system

Abstract

The system according to the embodiment aims to collect and analyze employee skill information and quickly identify the most suitable person. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects employee skill information. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies suitable candidates based on the information analyzed by the analysis unit. The provision unit provides the information identified by the identification 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] With conventional technology, the larger an organization becomes, the more difficult it becomes to grasp employee skill information, making it difficult to quickly identify the right person for the job.

[0005] The system according to the embodiment aims to collect and analyze employee skill information and quickly identify the most suitable person. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects employee skill information. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies a suitable candidate based on the information analyzed by the analysis unit. The provision unit provides the information identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze employee skill information and quickly identify the most suitable person. [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 information provision system according to an embodiment of the present invention utilizes a generative AI to enable all employees to access information about "who knows what." This information provision system allows employees to interactively consult with the generative AI, which then searches the company's internal database to find the appropriate person and provide that information to the employee. For example, when an employee asks the generative AI, "Tell me who is knowledgeable about XX," the generative AI analyzes the question, searches the company's internal database to identify an employee who is knowledgeable about XX, and provides information such as, "Mr. / Ms. XX is knowledgeable about XX." This system allows employees to use the generative AI to access information about knowledgeable people about things they do not know within their own knowledge or field of expertise. This makes it easier to understand information about "who knows what," even as an organization grows. For example, if a new employee wants to ask about a specific technology, the generative AI can find an employee who is knowledgeable about that technology. Furthermore, when a project team is working on a new field, the generative AI can be used to find the appropriate person. This promotes knowledge sharing throughout the organization and improves work efficiency. This allows the information provision system to efficiently collect, analyze, identify, and provide employee skill information.

[0029] An information provision system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects employee skill information. The employee skill information includes, but is not limited to, technical skills, soft skills, and qualification information. The collection unit may collect employee skill information using, for example, a questionnaire. Alternatively, the collection unit may collect employee skill information through interviews. Furthermore, the collection unit may extract employee skill information from a database. For example, the collection unit may conduct a monthly questionnaire to regularly update employee skill information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit may analyze employee skill information using data mining technology. Alternatively, the analysis unit may analyze employee skill information using statistical analysis. Furthermore, the analysis unit may analyze employee skill information using a machine learning algorithm. For example, the analysis unit may analyze employee skill information using a decision tree algorithm. The identification unit identifies suitable candidates based on the information analyzed by the analysis unit. For example, the identification unit may identify employees with a specific skill set. The identification unit can also identify employees with a certain number of years of experience or more. Furthermore, the identification unit can also identify employees with knowledge of a specific project. For example, the identification unit identifies suitable candidates using a random forest algorithm. The provision unit provides the employees with the information identified by the identification unit. The provision unit provides the employees with information about suitable candidates, for example, via email notification. The provision unit can also provide the employees with information about suitable candidates through a dashboard display. Furthermore, the provision unit can provide the employees with information about suitable candidates through report generation. For example, the provision unit automatically generates a report including information about suitable candidates and provides it to the employees. This allows the information provision system according to the embodiment to efficiently collect, analyze, identify, and provide employee skill information.

[0030] The collection unit can periodically update employee skill information. Examples of the frequency of periodic updates include, but are not limited to, monthly, weekly, and real-time updates. For example, the collection unit can conduct a monthly survey to update employee skill information. The collection unit can also conduct weekly interviews to update employee skill information. Furthermore, the collection unit can extract and update employee skill information from the database in real time. For example, the collection unit can immediately update the database when an employee acquires a new qualification. This allows employee skill information to be kept up to date.

[0031] The analysis unit may include an algorithm for identifying a suitable candidate based on the collected information. Examples of the algorithm include, but are not limited to, a decision tree, a random forest, and a neural network. For example, the analysis unit may analyze employee skill information using a decision tree algorithm to identify a suitable candidate. The analysis unit may also identify a suitable candidate using a random forest algorithm. Furthermore, the analysis unit may also identify a suitable candidate using a neural network. For example, the analysis unit may identify a suitable candidate using a neural network model that inputs employee skill information and outputs a suitable candidate. This improves the accuracy of identifying a suitable candidate.

[0032] The providing unit can provide information on suitable candidates to employees. Methods of providing the information include, but are not limited to, email notification, dashboard display, and report generation, for example. The providing unit can provide information on suitable candidates to employees, for example, through email notification. The providing unit can also provide information on suitable candidates to employees through dashboard display. Furthermore, the providing unit can provide information on suitable candidates to employees through report generation. For example, the providing unit can automatically generate a report including information on suitable candidates and provide it to employees. This allows employees to quickly obtain information on suitable candidates.

[0033] The information provision system includes a feedback unit that receives feedback from employees. The feedback unit can collect feedback from employees and use it to improve the system. Methods for collecting feedback include, but are not limited to, questionnaires, interviews, online forms, and the like. For example, the feedback unit collects feedback from employees through questionnaires. The feedback unit can also collect feedback from employees through interviews. Furthermore, the feedback unit can collect feedback from employees through online forms. For example, the feedback unit periodically conducts questionnaires to collect feedback from employees. In this way, feedback from employees can be collected and used to improve the system.

[0034] The information providing system includes a verification unit that verifies the accuracy of the information provided. The verification unit can improve the reliability of the information provided. Methods for verifying the accuracy include, but are not limited to, cross-checking data and review by a third party. For example, the verification unit verifies the accuracy of the information provided through cross-checking data. The verification unit can also verify the accuracy of the information provided through review by a third party. Furthermore, the verification unit can verify the accuracy of the information provided through a data consistency check. For example, the verification unit compares multiple data sources and checks the consistency of the information. This can improve the reliability of the information provided.

[0035] The information provision system includes a collection unit that analyzes an employee's past project history and selects an optimal skill information collection method. The collection unit can analyze an employee's past project history and select an optimal skill information collection method. Examples of project history analysis methods include, but are not limited to, project type, duration, and results. The collection unit collects related skill information based on, for example, the types of projects in which the employee has participated in the past. The collection unit can also preferentially collect information about projects that require specific skills from the employee's project history. Furthermore, the collection unit can analyze an employee's project history and identify and collect uncollected skill information. For example, the collection unit collects related skill information based on the results of projects in which the employee has participated in the past. This makes it possible to collect optimal skill information based on the employee's past project history.

