze the data and extract important patterns. For example, it can analyze employee communication patterns to identify which methods are most effective. This allows the Analysis Department to quickly and accurately analyze the collected data and propose the optimal communication methods. In addition, the Analysis Department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk for a specific theme or problem based on past communication data and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The proposal department proposes the optimal information sharing strategy based on the analysis results obtained by the analysis department. For example, the proposal department proposes how information should be shared. Specifically, the proposal department proposes an information sharing method using email. For example, it creates email templates for quickly sharing important information and provides them to employees. The proposal department can also propose an information sharing method using internal social networking services (SNS). For example, it creates a dedicated channel for each project and centrally manages related information. The proposal department can also propose an information sharing method using meetings. For example, it holds regular meetings to share project progress and challenges. Furthermore, the proposal department proposes the optimal information sharing method according to the progress of the project. For example, in the initial stages of a project, information is shared through frequent meetings and chat, and as the project progresses, email and internal SNS are utilized. In this way, the proposal department can propose the optimal information sharing strategy for each project and situation, and effectively improve communication within the company. Furthermore, the proposal department can evaluate the effectiveness of the proposed information sharing strategy and make improvements as needed. For example, it collects feedback from employees and continuously improves the accuracy and effectiveness of the proposal. Furthermore, the proposal department can combine multiple information-sharing methods to build the optimal strategy. This allows the proposal department to share information within the company efficiently and effectively, improving the quality of communication.

[0068] The Planning Department plans to implement the information sharing strategy proposed by the Proposal Department. For example, the Planning Department plans project categories and service / commercialization plans. Specifically, the Planning Department plans development projects. For example, it creates detailed plans for the development of new products or services, clarifying the execution procedures for each step. The Planning Department can also plan marketing projects. For example, it develops marketing strategies for launching new products and plans advertising and promotional activities. Furthermore, the Planning Department can plan service delivery methods and commercialization steps. For example, it plans how to deliver new services and proposes specific execution procedures. This allows the Planning Department to translate the information sharing strategy proposed by the Proposal Department into concrete plans and put them into action. In addition, the Planning Department can monitor the progress of the plans and revise them as needed. For example, it can regularly check the project progress and respond quickly if problems arise. The Planning Department also allocates resources and adjusts schedules to support the smooth progress of projects. This allows the Planning Department to effectively implement the proposed information sharing strategy and improve internal communication. Furthermore, the planning department can continuously improve its plans by evaluating their execution results and incorporating those improvements into future plans. This allows the planning department to support the company's growth and development and achieve effective information sharing.

[0069] The data collection unit can collect email and chat content and identify important topics. For example, the data collection unit can analyze email content and extract frequently occurring keywords. The data collection unit can also analyze chat content and identify topics related to specific themes. The data collection unit can also analyze phone call recordings and extract important conversation content. For example, the data collection unit can use speech recognition technology to convert phone call content into text data and identify important topics. This enables effective communication improvement by collecting email and chat content and identifying important topics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input email and chat content into a generating AI and have the generating AI perform the identification of important topics.

[0070] The analysis department can analyze the collected data and specifically suggest the optimal communication methods. For example, the analysis department can analyze the data using natural language processing technology and propose the optimal communication methods. The analysis department can also analyze the data using machine learning algorithms and learn the optimal communication methods. The analysis department can also analyze the data using data mining technology and extract important patterns. For example, the analysis department can use data mining technology to analyze employees' communication patterns and propose the optimal communication methods. In this way, the quality of communication can be improved by analyzing the collected data and specifically suggesting the optimal communication methods. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the collected data into a generating AI and have the generating AI perform the task of suggesting the optimal communication methods.

[0071] The proposal department can suggest how information should be shared. For example, the proposal department might suggest using email for information sharing. The proposal department could also suggest using an internal social networking service (SNS) for information sharing. The proposal department could also suggest using meetings for information sharing. For example, the proposal department could suggest the most suitable information sharing method depending on the project's progress. By suggesting how information should be shared, the efficiency of information sharing can be improved. 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 could have a generation AI generate suggestions for the most suitable information sharing method.

