System

Abstract

An object of the system according to the embodiment is to efficiently screen dangerous sexual preferences of candidates.SOLUTION: A system according to an embodiment includes a document analysis unit, a question and answer analysis unit, and an image reaction analysis unit. The document analysis unit analyzes a document submitted by a candidate. The question and answer analysis unit analyzes an online interactive question and answer. The image response analysis unit analyzes the response of the candidate to the image generated by the AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently screen candidates for risky sexual preferences.

[0005] The system according to the embodiment aims to efficiently screen candidates for risky sexual preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a document analysis unit, a question and response analysis unit, and an image response analysis unit. The document analysis unit analyzes documents submitted by candidates. The question and response analysis unit analyzes online interactive question and response. The image response analysis unit analyzes the candidate's response to images generated by AI. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently screen candidates for risky sexual preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The screening system according to an embodiment of the present invention analyzes candidate responses to documents submitted by candidates, online interactive question-and-answer sessions, and AI-generated images to perform in-depth analysis of personality traits, thereby assessing whether candidates have risky sexual preferences and identifying candidates who will contribute to the organization's values ​​and child safety.

[0029] A screening system according to an embodiment includes a document analysis unit, a question and response analysis unit, and an image response analysis unit. The document analysis unit analyzes documents submitted by candidates. For example, it analyzes documents such as resumes, letters of recommendation, and self-introductions to evaluate the candidate's personality traits and past behavioral history. The question and response analysis unit analyzes online interactive question and response responses. For example, it evaluates the candidate's stress tolerance and problem-solving ability based on answers to questions such as, "What is the most difficult situation you have ever experienced?" The image response analysis unit analyzes the candidate's reaction to images generated by AI. For example, it presents images such as photos of children and family, or scenes from everyday life, and analyzes the candidate's facial expressions and reactions. This allows the screening system according to an embodiment to perform an in-depth analysis of the candidate's personality traits, evaluate whether the candidate has risky sexual preferences, and identify individuals who will contribute to the organization's values ​​and the safety of children.

[0030] The document analysis unit can analyze the timing and frequency of document submissions to evaluate the candidate's behavioral patterns and time management ability. The document analysis unit, for example, analyzes the timing and frequency of document submissions to evaluate the candidate's behavioral patterns. For example, the candidate's planning ability and time management ability are evaluated based on the timing and frequency of submission. The submission timing is evaluated based on the date and timing of submission. The frequency is evaluated based on the number of submissions and the interval between submissions. The behavioral pattern is evaluated based on the regularity of submissions and the timing of submission. Time management ability is evaluated based on compliance with deadlines and planning for submission. In this way, by evaluating the candidate's behavioral patterns and time management ability, it is possible to analyze personality characteristics in more detail.

[0031] The document analysis unit can collect other candidates' reactions to the contents of the document and evaluate the candidate's social adaptability using the emotion estimation function. The document analysis unit, for example, collects other candidates' reactions to the contents of the submitted document and evaluates the candidate's social adaptability using the emotion estimation function. For example, it analyzes the comments and evaluations of other candidates. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Social adaptability is evaluated based on cooperation and communication skills. This makes it possible to analyze personality characteristics in more detail by evaluating the candidate's social adaptability.

[0032] In addition to document submission, the document analysis unit can analyze a candidate's past social media posts and online activity history to conduct a comprehensive personality evaluation. The document analysis unit, for example, analyzes a candidate's past social media posts to conduct a comprehensive personality evaluation. For example, the candidate's personality and interests are evaluated based on the content and frequency of posts. Social media posts are evaluated based on the content, frequency, and reactions of posts. Online activity history is evaluated based on browsing history and online events attended. A comprehensive personality evaluation is conducted by integrating multiple evaluation items and weighting the evaluations. This makes it possible to analyze a candidate's personality characteristics in more detail by analyzing a candidate's past social media posts and online activity history.