[0036] The information provision system includes a collection unit that filters skill information based on the employee's current work situation and areas of interest when collecting the skill information. The collection unit can filter the skill information based on the employee's current work situation and areas of interest when collecting the skill information. Methods for filtering the work situation and areas of interest include, but are not limited to, current projects and technical fields of interest. For example, the collection unit prioritizes collecting skill information related to the work the employee is currently working on. The collection unit can also filter and collect related skill information based on the employee's areas of interest. Furthermore, the collection unit can take the employee's work situation into consideration and collect skill information at a time that does not interfere with the work. For example, the collection unit checks the progress of the project the employee is currently working on and collects skill information at an appropriate time. This makes it possible to collect skill information according to the employee's work situation and areas of interest.

[0037] The information provision system includes a collection unit that, when collecting skill information, prioritizes collecting highly relevant information by taking into account the geographical location information of employees. When collecting skill information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if an employee works in a specific region, the collection unit prioritizes collecting skill information related to that region. Furthermore, if an employee works remotely, the collection unit can prioritize collecting skill information available online. Furthermore, if an employee is on a business trip, the collection unit can collect skill information related to the business trip destination. For example, the collection unit collects region-specific skill information based on the geographical location information of employees. This makes it possible to collect highly relevant skill information based on the geographical location information of employees.

[0038] The information provision system includes a collection unit that analyzes employees' social media activities and collects related information when collecting skill information. The collection unit can analyze employees' social media activities and collect related information when collecting skill information. Methods for analyzing social media activities include, but are not limited to, post content, number of followers, and engagement rate. The collection unit can collect related skill information, for example, based on expertise shared by employees on social media. The collection unit can also identify areas of interest from employees' social media activities and collect skill information in those areas. Furthermore, the collection unit can collect related skill information from experts and groups followed by employees on social media. For example, the collection unit can collect skill information related to the latest technology trends based on employees' social media activities. This makes it possible to collect related skill information based on employees' social media activities.

[0039] The information providing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the skill information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the skill information during analysis. Methods for evaluating the importance of skill information include, but are not limited to, the impact on business operations and the rarity of the skill. For example, the analysis unit performs a detailed analysis of skill information with high importance. The analysis unit can also perform a concise analysis of skill information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance and provide necessary information. For example, the analysis unit performs a detailed analysis of skill information with high impact on business operations and a concise analysis of skill information with low impact on business operations. This makes it possible to adjust the level of detail of the analysis according to the importance of the skill information.

[0040] The information providing system includes an analysis unit that applies different analysis algorithms depending on the category of skill information during analysis. The analysis unit can apply different analysis algorithms depending on the category of skill information during analysis. Definitions of skill information categories include, for example, technical skills, management skills, communication skills, etc., but are not limited to these examples. For example, the analysis unit applies a technical analysis algorithm to technical skill information. The analysis unit can also apply an analysis algorithm specialized for management to management skill information. Furthermore, the analysis unit can also apply a creative analysis algorithm to creative skill information. For example, the analysis unit uses a technical analysis algorithm when analyzing information related to technical skills. This makes it possible to perform optimal analysis depending on the category of skill information.

[0041] The information providing system includes an analysis unit that determines the priority of analysis based on the time of submission of skill information during analysis. The analysis unit can determine the priority of analysis based on the time of submission of skill information during analysis. Methods for evaluating the time of submission include, but are not limited to, for example, the submission date and the frequency of submission. For example, the analysis unit prioritizes analysis of recently submitted skill information. The analysis unit can also postpone analysis of skill information that has been submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of submission. For example, the analysis unit prioritizes analysis of skill information that has been submitted more recently and postpones analysis of skill information that has been submitted earlier. This makes it possible to determine the priority of analysis depending on the time of submission of skill information.

[0042] The information providing system includes an analysis unit that adjusts the order of analysis based on the relevance of skill information during analysis. The analysis unit can adjust the order of analysis based on the relevance of skill information during analysis. Methods for evaluating the relevance of skill information include, but are not limited to, for example, the degree of match with business content and project relevance. For example, the analysis unit prioritizes analysis of highly relevant skill information. The analysis unit can also postpone analysis of less relevant skill information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of skill information. For example, the analysis unit prioritizes analysis of skill information that is highly consistent with business content and postpones analysis of skill information that is less consistent. This makes it possible to adjust the order of analysis according to the relevance of skill information.

[0043] The identification unit can improve the accuracy of identification by taking into account the interrelationships of skill information during identification. The identification unit can improve the accuracy of identification by taking into account the interrelationships of skill information during identification. Methods for evaluating the interrelationships of skill information include, but are not limited to, skill complementarity and dependency, for example. The identification unit can, for example, analyze the interrelationships of skill information and identify employees with related skills. The identification unit can also improve the accuracy of identification by taking into account the interrelationships of skill information. Furthermore, the identification unit can identify the most suitable employee based on the interrelationships of skill information. For example, the identification unit can identify employees with multiple skills based on the complementarity of skills. This can improve the accuracy of identification based on the interrelationships of skill information.

[0044] The identification unit can perform identification by taking into consideration attribute information of the person who submitted the skill information. The identification unit can perform identification by taking into consideration attribute information of the person who submitted the skill information. Methods for collecting attribute information of the person who submitted the skill information include, but are not limited to, age, gender, and job position, for example. The identification unit can perform identification by taking into consideration, for example, the job title and years of experience of the person who submitted the skill information. The identification unit can also perform identification by taking into consideration the field of expertise of the person who submitted the skill information. Furthermore, the identification unit can perform identification by taking into consideration the past project history of the person who submitted the skill information. For example, the identification unit can identify employees who have specific skills based on the job title of the person who submitted the skill information. This allows for optimal identification based on the attribute information of the person who submitted the skill information.

[0045] The identification unit can perform identification by taking into consideration the geographical distribution of the skill information. The identification unit can perform identification by taking into consideration the geographical distribution of the skill information. Methods for evaluating the geographical distribution include, but are not limited to, skill distribution by region and skill distribution by city, for example. The identification unit, for example, analyzes the geographical distribution of the skill information to identify the most suitable employee. The identification unit can also prioritize identifying employees who are geographically close. Furthermore, the identification unit can improve the accuracy of identification by taking into consideration the geographical distribution. For example, the identification unit identifies employees with specific skills based on the skill distribution by region. This allows for optimal identification based on the geographical distribution of the skill information.

[0046] The identification unit can improve the accuracy of the identification by referring to literature related to the skill information during identification. The identification unit can improve the accuracy of the identification by referring to literature related to the skill information during identification. Methods of referring to related literature include, but are not limited to, academic papers, technical reports, industry reports, etc. For example, the identification unit can improve the accuracy of the identification by referring to literature related to the skill information. The identification unit can also identify the most suitable employee based on the related literature. Furthermore, the identification unit can analyze literature related to the skill information and improve the accuracy of the identification. For example, the identification unit can identify an employee with a specific skill based on the related literature. This can improve the accuracy of the identification based on the literature related to the skill information.