[0072] The proposal department can suggest when communication should take place. For example, the proposal department can suggest the optimal timing for communication based on the progress of the project. For example, the proposal department can suggest the optimal timing for communication considering both inside and outside of business hours. For example, the proposal department can suggest the optimal timing for communication before and after a specific event or meeting. For example, the proposal department can suggest the optimal timing for communication in line with important milestones in the project. In this way, the timing of communication can be optimized by suggesting when communication should take place. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI generate suggestions for the optimal timing of communication.

[0073] The planning department can plan project categories and service / commercialization plans. For example, the planning department can plan development projects. For example, the planning department can also plan marketing projects. For example, the planning department can plan service delivery methods and commercialization steps. For example, the planning department can plan service delivery methods and propose specific implementation procedures. For example, the planning department can plan commercialization steps and propose specific implementation procedures. In this way, by planning project categories and service / commercialization plans, a concrete action plan can be provided. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can have a generating AI perform the creation of project categories and service / commercialization plans.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of conversation data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and wait until the user is relaxed. If the user is relaxed, the data collection unit can also collect conversation data immediately and perform real-time analysis. If the user is in a hurry, the data collection unit can also advance the collection timing to collect data quickly. By adjusting the timing of conversation data collection based on the user's emotions, data can be collected at a more appropriate time. 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0075] The data collection unit can analyze an employee's past communication history and select the optimal collection method. For example, the data collection unit can prioritize collecting communication tools that an employee has frequently used in the past. The data collection unit can also analyze an employee's past communication patterns and select the most effective collection method. For example, the data collection unit can concentrate data collection on specific time periods based on an employee's past communication history. This allows the optimal collection method to be selected by analyzing an employee's past communication history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an employee's past communication history into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter conversation data based on an employee's current projects and areas of interest. For example, the data collection unit can prioritize collecting conversation data related to a project the employee is currently working on. The data collection unit can also filter highly relevant conversation data based on an employee's areas of interest. The data collection unit can also select conversation data to collect based on an employee's current work content. This allows for the collection of highly relevant data by filtering based on an employee's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on an employee's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of conversation data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting less important conversation data. For example, if the user is relaxed, the data collection unit may prioritize collecting more important conversation data. For example, if the user is in a hurry, the data collection unit may prioritize collecting conversation data that can be collected quickly. This allows for the priority collection of important data by determining the priority of conversation data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of conversation data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the employee's geographical location information when collecting conversation data. For example, if an employee is in a specific region, the data collection unit will prioritize the collection of conversation data related to that region. The data collection unit can also filter highly relevant conversation data based on the employee's geographical location information. For example, if an employee is on the move, the data collection unit can select conversation data to collect based on their current location. This enables more effective data collection by prioritizing the collection of highly relevant data based on the employee's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the employee's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze employees' social media activity and collect relevant data when collecting conversation data. For example, the data collection unit can collect relevant conversation data based on information shared by employees on social media. The data collection unit can also analyze employees' social media activity and prioritize the collection of highly relevant data. The data collection unit can also select conversation data to collect based on topics that employees have shown interest in on social media. This allows for the collection of highly relevant data by analyzing employees' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on employees' social media activity into a generating AI and have the generating AI collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the conversation data. For example, the analysis unit can perform a detailed analysis on conversation data with high importance. For example, the analysis unit can also perform a concise analysis on conversation data with low importance. For example, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the conversation data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the conversation data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the conversation data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the conversation data. For example, the analysis unit can apply a specialized analysis algorithm to technical conversation data. For example, the analysis unit can also apply a business analysis algorithm to business-related conversation data. For example, the analysis unit can apply a sentiment analysis algorithm to employees' personal conversation data. By applying different analysis algorithms depending on the category of the conversation data, more accurate analysis results can be provided. 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 can input the categories of conversation data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is in a hurry, the analysis unit can provide a short, easily understandable analysis. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on when the conversation data was collected. For example, the analysis unit may prioritize the analysis of the most recent conversation data. The analysis unit may also prioritize the most recent data while referring to past conversation data. The analysis unit may also adjust the priority of analysis in stages according to when the conversation data was collected. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on when the conversation data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the conversation data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the conversation data. For example, the analysis unit may prioritize the analysis of highly relevant conversation data. For example, the analysis unit may postpone the analysis of less relevant conversation data. For example, the analysis unit may also adjust the order of analysis in stages according to the relevance of the conversation data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the conversation data. 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 can input the relevance of the conversation data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0087] The proposal unit can adjust the level of detail of its proposals based on the importance of the information sharing strategies. For example, the proposal unit can provide detailed proposals for highly important information sharing strategies. For example, it can also provide concise proposals for less important information sharing strategies. The proposal unit can also adjust the level of detail of its proposals in stages according to the importance of the information sharing strategies. This allows for efficient information sharing by adjusting the level of detail of proposals based on the importance of the information sharing strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the information sharing strategies into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the category of information sharing strategy. For example, the proposal unit can apply a specialized proposal algorithm to a technical information sharing strategy. For example, the proposal unit can apply a business proposal algorithm to a business-related information sharing strategy. For example, the proposal unit can apply a sentiment analysis algorithm to an employee's personal information sharing strategy. By applying different proposal algorithms depending on the category of information sharing strategy, it is possible to provide more accurate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the category of information sharing strategy into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is in a hurry, for example, the suggestion unit can provide short, easily understandable suggestions. By adjusting the length of suggestions based on the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0090] The proposal department can determine the priority of proposals based on the implementation timing of information sharing strategies. For example, the proposal department may prioritize proposals for information sharing strategies to be implemented in the immediate future. The proposal department may also postpone proposals for long-term information sharing strategies. The proposal department may also adjust the priority of proposals in stages according to the implementation timing of information sharing strategies. This enables efficient information sharing by determining the priority of proposals based on the implementation timing of information sharing strategies. 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 can input the implementation timing of information sharing strategies into a generating AI and have the generating AI determine the priority of proposals.

