system

Abstract

The system according to this embodiment aims to quickly and efficiently find personnel who meet the desired conditions. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The reception unit receives input from the user regarding desired conditions. The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the conditions. The evaluation unit evaluates the degree of matching of the personnel identified by the analysis unit. The proposal unit proposes personnel evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly and efficiently find human resources that meet the desired conditions.

[0005] The system according to the embodiment aims to quickly and efficiently find human resources that meet the desired conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The reception unit receives input from the user regarding desired conditions. The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the conditions. The evaluation unit evaluates the degree of matching of the personnel identified by the analysis unit. The proposal unit proposes personnel evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and efficiently find personnel who meet the desired conditions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 1 has a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​scouting service according to an embodiment of the present invention is a system that quickly finds and proposes personnel who match the desired conditions of companies and individuals. In this system, the user inputs the desired conditions, and the AI ​​searches for and proposes personnel who match the conditions from all kinds of media such as blogs, social media, and videos. For example, the user may state desired conditions such as "I want someone with these skills" or "Are there any people with this kind of experience?" Next, the AI ​​searches for personnel who match the conditions from all kinds of media such as blogs, social media, and videos. For example, it analyzes blog posts, social media posts, and video content to find personnel with specific skills and experience. Finally, the AI ​​proposes personnel with a high degree of match. For example, by providing the profile and contact information of personnel who match the conditions, the user can quickly find personnel. This mechanism allows companies and individuals to quickly find personnel who match their desired conditions. For example, a company looking for personnel with specific skills can use the AI ​​scouting service to find suitable personnel in a short amount of time. Similarly, an individual looking for personnel with specific experience can also find personnel quickly. This improves the efficiency of personnel searching and makes it possible to quickly secure suitable personnel. This allows AI scouting services to quickly find and propose talent that matches the desired conditions of companies and individuals.

[0029] The AI ​​scouting service according to this embodiment comprises a reception unit, a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The reception unit receives input from the user regarding desired conditions. These conditions include, but are not limited to, skills, experience, and qualifications. For example, the user can input conditions such as "personnel with programming skills" or "personnel with project management experience" into the reception unit. The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. For example, the collection unit collects information such as blog posts, social media posts, and video content. The collection unit can collect information by methods such as web scraping and using APIs. The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the conditions. For example, the analysis unit analyzes the information using text mining or natural language processing techniques. The analysis unit analyzes the collected information in detail to identify personnel with specific skills and experience. The evaluation unit evaluates the degree of matching of the personnel identified by the analysis unit. For example, the evaluation unit evaluates the degree of matching using a scoring system or a matching algorithm. The evaluation unit calculates a score based on the analyzed information to assess the degree of matching with the conditions. The proposal unit proposes personnel evaluated by the evaluation unit. The proposal unit provides, for example, the profile and contact information of personnel who meet the conditions. The proposal unit lists and proposes personnel who best match the conditions to the user. As a result, the AI ​​scouting service according to this embodiment can quickly find and propose personnel who meet the conditions desired by the user.

[0030] The reception desk allows users to input their desired criteria. These criteria may include, but are not limited to, skills, experience, and qualifications. For example, users can input criteria such as "personnel with programming skills" or "personnel with project management experience." Specifically, the reception desk provides an intuitive interface to facilitate criteria input. For example, dropdown menus, checkboxes, and text input fields allow users to specify their desired criteria in detail. The reception desk also has a function to remember and reuse criteria previously entered by the user. This saves users the trouble of re-entering criteria they have already entered. Furthermore, the reception desk provides real-time feedback on the criteria entered by the user, allowing them to check the appropriateness and specificity of the criteria. For example, if the criteria are ambiguous or there are input errors, it displays a message prompting appropriate corrections. In this way, the reception desk supports users in accurately and efficiently entering their desired criteria.

[0031] The data collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. For example, the collection unit collects information such as blog posts, social media posts, and video content. Specifically, the collection unit automatically collects publicly available information from the internet using web scraping technology. Web scraping is a technique for extracting necessary information from specific websites, and the collection unit uses this to collect blog posts and social media posts. Furthermore, by utilizing APIs, it can also directly obtain data from social media platforms and video sharing sites. The collection unit combines these methods to collect a wide range of information from diverse media. In addition, the collection unit centrally manages the collected information and stores it in a database for efficient access by the analysis unit. This allows the collection unit to quickly and accurately collect information that matches the user's desired conditions, improving the overall system performance.

[0032] The analysis unit analyzes the information collected by the collection unit to identify personnel who meet the specified criteria. The analysis unit analyzes the information using techniques such as text mining and natural language processing. Specifically, it uses text mining techniques to extract useful information from the collected text data. For example, it extracts descriptions of specific skills and experiences from blog posts and social media posts to identify personnel who meet the criteria. It also uses natural language processing techniques to analyze the meaning of the text data and extract information with higher accuracy. For example, it uses named entity recognition (NER) to identify names, skills, qualifications, etc., in the text. Furthermore, the analysis unit uses machine learning algorithms to classify the collected information and identify personnel who meet the criteria. For example, it uses algorithms such as support vector machines (SVM) and random forests to classify the collected information based on skills and experience and identify personnel who best meet the criteria. As a result, the analysis unit can analyze the collected information in detail and quickly and accurately identify personnel who meet the user's desired criteria.