[0047] The providing unit can adjust the level of detail of the provided information based on the importance of the skill information when providing the information. The providing unit can adjust the level of detail of the provided information based on the importance of the skill information when providing the information. Methods for evaluating the importance of skill information include, for example, the granularity of the information, the presence or absence of a detailed explanation, and the like, but are not limited to these examples. For example, the providing unit provides detailed information for skill information with high importance. The providing unit can also provide concise information for skill information with low importance. Furthermore, the providing unit can adjust the level of detail of the provided information according to the importance and provide necessary information. For example, the providing unit provides detailed information for skill information with a high impact on business operations, and provides concise information for skill information with a low impact. In this way, the level of detail of the provided information can be adjusted according to the importance of the skill information.

[0048] The providing unit can apply different provision algorithms depending on the category of the skill information when providing the information. The providing unit can apply different provision algorithms depending on the category of the skill information when providing the information. Types of provision algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, the providing unit can apply a technical provision algorithm to technical skill information. Furthermore, the providing unit can apply a provision algorithm specialized for management to management skill information. Furthermore, the providing unit can apply a creative provision algorithm to creative skill information. For example, the providing unit uses a technical provision algorithm when providing information on technical skills. This allows the optimal provision according to the category of skill information.

[0049] The providing unit can determine the priority of provision based on the time of submission of the skill information at the time of provision. The providing unit can determine the priority of provision based on the time of submission of the skill information at the time of provision. Methods for evaluating the time of submission include, but are not limited to, for example, the submission date and the frequency of submission. For example, the providing unit can provide skill information that has been submitted recently with priority. The providing unit can also provide skill information that has been submitted older at a later date. Furthermore, the providing unit can dynamically adjust the priority of provision depending on the time of submission. For example, the providing unit can provide skill information that has been submitted more recently with priority and provide skill information that has been submitted older at a later date. In this way, the priority of provision can be determined depending on the time of submission of the skill information.

[0050] The providing unit can adjust the order of provision based on the relevance of the skill information when providing the skill information. The providing unit can adjust the order of provision based on the relevance of the skill information when providing the skill information. Methods for evaluating the relevance of the skill information include, but are not limited to, for example, the degree of match with the business content, the relevance of the project, etc. For example, the providing unit can provide highly relevant skill information preferentially. The providing unit can also provide less relevant skill information later. Furthermore, the providing unit can dynamically adjust the order of provision according to the relevance of the skill information. For example, the providing unit can provide skill information that has a high degree of match with the business content preferentially, and skill information that has a low degree of match later. This makes it possible to adjust the order of provision according to the relevance of the skill information.

[0051] The feedback unit may select an optimal collection method by referring to past feedback data when collecting feedback. The feedback unit may select an optimal collection method by referring to past feedback data when collecting feedback. Methods of referring to past feedback data include, but are not limited to, database search, history analysis, etc. For example, the feedback unit may analyze past feedback data and select the most effective collection method. The feedback unit may also confirm from past feedback data that a specific collection method is effective and apply that method. Furthermore, the feedback unit may dynamically adjust the collection method based on past feedback data. For example, the feedback unit may select an optimal collection method based on past feedback data. This allows the optimal collection method to be selected based on past feedback data.

[0052] The feedback unit may select an optimal feedback collection method by taking into account the employee's geographic location information when collecting feedback. The feedback unit may select an optimal feedback collection method by taking into account the employee's geographic location information when collecting feedback. Examples of methods for collecting geographic location information include, but are not limited to, GPS data, IP addresses, etc. For example, if an employee works in a specific region, the feedback unit may prioritize collecting feedback related to that region. Furthermore, if an employee works remotely, the feedback unit may select a feedback collection method that is available online. Furthermore, if an employee is on a business trip, the feedback unit may collect feedback related to the business trip destination. For example, the feedback unit may collect region-specific feedback based on the employee's geographic location information. This allows the optimal feedback collection method to be selected based on the employee's geographic location information.

[0053] The verification unit can select the optimal verification method by referring to past verification data during verification. The verification unit can select the optimal verification method by referring to past verification data during verification. Methods for referring to past verification data include, but are not limited to, database search, history analysis, and the like. The verification unit, for example, analyzes past verification data and selects the most effective verification method. The verification unit can also confirm from the past verification data that a specific verification method is effective and apply that method. Furthermore, the verification unit can dynamically adjust the verification method based on the past verification data. For example, the verification unit selects the optimal verification method based on the past verification data. This makes it possible to select the optimal verification method based on the past verification data.

[0054] During verification, the verification unit can weight the verification data based on the time of submission of the skill information. During verification, the verification unit can weight the verification data based on the time of submission of the skill information. Methods for weighting the verification data include, for example, data reliability and time of submission, but are not limited to these examples. For example, the verification unit prioritizes verification of recently submitted skill information. The verification unit can also postpone verification of skill information that has been submitted earlier. Furthermore, the verification unit can dynamically adjust the weighting of the verification data depending on the time of submission. For example, the verification unit prioritizes verification of skill information that has been submitted more recently and postpones verification of skill information that has been submitted earlier. This allows the weighting of the verification data to be performed depending on the time of submission of the skill information.

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

[0056] When collecting employee skill information, the information provision system can combine the employee's self-assessment and the assessments of others to collect the skill information. For example, the collection unit conducts a survey in which employees evaluate themselves and collects the results. The collection unit can also collect assessments from the employee's superiors and colleagues. Furthermore, the collection unit can integrate the self-assessment and the assessments of others to obtain more accurate skill information. This improves the reliability of employee skill information and increases the accuracy of identifying suitable candidates.