[0033] The document analysis unit can visualize the contents of the document and visually evaluate the candidate's characteristics using graphs and charts. The document analysis unit, for example, visualizes the contents of the submitted document and visually evaluates the candidate's characteristics using graphs and charts. For example, work history and educational background can be displayed in a graph. Visualization can be performed using tools such as graphs, charts, and infographics. Graphs used include bar graphs, line graphs, and pie charts. Charts used include flowcharts and heat maps. Characteristics are evaluated based on personality traits and behavioral characteristics. This makes it possible to visually evaluate the candidate's characteristics and perform a more detailed analysis of their personality traits.

[0034] The question and answer analysis unit can analyze the content of questions and answers in chronological order to evaluate the consistency and logic of the candidate. The question and answer analysis unit, for example, analyzes the content of questions and answers in chronological order to evaluate the consistency and logic of the candidate. For example, it analyzes the order of answers and the consistency of their content. The analysis of the chronological order is performed based on organizing data along the flow of time and analyzing changes over time. Consistency is evaluated based on the consistency of answers and the presence or absence of contradictions. Logic is evaluated based on the logical structure and clarity of causal relationships. In this way, by evaluating the consistency and logic of the candidate, it becomes possible to analyze personality characteristics in more detail.

[0035] The question and answer analysis unit can record the content of questions and answers not only in text format but also in video format and analyze facial expressions and gestures. The question and answer analysis unit, for example, records the content of questions and answers in video format and analyzes facial expressions and gestures. For example, it analyzes facial movements and hand movements. Video recording is performed based on the resolution and length of the recording. Facial expressions are evaluated based on the type of expression, such as smiling, anger, or surprise. Gestures are evaluated based on hand movements, body movements, and eye movements. This makes it possible to analyze a candidate's personality characteristics in more detail by analyzing their facial expressions and gestures.

[0036] The question and answer analysis unit can automatically translate the content of questions and answers into different languages ​​and evaluate candidates from an international perspective. The question and answer analysis unit, for example, automatically translates the content of questions and answers into different languages ​​and evaluates candidates from an international perspective. For example, it translates into English and French. Different languages ​​are evaluated based on English, Chinese, Spanish, etc. Automatic translation can be performed using technologies such as machine translation and neural network translation. An international perspective is evaluated based on consideration of cultural background and multilingual support. This makes it possible to analyze personality characteristics in more detail by evaluating candidates from an international perspective.

[0037] The image reaction analysis unit can diversify the types of images presented by the generation AI and evaluate candidate reactions from multiple angles. The image reaction analysis unit, for example, diversifies the types of images presented by the generation AI and evaluates candidate reactions from multiple angles. For example, landscape images or animal images can be presented. The types are evaluated based on the theme, content, and format of the image. Diversification can be achieved by using images with different themes or different styles. Multifaceted evaluation is carried out by evaluating from multiple evaluation items and different perspectives. This makes it possible to evaluate candidate reactions from multiple angles and analyze personality characteristics in more detail.

[0038] The image response analysis unit can evaluate the candidate's eye movement and gaze duration when analyzing responses to images. For example, the image response analysis unit evaluates the candidate's eye movement and gaze duration when analyzing responses to images. For example, it analyzes the eye movement pattern and gaze duration. Eye movement is evaluated based on the direction of gaze and the speed of gaze movement. Gaze duration is evaluated based on the length of time spent gazing at a specific part and the frequency of gaze. In this way, by evaluating the candidate's eye movement and gaze duration, it becomes possible to analyze personality characteristics in more detail.

[0039] The image response analysis unit can present audio and video in addition to images to comprehensively evaluate the candidate's response. The image response analysis unit, for example, presents audio in addition to images to comprehensively evaluate the candidate's response. For example, it analyzes responses to the content and tone of the audio. Audio is evaluated based on the content, sound quality, and volume of the audio. Video is evaluated based on the content, length, and resolution of the video. A comprehensive evaluation is made by integrating multiple evaluation items and weighting the evaluation. This makes it possible to comprehensively evaluate the candidate's response and analyze personality characteristics in more detail.