[0033] The evaluation unit assesses the degree of match of the personnel identified by the analysis unit. The evaluation unit evaluates the degree of match using, for example, a scoring system or a matching algorithm. Specifically, it uses a scoring system to quantify the degree of match for specific skills and experience. For example, it evaluates the degree of match for programming skills on a score from 0 to 100, visually showing the degree of match to the conditions desired by the user. It also uses a matching algorithm to comprehensively evaluate the degree of match for multiple conditions. For example, by weighting each condition and calculating an overall score, it identifies the personnel who best match the conditions. Furthermore, the evaluation unit continuously improves evaluation accuracy by utilizing past evaluation data and feedback. For example, it learns the characteristics of personnel who have received high evaluations in the past and reflects them in future evaluations. As a result, the evaluation unit can accurately and efficiently evaluate the degree of match to the conditions desired by the user and identify the optimal personnel.

[0034] The Proposal Department proposes candidates who have been evaluated by the Evaluation Department. For example, the Proposal Department provides profiles and contact information of candidates who meet the criteria. Specifically, it provides users with profile information of candidates who have received high ratings from the Evaluation Department, allowing them to check detailed skills, experience, qualifications, etc. The Proposal Department also provides contact information for users to contact candidates directly. For example, it provides email addresses, phone numbers, and social media account information so that users can quickly make contact. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of the proposals. For example, it collects user evaluations and comments on proposed candidates and incorporates them into future proposals. This allows the Proposal Department to quickly and accurately propose candidates who best match the user's desired conditions and meet the user's needs.

[0035] The collection unit collects information such as blog posts, social media posts, and video content. For example, the collection unit can collect blog posts using specific platforms or keywords. The collection unit can collect social media posts using specific hashtags or user accounts. The collection unit can collect video content using specific channels or keywords. This allows the collection unit to collect information from diverse media, making it easier to find suitable candidates. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to perform web scraping in order to collect blog posts containing specific keywords.

[0036] The analysis unit can analyze the collected information and identify individuals with specific skills and experience. For example, the analysis unit can analyze the collected information using text mining techniques. The analysis unit can also analyze the information using natural language processing techniques to identify individuals with specific skills and experience. Furthermore, the analysis unit can analyze the information using machine learning algorithms to identify individuals who meet specific criteria. Thus, by analyzing the collected information, the analysis unit can identify individuals with specific skills and experience. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected text data into an AI and have the AI ​​perform the analysis to identify individuals with specific skills and experience.

[0037] The evaluation unit can assess the degree of matching to the conditions. The evaluation unit can assess the degree of matching using, for example, a scoring system. The evaluation unit can also assess the degree of matching using a matching algorithm. The evaluation unit can also calculate a score based on the analyzed information in order to assess the degree of matching to the conditions. In this way, the evaluation unit can find the most suitable personnel by assessing the degree of matching to the conditions. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the analyzed information into AI and have the AI ​​perform scoring to assess the degree of matching.

[0038] The proposal department can provide profiles and contact information of candidates who meet the criteria. For example, the proposal department can provide resumes and portfolios of candidates who meet the criteria. The proposal department can also provide email addresses and phone numbers of candidates who meet the criteria. By providing detailed information about candidates who meet the criteria, the proposal department can enable users to quickly find candidates. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information about candidates who meet the criteria into AI and have the AI ​​perform the processing to generate proposals.

[0039] The reception desk can analyze the user's past input history of desired conditions and suggest the optimal input method. For example, the reception desk can automatically display desired conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​perform analysis to suggest the optimal input method.

[0040] The reception desk can customize input fields based on the user's current work situation and areas of interest when they enter their desired conditions. For example, the reception desk can prioritize inputting skills and experience related to the user's current project. The reception desk can also automatically display relevant skills and experience in the input fields based on the user's areas of interest. The reception desk can also reflect necessary skills and experience in the input fields according to the user's work situation. This allows the reception desk to input more appropriate conditions by customizing the input fields according to the user's work situation and areas of interest. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work situation data into AI and have the AI ​​perform the customization of the input fields.

[0041] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user is looking for personnel in a specific region, the reception desk can prioritize conditions related to that region. The reception desk can also prioritize conditions related to nearby personnel based on the user's current location. If a user is looking for personnel in a specific city, the reception desk can prioritize conditions related to that city. In this way, the reception desk can prioritize the input of more relevant conditions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​perform the processing to prioritize the input of highly relevant conditions.

[0042] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk may suggest skills and experiences that the user frequently mentions on social media in the input fields. The reception desk can also suggest conditions related to areas of interest based on the user's social media activity. The reception desk can also suggest conditions based on the skills and experience of experts that the user follows on social media. In this way, the reception desk can suggest relevant conditions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform analysis to suggest relevant conditions.