[0057] When collecting employee skill information, the information provision system can supplement the skill information by analyzing the employee's past work performance. For example, the collection department collects deliverables from projects that the employee has worked on in the past and analyzes their contents. The collection department can also collect employee work evaluation reports to supplement the skill information. Furthermore, the collection department can update the skill information based on the employee's work performance and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0058] When collecting employee skill information, the information provision system can take the employee's learning history into consideration. For example, the collection unit collects the history of training and seminars attended by the employee and analyzes the content. The collection unit can also collect information on the qualifications and certifications that the employee has obtained. Furthermore, the collection unit can update the skill information based on the employee's learning history and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0059] When collecting employee skill information, the information provision system can take into account the employee's self-learning activities. For example, the collection unit collects records of learning activities independently undertaken by employees and analyzes the content. The collection unit can also collect the history of online courses and webinars in which employees have participated. Furthermore, the collection unit can update the skill information based on the employee's self-learning activities and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0060] When collecting employee skill information, the information provision system can take into account the employee's work performance data. For example, the collection department collects employee work performance data and analyzes the content. The collection department can also collect the employee's work goal achievement rate and KPIs (key performance indicators). Furthermore, the collection department can update the skill information based on the employee's work performance data and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects employee skill information. Employee skill information includes technical skills, soft skills, and qualification information. The collection department collects skill information using methods such as questionnaires, interviews, and extraction from databases. For example, the department conducts a monthly questionnaire to regularly update the skill information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes employee skill information using data mining techniques, statistical analysis, and machine learning algorithms (e.g., decision tree algorithms). Step 3: The identification department identifies suitable candidates based on the information analyzed by the analysis department. The identification department identifies employees with specific skill sets, employees with a certain number of years of experience, and employees with knowledge of specific projects. For example, the department may use a random forest algorithm to identify suitable candidates. Step 4: The provision unit provides the information identified by the identification unit to employees. The provision unit provides information on suitable candidates using methods such as email notification, dashboard display, and report generation. For example, the provision unit automatically generates a report including information on suitable candidates and provides it to employees.

[0063] (Example 2) An information provision system according to an embodiment of the present invention utilizes a generative AI to enable all employees to access information about "who knows what." This information provision system allows employees to interactively consult with the generative AI, which then searches the company's internal database to find the appropriate person and provide that information to the employee. For example, when an employee asks the generative AI, "Tell me who is knowledgeable about XX," the generative AI analyzes the question, searches the company's internal database to identify an employee who is knowledgeable about XX, and provides information such as, "Mr. / Ms. XX is knowledgeable about XX." This system allows employees to use the generative AI to access information about knowledgeable people about things they do not know within their own knowledge or field of expertise. This makes it easier to understand information about "who knows what," even as an organization grows. For example, if a new employee wants to ask about a specific technology, the generative AI can find an employee who is knowledgeable about that technology. Furthermore, when a project team is working on a new field, the generative AI can be used to find the appropriate person. This promotes knowledge sharing throughout the organization and improves work efficiency. This allows the information provision system to efficiently collect, analyze, identify, and provide employee skill information.

[0064] An information provision system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects employee skill information. The employee skill information includes, but is not limited to, technical skills, soft skills, and qualification information. The collection unit may collect employee skill information using, for example, a questionnaire. Alternatively, the collection unit may collect employee skill information through interviews. Furthermore, the collection unit may extract employee skill information from a database. For example, the collection unit may conduct a monthly questionnaire to regularly update employee skill information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit may analyze employee skill information using data mining technology. Alternatively, the analysis unit may analyze employee skill information using statistical analysis. Furthermore, the analysis unit may analyze employee skill information using a machine learning algorithm. For example, the analysis unit may analyze employee skill information using a decision tree algorithm. The identification unit identifies suitable candidates based on the information analyzed by the analysis unit. For example, the identification unit may identify employees with a specific skill set. The identification unit can also identify employees with a certain number of years of experience or more. Furthermore, the identification unit can also identify employees with knowledge of a specific project. For example, the identification unit identifies suitable candidates using a random forest algorithm. The provision unit provides the employees with the information identified by the identification unit. The provision unit provides the employees with information about suitable candidates, for example, via email notification. The provision unit can also provide the employees with information about suitable candidates through a dashboard display. Furthermore, the provision unit can provide the employees with information about suitable candidates through report generation. For example, the provision unit automatically generates a report including information about suitable candidates and provides it to the employees. This allows the information provision system according to the embodiment to efficiently collect, analyze, identify, and provide employee skill information.

[0065] The collection unit can periodically update employee skill information. Examples of the frequency of periodic updates include, but are not limited to, monthly, weekly, and real-time updates. For example, the collection unit can conduct a monthly survey to update employee skill information. The collection unit can also conduct weekly interviews to update employee skill information. Furthermore, the collection unit can extract and update employee skill information from the database in real time. For example, the collection unit can immediately update the database when an employee acquires a new qualification. This allows employee skill information to be kept up to date.

[0066] The analysis unit may include an algorithm for identifying a suitable candidate based on the collected information. Examples of the algorithm include, but are not limited to, a decision tree, a random forest, and a neural network. For example, the analysis unit may analyze employee skill information using a decision tree algorithm to identify a suitable candidate. The analysis unit may also identify a suitable candidate using a random forest algorithm. Furthermore, the analysis unit may also identify a suitable candidate using a neural network. For example, the analysis unit may identify a suitable candidate using a neural network model that inputs employee skill information and outputs a suitable candidate. This improves the accuracy of identifying a suitable candidate.

[0067] The providing unit can provide information on suitable candidates to employees. Methods of providing the information include, but are not limited to, email notification, dashboard display, and report generation, for example. The providing unit can provide information on suitable candidates to employees, for example, through email notification. The providing unit can also provide information on suitable candidates to employees through dashboard display. Furthermore, the providing unit can provide information on suitable candidates to employees through report generation. For example, the providing unit can automatically generate a report including information on suitable candidates and provide it to employees. This allows employees to quickly obtain information on suitable candidates.

[0068] The information provision system includes a feedback unit that receives feedback from employees. The feedback unit can collect feedback from employees and use it to improve the system. Methods for collecting feedback include, but are not limited to, questionnaires, interviews, online forms, and the like. For example, the feedback unit collects feedback from employees through questionnaires. The feedback unit can also collect feedback from employees through interviews. Furthermore, the feedback unit can collect feedback from employees through online forms. For example, the feedback unit periodically conducts questionnaires to collect feedback from employees. In this way, feedback from employees can be collected and used to improve the system.

[0069] The information providing system includes a verification unit that verifies the accuracy of the information provided. The verification unit can improve the reliability of the information provided. Methods for verifying the accuracy include, but are not limited to, cross-checking data and review by a third party. For example, the verification unit verifies the accuracy of the information provided through cross-checking data. The verification unit can also verify the accuracy of the information provided through review by a third party. Furthermore, the verification unit can verify the accuracy of the information provided through a data consistency check. For example, the verification unit compares multiple data sources and checks the consistency of the information. This can improve the reliability of the information provided.

[0070] The information provision system includes a collection unit that estimates an employee's emotion and adjusts the timing of collecting skill information based on the estimated employee emotion. The collection unit can estimate the employee's emotion and adjust the timing of collecting skill information based on the estimated employee emotion. Methods of emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is feeling stressed, the collection unit delays the collection timing to collect skill information when the employee is relaxed. Furthermore, if the employee is relaxed, the collection unit can immediately collect skill information to obtain detailed information. Furthermore, if an employee is busy, the collection unit can adjust the collection timing to collect skill information when work is calm. For example, the collection unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows skill information to be collected at the optimal timing depending on the employee's emotion.