[0040] The image reaction analysis unit can compare reactions to images between candidates from different cultural areas and backgrounds, and perform evaluations from a global perspective. The image reaction analysis unit, for example, compares reactions to images between candidates from different cultural areas and performs evaluations from a global perspective. For example, it analyzes differences in reactions due to cultural background. Different cultural areas are evaluated based on differences in cultural background and values ​​in each region. Backgrounds are evaluated based on the candidate's educational background and occupational background. A global perspective is evaluated based on the application of international standards and cross-cultural understanding. This makes it possible to analyze personality characteristics in more detail by evaluating candidate reactions from a global perspective.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The screening system can further include a work history analysis unit that performs a detailed analysis of a candidate's past work history. The work history analysis unit analyzes, for example, the candidate's past job content, position, and project results to evaluate the candidate's expertise and leadership ability. Job content is evaluated based on the specific work content and projects in which the candidate was in charge. Positions are evaluated based on past positions and promotion history. Project results are evaluated based on goals achieved and awards received. This enables a more comprehensive analysis of a candidate's personality characteristics by analyzing their work history in detail.

[0043] The screening system can further include a hobby analysis unit that analyzes the hobbies and interests of candidates. The hobby analysis unit extracts hobbies and interests from, for example, the self-introduction text submitted by the candidate or posts on social media, and evaluates the candidate's personality characteristics. Hobbies are evaluated based on activities such as sports, music, and reading. Interests are evaluated based on interest in specific fields or themes. This makes it possible to analyze the candidate's personality characteristics in more detail by analyzing the candidate's hobbies and interests.

[0044] The screening system may further include a health analysis unit that analyzes the candidate's health condition. The health analysis unit analyzes, for example, the health certificate submitted by the candidate and self-reported health information to evaluate the candidate's health condition. The health certificate is evaluated based on the results of blood tests and physical measurements. The self-reported health information is evaluated based on lifestyle habits and medical history. This allows for a more comprehensive analysis of the candidate's personality traits by analyzing the candidate's health condition.

[0045] The screening system can further include a learning ability analysis unit that analyzes the candidate's learning ability. The learning ability analysis unit analyzes, for example, the academic history, qualifications, and online course completion certificates submitted by the candidate to evaluate the candidate's learning ability. The academic history is evaluated based on the degree and major obtained. The qualifications are evaluated based on the qualifications and certifications obtained. The online course completion certificates are evaluated based on the courses taken and the courses completed. In this way, analyzing the candidate's learning ability makes it possible to analyze personality characteristics in more detail.

[0046] The screening system can further include an ethical analysis unit that analyzes the candidate's ethical values. The ethical analysis unit evaluates the candidate's ethical values, for example, by analyzing the essays submitted by the candidate and their past behavioral history. The essays are evaluated based on the candidate's thoughts and values ​​regarding ethical issues. The past behavioral history is evaluated based on ethical behavior and social contributions. In this way, analyzing the candidate's ethical values ​​enables a more detailed analysis of their personality traits.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The document analysis unit analyzes documents submitted by candidates, such as resumes, letters of recommendation, and self-introductions, to evaluate the candidate's personality traits and past behavioral history. Step 2: The Q&A analysis unit analyzes online interactive Q&A. For example, the candidate's stress tolerance and problem-solving ability are evaluated based on their responses to questions such as, "What is the most difficult situation you have ever experienced?" Step 3: The image reaction analysis unit analyzes the candidate's reaction to the AI-generated images. For example, it presents images of children, family, and everyday life scenes, and analyzes the candidate's facial expressions and reactions.

[0049] (Example 2) The screening system according to an embodiment of the present invention analyzes candidate responses to documents submitted by candidates, online interactive question-and-answer sessions, and AI-generated images to perform in-depth analysis of personality traits, thereby assessing whether candidates have risky sexual preferences and identifying candidates who will contribute to the organization's values ​​and child safety.

[0050] A screening system according to an embodiment includes a document analysis unit, a question and response analysis unit, and an image response analysis unit. The document analysis unit analyzes documents submitted by candidates. For example, it analyzes documents such as resumes, letters of recommendation, and self-introductions to evaluate the candidate's personality traits and past behavioral history. The question and response analysis unit analyzes online interactive question and response responses. For example, it evaluates the candidate's stress tolerance and problem-solving ability based on answers to questions such as, "What is the most difficult situation you have ever experienced?" The image response analysis unit analyzes the candidate's reaction to images generated by AI. For example, it presents images such as photos of children and family, or scenes from everyday life, and analyzes the candidate's facial expressions and reactions. This allows the screening system according to an embodiment to perform an in-depth analysis of the candidate's personality traits, evaluate whether the candidate has risky sexual preferences, and identify individuals who will contribute to the organization's values ​​and the safety of children.

