system

Abstract

The system according to the embodiment aims to identify experts from an employee directory and effectively utilize the human resources of group companies. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a utilization unit. The collection unit collects information from an employee directory. The analysis unit analyzes the information collected by the collection unit and identifies experts. The utilization unit utilizes human resources from group companies based on the experts identified by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently identify experts from employee directories and communication tools, and do not fully utilize the human resources of group companies, so there is room for improvement.

[0005] The system according to the embodiment aims to identify experts from an employee directory and effectively utilize the human resources of group companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an utilization unit. The collection unit collects information from an employee directory. The analysis unit analyzes the information collected by the collection unit and identifies experts. The utilization unit utilizes human resources from group companies based on the experts identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify experts from an employee directory and effectively utilize the human resources of group companies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An expert identification system according to an embodiment of the present invention is a system for finding experts by utilizing information stored on an intranet, such as an employee directory, communication tools, and internal newsletters. The expert identification system collects information from the employee directory, communication tools, internal newsletters, and other sources, and uses AI to analyze it to identify experts. For example, experts can be found based on information such as "This person is knowledgeable about XX!" or "This person has certain qualifications." The expert identification system can then utilize human resources from group companies based on the identified experts, which can be useful for service development. For example, the expert identification system collects information such as each employee's job title, department, and field of expertise from the employee directory. For example, the expert identification system collects information such as each employee's comments and project participation history from communication tools. The expert identification system then uses AI to analyze the collected information. The AI ​​identifies each employee's field of expertise and skills based on the collected information. For example, by analyzing past work history and qualifications, it can identify information such as "This person is knowledgeable about XX!" The expert identification system can also understand each employee's expertise and skills by analyzing the content of comments made on communication tools. The expert identification system can then utilize human resources from group companies based on the identified experts. For example, by having the identified experts participate in projects of the group companies to which they belong, they can be useful in service development. Furthermore, the expert identification system can promote networking between experts, which is expected to lead to knowledge sharing and the creation of new ideas. This allows the expert identification system to find experts by utilizing information such as employee directories, communication tools, and internal public relations. This allows the expert identification system to effectively utilize the human resources of group companies and utilize them in service development.

[0029] The expert identification system according to the embodiment includes a collection unit, an analysis unit, and an utilization unit. The collection unit collects information from an employee directory. For example, the collection unit collects information such as each employee's job title, department, and field of expertise from the employee directory. The collection unit can also collect information such as each employee's comments and project participation history from communication tools. For example, the collection unit collects meeting minutes and chat logs to understand each employee's comments. The collection unit can also collect each employee's achievements and qualifications from internal public relations materials. For example, the collection unit collects internal newsletters and internal reports to understand each employee's field of expertise and skills. The analysis unit analyzes the information collected by the collection unit and identifies an expert. The analysis unit analyzes the collected information using, for example, natural language processing technology. For example, the analysis unit analyzes each employee's comments using morphological analysis and grammatical analysis. The analysis unit can also analyze each employee's work history and qualifications using data mining technology. For example, the analysis unit analyzes past project participation history and performance data to identify each employee's area of ​​expertise. Furthermore, the analysis unit can also use AI to identify experts based on the collected information. For example, the analysis unit uses an AI model to evaluate each employee's skill set and years of experience and identify experts. The utilization unit utilizes human resources from group companies based on the experts identified by the analysis unit. The utilization unit contributes to service development by, for example, having the identified experts participate in projects of the group companies to which they belong. For example, the utilization unit adds the identified experts to project teams to contribute to the development of new products and the improvement of existing services. The utilization unit can also promote networking among experts. For example, the utilization unit holds regular meetings and online forums to promote knowledge sharing and idea creation among experts. As a result, the expert identification system according to the embodiment can find experts by utilizing information such as employee directories, communication tools, and internal newsletters. As a result, the expert identification system can effectively utilize human resources from group companies and contribute to service development.

[0030] The collection unit can collect information on each employee's job title, department, and specialty from the employee directory. For example, the collection unit collects information on each employee's job title, department, and specialty from the employee directory. For example, the collection unit collects job title information for each employee from the employee directory. Job titles include, for example, manager, director, and engineer. The collection unit also collects department information for each employee from the employee directory. Departments include, for example, sales department, development department, and marketing department. The collection unit also collects specialty information for each employee from the employee directory. Specialized fields include, for example, machine learning, marketing, and project management. This makes it easier to identify experts by collecting detailed information from the employee directory. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information obtained from the employee directory into a generation AI and have the generation AI analyze the information.

[0031] The collection unit can collect information on each employee's comments and project participation history from communication tools. For example, the collection unit collects information on each employee's comments and project participation history from communication tools. For example, the collection unit collects meeting minutes and chat logs to understand each employee's comments. The collection unit can also collect each employee's project participation history from a project management tool. For example, the collection unit analyzes data from the project management tool to understand each employee's project participation history. Furthermore, the collection unit can collect each employee's comments from an internal social networking site or forum. For example, the collection unit analyzes posts on the internal social networking site to understand each employee's expertise and skills. This allows for more accurate identification of experts by collecting information from communication tools. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input data acquired from communication tools into a generation AI and have the generation AI analyze the data.

[0032] The analysis unit can analyze the content of the utterances using natural language processing and identify the field of expertise. The analysis unit can analyze the content of the utterances using, for example, natural language processing and identify the field of expertise. For example, the analysis unit can analyze the content of the utterances of each employee using morphological analysis. The analysis unit can also analyze the content of the utterances of each employee using grammatical analysis. Furthermore, the analysis unit can analyze the content of the utterances of each employee using semantic analysis. For example, the analysis unit can extract keywords from the content of the utterances and identify the field of expertise of each employee. In this way, by using natural language processing, the field of expertise can be accurately identified from the content of the utterances. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the content of the utterances to a generation AI and have the generation AI identify the field of expertise.