[0043] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting information. For example, the data collection unit can analyze patterns in information previously collected by the user and select the optimal data collection method. The data collection unit can also prioritize the collection of specific media based on the user's past data collection history. The data collection unit can also suggest efficient data collection methods based on the user's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​perform analysis to select the optimal data collection method.

[0044] The data collection unit can filter the data to be collected based on the user's current work situation and areas of interest. For example, the data collection unit may prioritize collecting information related to a project the user is currently working on. The data collection unit can also filter and collect relevant information based on the user's areas of interest. The data collection unit can also filter and collect necessary information according to the user's work situation. This allows the data collection unit to collect more appropriate information by filtering the data to be collected according to the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work situation data into AI and have the AI ​​perform the filtering of the data to be collected.

[0045] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user is looking for talent in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also prioritize collecting information related to nearby talent based on the user's current location. If the user is looking for talent in a specific city, the data collection unit can also prioritize collecting information related to that city. In this way, the data collection unit can prioritize collecting more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform processing to prioritize the collection of highly relevant information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to skills and experiences that the user frequently mentions on social media. The data collection unit can also collect information related to areas of interest from the user's social media activity. The data collection unit can also collect information related to experts that the user follows on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform analysis to collect relevant information.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to information about technical skills. The analysis unit can also apply a historical analysis algorithm to information about experience. The analysis unit can also apply a certification analysis algorithm to information about qualifications. This improves the accuracy of the analysis by allowing the analysis unit to apply the most appropriate analysis algorithm for each category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​perform the process of selecting an appropriate analysis algorithm.

[0049] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also simplify the analysis of older information. The analysis unit can also determine the priority of analysis according to the timing of information collection. As a result, the analysis unit can prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information collection timing data into AI and have the AI ​​perform the process of determining the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the information into the AI ​​and have the AI ​​perform the processing to adjust the order of analysis.

[0051] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the analyzed information during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation based on the interrelationships of the analyzed information. The evaluation unit can also improve the accuracy of its evaluation by considering the relationships between the information. The evaluation unit can also improve the accuracy of its evaluation by analyzing the interrelationships of the information. In this way, the evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the analyzed information. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the data on the interrelationships of the analyzed information into the AI ​​and have the AI ​​perform processes to improve the accuracy of its evaluation.

[0052] The evaluation unit can perform evaluations while considering the attribute information of the information submitter. For example, the evaluation unit will give a higher rating to information submitted by an expert. The evaluation unit may also give a higher rating to information submitted by a reliable source. The evaluation unit may also perform evaluations while considering the past performance of the information submitter. In this way, the evaluation unit can perform more reliable evaluations by considering the attribute information of the information submitter. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input attribute information data of the information submitter into AI and have the AI ​​perform the processing for evaluation.

[0053] The evaluation unit can perform evaluations while considering the geographical distribution of information. For example, the evaluation unit may give higher evaluation to information related to a specific region. The evaluation unit may also prioritize the evaluation of information that is geographically close. The evaluation unit may also determine the priority of evaluations based on geographical distribution. This allows the evaluation unit to perform more appropriate evaluations by considering the geographical distribution of information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input geographical distribution data of information into AI and have the AI ​​perform the processing for evaluation.

[0054] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also perform the evaluation considering the reliability of the relevant literature. The evaluation unit can also improve the accuracy of its evaluation based on the content of the relevant literature. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input relevant literature data into AI and have AI perform processes to improve the accuracy of its evaluation.

[0055] The proposal unit can adjust the level of detail of its proposals based on the importance of the evaluated information. For example, the proposal unit can provide detailed proposals for highly important information, and simplified proposals for less important information. The proposal unit can also prioritize proposals according to their importance. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the importance of the evaluated information. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the importance data of the evaluated information into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the proposals.

[0056] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit can apply a specialized proposal algorithm to information about technical skills. The proposal unit can also apply a history proposal algorithm to information about experience. The proposal unit can also apply a certification proposal algorithm to information about qualifications. This improves the accuracy of the proposal by allowing the proposal unit to apply the most appropriate proposal algorithm for each category of information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information category data into an AI and have the AI ​​perform the processing to select an appropriate proposal algorithm.

[0057] The proposal department can determine the priority of proposals based on the timing of information submission when submitting a proposal. For example, the proposal department may prioritize the most recent information. The proposal department may also simplify older information before submitting it. The proposal department can also determine the priority of proposals based on the timing of information submission. This allows the proposal department to prioritize the most recent information by determining the priority of proposals based on the timing of information submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information submission timing data into an AI and have the AI ​​perform the process of determining the priority of proposals.

[0058] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant information. The proposal unit may also postpone proposing less relevant information. The proposal unit can also adjust the order of proposals according to the relevance of the information. In this way, the proposal unit can prioritize proposing more relevant information by adjusting the order of proposals based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information relevance data into AI and have the AI ​​perform the processing to adjust the order of proposals.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception desk can analyze the user's past input history of desired conditions and suggest the optimal input method. For example, it can automatically display desired conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​perform analysis to suggest the optimal input method.