[0051] The document analysis unit can perform sentiment analysis on the content of documents to evaluate the emotional tendencies of candidates. The document analysis unit analyzes text data, such as submitted resumes and letters of recommendation, to perform sentiment analysis. For example, it extracts positive and negative expressions to evaluate the emotional tendencies of candidates. Sentiment analysis can be performed using text mining and sentiment dictionaries. Emotional tendencies are evaluated based on positive / negative scores and the intensity of emotions. This allows for a more detailed analysis of personality characteristics by evaluating the emotional tendencies of candidates.

[0052] The document analysis unit can analyze the timing and frequency of document submissions to evaluate the candidate's behavioral patterns and time management ability. The document analysis unit, for example, analyzes the timing and frequency of document submissions to evaluate the candidate's behavioral patterns. For example, the candidate's planning ability and time management ability are evaluated based on the timing and frequency of submission. The submission timing is evaluated based on the date and timing of submission. The frequency is evaluated based on the number of submissions and the interval between submissions. The behavioral pattern is evaluated based on the regularity of submissions and the timing of submission. Time management ability is evaluated based on compliance with deadlines and planning for submission. In this way, by evaluating the candidate's behavioral patterns and time management ability, it is possible to analyze personality characteristics in more detail.

[0053] The document analysis unit can collect other candidates' reactions to the contents of the document and evaluate the candidate's social adaptability using the emotion estimation function. The document analysis unit, for example, collects other candidates' reactions to the contents of the submitted document and evaluates the candidate's social adaptability using the emotion estimation function. For example, it analyzes the comments and evaluations of other candidates. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Social adaptability is evaluated based on cooperation and communication skills. This makes it possible to analyze personality characteristics in more detail by evaluating the candidate's social adaptability.

[0054] In addition to document submission, the document analysis unit can analyze a candidate's past social media posts and online activity history to conduct a comprehensive personality evaluation. The document analysis unit, for example, analyzes a candidate's past social media posts to conduct a comprehensive personality evaluation. For example, the candidate's personality and interests are evaluated based on the content and frequency of posts. Social media posts are evaluated based on the content, frequency, and reactions of posts. Online activity history is evaluated based on browsing history and online events attended. A comprehensive personality evaluation is conducted by integrating multiple evaluation items and weighting the evaluations. This makes it possible to analyze a candidate's personality characteristics in more detail by analyzing a candidate's past social media posts and online activity history.

[0055] The document analysis unit can visualize the contents of the document and visually evaluate the candidate's characteristics using graphs and charts. The document analysis unit, for example, visualizes the contents of the submitted document and visually evaluates the candidate's characteristics using graphs and charts. For example, work history and educational background can be displayed in a graph. Visualization can be performed using tools such as graphs, charts, and infographics. Graphs used include bar graphs, line graphs, and pie charts. Charts used include flowcharts and heat maps. Characteristics are evaluated based on personality traits and behavioral characteristics. This makes it possible to visually evaluate the candidate's characteristics and perform a more detailed analysis of their personality traits.

[0056] The document analysis unit can use the emotion estimation function to monitor the emotional state of candidates in real time when submitting documents and evaluate the reliability of the submitted content. The document analysis unit, for example, monitors the emotional state of candidates in real time when submitting documents and evaluates the reliability of the submitted content. For example, it analyzes facial expressions and voice tone. The emotion estimation function can use technologies such as facial expression recognition, voice analysis, and text analysis. The emotional state is evaluated based on stress levels and positive / negative scores. Real-time monitoring is performed based on the frequency of data updates and the timing of monitoring. Reliability is evaluated based on the accuracy and consistency of the data. This makes it possible to evaluate the reliability of the submitted content by monitoring the emotional state of candidates in real time.