[0033] The utilization department can utilize the identified experts for service development by having them participate in projects of group companies to which they belong. For example, the utilization department can utilize the identified experts for service development by having them participate in projects of group companies to which they belong. For example, the utilization department can add the identified experts to a project team to contribute to the development of new products or the improvement of existing services. The utilization department can also have the identified experts participate in projects as consultants. For example, the utilization department can utilize the identified experts' knowledge and experience to contribute to the success of the project. Furthermore, the utilization department can have the identified experts participate in the project team as trainers. For example, the utilization department can have the identified experts train project team members to improve their skills. In this way, having the identified experts participate in projects of group companies promotes service development. Some or all of the above-described processing in the utilization department can be performed using, for example, AI, or can be performed without using AI. For example, the utilization department can input information about the identified experts into a generation AI and have the generation AI suggest optimal projects for them to participate in.

[0034] The utilization department can promote networking among experts. For example, the utilization department may promote networking among experts. For example, the utilization department may hold regular meetings to promote knowledge sharing and idea generation among experts. The utilization department may also set up an online forum to provide a place where experts can freely exchange opinions. For example, the utilization department may find new ideas and solutions through discussions in the online forum. Furthermore, the utilization department may provide a platform to promote collaboration among experts. For example, the utilization department may use a project management tool to create an environment where experts can work together on projects. This promotes networking among experts, which is expected to lead to knowledge sharing and the generation of new ideas. Some or all of the above-mentioned processing in the utilization department may be performed using, for example, AI, or may be performed without AI. For example, the utilization department may input networking data among experts into a generation AI and have the generation AI perform networking optimization.

[0035] The collection unit can analyze each employee's past work history and select the optimal information collection method. The collection unit, for example, analyzes each employee's past work history and selects the optimal information collection method. For example, the collection unit prioritizes collecting relevant information based on each employee's past project participation history. The collection unit can also analyze each employee's past work content and select the optimal information collection method (text, audio, etc.). Furthermore, the collection unit can improve the accuracy of information collection by referring to each employee's past work results. For example, the collection unit determines the priority of information collection based on past work results. In this way, the optimal information collection method can be selected by analyzing the past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into a generation AI and have the generation AI select the optimal information collection method.

[0036] The collection unit may filter information based on each employee's current project or area of ​​interest when collecting information. For example, the collection unit may prioritize collecting information related to each employee's current project. The collection unit may also filter and provide relevant information based on each employee's area of ​​interest. Furthermore, the collection unit may collect only necessary information, taking into account each employee's current work situation. For example, the collection unit may adjust the frequency and content of information collection according to the current work situation. By filtering information based on the current project or area of ​​interest, highly relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the current project or area of ​​interest into the generation AI and have the generation AI perform information filtering.

[0037] The collection unit can select an appropriate collection means depending on each employee's input method when collecting information. For example, the collection unit can select an appropriate collection means depending on each employee's input method (voice, text, image, etc.) when collecting information. For example, if each employee uses voice input, the collection unit can prioritize collecting voice data. Also, if each employee uses text input, the collection unit can prioritize collecting text data. Furthermore, if each employee uses image data, the collection unit can prioritize collecting image data. For example, the collection unit can convert voice data into text data using voice recognition technology and collect information. This enables efficient information collection by selecting the optimal collection means depending on each employee's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input voice data or text data into a generation AI and have the generation AI collect information.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each employee. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of each employee. For example, the collection unit prioritizes collecting relevant information based on the current location of each employee. The collection unit can also collect highly relevant information by referring to the past movement history of each employee. Furthermore, the collection unit can collect region-specific information based on the geographical location information of each employee. For example, the collection unit uses GPS data and address information to determine the current location and movement history of each employee and collect relevant information. In this way, by taking the geographical location information into account, highly relevant information can be prioritized. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect information.

[0039] The collection unit can analyze each employee's social media activity and collect related information when collecting information. For example, the collection unit can analyze each employee's social media activity and collect related information when collecting information. For example, the collection unit can analyze each employee's social media posts and collect related information. The collection unit can also collect related information based on each employee's social media activity history. Furthermore, the collection unit can collect related information by referring to the activities of each employee's friends on social media. For example, the collection unit can analyze the content of social media posts and the number of followers to identify each employee's areas of interest and expertise. This allows for efficient collection of related information by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect information.

[0040] The collection unit can customize the collection method by reflecting each employee's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting each employee's past feedback when collecting information. For example, the collection unit improves the information collection method based on each employee's past feedback. The collection unit can also adjust the type of information to be collected by referring to each employee's past feedback. Furthermore, the collection unit can adjust the frequency of information collection by reflecting each employee's past feedback. For example, the collection unit optimizes the information collection method and frequency based on survey results and user reviews. In this way, the information collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the information collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can adjust the accuracy of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of the information based on the impact on business and urgency, and determines the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical information. The analysis unit can also apply a business-oriented analysis algorithm to business information. The analysis unit can also apply a public relations-oriented analysis algorithm to internal public relations information. For example, the analysis unit performs analysis depending on the category of information using techniques such as clustering or regression analysis. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to each employee's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to each employee's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on each employee's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to each employee's past analysis results. Furthermore, the analysis unit can improve the analysis method by reflecting each employee's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past reports and databases. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of information submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of information submission during analysis. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone analysis of information submitted later. Furthermore, the analysis unit can adjust the analysis schedule depending on the submission time. For example, the analysis unit evaluates the time of information submission based on the submission date and submission time and determines the analysis priority. This enables efficient analysis by determining the analysis priority based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of information. For example, the analysis unit evaluates the relevance of information based on common keywords and related topics and determines the order of analysis. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terminology during analysis according to each employee's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to each employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to each employee's level of expertise. For example, the analysis unit can evaluate each employee's level of expertise based on their qualifications and years of experience and determine the use of technical terminology during analysis. By adjusting the use of technical terminology during analysis according to each employee's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input each employee's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology during analysis.