[0091] The proposal unit can adjust the order of proposals based on the relevance of the information sharing strategies. For example, the proposal unit may prioritize proposing highly relevant information sharing strategies. For example, the proposal unit may postpone less relevant information sharing strategies. For example, the proposal unit may also adjust the order of proposals in stages according to the relevance of the information sharing strategies. This allows for efficient information sharing by adjusting the order of proposals based on the relevance of the information sharing strategies. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the information sharing strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0092] The planning unit can estimate the user's emotions and adjust the planning method based on the estimated emotions. For example, if the user is nervous, the planning unit can provide a simple and easy-to-understand plan. For example, if the user is relaxed, the planning unit can also provide a detailed plan. For example, if the user is in a hurry, the planning unit can provide a concise plan that gets straight to the point. This allows for the provision of a more appropriate plan by adjusting the planning method based on the user's emotions. 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 planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the planning method.

[0093] The planning department can analyze an employee's past project history to select the optimal planning method. For example, the planning department can select the optimal planning method based on an employee's past successful project history. For example, the planning department can also select a planning method that reflects lessons learned from failed projects based on an employee's past project history. For example, the planning department can analyze an employee's past project history to select the most efficient planning method. In this way, the optimal planning method can be selected by analyzing an employee's past project history. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input an employee's past project history into a generating AI and have the generating AI select the optimal planning method.

[0094] The planning department can customize the planning methods based on the employee's current project status. For example, the planning department can customize the planning methods based on the progress of the project the employee is currently working on. The planning department can also suggest the optimal planning methods, taking into account the employee's current project status. The planning department can also adjust the planning methods step by step, depending on the employee's current project status. This allows for the provision of more effective plans by customizing the planning methods based on the employee's current project status. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input the employee's current project status into a generating AI and have the generating AI perform the customization of the planning methods.

[0095] The planning unit can estimate the user's emotions and determine the priority of plans based on the estimated emotions. For example, if the user is stressed, the planning unit may prioritize providing plans of lower importance. For example, if the user is relaxed, the planning unit may also prioritize providing plans of higher importance. For example, if the user is in a hurry, the planning unit may also prioritize providing plans that can be executed quickly. This allows for the provision of more appropriate plans by prioritizing plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not using AI. For example, the planning unit can input user emotion data into a generative AI and have the generative AI determine the priority of plans.