[0061] The reception desk can customize input fields based on the user's current work situation and areas of interest when they enter their desired conditions. For example, it can prioritize the input of skills and experience related to the user's current project. It can also automatically display relevant skills and experience in the input fields based on the user's areas of interest. Furthermore, it can reflect necessary skills and experience in the input fields according to the user's work situation. This allows the reception desk to input more appropriate conditions by customizing the input fields according to the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work situation data into AI and have the AI ​​perform the customization of the input fields.

[0062] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting information. For example, it can analyze patterns in the information the user has collected in the past and select the optimal data collection method. It can also prioritize the collection of specific media based on the user's past data collection history. Furthermore, it can suggest efficient data collection methods based on the user's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​perform analysis to select the optimal data collection method.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, it can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. Furthermore, it can also determine the priority of the analysis according to its importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the processing to adjust the level of detail of the analysis.

[0064] The proposal unit can adjust the level of detail of its proposals based on the importance of the evaluated information. For example, it can provide detailed proposals for highly important information and simplified proposals for less important information. Furthermore, it can prioritize proposals according to their importance. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the importance of the evaluated information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the evaluated information into the AI ​​and have the AI ​​perform the processing to adjust the level of detail of the proposals.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk enters the user's desired conditions. These conditions include skills, experience, and qualifications. For example, the user might enter conditions such as "personnel with programming skills" or "personnel with project management experience." Step 2: The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. The collection unit can collect information such as blog posts, social media posts, and video content, and can collect this information through methods such as web scraping and the use of APIs. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the criteria. The analysis unit uses text mining and natural language processing techniques to analyze the information and analyze the collected information in detail to identify personnel with specific skills and experience. Step 4: The evaluation unit assesses the degree of match of the personnel identified by the analysis unit. The evaluation unit uses a scoring system and matching algorithm to assess the degree of match and calculates a score based on the analyzed information to evaluate the degree of match to the conditions. Step 5: The proposal team proposes candidates evaluated by the evaluation team. The proposal team provides profiles and contact information of candidates who meet the criteria and lists and proposes the candidates who best match the criteria to the user.

[0067] (Example of form 2) The AI ​​scouting service according to an embodiment of the present invention is a system that quickly finds and proposes personnel who match the desired conditions of companies and individuals. In this system, the user inputs the desired conditions, and the AI ​​searches for and proposes personnel who match the conditions from all kinds of media such as blogs, social media, and videos. For example, the user may state desired conditions such as "I want someone with these skills" or "Are there any people with this kind of experience?" Next, the AI ​​searches for personnel who match the conditions from all kinds of media such as blogs, social media, and videos. For example, it analyzes blog posts, social media posts, and video content to find personnel with specific skills and experience. Finally, the AI ​​proposes personnel with a high degree of match. For example, by providing the profile and contact information of personnel who match the conditions, the user can quickly find personnel. This mechanism allows companies and individuals to quickly find personnel who match their desired conditions. For example, a company looking for personnel with specific skills can use the AI ​​scouting service to find suitable personnel in a short amount of time. Similarly, an individual looking for personnel with specific experience can also find personnel quickly. This improves the efficiency of personnel searching and makes it possible to quickly secure suitable personnel. This allows AI scouting services to quickly find and propose talent that matches the desired conditions of companies and individuals.

[0068] The AI ​​scouting service according to this embodiment comprises a reception unit, a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The reception unit receives input from the user regarding desired conditions. These conditions include, but are not limited to, skills, experience, and qualifications. For example, the user can input conditions such as "personnel with programming skills" or "personnel with project management experience" into the reception unit. The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. For example, the collection unit collects information such as blog posts, social media posts, and video content. The collection unit can collect information by methods such as web scraping and using APIs. The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the conditions. For example, the analysis unit analyzes the information using text mining or natural language processing techniques. The analysis unit analyzes the collected information in detail to identify personnel with specific skills and experience. The evaluation unit evaluates the degree of matching of the personnel identified by the analysis unit. For example, the evaluation unit evaluates the degree of matching using a scoring system or a matching algorithm. The evaluation unit calculates a score based on the analyzed information to assess the degree of matching with the conditions. The proposal unit proposes personnel evaluated by the evaluation unit. The proposal unit provides, for example, the profile and contact information of personnel who meet the conditions. The proposal unit lists and proposes personnel who best match the conditions to the user. As a result, the AI ​​scouting service according to this embodiment can quickly find and propose personnel who meet the conditions desired by the user.

[0069] The reception desk allows users to input their desired criteria. These criteria may include, but are not limited to, skills, experience, and qualifications. For example, users can input criteria such as "personnel with programming skills" or "personnel with project management experience." Specifically, the reception desk provides an intuitive interface to facilitate criteria input. For example, dropdown menus, checkboxes, and text input fields allow users to specify their desired criteria in detail. The reception desk also has a function to remember and reuse criteria previously entered by the user. This saves users the trouble of re-entering criteria they have already entered. Furthermore, the reception desk provides real-time feedback on the criteria entered by the user, allowing them to check the appropriateness and specificity of the criteria. For example, if the criteria are ambiguous or there are input errors, it displays a message prompting appropriate corrections. In this way, the reception desk supports users in accurately and efficiently entering their desired criteria.