[0047] The utilization unit can analyze the identified expert's past project participation history and select the optimal utilization method during utilization. For example, the utilization unit analyzes the identified expert's past project participation history and selects the optimal utilization method during utilization. For example, the utilization unit selects the optimal utilization method based on the identified expert's past project participation history. The utilization unit can also analyze the identified expert's past work content and select the optimal utilization method. Furthermore, the utilization unit can optimize the utilization method by referring to the identified expert's past work results. For example, the utilization unit analyzes data from a project management tool to understand the identified expert's project participation history. By analyzing the past project participation history, the optimal utilization method can be selected. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input past project participation history data into a generation AI and cause the generation AI to select the optimal utilization method.

[0048] The utilization unit can customize the utilization means based on the current work situation of the identified expert at the time of utilization. For example, the utilization unit customizes the utilization means based on the current work situation of the identified expert at the time of utilization. For example, the utilization unit selects the optimal utilization means taking into account the current work situation of the identified expert. The utilization unit can also provide information related to the identified expert's current project. Furthermore, the utilization unit can adjust the utilization method taking into account the identified expert's current workload. For example, the utilization unit evaluates the work situation of the identified expert based on the current project and work assigned to the expert, and determines the optimal utilization means. This enables efficient utilization by customizing the utilization means based on the current work situation. Some or all of the above-mentioned processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input current work situation data into a generation AI and cause the generation AI to customize the utilization means.

[0049] The utilization unit can improve the utilization method by reflecting the feedback of the identified expert during utilization. For example, the utilization unit improves the utilization method by reflecting the feedback of the identified expert during utilization. For example, the utilization unit improves the utilization method based on the feedback of the identified expert. The utilization unit can also optimize the utilization means by referring to the feedback of the identified expert. Furthermore, the utilization unit can improve the utilization method by reflecting the feedback of the identified expert. For example, the utilization unit optimizes the utilization method based on survey results and user reviews. In this way, the utilization method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the utilization unit may be performed using AI, for example, or may be performed without using AI. For example, the utilization unit can input feedback data into a generation AI and cause the generation AI to improve the utilization method.

[0050] The utilization unit can select an appropriate utilization method by taking into account the geographical location information of the identified expert during utilization. For example, the utilization unit selects an appropriate utilization method by taking into account the geographical location information of the identified expert during utilization. For example, the utilization unit selects an optimal utilization method based on the current location of the identified expert. The utilization unit can also select a highly relevant utilization method by referring to the past movement history of the identified expert. Furthermore, the utilization unit can select a utilization method specialized for a region based on the geographical location information of the identified expert. For example, the utilization unit uses GPS data or address information to determine the current location and movement history of the identified expert and selects an optimal utilization method. In this way, the optimal utilization method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input geographical location information data to a generation AI and cause the generation AI to select a utilization method.

[0051] The utilization unit can analyze the social media activity of the identified expert and suggest utilization methods during utilization. For example, the utilization unit can analyze the social media activity of the identified expert and suggest utilization methods during utilization. For example, the utilization unit can analyze the content of the identified expert's social media posts and suggest optimal utilization methods. The utilization unit can also suggest related utilization methods based on the identified expert's social media activity history. Furthermore, the utilization unit can also suggest optimal utilization methods by referring to the activity of the identified expert's friends on social media. For example, the utilization unit can analyze the content of social media posts and the number of followers to understand the identified expert's areas of interest and expertise. This allows the analysis of social media activity to suggest optimal utilization methods. Some or all of the above-mentioned processing in the utilization unit can be performed using, for example, AI, or without AI. For example, the utilization unit can input social media data into a generation AI and have the generation AI execute the utilization method suggestions.

[0052] The utilization unit can customize the utilization method by reflecting the identified expert's past feedback during utilization. For example, the utilization unit customizes the utilization method by reflecting the identified expert's past feedback during utilization. For example, the utilization unit customizes the utilization method based on the identified expert's past feedback. The utilization unit can also optimize the utilization means by referring to the identified expert's past feedback. Furthermore, the utilization unit can improve the utilization method by reflecting the identified expert's past feedback. For example, the utilization unit optimizes the utilization method based on survey results and user reviews. In this way, the utilization method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the utilization unit may be performed using AI, for example, or may be performed without using AI. For example, the utilization unit can input past feedback data into the generation AI and cause the generation AI to customize the utilization method.

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

[0054] The collection department collects each employee's health data, and the analysis department can use that data to evaluate the health status of the specialist. For example, the collection department collects data from employees' fitness trackers and health apps, and the analysis department analyzes that data to evaluate the specialist's health status. The collection department can also collect the results of employees' regular health checkups, and the analysis department can identify health risks based on those results. Furthermore, the collection department can monitor employees' stress levels, and the analysis department can use that data to make stress management suggestions. This allows the health status of specialists to be understood and appropriate support to be provided.