[0096] The planning department can select the optimal planning method based on the geographical location information of employees. For example, if an employee is in a specific region, the planning department will select a planning method relevant to that region. The planning department can also propose the optimal planning method based on the geographical location information of employees. For example, if an employee is on the move, the planning department can select a planning method based on their current location. By selecting the optimal planning method based on the geographical location information of employees, more effective plans can be provided. Some or all of the above processes in the planning department may be performed using AI, for example, or without AI. For example, the planning department can input the geographical location information of employees into a generating AI and have the generating AI select the optimal planning method.

[0097] The planning department can analyze employees' social media activity and propose relevant planning measures. For example, the planning department can propose the most suitable planning measures based on information shared by employees on social media. The planning department can also analyze employees' social media activity and propose highly relevant planning measures. The planning department can also select planning measures based on topics that employees have shown interest in on social media. This allows the planning department to propose highly relevant planning measures by analyzing employees' social media activity. Some or all of the above processes in the planning department may be performed using AI, for example, or not using AI. For example, the planning department can input data on employees' social media activity into a generating AI and have the generating AI propose relevant planning measures.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The AI ​​agent system can also be equipped with a feedback unit. This feedback unit collects feedback from employees and uses it to improve the system. For example, the feedback unit can collect how employees feel about proposed communication methods. It can also collect how employees feel about proposed information sharing strategies. Furthermore, it can collect how employees feel about proposed plans. This allows the feedback unit to improve the system by incorporating employee feedback.

[0100] The data collection department can collect data while taking employees' schedules into consideration. For example, the department can choose to collect data during times when employees are not in meetings or engaged in important work. The department can also collect data during employees' breaks or lunch breaks. Furthermore, the department can choose to collect data after employees have finished their workday. This allows the department to collect data without disrupting employees' work.

[0101] The analytics department can conduct analyses that take into account the individual communication styles of employees. For example, the analytics department can analyze employees by considering their preferred means of communication (email, chat, phone, etc.). It can also analyze the frequency and timing of communication preferred by employees. Furthermore, the analytics department can analyze employees by considering their preferred tone and style of communication. This allows the analytics department to propose optimal communication methods tailored to each employee's individual communication style.

[0102] The proposal department can customize information sharing strategies according to employees' positions and job responsibilities. For example, the proposal department can propose different information sharing strategies for managers and general employees. It can also propose different strategies for sales departments and technical departments. Furthermore, it can propose different strategies for project managers and team members. This allows the proposal department to propose the optimal information sharing strategy tailored to each employee's position and job responsibilities.

[0103] The planning department can create plans that take employees' skill sets into account. For example, the planning department can determine project roles based on employees' expertise and experience. The planning department can also plan training programs based on employees' skill sets. Furthermore, the planning department can plan career paths based on employees' skill sets. This allows the planning department to create plans that make the most of employees' skill sets.

[0104] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those estimates. For example, if the user is stressed, the unit will prioritize collecting data related to emotions. If the user is relaxed, for example, the unit may prioritize collecting data related to work. Furthermore, if the user is in a hurry, the unit may prioritize collecting data that can be collected quickly. By adjusting the types of data collected based on the user's emotions, more relevant data can be collected.

[0105] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on those emotions. For example, if the user is stressed, the analysis unit can delay the analysis and wait until the user is relaxed. If the user is relaxed, the analysis unit can perform the analysis immediately and provide real-time results. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis quickly and provide results in a short time. By adjusting the timing of the analysis based on the user's emotions, the analysis results can be provided at a more appropriate time.

[0106] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easy-to-implement suggestions. If the user is relaxed, for example, the suggestion function can also provide detailed and comprehensive suggestions. Furthermore, if the user is in a hurry, the suggestion function can provide suggestions that can be implemented quickly. In this way, by adjusting the content of suggestions based on the user's emotions, it is possible to provide more appropriate suggestions.

[0107] The planning department can estimate the user's emotions and adjust the progress of the plan based on those emotions. For example, if the user is stressed, the planning department can slow down the plan and wait until the user is relaxed. If the user is relaxed, the planning department can also accelerate the plan. Furthermore, if the user is in a hurry, the planning department can expedite the plan. By adjusting the progress of the plan based on the user's emotions, the plan can be advanced at a more appropriate time.