[0071] The information provision system includes a collection unit that analyzes an employee's past project history and selects an optimal skill information collection method. The collection unit can analyze an employee's past project history and select an optimal skill information collection method. Examples of project history analysis methods include, but are not limited to, project type, duration, and results. The collection unit collects related skill information based on, for example, the types of projects in which the employee has participated in the past. The collection unit can also preferentially collect information about projects that require specific skills from the employee's project history. Furthermore, the collection unit can analyze an employee's project history and identify and collect uncollected skill information. For example, the collection unit collects related skill information based on the results of projects in which the employee has participated in the past. This makes it possible to collect optimal skill information based on the employee's past project history.

[0072] The information provision system includes a collection unit that filters skill information based on the employee's current work situation and areas of interest when collecting the skill information. The collection unit can filter the skill information based on the employee's current work situation and areas of interest when collecting the skill information. Methods for filtering the work situation and areas of interest include, but are not limited to, current projects and technical fields of interest. For example, the collection unit prioritizes collecting skill information related to the work the employee is currently working on. The collection unit can also filter and collect related skill information based on the employee's areas of interest. Furthermore, the collection unit can take the employee's work situation into consideration and collect skill information at a time that does not interfere with the work. For example, the collection unit checks the progress of the project the employee is currently working on and collects skill information at an appropriate time. This makes it possible to collect skill information according to the employee's work situation and areas of interest.

[0073] The information provision system includes a collection unit that estimates an employee's emotion and determines the priority of skill information to be collected based on the estimated employee emotion. The collection unit can estimate an employee's emotion and determine the priority of skill information to be collected based on the estimated employee emotion. Methods for emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is feeling stressed, the collection unit can postpone collecting less important skill information. Furthermore, if an employee is relaxed, the collection unit can also prioritize collecting more important skill information. Furthermore, if an employee is busy, the collection unit can prioritize collecting skill information directly related to work. For example, the collection unit can capture an employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows the priority of skill information to be determined according to the employee's emotion.

[0074] The information provision system includes a collection unit that, when collecting skill information, prioritizes collecting highly relevant information by taking into account the geographical location information of employees. When collecting skill information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees. Methods for collecting geographical location information include, but are not limited to, GPS data, IP addresses, etc. For example, if an employee works in a specific region, the collection unit prioritizes collecting skill information related to that region. Furthermore, if an employee works remotely, the collection unit can prioritize collecting skill information available online. Furthermore, if an employee is on a business trip, the collection unit can collect skill information related to the business trip destination. For example, the collection unit collects region-specific skill information based on the geographical location information of employees. This makes it possible to collect highly relevant skill information based on the geographical location information of employees.

[0075] The information provision system includes a collection unit that analyzes employees' social media activities and collects related information when collecting skill information. The collection unit can analyze employees' social media activities and collect related information when collecting skill information. Methods for analyzing social media activities include, but are not limited to, post content, number of followers, and engagement rate. The collection unit can collect related skill information, for example, based on expertise shared by employees on social media. The collection unit can also identify areas of interest from employees' social media activities and collect skill information in those areas. Furthermore, the collection unit can collect related skill information from experts and groups followed by employees on social media. For example, the collection unit can collect skill information related to the latest technology trends based on employees' social media activities. This makes it possible to collect related skill information based on employees' social media activities.

[0076] The information provision system includes an analysis unit that estimates an employee's emotions and adjusts the presentation method of the analysis based on the estimated employee emotions. The analysis unit can estimate an employee's emotions and adjust the presentation method of the analysis based on the estimated employee emotions. Methods of emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is nervous, the analysis unit provides a simple, highly visible analysis result. If the employee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the employee is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. For example, the analysis unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows the analysis result to be presented in an optimal presentation method depending on the employee's emotions.

[0077] The information providing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the skill information during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the skill information during analysis. Methods for evaluating the importance of skill information include, but are not limited to, the impact on business operations and the rarity of the skill. For example, the analysis unit performs a detailed analysis of skill information with high importance. The analysis unit can also perform a concise analysis of skill information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance and provide necessary information. For example, the analysis unit performs a detailed analysis of skill information with high impact on business operations and a concise analysis of skill information with low impact on business operations. This makes it possible to adjust the level of detail of the analysis according to the importance of the skill information.

[0078] The information providing system includes an analysis unit that applies different analysis algorithms depending on the category of skill information during analysis. The analysis unit can apply different analysis algorithms depending on the category of skill information during analysis. Definitions of skill information categories include, for example, technical skills, management skills, communication skills, etc., but are not limited to these examples. For example, the analysis unit applies a technical analysis algorithm to technical skill information. The analysis unit can also apply an analysis algorithm specialized for management to management skill information. Furthermore, the analysis unit can also apply a creative analysis algorithm to creative skill information. For example, the analysis unit uses a technical analysis algorithm when analyzing information related to technical skills. This makes it possible to perform optimal analysis depending on the category of skill information.

[0079] The information provision system includes an analysis unit that estimates an employee's emotion and adjusts the length of the analysis based on the estimated employee emotion. The analysis unit can estimate an employee's emotion and adjust the length of the analysis based on the estimated employee emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is in a hurry, the analysis unit can provide a short and concise analysis result. If the employee is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the employee is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit can capture the employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows the analysis result to be provided at an optimal length depending on the employee's emotion.

[0080] The information providing system includes an analysis unit that determines the priority of analysis based on the time of submission of skill information during analysis. The analysis unit can determine the priority of analysis based on the time of submission of skill information during analysis. Methods for evaluating the time of submission include, but are not limited to, for example, the submission date and the frequency of submission. For example, the analysis unit prioritizes analysis of recently submitted skill information. The analysis unit can also postpone analysis of skill information that has been submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the time of submission. For example, the analysis unit prioritizes analysis of skill information that has been submitted more recently and postpones analysis of skill information that has been submitted earlier. This makes it possible to determine the priority of analysis depending on the time of submission of skill information.

[0081] The information providing system includes an analysis unit that adjusts the order of analysis based on the relevance of skill information during analysis. The analysis unit can adjust the order of analysis based on the relevance of skill information during analysis. Methods for evaluating the relevance of skill information include, but are not limited to, for example, the degree of match with business content and project relevance. For example, the analysis unit prioritizes analysis of highly relevant skill information. The analysis unit can also postpone analysis of less relevant skill information. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of skill information. For example, the analysis unit prioritizes analysis of skill information that is highly consistent with business content and postpones analysis of skill information that is less consistent. This makes it possible to adjust the order of analysis according to the relevance of skill information.