[0055] The analysis department can analyze each employee's learning history and propose optimal learning plans to improve the experts' skills. For example, the analysis department can analyze the history of online courses and training programs that employees have taken and identify skill gaps. The analysis department can also evaluate the employees' learning outcomes and suggest what they should learn next. Furthermore, the analysis department can analyze the employees' learning styles and propose optimal learning methods. This makes it possible to effectively support the improvement of experts' skills.

[0056] The collection department collects information on each employee's hobbies and interests, and the analysis department can use that data to suggest personalized projects to experts. For example, the collection department collects information on hobbies and interests from employees' internal social media accounts and surveys, and the analysis department analyzes that data to suggest projects that are suitable for experts. The collection department can also collect employees' past project history, and the analysis department can use that data to identify projects that match their interests. Furthermore, the collection department can collect employees' self-introductions and profile information, and the analysis department can use that data to make personalized suggestions. This makes it possible to provide experts with projects that match their interests.

[0057] The collection department collects information on each employee's communication style, and the analysis department can suggest the optimal communication method based on that data. For example, the collection department collects employees' email and chat history, and the analysis department analyzes that data to suggest a communication method that suits the employee. The collection department can also collect employees' meeting participation history, and the analysis department can identify effective communication methods based on that data. Furthermore, the collection department collects employees' feedback history, and the analysis department can suggest areas for improving communication based on that data. This allows for effective support of employee communication.

[0058] The collection department collects each employee's career goals, and the analysis department can propose career paths based on that data. For example, the collection department collects career goals from employees' self-evaluations and goal-setting sheets, and the analysis department analyzes that data to propose career paths that are suitable for the employee. The collection department also collects employees' past work history, and the analysis department can predict career progress based on that data. Furthermore, the collection department collects employees' skill sets, and the analysis department can propose skill development plans for career advancement based on that data. This makes it possible to provide support that is tailored to employees' career goals.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection department collects information from the employee directory. Specifically, they collect information such as each employee's job title, department, and area of ​​expertise. The collection department can also collect information such as each employee's comments and project participation history from communication tools. For example, they collect meeting minutes and chat logs to understand what each employee has said. They can also collect information on each employee's achievements and qualifications from internal communications. For example, they collect internal newsletters and internal reports to understand each employee's area of ​​expertise and skills. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies experts. Specifically, it analyzes the collected information using natural language processing technology. For example, it uses morphological analysis and grammatical analysis to analyze the content of each employee's speech. It can also use data mining technology to analyze each employee's work history and qualifications. For example, it can analyze past project participation history and performance data to identify each employee's area of ​​expertise. It can also identify experts based on information collected using AI. For example, it can use an AI model to evaluate each employee's skill set and years of experience to identify experts. Step 3: The Utilization Department utilizes the human resources of group companies based on the experts identified by the Analysis Department. Specifically, the identified experts can be involved in projects at the group companies to which they belong, thereby helping with service development. For example, the identified experts can be added to project teams to contribute to the development of new products or the improvement of existing services. Networking between experts can also be promoted. For example, regular meetings and online forums can be held to encourage knowledge sharing and idea generation among experts.

[0061] (Example 2) An expert identification system according to an embodiment of the present invention is a system for finding experts by utilizing information stored on an intranet, such as an employee directory, communication tools, and internal newsletters. The expert identification system collects information from the employee directory, communication tools, internal newsletters, and other sources, and uses AI to analyze it to identify experts. For example, experts can be found based on information such as "This person is knowledgeable about XX!" or "This person has certain qualifications." The expert identification system can then utilize human resources from group companies based on the identified experts, which can be useful for service development. For example, the expert identification system collects information such as each employee's job title, department, and field of expertise from the employee directory. For example, the expert identification system collects information such as each employee's comments and project participation history from communication tools. The expert identification system then uses AI to analyze the collected information. The AI ​​identifies each employee's field of expertise and skills based on the collected information. For example, by analyzing past work history and qualifications, it can identify information such as "This person is knowledgeable about XX!" The expert identification system can also understand each employee's expertise and skills by analyzing the content of comments made on communication tools. The expert identification system can then utilize human resources from group companies based on the identified experts. For example, by having the identified experts participate in projects of the group companies to which they belong, they can be useful in service development. Furthermore, the expert identification system can promote networking between experts, which is expected to lead to knowledge sharing and the creation of new ideas. This allows the expert identification system to find experts by utilizing information such as employee directories, communication tools, and internal public relations. This allows the expert identification system to effectively utilize the human resources of group companies and utilize them in service development.

[0062] The expert identification system according to the embodiment includes a collection unit, an analysis unit, and an utilization unit. The collection unit collects information from an employee directory. For example, the collection unit collects information such as each employee's job title, department, and field of expertise from the employee directory. The collection unit can also collect information such as each employee's comments and project participation history from communication tools. For example, the collection unit collects meeting minutes and chat logs to understand each employee's comments. The collection unit can also collect each employee's achievements and qualifications from internal public relations materials. For example, the collection unit collects internal newsletters and internal reports to understand each employee's field of expertise and skills. The analysis unit analyzes the information collected by the collection unit and identifies an expert. The analysis unit analyzes the collected information using, for example, natural language processing technology. For example, the analysis unit analyzes each employee's comments using morphological analysis and grammatical analysis. The analysis unit can also analyze each employee's work history and qualifications using data mining technology. For example, the analysis unit analyzes past project participation history and performance data to identify each employee's area of ​​expertise. Furthermore, the analysis unit can also use AI to identify experts based on the collected information. For example, the analysis unit uses an AI model to evaluate each employee's skill set and years of experience and identify experts. The utilization unit utilizes human resources from group companies based on the experts identified by the analysis unit. The utilization unit contributes to service development by, for example, having the identified experts participate in projects of the group companies to which they belong. For example, the utilization unit adds the identified experts to project teams to contribute to the development of new products and the improvement of existing services. The utilization unit can also promote networking among experts. For example, the utilization unit holds regular meetings and online forums to promote knowledge sharing and idea creation among experts. As a result, the expert identification system according to the embodiment can find experts by utilizing information such as employee directories, communication tools, and internal newsletters. As a result, the expert identification system can effectively utilize human resources from group companies and contribute to service development.