[0108] The planning department can create plans that take into account the health status of employees. For example, the planning department can set realistic schedules based on the results of employees' health checkups. The planning department can also incorporate vacations and refresh time into the plan according to the health status of employees. Furthermore, the planning department can adjust the workload while taking into account the health status of employees. In this way, the planning department can create plans that take into account the health status of employees, thereby achieving efficient work execution while maintaining employee health.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects internal conversation data and communication history. This includes emails, chats, and phone call records. The data collection unit collects the content of emails and chats and identifies important topics. For example, it analyzes the content of emails and extracts frequently occurring keywords. It can also analyze the content of chats and identify topics related to specific themes. Furthermore, the data collection unit analyzes phone call records and extracts important conversation content. For example, it uses speech recognition technology to convert phone call content into text data and identify important topics. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the collected data and specifically proposes the optimal communication method. For example, it can use natural language processing technology to analyze the data and propose the optimal communication method. It can also use machine learning algorithms to analyze the data and learn the optimal communication method. Furthermore, it can use data mining technology to analyze the data and extract important patterns. For example, it can analyze employees' communication patterns and propose the optimal communication method. Step 3: The proposal team proposes the optimal information sharing strategy based on the analysis results obtained by the analysis team. The proposal team proposes how information should be shared. For example, they may propose methods such as information sharing via email, information sharing via internal social networking services, or information sharing via meetings. They propose the most suitable information sharing method according to the progress of the project. Step 4: The Planning Department plans to implement the information sharing strategy proposed by the Proposal Department. The Planning Department plans the project categories and service / commercialization plans. For example, it plans development projects and marketing projects. Furthermore, it plans the service delivery methods and commercialization steps. It proposes specific implementation procedures to effectively improve internal communication.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and planning unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects internal conversation data and communication history using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A identifies important topics. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and specifically proposes the optimal communication method. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal information sharing strategy based on the analysis results. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which plans to implement the proposed information sharing strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and planning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects internal conversation data and communication history using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A identifies important topics. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data and specifically proposes the optimal communication method. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which proposes the optimal information sharing strategy based on the analysis results. The planning unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which plans to implement the proposed information sharing strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and planning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects internal conversation data and communication history using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A identifies important topics. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to specifically suggest the optimal communication method. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal information sharing strategy based on the analysis results. The planning unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and plans to implement the proposed information sharing strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and planning unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects internal conversation data and communication history using the camera 42 and microphone 238 of the robot 414, and the control unit 46A identifies important topics. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and specifically proposes the optimal communication method. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal information sharing strategy based on the analysis results. The planning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which plans to implement the proposed information sharing strategy. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The data collection department collects internal conversation data and communication history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal information sharing strategy. The system comprises a planning unit that implements the information sharing strategy proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect email and chat content and identify important topics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze the collected data and specifically suggest the most optimal communication methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose how information should be shared. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, I propose the timing for communication. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, Plan the project category and service / commercialization. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze employees' past communication history and select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting conversation data, filtering is performed based on employees' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the conversation data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting conversation data, the system prioritizes collecting highly relevant data based on employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting conversation data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Adjust the level of detail in the analysis based on the importance of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different analysis algorithms depending on the category of conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on when conversation data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the conversation data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the information sharing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Apply different proposed algorithms depending on the category of information sharing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Prioritize proposals based on the implementation timeline of the information sharing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Adjust the order of proposals based on the relevance of the information sharing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planning department, We estimate the user's emotions and adjust the planning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned planning department, Analyze employees' past project history to select the optimal planning method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned planning department, Customize the planning process based on the current project status of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned planning department, It estimates user sentiment and prioritizes plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned planning department, Select the optimal planning method based on the geographical location information of employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned planning department, Analyze employees' social media activity and propose relevant planning strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 data collection department collects internal conversation data and communication history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal information sharing strategy. The system comprises a planning unit that implements the information sharing strategy proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect email and chat content and identify important topics. The system according to feature 1.

3. The aforementioned analysis unit is We analyze the collected data and specifically suggest the most optimal communication methods. The system according to feature 1.

4. The aforementioned proposal section is, We propose how information should be shared. The system according to feature 1.

5. The aforementioned proposal section is, I propose the timing for communication. The system according to feature 1.

6. The aforementioned planning department, Plan the project category and service / commercialization. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze employees' past communication history and select the most suitable data collection method. The system according to feature 1.

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