[0057] The question and answer analysis unit can analyze the candidate's tone of voice and speaking style when answering questions to evaluate their emotional state. The question and answer analysis unit, for example, analyzes the candidate's tone of voice and speaking style when answering questions to evaluate their emotional state. For example, it analyzes the pitch and speed of the voice. The tone of voice is evaluated based on the pitch, intensity, and emotional expression. The speaking style is evaluated based on the speaking speed, rhythm, and word choice. The emotional state is evaluated based on the stress level and positive / negative score. This makes it possible to evaluate a candidate's emotional state by analyzing the tone of voice and speaking style.

[0058] The question and answer analysis unit can analyze the content of questions and answers in chronological order to evaluate the consistency and logic of the candidate. The question and answer analysis unit, for example, analyzes the content of questions and answers in chronological order to evaluate the consistency and logic of the candidate. For example, it analyzes the order of answers and the consistency of their content. The analysis of the chronological order is performed based on organizing data along the flow of time and analyzing changes over time. Consistency is evaluated based on the consistency of answers and the presence or absence of contradictions. Logic is evaluated based on the logical structure and clarity of causal relationships. In this way, by evaluating the consistency and logic of the candidate, it becomes possible to analyze personality characteristics in more detail.

[0059] The question and answer analysis unit can use the emotion estimation function to monitor the emotional changes of candidates while they are answering questions in real time and evaluate their stress tolerance. The question and answer analysis unit, for example, monitors the emotional changes of candidates while they are answering questions in real time and evaluates their stress tolerance. For example, it analyzes facial expressions and voice tone. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Emotional changes are evaluated based on changes in the intensity and type of emotion. Stress tolerance is evaluated based on performance under stress and the intensity of the stress response. This makes it possible to evaluate stress tolerance by monitoring the emotional changes of candidates in real time.

[0060] The question and answer analysis unit can record the content of questions and answers not only in text format but also in video format and analyze facial expressions and gestures. The question and answer analysis unit, for example, records the content of questions and answers in video format and analyzes facial expressions and gestures. For example, it analyzes facial movements and hand movements. Video recording is performed based on the resolution and length of the recording. Facial expressions are evaluated based on the type of expression, such as smiling, anger, or surprise. Gestures are evaluated based on hand movements, body movements, and eye movements. This makes it possible to analyze a candidate's personality characteristics in more detail by analyzing their facial expressions and gestures.

[0061] The question and answer analysis unit can automatically translate the content of questions and answers into different languages ​​and evaluate candidates from an international perspective. The question and answer analysis unit, for example, automatically translates the content of questions and answers into different languages ​​and evaluates candidates from an international perspective. For example, it translates into English and French. Different languages ​​are evaluated based on English, Chinese, Spanish, etc. Automatic translation can be performed using technologies such as machine translation and neural network translation. An international perspective is evaluated based on consideration of cultural background and multilingual support. This makes it possible to analyze personality characteristics in more detail by evaluating candidates from an international perspective.

[0062] The image reaction analysis unit can diversify the types of images presented by the generation AI and evaluate candidate reactions from multiple angles. The image reaction analysis unit, for example, diversifies the types of images presented by the generation AI and evaluates candidate reactions from multiple angles. For example, landscape images or animal images can be presented. The types are evaluated based on the theme, content, and format of the image. Diversification can be achieved by using images with different themes or different styles. Multifaceted evaluation is carried out by evaluating from multiple evaluation items and different perspectives. This makes it possible to evaluate candidate reactions from multiple angles and analyze personality characteristics in more detail.

[0063] The image response analysis unit can evaluate the candidate's eye movement and gaze duration when analyzing responses to images. For example, the image response analysis unit evaluates the candidate's eye movement and gaze duration when analyzing responses to images. For example, it analyzes the eye movement pattern and gaze duration. Eye movement is evaluated based on the direction of gaze and the speed of gaze movement. Gaze duration is evaluated based on the length of time spent gazing at a specific part and the frequency of gaze. In this way, by evaluating the candidate's eye movement and gaze duration, it becomes possible to analyze personality characteristics in more detail.

[0064] The image response analysis unit can use the emotion estimation function to monitor the candidate's emotional response to the image in real time and evaluate their latent preferences. The image response analysis unit, for example, monitors the candidate's emotional response to the image in real time and evaluates their latent preferences. For example, it analyzes facial expressions and vocal tone. The emotion estimation function can use technologies such as facial expression recognition, voice analysis, and text analysis. Emotional responses are evaluated based on the intensity and type of emotion. Latent preferences are evaluated based on preferences for specific themes or styles and patterns of emotional responses. This makes it possible to evaluate potential preferences by monitoring the candidate's emotional responses in real time.