[0063] The collection unit can collect information on each employee's job title, department, and specialty from the employee directory. For example, the collection unit collects information on each employee's job title, department, and specialty from the employee directory. For example, the collection unit collects job title information for each employee from the employee directory. Job titles include, for example, manager, director, and engineer. The collection unit also collects department information for each employee from the employee directory. Departments include, for example, sales department, development department, and marketing department. The collection unit also collects specialty information for each employee from the employee directory. Specialized fields include, for example, machine learning, marketing, and project management. This makes it easier to identify experts by collecting detailed information from the employee directory. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input information obtained from the employee directory into a generation AI and have the generation AI analyze the information.

[0064] The collection unit can collect information on each employee's comments and project participation history from communication tools. For example, the collection unit collects information on each employee's comments and project participation history from communication tools. For example, the collection unit collects meeting minutes and chat logs to understand each employee's comments. The collection unit can also collect each employee's project participation history from a project management tool. For example, the collection unit analyzes data from the project management tool to understand each employee's project participation history. Furthermore, the collection unit can collect each employee's comments from an internal social networking site or forum. For example, the collection unit analyzes posts on the internal social networking site to understand each employee's expertise and skills. This allows for more accurate identification of experts by collecting information from communication tools. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input data acquired from communication tools into a generation AI and have the generation AI analyze the data.

[0065] The analysis unit can analyze the content of the utterances using natural language processing and identify the field of expertise. The analysis unit can analyze the content of the utterances using, for example, natural language processing and identify the field of expertise. For example, the analysis unit can analyze the content of the utterances of each employee using morphological analysis. The analysis unit can also analyze the content of the utterances of each employee using grammatical analysis. Furthermore, the analysis unit can analyze the content of the utterances of each employee using semantic analysis. For example, the analysis unit can extract keywords from the content of the utterances and identify the field of expertise of each employee. In this way, by using natural language processing, the field of expertise can be accurately identified from the content of the utterances. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data of the content of the utterances to a generation AI and have the generation AI identify the field of expertise.

[0066] The utilization department can utilize the identified experts for service development by having them participate in projects of group companies to which they belong. For example, the utilization department can utilize the identified experts for service development by having them participate in projects of group companies to which they belong. For example, the utilization department can add the identified experts to a project team to contribute to the development of new products or the improvement of existing services. The utilization department can also have the identified experts participate in projects as consultants. For example, the utilization department can utilize the identified experts' knowledge and experience to contribute to the success of the project. Furthermore, the utilization department can have the identified experts participate in the project team as trainers. For example, the utilization department can have the identified experts train project team members to improve their skills. In this way, having the identified experts participate in projects of group companies promotes service development. Some or all of the above-described processing in the utilization department can be performed using, for example, AI, or can be performed without using AI. For example, the utilization department can input information about the identified experts into a generation AI and have the generation AI suggest optimal projects for them to participate in.

[0067] The utilization department can promote networking among experts. For example, the utilization department may promote networking among experts. For example, the utilization department may hold regular meetings to promote knowledge sharing and idea generation among experts. The utilization department may also set up an online forum to provide a place where experts can freely exchange opinions. For example, the utilization department may find new ideas and solutions through discussions in the online forum. Furthermore, the utilization department may provide a platform to promote collaboration among experts. For example, the utilization department may use a project management tool to create an environment where experts can work together on projects. This promotes networking among experts, which is expected to lead to knowledge sharing and the generation of new ideas. Some or all of the above-mentioned processing in the utilization department may be performed using, for example, AI, or may be performed without AI. For example, the utilization department may input networking data among experts into a generation AI and have the generation AI perform networking optimization.

[0068] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection and provide detailed information. Furthermore, if the user is in a hurry, the collection unit can quickly collect information and provide it immediately. This reduces the burden on the user by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0069] The collection unit can analyze each employee's past work history and select the optimal information collection method. The collection unit, for example, analyzes each employee's past work history and selects the optimal information collection method. For example, the collection unit prioritizes collecting relevant information based on each employee's past project participation history. The collection unit can also analyze each employee's past work content and select the optimal information collection method (text, audio, etc.). Furthermore, the collection unit can improve the accuracy of information collection by referring to each employee's past work results. For example, the collection unit determines the priority of information collection based on past work results. In this way, the optimal information collection method can be selected by analyzing the past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into a generation AI and have the generation AI select the optimal information collection method.

[0070] The collection unit may filter information based on each employee's current project or area of ​​interest when collecting information. For example, the collection unit may prioritize collecting information related to each employee's current project. The collection unit may also filter and provide relevant information based on each employee's area of ​​interest. Furthermore, the collection unit may collect only necessary information, taking into account each employee's current work situation. For example, the collection unit may adjust the frequency and content of information collection according to the current work situation. By filtering information based on the current project or area of ​​interest, highly relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the current project or area of ​​interest into the generation AI and have the generation AI perform information filtering.