[0082] The information provision system includes an identification unit that estimates an employee's emotion and adjusts the identification criteria based on the estimated employee's emotion. The identification unit can estimate the employee's emotion and adjust the identification criteria based on the estimated employee's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the employee is nervous, the identification unit provides simple, highly visible identification criteria. If the employee is relaxed, the identification unit can also provide detailed identification criteria. Furthermore, if the employee is in a hurry, the identification unit can provide concise identification criteria that focus on the main points. For example, the identification unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows identification to be performed using optimal criteria depending on the employee's emotion.

[0083] The identification unit can improve the accuracy of identification by taking into account the interrelationships of skill information during identification. The identification unit can improve the accuracy of identification by taking into account the interrelationships of skill information during identification. Methods for evaluating the interrelationships of skill information include, but are not limited to, skill complementarity and dependency, for example. The identification unit can, for example, analyze the interrelationships of skill information and identify employees with related skills. The identification unit can also improve the accuracy of identification by taking into account the interrelationships of skill information. Furthermore, the identification unit can identify the most suitable employee based on the interrelationships of skill information. For example, the identification unit can identify employees with multiple skills based on the complementarity of skills. This can improve the accuracy of identification based on the interrelationships of skill information.

[0084] The identification unit can perform identification by taking into consideration attribute information of the person who submitted the skill information. The identification unit can perform identification by taking into consideration attribute information of the person who submitted the skill information. Methods for collecting attribute information of the person who submitted the skill information include, but are not limited to, age, gender, and job position, for example. The identification unit can perform identification by taking into consideration, for example, the job title and years of experience of the person who submitted the skill information. The identification unit can also perform identification by taking into consideration the field of expertise of the person who submitted the skill information. Furthermore, the identification unit can perform identification by taking into consideration the past project history of the person who submitted the skill information. For example, the identification unit can identify employees who have specific skills based on the job title of the person who submitted the skill information. This allows for optimal identification based on the attribute information of the person who submitted the skill information.

[0085] The identification unit can estimate the employee's emotions and adjust the order in which specific results are displayed based on the estimated employee's emotions. The identification unit can estimate the employee's emotions and adjust the order in which specific results are displayed based on the estimated employee's emotions. Methods for emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the employee is nervous, the identification unit can display the specific results in a simple, highly visible order. Alternatively, if the employee is relaxed, the identification unit can display the specific results in a detailed order. Furthermore, if the employee is in a hurry, the identification unit can display the specific results in an order that emphasizes the main points. For example, the identification unit can capture the employee's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. This allows the specific results to be displayed in an optimal order depending on the employee's emotions.

[0086] The identification unit can perform identification by taking into consideration the geographical distribution of the skill information. The identification unit can perform identification by taking into consideration the geographical distribution of the skill information. Methods for evaluating the geographical distribution include, but are not limited to, skill distribution by region and skill distribution by city, for example. The identification unit, for example, analyzes the geographical distribution of the skill information to identify the most suitable employee. The identification unit can also prioritize identifying employees who are geographically close. Furthermore, the identification unit can improve the accuracy of identification by taking into consideration the geographical distribution. For example, the identification unit identifies employees with specific skills based on the skill distribution by region. This allows for optimal identification based on the geographical distribution of the skill information.

[0087] The identification unit can improve the accuracy of the identification by referring to literature related to the skill information during identification. The identification unit can improve the accuracy of the identification by referring to literature related to the skill information during identification. Methods of referring to related literature include, but are not limited to, academic papers, technical reports, industry reports, etc. For example, the identification unit can improve the accuracy of the identification by referring to literature related to the skill information. The identification unit can also identify the most suitable employee based on the related literature. Furthermore, the identification unit can analyze literature related to the skill information and improve the accuracy of the identification. For example, the identification unit can identify an employee with a specific skill based on the related literature. This can improve the accuracy of the identification based on the literature related to the skill information.

[0088] The information provision system includes a provision unit that estimates an employee's emotion and adjusts the presentation method of the information to be provided based on the estimated employee's emotion. The provision unit can estimate the employee's emotion and adjust the presentation method of the information to be provided based on the estimated employee's emotion. Methods for emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the employee is nervous, the provision unit can provide a simple, highly visible presentation method. Furthermore, if the employee is relaxed, the provision unit can provide a presentation method that includes detailed information. Furthermore, if the employee is in a hurry, the provision unit can provide a presentation method that focuses on the main points. For example, the provision unit can capture the employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide information in the optimal presentation method depending on the employee's emotion.

[0089] The providing unit can adjust the level of detail of the provided information based on the importance of the skill information when providing the information. The providing unit can adjust the level of detail of the provided information based on the importance of the skill information when providing the information. Methods for evaluating the importance of skill information include, for example, the granularity of the information, the presence or absence of a detailed explanation, and the like, but are not limited to these examples. For example, the providing unit provides detailed information for skill information with high importance. The providing unit can also provide concise information for skill information with low importance. Furthermore, the providing unit can adjust the level of detail of the provided information according to the importance and provide necessary information. For example, the providing unit provides detailed information for skill information with a high impact on business operations, and provides concise information for skill information with a low impact. In this way, the level of detail of the provided information can be adjusted according to the importance of the skill information.

[0090] The providing unit can apply different provision algorithms depending on the category of the skill information when providing the information. The providing unit can apply different provision algorithms depending on the category of the skill information when providing the information. Types of provision algorithms include, but are not limited to, recommendation systems and filtering algorithms. For example, the providing unit can apply a technical provision algorithm to technical skill information. Furthermore, the providing unit can apply a provision algorithm specialized for management to management skill information. Furthermore, the providing unit can apply a creative provision algorithm to creative skill information. For example, the providing unit uses a technical provision algorithm when providing information on technical skills. This allows the optimal provision according to the category of skill information.

[0091] The information provision system includes a provision unit that estimates an employee's emotion and adjusts the length of information to be provided based on the estimated employee emotion. The provision unit can estimate the employee's emotion and adjust the length of information to be provided based on the estimated employee emotion. Methods for emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is in a hurry, the provision unit can provide short, concise information. If the employee is relaxed, the provision unit can also provide detailed information. Furthermore, if the employee is excited, the provision unit can provide information with visually stimulating effects. For example, the provision unit can capture the employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows information to be provided at an optimal length depending on the employee's emotion.