[0070] The data collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. For example, the collection unit collects information such as blog posts, social media posts, and video content. Specifically, the collection unit automatically collects publicly available information from the internet using web scraping technology. Web scraping is a technique for extracting necessary information from specific websites, and the collection unit uses this to collect blog posts and social media posts. Furthermore, by utilizing APIs, it can also directly obtain data from social media platforms and video sharing sites. The collection unit combines these methods to collect a wide range of information from diverse media. In addition, the collection unit centrally manages the collected information and stores it in a database for efficient access by the analysis unit. This allows the collection unit to quickly and accurately collect information that matches the user's desired conditions, improving the overall system performance.

[0071] The analysis unit analyzes the information collected by the collection unit to identify personnel who meet the specified criteria. The analysis unit analyzes the information using techniques such as text mining and natural language processing. Specifically, it uses text mining techniques to extract useful information from the collected text data. For example, it extracts descriptions of specific skills and experiences from blog posts and social media posts to identify personnel who meet the criteria. It also uses natural language processing techniques to analyze the meaning of the text data and extract information with higher accuracy. For example, it uses named entity recognition (NER) to identify names, skills, qualifications, etc., in the text. Furthermore, the analysis unit uses machine learning algorithms to classify the collected information and identify personnel who meet the criteria. For example, it uses algorithms such as support vector machines (SVM) and random forests to classify the collected information based on skills and experience and identify personnel who best meet the criteria. As a result, the analysis unit can analyze the collected information in detail and quickly and accurately identify personnel who meet the user's desired criteria.

[0072] The evaluation unit assesses the degree of match of the personnel identified by the analysis unit. The evaluation unit evaluates the degree of match using, for example, a scoring system or a matching algorithm. Specifically, it uses a scoring system to quantify the degree of match for specific skills and experience. For example, it evaluates the degree of match for programming skills on a score from 0 to 100, visually showing the degree of match to the conditions desired by the user. It also uses a matching algorithm to comprehensively evaluate the degree of match for multiple conditions. For example, by weighting each condition and calculating an overall score, it identifies the personnel who best match the conditions. Furthermore, the evaluation unit continuously improves evaluation accuracy by utilizing past evaluation data and feedback. For example, it learns the characteristics of personnel who have received high evaluations in the past and reflects them in future evaluations. As a result, the evaluation unit can accurately and efficiently evaluate the degree of match to the conditions desired by the user and identify the optimal personnel.

[0073] The Proposal Department proposes candidates who have been evaluated by the Evaluation Department. For example, the Proposal Department provides profiles and contact information of candidates who meet the criteria. Specifically, it provides users with profile information of candidates who have received high ratings from the Evaluation Department, allowing them to check detailed skills, experience, qualifications, etc. The Proposal Department also provides contact information for users to contact candidates directly. For example, it provides email addresses, phone numbers, and social media account information so that users can quickly make contact. Furthermore, the Proposal Department collects user feedback and continuously improves the accuracy and effectiveness of the proposals. For example, it collects user evaluations and comments on proposed candidates and incorporates them into future proposals. This allows the Proposal Department to quickly and accurately propose candidates who best match the user's desired conditions and meet the user's needs.

[0074] The collection unit collects information such as blog posts, social media posts, and video content. For example, the collection unit can collect blog posts using specific platforms or keywords. The collection unit can collect social media posts using specific hashtags or user accounts. The collection unit can collect video content using specific channels or keywords. This allows the collection unit to collect information from diverse media, making it easier to find suitable candidates. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to perform web scraping in order to collect blog posts containing specific keywords.

[0075] The analysis unit can analyze the collected information and identify individuals with specific skills and experience. For example, the analysis unit can analyze the collected information using text mining techniques. The analysis unit can also analyze the information using natural language processing techniques to identify individuals with specific skills and experience. Furthermore, the analysis unit can analyze the information using machine learning algorithms to identify individuals who meet specific criteria. Thus, by analyzing the collected information, the analysis unit can identify individuals with specific skills and experience. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected text data into an AI and have the AI ​​perform the analysis to identify individuals with specific skills and experience.

[0076] The evaluation unit can assess the degree of matching to the conditions. The evaluation unit can assess the degree of matching using, for example, a scoring system. The evaluation unit can also assess the degree of matching using a matching algorithm. The evaluation unit can also calculate a score based on the analyzed information in order to assess the degree of matching to the conditions. In this way, the evaluation unit can find the most suitable personnel by assessing the degree of matching to the conditions. Some or all of the above processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input the analyzed information into AI and have the AI ​​perform scoring to assess the degree of matching.

[0077] The proposal department can provide profiles and contact information of candidates who meet the criteria. For example, the proposal department can provide resumes and portfolios of candidates who meet the criteria. The proposal department can also provide email addresses and phone numbers of candidates who meet the criteria. By providing detailed information about candidates who meet the criteria, the proposal department can enable users to quickly find candidates. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information about candidates who meet the criteria into AI and have the AI ​​perform the processing to generate proposals.