[0071] The collection unit can select an appropriate collection means depending on each employee's input method when collecting information. For example, the collection unit can select an appropriate collection means depending on each employee's input method (voice, text, image, etc.) when collecting information. For example, if each employee uses voice input, the collection unit can prioritize collecting voice data. Also, if each employee uses text input, the collection unit can prioritize collecting text data. Furthermore, if each employee uses image data, the collection unit can prioritize collecting image data. For example, the collection unit can convert voice data into text data using voice recognition technology and collect information. This enables efficient information collection by selecting the optimal collection means depending on each employee's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without AI. For example, the collection unit can input voice data or text data into a generation AI and have the generation AI collect information.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. In this way, by determining the priority of information based on the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0073] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each employee. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of each employee. For example, the collection unit prioritizes collecting relevant information based on the current location of each employee. The collection unit can also collect highly relevant information by referring to the past movement history of each employee. Furthermore, the collection unit can collect region-specific information based on the geographical location information of each employee. For example, the collection unit uses GPS data and address information to determine the current location and movement history of each employee and collect relevant information. In this way, by taking the geographical location information into account, highly relevant information can be prioritized. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to collect information.

[0074] The collection unit can analyze each employee's social media activity and collect related information when collecting information. For example, the collection unit can analyze each employee's social media activity and collect related information when collecting information. For example, the collection unit can analyze each employee's social media posts and collect related information. The collection unit can also collect related information based on each employee's social media activity history. Furthermore, the collection unit can collect related information by referring to the activities of each employee's friends on social media. For example, the collection unit can analyze the content of social media posts and the number of followers to identify each employee's areas of interest and expertise. This allows for efficient collection of related information by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect information.

[0075] The collection unit can customize the collection method by reflecting each employee's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting each employee's past feedback when collecting information. For example, the collection unit improves the information collection method based on each employee's past feedback. The collection unit can also adjust the type of information to be collected by referring to each employee's past feedback. Furthermore, the collection unit can adjust the frequency of information collection by reflecting each employee's past feedback. For example, the collection unit optimizes the information collection method and frequency based on survey results and user reviews. In this way, the information collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and cause the generation AI to customize the information collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can adjust the accuracy of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of the information based on the impact on business and urgency, and determines the level of detail of the analysis. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specialized analysis algorithm to technical information. The analysis unit can also apply a business-oriented analysis algorithm to business information. The analysis unit can also apply a public relations-oriented analysis algorithm to internal public relations information. For example, the analysis unit performs analysis depending on the category of information using techniques such as clustering or regression analysis. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by referring to each employee's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to each employee's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on each employee's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to each employee's past analysis results. Furthermore, the analysis unit can improve the analysis method by reflecting each employee's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on past reports and databases. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] The analysis unit can determine the analysis priority based on the time of information submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of information submission during analysis. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone analysis of information submitted later. Furthermore, the analysis unit can adjust the analysis schedule depending on the submission time. For example, the analysis unit evaluates the time of information submission based on the submission date and submission time and determines the analysis priority. This enables efficient analysis by determining the analysis priority based on the time of information submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of information. For example, the analysis unit evaluates the relevance of information based on common keywords and related topics and determines the order of analysis. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terminology during analysis according to each employee's level of expertise. For example, the analysis unit can adjust the use of technical terminology during analysis according to each employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to employees with a high level of expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with a low level of expertise. The analysis unit can also adjust the way the analysis results are presented according to each employee's level of expertise. For example, the analysis unit can evaluate each employee's level of expertise based on their qualifications and years of experience and determine the use of technical terminology during analysis. By adjusting the use of technical terminology during analysis according to each employee's level of expertise, it is possible to provide analysis results that are easy to understand. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input each employee's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology during analysis.

[0084] The utilization unit can estimate the user's emotions and adjust the utilization method based on the estimated user emotions. For example, the utilization unit can estimate the user's emotions and adjust the utilization method based on the estimated user emotions. For example, if the user is nervous, the utilization unit can provide a simple and highly visible utilization method. If the user is relaxed, the utilization unit can provide a utilization method that includes detailed information. If the user is in a hurry, the utilization unit can provide a utilization method that focuses on the main points. This allows the utilization method to be adjusted according to the user's emotions, thereby providing an optimal utilization method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the utilization unit may be performed using an AI, for example, or without an AI. For example, the utilization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the utilization method.

[0085] The utilization unit can analyze the identified expert's past project participation history and select the optimal utilization method during utilization. For example, the utilization unit analyzes the identified expert's past project participation history and selects the optimal utilization method during utilization. For example, the utilization unit selects the optimal utilization method based on the identified expert's past project participation history. The utilization unit can also analyze the identified expert's past work content and select the optimal utilization method. Furthermore, the utilization unit can optimize the utilization method by referring to the identified expert's past work results. For example, the utilization unit analyzes data from a project management tool to understand the identified expert's project participation history. By analyzing the past project participation history, the optimal utilization method can be selected. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input past project participation history data into a generation AI and cause the generation AI to select the optimal utilization method.

[0086] The utilization unit can customize the utilization means based on the current work situation of the identified expert at the time of utilization. For example, the utilization unit customizes the utilization means based on the current work situation of the identified expert at the time of utilization. For example, the utilization unit selects the optimal utilization means taking into account the current work situation of the identified expert. The utilization unit can also provide information related to the identified expert's current project. Furthermore, the utilization unit can adjust the utilization method taking into account the identified expert's current workload. For example, the utilization unit evaluates the work situation of the identified expert based on the current project and work assigned to the expert, and determines the optimal utilization means. This enables efficient utilization by customizing the utilization means based on the current work situation. Some or all of the above-mentioned processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input current work situation data into a generation AI and cause the generation AI to customize the utilization means.