[0092] The providing unit can determine the priority of provision based on the time of submission of the skill information at the time of provision. The providing unit can determine the priority of provision based on the time of submission of the skill information at the time of provision. Methods for evaluating the time of submission include, but are not limited to, for example, the submission date and the frequency of submission. For example, the providing unit can provide skill information that has been submitted recently with priority. The providing unit can also provide skill information that has been submitted older at a later date. Furthermore, the providing unit can dynamically adjust the priority of provision depending on the time of submission. For example, the providing unit can provide skill information that has been submitted more recently with priority and provide skill information that has been submitted older at a later date. In this way, the priority of provision can be determined depending on the time of submission of the skill information.

[0093] The providing unit can adjust the order of provision based on the relevance of the skill information when providing the skill information. The providing unit can adjust the order of provision based on the relevance of the skill information when providing the skill information. Methods for evaluating the relevance of the skill information include, but are not limited to, for example, the degree of match with the business content, the relevance of the project, etc. For example, the providing unit can provide highly relevant skill information preferentially. The providing unit can also provide less relevant skill information later. Furthermore, the providing unit can dynamically adjust the order of provision according to the relevance of the skill information. For example, the providing unit can provide skill information that has a high degree of match with the business content preferentially, and skill information that has a low degree of match later. This makes it possible to adjust the order of provision according to the relevance of the skill information.

[0094] The information provision system includes a feedback unit that estimates an employee's emotion and adjusts a feedback collection method based on the estimated employee's emotion. The feedback unit can estimate an employee's emotion and adjust the feedback collection method based on the estimated employee's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is nervous, the feedback unit can provide a simple, highly visible feedback collection method. If the employee is relaxed, the feedback unit can also provide a detailed feedback collection method. Furthermore, if the employee is in a hurry, the feedback unit can provide a concise feedback collection method that focuses on the main points. For example, the feedback unit can capture an employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This makes it possible to collect feedback in an optimal manner depending on the employee's emotion.

[0095] The feedback unit may select an optimal collection method by referring to past feedback data when collecting feedback. The feedback unit may select an optimal collection method by referring to past feedback data when collecting feedback. Methods of referring to past feedback data include, but are not limited to, database search, history analysis, etc. For example, the feedback unit may analyze past feedback data and select the most effective collection method. The feedback unit may also confirm from past feedback data that a specific collection method is effective and apply that method. Furthermore, the feedback unit may dynamically adjust the collection method based on past feedback data. For example, the feedback unit may select an optimal collection method based on past feedback data. This allows the optimal collection method to be selected based on past feedback data.

[0096] The information provision system includes a feedback unit that estimates an employee's emotions and determines the priority of feedback based on the estimated employee emotions. The feedback unit can estimate an employee's emotions and determine the priority of feedback based on the estimated employee emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is nervous, the feedback unit can postpone feedback with low importance. Furthermore, if an employee is relaxed, the feedback unit can also prioritize collecting feedback with high importance. Furthermore, if an employee is busy, the feedback unit can prioritize collecting feedback directly related to work. For example, the feedback unit can capture an employee's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This makes it possible to determine the priority of feedback based on the employee's emotions.

[0097] The feedback unit may select an optimal feedback collection method by taking into account the employee's geographic location information when collecting feedback. The feedback unit may select an optimal feedback collection method by taking into account the employee's geographic location information when collecting feedback. Examples of methods for collecting geographic location information include, but are not limited to, GPS data, IP addresses, etc. For example, if an employee works in a specific region, the feedback unit may prioritize collecting feedback related to that region. Furthermore, if an employee works remotely, the feedback unit may select a feedback collection method that is available online. Furthermore, if an employee is on a business trip, the feedback unit may collect feedback related to the business trip destination. For example, the feedback unit may collect region-specific feedback based on the employee's geographic location information. This allows the optimal feedback collection method to be selected based on the employee's geographic location information.

[0098] The information provision system includes a verification unit that estimates an employee's emotion and adjusts a verification method based on the estimated employee's emotion. The verification unit can estimate an employee's emotion and adjust the verification method based on the estimated employee's emotion. Emotion estimation methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is nervous, the verification unit provides a simple, highly visible verification method. If the employee is relaxed, the verification unit can also provide a detailed verification method. Furthermore, if the employee is in a hurry, the verification unit can provide a concise verification method that focuses on the main points. For example, the verification unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows verification to be performed in the optimal method depending on the employee's emotion.

[0099] The verification unit can select the optimal verification method by referring to past verification data during verification. The verification unit can select the optimal verification method by referring to past verification data during verification. Methods for referring to past verification data include, but are not limited to, database search, history analysis, and the like. The verification unit, for example, analyzes past verification data and selects the most effective verification method. The verification unit can also confirm from the past verification data that a specific verification method is effective and apply that method. Furthermore, the verification unit can dynamically adjust the verification method based on the past verification data. For example, the verification unit selects the optimal verification method based on the past verification data. This makes it possible to select the optimal verification method based on the past verification data.

[0100] The information provision system includes a verification unit that estimates an employee's emotions and determines the priority of verification based on the estimated employee emotions. The verification unit can estimate the employee's emotions and determine the priority of verification based on the estimated employee emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if an employee is nervous, the verification unit postpones verification of lower importance. Furthermore, if an employee is relaxed, the verification unit can prioritize verification of higher importance. Furthermore, if an employee is busy, the verification unit can prioritize verification directly related to work. For example, the verification unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This makes it possible to determine the priority of verification based on the employee's emotions.

[0101] During verification, the verification unit can weight the verification data based on the time of submission of the skill information. During verification, the verification unit can weight the verification data based on the time of submission of the skill information. Methods for weighting the verification data include, for example, data reliability and time of submission, but are not limited to these examples. For example, the verification unit prioritizes verification of recently submitted skill information. The verification unit can also postpone verification of skill information that has been submitted earlier. Furthermore, the verification unit can dynamically adjust the weighting of the verification data depending on the time of submission. For example, the verification unit prioritizes verification of skill information that has been submitted more recently and postpones verification of skill information that has been submitted earlier. This allows the weighting of the verification data to be performed depending on the time of submission of the skill information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and provision 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 is realized by the control unit 46A of the smart device 14 and collects employee skill information through questionnaires and interviews. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the employee skill information using data mining technology or machine learning algorithms. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies suitable candidates based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides employees with information about suitable candidates through email notifications or dashboard displays. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and provision 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 is realized by the control unit 46A of the smart glasses 214 and collects employee skill information through questionnaires and interviews. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the employee skill information using data mining technology or machine learning algorithms. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies suitable candidates based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides suitable candidate information to employees through email notifications or dashboard displays. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and provision 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 is realized by the control unit 46A of the headset type terminal 314 and collects employee skill information through questionnaires and interviews. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the employee skill information using data mining technology or machine learning algorithms. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies suitable candidates based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides employees with information about suitable candidates through email notifications or dashboard displays. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and provision 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 is realized by the control unit 46A of the robot 414 and collects employee skill information through questionnaires and interviews. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the employee skill information using data mining technology or machine learning algorithms. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies suitable candidates based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides employees with information about suitable candidates through email notifications or dashboard displays.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When collecting employee skill information, the information provision system can combine the employee's self-assessment and the assessments of others to collect the skill information. For example, the collection unit conducts a survey in which employees evaluate themselves and collects the results. The collection unit can also collect assessments from the employee's superiors and colleagues. Furthermore, the collection unit can integrate the self-assessment and the assessments of others to obtain more accurate skill information. This improves the reliability of employee skill information and increases the accuracy of identifying suitable candidates.