[0065] The image response analysis unit can present audio and video in addition to images to comprehensively evaluate the candidate's response. The image response analysis unit, for example, presents audio in addition to images to comprehensively evaluate the candidate's response. For example, it analyzes responses to the content and tone of the audio. Audio is evaluated based on the content, sound quality, and volume of the audio. Video is evaluated based on the content, length, and resolution of the video. A comprehensive evaluation is made by integrating multiple evaluation items and weighting the evaluation. This makes it possible to comprehensively evaluate the candidate's response and analyze personality characteristics in more detail.

[0066] The image reaction analysis unit can compare reactions to images between candidates from different cultural areas and backgrounds, and perform evaluations from a global perspective. The image reaction analysis unit, for example, compares reactions to images between candidates from different cultural areas and performs evaluations from a global perspective. For example, it analyzes differences in reactions due to cultural background. Different cultural areas are evaluated based on differences in cultural background and values ​​in each region. Backgrounds are evaluated based on the candidate's educational background and occupational background. A global perspective is evaluated based on the application of international standards and cross-cultural understanding. This makes it possible to analyze personality characteristics in more detail by evaluating candidate reactions from a global perspective.

[0067] The image reaction analysis unit can use the emotion estimation function to analyze the candidate's emotional reaction to an image and generate an image that elicits positive emotions. The image reaction analysis unit, for example, analyzes the candidate's emotional reaction to an image and generates an image that elicits positive emotions. For example, the image is adjusted based on the emotion score. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Emotional reactions are evaluated based on the intensity and type of emotion. Positive emotions are evaluated based on joy, satisfaction, excitement, etc. Image generation is performed based on the generation algorithm and the dataset used. This makes it possible to analyze the candidate's emotional reaction and generate images that elicit positive emotions, thereby enabling a more detailed analysis of personality characteristics.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The screening system can further include a work history analysis unit that performs a detailed analysis of a candidate's past work history. The work history analysis unit analyzes, for example, the candidate's past job content, position, and project results to evaluate the candidate's expertise and leadership ability. Job content is evaluated based on the specific work content and projects in which the candidate was in charge. Positions are evaluated based on past positions and promotion history. Project results are evaluated based on goals achieved and awards received. This enables a more comprehensive analysis of a candidate's personality characteristics by analyzing their work history in detail.

[0070] The screening system can further include a hobby analysis unit that analyzes the hobbies and interests of candidates. The hobby analysis unit extracts hobbies and interests from, for example, the self-introduction text submitted by the candidate or posts on social media, and evaluates the candidate's personality characteristics. Hobbies are evaluated based on activities such as sports, music, and reading. Interests are evaluated based on interest in specific fields or themes. This makes it possible to analyze the candidate's personality characteristics in more detail by analyzing the candidate's hobbies and interests.

[0071] The screening system may further include a health analysis unit that analyzes the candidate's health condition. The health analysis unit analyzes, for example, the health certificate submitted by the candidate and self-reported health information to evaluate the candidate's health condition. The health certificate is evaluated based on the results of blood tests and physical measurements. The self-reported health information is evaluated based on lifestyle habits and medical history. This allows for a more comprehensive analysis of the candidate's personality traits by analyzing the candidate's health condition.

[0072] The screening system can further include a learning ability analysis unit that analyzes the candidate's learning ability. The learning ability analysis unit analyzes, for example, the academic history, qualifications, and online course completion certificates submitted by the candidate to evaluate the candidate's learning ability. The academic history is evaluated based on the degree and major obtained. The qualifications are evaluated based on the qualifications and certifications obtained. The online course completion certificates are evaluated based on the courses taken and the courses completed. In this way, analyzing the candidate's learning ability makes it possible to analyze personality characteristics in more detail.