[0087] The utilization unit can improve the utilization method by reflecting the feedback of the identified expert during utilization. For example, the utilization unit improves the utilization method by reflecting the feedback of the identified expert during utilization. For example, the utilization unit improves the utilization method based on the feedback of the identified expert. The utilization unit can also optimize the utilization means by referring to the feedback of the identified expert. Furthermore, the utilization unit can improve the utilization method by reflecting the feedback of the identified expert. For example, the utilization unit optimizes the utilization method based on survey results and user reviews. In this way, the utilization method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the utilization unit may be performed using AI, for example, or may be performed without using AI. For example, the utilization unit can input feedback data into a generation AI and cause the generation AI to improve the utilization method.

[0088] The utilization unit can estimate the user's emotions and determine utilization priorities based on the estimated user emotions. For example, the utilization unit can estimate the user's emotions and determine utilization priorities based on the estimated user emotions. For example, when the user is feeling stressed, the utilization unit can prioritize utilization of information with high importance. Furthermore, when the user is relaxed, the utilization unit can prioritize utilization of detailed information. Furthermore, when the user is in a hurry, the utilization unit can prioritize providing information that can be used quickly. In this way, by determining utilization priorities based on the user's emotions, important information can be prioritized for utilization. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the utilization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the utilization unit can input the user's emotion data into the generation AI and have the generation AI determine the utilization priorities.

[0089] The utilization unit can select an appropriate utilization method by taking into account the geographical location information of the identified expert during utilization. For example, the utilization unit selects an appropriate utilization method by taking into account the geographical location information of the identified expert during utilization. For example, the utilization unit selects an optimal utilization method based on the current location of the identified expert. The utilization unit can also select a highly relevant utilization method by referring to the past movement history of the identified expert. Furthermore, the utilization unit can select a utilization method specialized for a region based on the geographical location information of the identified expert. For example, the utilization unit uses GPS data or address information to determine the current location and movement history of the identified expert and selects an optimal utilization method. In this way, the optimal utilization method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the utilization unit may be performed using, for example, AI, or may be performed without using AI. For example, the utilization unit can input geographical location information data to a generation AI and cause the generation AI to select a utilization method.

[0090] The utilization unit can analyze the social media activity of the identified expert and suggest utilization methods during utilization. For example, the utilization unit can analyze the social media activity of the identified expert and suggest utilization methods during utilization. For example, the utilization unit can analyze the content of the identified expert's social media posts and suggest optimal utilization methods. The utilization unit can also suggest related utilization methods based on the identified expert's social media activity history. Furthermore, the utilization unit can also suggest optimal utilization methods by referring to the activity of the identified expert's friends on social media. For example, the utilization unit can analyze the content of social media posts and the number of followers to understand the identified expert's areas of interest and expertise. This allows the analysis of social media activity to suggest optimal utilization methods. Some or all of the above-mentioned processing in the utilization unit can be performed using, for example, AI, or without AI. For example, the utilization unit can input social media data into a generation AI and have the generation AI execute the utilization method suggestions.

[0091] The utilization unit can customize the utilization method by reflecting the identified expert's past feedback during utilization. For example, the utilization unit customizes the utilization method by reflecting the identified expert's past feedback during utilization. For example, the utilization unit customizes the utilization method based on the identified expert's past feedback. The utilization unit can also optimize the utilization means by referring to the identified expert's past feedback. Furthermore, the utilization unit can improve the utilization method by reflecting the identified expert's past feedback. For example, the utilization unit optimizes the utilization method based on survey results and user reviews. In this way, the utilization method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the utilization unit may be performed using AI, for example, or may be performed without using AI. For example, the utilization unit can input past feedback data into the generation AI and cause the generation AI to customize the utilization method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and utilization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information from an employee directory or communication tools using the control unit 46A of the smart device 14. The analysis unit, for example, analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies experts. For example, the utilization unit can utilize human resources of group companies based on the experts identified by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and utilization unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information from an employee directory or communication tools using the control unit 46A of the smart glasses 214. The analysis unit, for example, analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies experts. For example, the utilization unit can utilize human resources of group companies based on the experts identified by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and utilization unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect information from an employee directory or communication tools using the control unit 46A of the headset type terminal 314. The analysis unit, for example, analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies experts. The utilization unit can utilize human resources of group companies based on the experts identified by the identification processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and utilization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information from an employee directory or communication tools using the control unit 46A of the robot 414. The analysis unit, for example, analyzes the information collected by the identification processing unit 290 of the data processing device 12 and identifies experts. The utilization unit can utilize human resources of group companies based on the experts identified by the identification processing unit 290 of the data processing device 12, for example.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection department collects each employee's health data, and the analysis department can use that data to evaluate the health status of the specialist. For example, the collection department collects data from employees' fitness trackers and health apps, and the analysis department analyzes that data to evaluate the specialist's health status. The collection department can also collect the results of employees' regular health checkups, and the analysis department can identify health risks based on those results. Furthermore, the collection department can monitor employees' stress levels, and the analysis department can use that data to make stress management suggestions. This allows the health status of specialists to be understood and appropriate support to be provided.

[0094] The analysis department can analyze each employee's learning history and propose optimal learning plans to improve the experts' skills. For example, the analysis department can analyze the history of online courses and training programs that employees have taken and identify skill gaps. The analysis department can also evaluate the employees' learning outcomes and suggest what they should learn next. Furthermore, the analysis department can analyze the employees' learning styles and propose optimal learning methods. This makes it possible to effectively support the improvement of experts' skills.