[0078] The reception desk can estimate the user's emotions and adjust the input interface for desired conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of desired conditions. In this way, the reception desk can provide a more user-friendly system by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past input history of desired conditions and suggest the optimal input method. For example, the reception desk can automatically display desired conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​perform analysis to suggest the optimal input method.

[0080] The reception desk can customize input fields based on the user's current work situation and areas of interest when they enter their desired conditions. For example, the reception desk can prioritize inputting skills and experience related to the user's current project. The reception desk can also automatically display relevant skills and experience in the input fields based on the user's areas of interest. The reception desk can also reflect necessary skills and experience in the input fields according to the user's work situation. This allows the reception desk to input more appropriate conditions by customizing the input fields according to the user's work situation and areas of interest. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work situation data into AI and have the AI ​​perform the customization of the input fields.

[0081] The reception desk can estimate the user's emotions and determine the priority of the input preferences based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize the most important preferences. If the user is relaxed, the reception desk can also prioritize preferences including more detailed ones. If the user is in a hurry, the reception desk can also prioritize preferences that can be processed quickly. In this way, the reception desk can prioritize more appropriate preferences by determining the priority of preferences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can prioritize the input of highly relevant conditions when users enter their desired conditions, taking into account their geographical location. For example, if a user is looking for personnel in a specific region, the reception desk can prioritize conditions related to that region. The reception desk can also prioritize conditions related to nearby personnel based on the user's current location. If a user is looking for personnel in a specific city, the reception desk can prioritize conditions related to that city. In this way, the reception desk can prioritize the input of more relevant conditions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​perform the processing to prioritize the input of highly relevant conditions.

[0083] The reception desk can analyze the user's social media activity when they input their desired conditions and suggest relevant conditions. For example, the reception desk may suggest skills and experiences that the user frequently mentions on social media in the input fields. The reception desk can also suggest conditions related to areas of interest based on the user's social media activity. The reception desk can also suggest conditions based on the skills and experience of experts that the user follows on social media. In this way, the reception desk can suggest relevant conditions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​perform analysis to suggest relevant conditions.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. If the user is relaxed, the data collection unit can also collect detailed information. If the user is in a hurry, the data collection unit can collect information quickly and provide it immediately. This allows the data collection unit to collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting information. For example, the data collection unit can analyze patterns in information previously collected by the user and select the optimal data collection method. The data collection unit can also prioritize the collection of specific media based on the user's past data collection history. The data collection unit can also suggest efficient data collection methods based on the user's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​perform analysis to select the optimal data collection method.

[0086] The data collection unit can filter the data to be collected based on the user's current work situation and areas of interest. For example, the data collection unit may prioritize collecting information related to a project the user is currently working on. The data collection unit can also filter and collect relevant information based on the user's areas of interest. The data collection unit can also filter and collect necessary information according to the user's work situation. This allows the data collection unit to collect more appropriate information by filtering the data to be collected according to the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's work situation data into AI and have the AI ​​perform the filtering of the data to be collected.

[0087] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting the most important information. If the user is relaxed, the data collection unit can also collect detailed information. If the user is in a hurry, the data collection unit can also prioritize collecting information that can be collected quickly. In this way, the data collection unit can prioritize collecting more important information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during data collection. For example, if the user is looking for talent in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also prioritize collecting information related to nearby talent based on the user's current location. If the user is looking for talent in a specific city, the data collection unit can also prioritize collecting information related to that city. In this way, the data collection unit can prioritize collecting more relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform processing to prioritize the collection of highly relevant information.

[0089] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to skills and experiences that the user frequently mentions on social media. The data collection unit can also collect information related to areas of interest from the user's social media activity. The data collection unit can also collect information related to experts that the user follows on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform analysis to collect relevant information.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specialized analysis algorithm to information about technical skills. The analysis unit can also apply a historical analysis algorithm to information about experience. The analysis unit can also apply a certification analysis algorithm to information about qualifications. This improves the accuracy of the analysis by allowing the analysis unit to apply the most appropriate analysis algorithm for each category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into the AI ​​and have the AI ​​perform the process of selecting an appropriate analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also simplify the analysis of older information. The analysis unit can also determine the priority of analysis according to the timing of information collection. As a result, the analysis unit can prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input information collection timing data into AI and have the AI ​​perform the process of determining the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information. The analysis unit may also postpone the analysis of less relevant information. The analysis unit can also adjust the order of analysis according to the relevance of the information. In this way, the analysis unit can prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the relevance data of the information into the AI ​​and have the AI ​​perform the processing to adjust the order of analysis.

[0096] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide simple and clear evaluation criteria. If the user is relaxed, the evaluation unit can also provide detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also provide criteria that allow for quick evaluation. This allows the evaluation unit to provide more appropriate evaluation results by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the analyzed information during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation based on the interrelationships of the analyzed information. The evaluation unit can also improve the accuracy of its evaluation by considering the relationships between the information. The evaluation unit can also improve the accuracy of its evaluation by analyzing the interrelationships of the information. In this way, the evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of the analyzed information. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the data on the interrelationships of the analyzed information into the AI ​​and have the AI ​​perform processes to improve the accuracy of its evaluation.