[0104] When collecting employee skill information, the information provision system can supplement the skill information by analyzing the employee's past work performance. For example, the collection department collects deliverables from projects that the employee has worked on in the past and analyzes their contents. The collection department can also collect employee work evaluation reports to supplement the skill information. Furthermore, the collection department can update the skill information based on the employee's work performance and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0105] When collecting employee skill information, the information provision system can take the employee's learning history into consideration. For example, the collection unit collects the history of training and seminars attended by the employee and analyzes the content. The collection unit can also collect information on the qualifications and certifications that the employee has obtained. Furthermore, the collection unit can update the skill information based on the employee's learning history and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0106] When collecting employee skill information, the information provision system can take into account the employee's self-learning activities. For example, the collection unit collects records of learning activities independently undertaken by employees and analyzes the content. The collection unit can also collect the history of online courses and webinars in which employees have participated. Furthermore, the collection unit can update the skill information based on the employee's self-learning activities and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0107] When collecting employee skill information, the information provision system can take into account the employee's work performance data. For example, the collection department collects employee work performance data and analyzes the content. The collection department can also collect the employee's work goal achievement rate and KPIs (key performance indicators). Furthermore, the collection department can update the skill information based on the employee's work performance data and keep it up to date. This improves the accuracy of employee skill information and makes it possible to more accurately identify suitable candidates.

[0108] The information provision system can estimate the employee's emotions and adjust the method of collecting skill information based on the estimated employee's emotions. For example, if the employee is feeling stressed, the collection department can collect skill information in an interview format rather than a questionnaire format. Also, if the employee is relaxed, the collection department can collect skill information by conducting a detailed questionnaire. Furthermore, if the employee is busy, the collection department can collect skill information in a simple question format. This makes it possible to collect skill information in the optimal way depending on the employee's emotions.

[0109] The information provision system can estimate the employee's emotions and adjust the frequency of collecting skill information based on the estimated employee's emotions. For example, the collection unit can reduce the frequency of collecting skill information when the employee is feeling stressed. The collection unit can also increase the frequency of collecting skill information when the employee is relaxed. Furthermore, the collection unit can adjust the frequency of collecting skill information when the employee is busy so as not to disrupt work. This makes it possible to collect skill information at an optimal frequency depending on the employee's emotions.

[0110] The information provision system can estimate the employee's emotions and adjust the content of skill information collection based on the estimated employee's emotions. For example, if the employee is feeling stressed, the collection unit can collect only basic skill information. Alternatively, if the employee is relaxed, the collection unit can collect detailed skill information. Furthermore, if the employee is busy, the collection unit can prioritize collecting skill information directly related to work. This makes it possible to collect skill information with optimal content according to the employee's emotions.

[0111] The information provision system can estimate an employee's emotions and adjust the means for collecting skill information based on the estimated employee emotions. For example, if an employee is feeling stressed, the collection department can conduct a face-to-face interview instead of an online questionnaire. Alternatively, if an employee is relaxed, the collection department can collect skill information by conducting an online questionnaire. Furthermore, if an employee is busy, the collection department can use a simple online form to collect skill information. This allows skill information to be collected by the most appropriate means depending on the employee's emotions.

[0112] The information provision system can estimate the emotions of employees and adjust the timing of collecting skill information based on the estimated emotions of employees. For example, the collection unit delays the timing of collection if an employee is feeling stressed. The collection unit can also collect skill information immediately if an employee is relaxed. Furthermore, if an employee is busy, the collection unit can collect skill information when work is calm. This makes it possible to collect skill information at the optimal timing depending on the emotions of employees.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection department collects employee skill information. Employee skill information includes technical skills, soft skills, and qualification information. The collection department collects skill information using methods such as questionnaires, interviews, and extraction from databases. For example, the department conducts a monthly questionnaire to regularly update the skill information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes employee skill information using data mining techniques, statistical analysis, and machine learning algorithms (e.g., decision tree algorithms). Step 3: The identification department identifies suitable candidates based on the information analyzed by the analysis department. The identification department identifies employees with specific skill sets, employees with a certain number of years of experience, and employees with knowledge of specific projects. For example, the department may use a random forest algorithm to identify suitable candidates. Step 4: The provision unit provides the information identified by the identification unit to employees. The provision unit provides information on suitable candidates using methods such as email notification, dashboard display, and report generation. For example, the provision unit automatically generates a report including information on suitable candidates and provides it to employees.

[0115] 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.

[0116] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

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

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 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.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0145] In the 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.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

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

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The 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.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 employee skill information; an analysis unit that analyzes the information collected by the collection unit; an identification unit that identifies a suitable person based on the information analyzed by the analysis unit; a providing unit that provides the information identified by the identifying unit. A system characterized by:

2. The collecting unit Regularly update employee skills information 2. The system of claim 1.

3. The analysis unit Includes algorithms that identify suitable candidates based on collected information 2. The system of claim 1.

4. The providing unit Providing employees with information on suitable candidates 2. The system of claim 1.

5. Have a feedback department that accepts feedback from employees 2. The system of claim 1.

6. Equipped with a verification department that verifies the accuracy of the information provided 2. The system of claim 1.

7. The collecting unit Estimate employee emotions and adjust the timing of collecting skill information based on the estimated employee emotions 2. The system of claim 1.

8. The collecting unit Analyze employees' past project history and select the best method for collecting skill information 2. The system of claim 1.

9. The collecting unit When collecting skills information, filter it based on the employee's current work situation and areas of interest.

2. The system of claim 1.

10. The collecting unit Estimate employee sentiment and prioritize skill information to be collected based on the estimated employee sentiment 2. The system of claim 1.

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