[0095] The utilization department can estimate the emotions of the identified experts and adjust the project roles based on the estimated emotions. For example, if the utilization department estimates the emotions of the experts, it can assign a less demanding role to the experts. If the experts are highly motivated, it can also provide them with a challenging role. Furthermore, if the experts are tired, it can encourage them to take a rest. This makes it possible to provide the most appropriate role according to the experts' emotions.

[0096] The collection department collects information on each employee's hobbies and interests, and the analysis department can use that data to suggest personalized projects to experts. For example, the collection department collects information on hobbies and interests from employees' internal social media accounts and surveys, and the analysis department analyzes that data to suggest projects that are suitable for experts. The collection department can also collect employees' past project history, and the analysis department can use that data to identify projects that match their interests. Furthermore, the collection department can collect employees' self-introductions and profile information, and the analysis department can use that data to make personalized suggestions. This makes it possible to provide experts with projects that match their interests.

[0097] The analysis unit can estimate the emotions of each employee and adjust the content of the feedback based on the estimated emotions. For example, if an employee is feeling stressed, the analysis unit prioritizes positive feedback. The analysis unit can also provide detailed feedback if the employee is relaxed. Furthermore, the analysis unit can provide concise feedback if the employee is in a hurry. This makes it possible to provide optimal feedback according to the employee's emotions.

[0098] The collection department collects information on each employee's communication style, and the analysis department can suggest the optimal communication method based on that data. For example, the collection department collects employees' email and chat history, and the analysis department analyzes that data to suggest a communication method that suits the employee. The collection department can also collect employees' meeting participation history, and the analysis department can identify effective communication methods based on that data. Furthermore, the collection department collects employees' feedback history, and the analysis department can suggest areas for improving communication based on that data. This allows for effective support of employee communication.

[0099] The utilization department can estimate the emotions of the identified experts and adjust the content of the networking event based on the estimated emotions. For example, if the experts are nervous, the utilization department can provide them with a relaxing activity. If the experts are excited, the utilization department can also provide them with a discussion or brainstorming session. Furthermore, if the experts are tired, the utilization department can adjust the event to provide more breaks. In this way, it is possible to provide an optimal networking event according to the experts' emotions.

[0100] The collection department collects each employee's career goals, and the analysis department can propose career paths based on that data. For example, the collection department collects career goals from employees' self-evaluations and goal-setting sheets, and the analysis department analyzes that data to propose career paths that are suitable for the employee. The collection department also collects employees' past work history, and the analysis department can predict career progress based on that data. Furthermore, the collection department collects employees' skill sets, and the analysis department can propose skill development plans for career advancement based on that data. This makes it possible to provide support that is tailored to employees' career goals.

[0101] The analysis unit can estimate the emotions of each employee and adjust the content of the training program based on the estimated emotions. For example, if an employee is feeling stressed, the analysis unit can provide relaxing training. If an employee is highly motivated, the analysis unit can also provide challenging training. Furthermore, if an employee is tired, the analysis unit can also provide lighter training. In this way, it is possible to provide an optimal training program according to the emotions of the employee.

[0102] The utilization department can estimate the emotion of the identified expert and adjust the project progress method based on the estimated emotion. For example, if the expert is feeling stressed, the utilization department can slow down the progress speed. Also, if the expert is highly motivated, the utilization department can increase the progress speed. Furthermore, if the expert is tired, the utilization department can adjust the expert to take more breaks. In this way, it is possible to provide the optimal project progress method according to the emotion of the expert.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection department collects information from the employee directory. Specifically, they collect information such as each employee's job title, department, and area of ​​expertise. The collection department can also collect information such as each employee's comments and project participation history from communication tools. For example, they collect meeting minutes and chat logs to understand what each employee has said. They can also collect information on each employee's achievements and qualifications from internal communications. For example, they collect internal newsletters and internal reports to understand each employee's area of ​​expertise and skills. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies experts. Specifically, it analyzes the collected information using natural language processing technology. For example, it uses morphological analysis and grammatical analysis to analyze the content of each employee's speech. It can also use data mining technology to analyze each employee's work history and qualifications. For example, it can analyze past project participation history and performance data to identify each employee's area of ​​expertise. It can also identify experts based on information collected using AI. For example, it can use an AI model to evaluate each employee's skill set and years of experience to identify experts. Step 3: The Utilization Department utilizes the human resources of group companies based on the experts identified by the Analysis Department. Specifically, the identified experts can be involved in projects at the group companies to which they belong, thereby helping with service development. For example, the identified experts can be added to project teams to contribute to the development of new products or the improvement of existing services. Networking between experts can also be promoted. For example, regular meetings and online forums can be held to encourage knowledge sharing and idea generation among experts.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information from employee directories; an analysis unit that analyzes the information collected by the collection unit and identifies an expert; and a utilization department that utilizes human resources of group companies based on the experts identified by the analysis department. A system characterized by:

2. The collecting unit Collect information about each employee's job title, department, and area of ​​expertise from the employee directory 2. The system of claim 1.

3. The collecting unit Collect information on each employee's comments and project participation history from communication tools 2. The system of claim 1.

4. The analysis unit Analyze speech content using natural language processing to identify areas of expertise 2. The system of claim 1.

5. The utilization part is By participating in projects of group companies to which the identified experts belong, they will contribute to service development.

2. The system of claim 1.

6. The utilization part is Facilitate professional networking 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze each employee's past work history and select the appropriate method of collecting information 2. The system of claim 1.

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