[0073] The screening system can further include an ethical analysis unit that analyzes the candidate's ethical values. The ethical analysis unit evaluates the candidate's ethical values, for example, by analyzing the essays submitted by the candidate and their past behavioral history. The essays are evaluated based on the candidate's thoughts and values ​​regarding ethical issues. The past behavioral history is evaluated based on ethical behavior and social contributions. In this way, analyzing the candidate's ethical values ​​enables a more detailed analysis of their personality traits.

[0074] The screening system can also use a candidate's emotion estimation function to evaluate a candidate's stress tolerance. For example, it can monitor a candidate's emotional state in real time when submitting documents or answering questions to evaluate their stress tolerance. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Stress tolerance is evaluated based on performance under stress and the strength of the stress response. This makes it possible to evaluate a candidate's stress tolerance by monitoring their emotional state in real time.

[0075] The screening system can also use a candidate's emotion estimation function to evaluate the candidate's cooperativeness. For example, the candidate's emotional state can be monitored in real time during online interactive question-and-answer sessions to evaluate their cooperativeness. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. The cooperativeness is evaluated based on the degree of communication and cooperation with others. This makes it possible to evaluate the candidate's cooperativeness by monitoring their emotional state in real time.

[0076] The screening system can also use a candidate's emotion estimation function to evaluate the candidate's leadership ability. For example, the candidate's emotional state can be monitored in real time while answering questions to evaluate their leadership ability. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Leadership ability is evaluated based on the degree of leadership demonstrated and the influence the candidate has on others. This makes it possible to evaluate a candidate's leadership ability by monitoring their emotional state in real time.

[0077] The screening system can also use a candidate's emotion estimation function to evaluate the candidate's creativity. For example, the candidate's emotional state can be monitored in real time during image reaction analysis to evaluate creativity. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Creativity is evaluated based on original ideas and new perspectives. This makes it possible to evaluate creativity by monitoring the candidate's emotional state in real time.

[0078] The screening system can also use a candidate's emotion estimation function to evaluate the candidate's adaptability. For example, the candidate's emotional state can be monitored in real time while answering questions to evaluate their adaptability. The emotion estimation function can be performed using technologies such as facial expression recognition, voice analysis, and text analysis. Adaptability is evaluated based on their flexibility to change and their adaptability to new environments. This makes it possible to evaluate a candidate's adaptability by monitoring their emotional state in real time.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The document analysis unit analyzes documents submitted by candidates, such as resumes, letters of recommendation, and self-introductions, to evaluate the candidate's personality traits and past behavioral history. Step 2: The Q&A analysis unit analyzes online interactive Q&A. For example, the candidate's stress tolerance and problem-solving ability are evaluated based on their responses to questions such as, "What is the most difficult situation you have ever experienced?" Step 3: The image reaction analysis unit analyzes the candidate's reaction to the AI-generated images. For example, it presents images of children, family, and everyday life scenes, and analyzes the candidate's facial expressions and reactions.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a document analysis unit that analyzes documents submitted by candidates; a question and answer analysis unit that analyzes online interactive question and answering; and an image reaction analysis unit that analyzes the candidate's reaction to the image generated by AI. A system characterized by:

2. The document analysis unit In addition to submitting the above documents, we will analyze the candidate's past social media posts and online activity history to conduct a comprehensive personality assessment.

2. The system of claim 1.

3. The question and answer analysis unit Analyze the content of questions and answers in chronological order and evaluate the candidate's consistency and logic 2. The system of claim 1.

4. The image response analysis unit The types of images presented by the generating AI are diversified, and the responses of the candidates are evaluated from multiple angles.

2. The system of claim 1.

5. The document analysis unit Conduct sentiment analysis on the content of the documents to assess the candidate's emotional tendencies 2. The system of claim 1.

6. The question and answer analysis unit Using an emotion estimation function, the candidate's emotional changes during question and answer sessions are monitored in real time to evaluate the candidate's stress tolerance.

2. The system of claim 1.

7. The image response analysis unit Using an emotion estimation function, the candidate's emotional response to the image is monitored in real time to assess the candidate's potential preferences.

2. The system of claim 1.

8. The question and answer analysis unit Analyzing the candidate's tone of voice and manner of speaking during question-and-answer sessions to assess the candidate's emotional state 2. The system of claim 1.

Images