[0098] The evaluation unit can perform evaluations while considering the attribute information of the information submitter. For example, the evaluation unit will give a higher rating to information submitted by an expert. The evaluation unit may also give a higher rating to information submitted by a reliable source. The evaluation unit may also perform evaluations while considering the past performance of the information submitter. In this way, the evaluation unit can perform more reliable evaluations by considering the attribute information of the information submitter. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input attribute information data of the information submitter into AI and have the AI ​​perform the processing for evaluation.

[0099] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can display the most important evaluation results first. If the user is relaxed, the evaluation unit can also display detailed evaluation results in a sequential manner. If the user is in a hurry, the evaluation unit can also display evaluation results that can be quickly reviewed first. In this way, the evaluation unit can provide more appropriate evaluation results by adjusting the order in which the evaluation results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The evaluation unit can perform evaluations while considering the geographical distribution of information. For example, the evaluation unit may give higher evaluation to information related to a specific region. The evaluation unit may also prioritize the evaluation of information that is geographically close. The evaluation unit may also determine the priority of evaluations based on geographical distribution. This allows the evaluation unit to perform more appropriate evaluations by considering the geographical distribution of information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input geographical distribution data of information into AI and have the AI ​​perform the processing for evaluation.

[0101] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also perform the evaluation considering the reliability of the relevant literature. The evaluation unit can also improve the accuracy of its evaluation based on the content of the relevant literature. In this way, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input relevant literature data into AI and have AI perform processes to improve the accuracy of its evaluation.

[0102] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can offer simple and clear suggestions. If the user is relaxed, it can offer more detailed suggestions. If the user is in a hurry, it can offer suggestions that can be quickly reviewed. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The proposal unit can adjust the level of detail of its proposals based on the importance of the evaluated information. For example, the proposal unit can provide detailed proposals for highly important information, and simplified proposals for less important information. The proposal unit can also prioritize proposals according to their importance. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the importance of the evaluated information. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the importance data of the evaluated information into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the proposals.

[0104] The proposal unit can apply different proposal algorithms depending on the category of information when making a proposal. For example, the proposal unit can apply a specialized proposal algorithm to information about technical skills. The proposal unit can also apply a history proposal algorithm to information about experience. The proposal unit can also apply a certification proposal algorithm to information about qualifications. This improves the accuracy of the proposal by allowing the proposal unit to apply the most appropriate proposal algorithm for each category of information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information category data into an AI and have the AI ​​perform the processing to select an appropriate proposal algorithm.

[0105] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can also make a detailed suggestion. If the user is excited, the suggestion unit can also make a visually stimulating suggestion. In this way, the suggestion unit can make more appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The proposal department can determine the priority of proposals based on the timing of information submission when submitting a proposal. For example, the proposal department may prioritize the most recent information. The proposal department may also simplify older information before submitting it. The proposal department can also determine the priority of proposals based on the timing of information submission. This allows the proposal department to prioritize the most recent information by determining the priority of proposals based on the timing of information submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input information submission timing data into an AI and have the AI ​​perform the process of determining the priority of proposals.

[0107] The proposal unit can adjust the order of proposals based on the relevance of the information during the proposal process. For example, the proposal unit may prioritize proposing highly relevant information. The proposal unit may also postpone proposing less relevant information. The proposal unit can also adjust the order of proposals according to the relevance of the information. In this way, the proposal unit can prioritize proposing more relevant information by adjusting the order of proposals based on the relevance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information relevance data into AI and have the AI ​​perform the processing to adjust the order of proposals.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception desk can estimate the user's emotions and adjust the input interface for desired conditions based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of desired conditions. In this way, the reception desk can provide a more user-friendly system by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the frequency of information collection can be reduced, and only important information can be collected. If the user is relaxed, detailed information can be collected. Furthermore, if the user is in a hurry, information can be collected quickly and provided immediately. In this way, the data collection unit can collect more appropriate information by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0112] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, it can provide simple and clear evaluation criteria. If the user is relaxed, it can provide detailed evaluation criteria. Furthermore, if the user is in a hurry, it can provide criteria that allow for quick evaluation. In this way, the evaluation unit can provide more appropriate evaluation results by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0113] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is nervous, it can offer simple and clear suggestions. If the user is relaxed, it can offer more detailed suggestions. Furthermore, if the user is in a hurry, it can offer suggestions that can be quickly reviewed. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0114] The reception desk can analyze the user's past input history of desired conditions and suggest the optimal input method. For example, it can automatically display desired conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest desired conditions to be used during specific time periods based on the user's past input history. In this way, the reception desk can suggest the optimal input method to the user by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI and have the AI ​​perform analysis to suggest the optimal input method.

[0115] The reception desk can customize input fields based on the user's current work situation and areas of interest when they enter their desired conditions. For example, it can prioritize the input of skills and experience related to the user's current project. It can also automatically display relevant skills and experience in the input fields based on the user's areas of interest. Furthermore, it can reflect necessary skills and experience in the input fields according to the user's work situation. This allows the reception desk to input more appropriate conditions by customizing the input fields according to the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's work situation data into AI and have the AI ​​perform the customization of the input fields.

[0116] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting information. For example, it can analyze patterns in the information the user has collected in the past and select the optimal data collection method. It can also prioritize the collection of specific media based on the user's past data collection history. Furthermore, it can suggest efficient data collection methods based on the user's past data collection history. In this way, the data collection unit can select the optimal data collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI and have the AI ​​perform analysis to select the optimal data collection method.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, it can perform a detailed analysis on information of high importance, and a simplified analysis on information of low importance. Furthermore, it can also determine the priority of the analysis according to its importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the collected information into the AI ​​and have the AI ​​perform the processing to adjust the level of detail of the analysis.

[0118] The proposal unit can adjust the level of detail of its proposals based on the importance of the evaluated information. For example, it can provide detailed proposals for highly important information and simplified proposals for less important information. Furthermore, it can prioritize proposals according to their importance. This allows the proposal unit to provide more appropriate proposals by adjusting the level of detail based on the importance of the evaluated information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the evaluated information into the AI ​​and have the AI ​​perform the processing to adjust the level of detail of the proposals.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk enters the user's desired conditions. These conditions include skills, experience, and qualifications. For example, the user might enter conditions such as "personnel with programming skills" or "personnel with project management experience." Step 2: The collection unit collects information from media such as blogs, social media, and videos based on the conditions entered by the reception unit. The collection unit can collect information such as blog posts, social media posts, and video content, and can collect this information through methods such as web scraping and the use of APIs. Step 3: The analysis unit analyzes the information collected by the collection unit and identifies personnel who meet the criteria. The analysis unit uses text mining and natural language processing techniques to analyze the information and analyze the collected information in detail to identify personnel with specific skills and experience. Step 4: The evaluation unit assesses the degree of match of the personnel identified by the analysis unit. The evaluation unit uses a scoring system and matching algorithm to assess the degree of match and calculates a score based on the analyzed information to evaluate the degree of match to the conditions. Step 5: The proposal team proposes candidates evaluated by the evaluation team. The proposal team provides profiles and contact information of candidates who meet the criteria and lists and proposes the candidates who best match the criteria to the user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, evaluation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs desired conditions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12, where information is collected from media such as blogs, social media, and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the collected information is analyzed. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, where the degree of matching of the analyzed information is evaluated. The proposal unit is implemented by the control unit 46A of the smart device 14, where the evaluated personnel are proposed to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, evaluation unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs desired conditions. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where information is collected from media such as blogs, social media, and videos. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the collected information is analyzed. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the degree of matching of the analyzed information is evaluated. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the evaluated personnel are proposed to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, evaluation unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user inputs their desired conditions. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where information is collected from media such as blogs, social media, and videos. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the collected information is analyzed. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the degree of matching of the analyzed information is evaluated. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314, where the evaluated personnel are proposed to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, evaluation unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user inputs the desired conditions. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where information is collected from media such as blogs, social media, and videos. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the collected information is analyzed. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the degree of matching of the analyzed information is evaluated. The proposal unit is implemented by, for example, the control unit 46A of the robot 414, where the evaluated personnel are proposed to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A reception desk where the user enters their desired conditions, Based on the conditions entered by the reception unit, the collection unit collects information from media such as blogs, social media, and videos. An analysis unit analyzes the information collected by the aforementioned collection unit and identifies personnel who meet the criteria, An evaluation unit that evaluates the degree of matching of personnel identified by the analysis unit, The system comprises a proposal unit that proposes personnel evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as blog posts, social media posts, and video content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to identify individuals with specific skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit described above, Evaluate the degree of match to the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Provide profiles and contact information of candidates who meet the criteria. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for desired conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past input history of desired conditions and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their desired conditions, the input fields are customized based on their current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the entered preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users enter their desired conditions, the system prioritizes highly relevant conditions by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When you enter your desired criteria, the system analyzes your social media activity and suggests relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting information, the system analyzes the user's past data collection history and selects the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting information, the data to be collected is filtered based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit described above, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit described above, During evaluation, consider the interrelationships of the analyzed information to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, During the evaluation process, the attribute information of the information submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit described above, During the evaluation, the geographical distribution of information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit described above, During evaluation, refer to relevant literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the information evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the user enters their desired conditions, Based on the conditions entered by the reception unit, the collection unit collects information from media such as blogs, social media, and videos. An analysis unit analyzes the information collected by the aforementioned collection unit and identifies personnel who meet the criteria, An evaluation unit that evaluates the degree of matching of personnel identified by the analysis unit, The system comprises a proposal unit that proposes personnel evaluated by the evaluation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as blog posts, social media posts, and video content. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to identify individuals with specific skills and experience. The system according to feature 1.

4. The evaluation unit, Evaluate the degree of match to the conditions. The system according to feature 1.

5. The aforementioned proposal section is, Provide profiles and contact information of candidates who meet the criteria. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for desired conditions based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past input history of desired conditions and suggest the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When users enter their desired conditions, the input fields are customized based on their current work situation and areas of interest. The system according to feature 1.

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