System

Abstract

An object of the system according to the embodiment is to investigate the personality of an examinee in detail in an interactive manner and to perform a precise personality evaluation.SOLUTION: A system includes an interaction unit, an analysis unit, an evaluation unit, and a proposal unit. The interaction part interactively investigates the character of the examinee. The analysis unit analyzes the dialogue data collected by the dialogue unit. The evaluation unit evaluates the personality traits based on the analysis result obtained by the analysis unit. The proposal unit proposes the type of occupation or the workplace environment based on the evaluation result obtained by the evaluation unit.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 that personality tests have low reliability and it is difficult to grasp the test-taker's deep psychology.

[0005] The system according to the embodiment aims to investigate the personality of the examinee in detail in an interactive format and to perform a precise personality evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a proposal unit. The dialogue unit investigates the personality of the examinee in a dialogue format. The analysis unit analyzes the dialogue data collected by the dialogue unit. The evaluation unit evaluates the personality traits based on the analysis results obtained by the analysis unit. The proposal unit proposes job types and work environments based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can investigate the personality of the examinee in detail in an interactive manner and perform a precise personality evaluation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An evaluation system according to an embodiment of the present invention interactively surveys an examinee's personality, and a generation AI analyzes the dialogue data, evaluates the examinee's personality traits, and suggests an appropriate job type and work environment. The evaluation system interactively surveys an examinee's personality, and a generation AI analyzes the dialogue data and evaluates the examinee's personality traits, thereby suggesting an appropriate job type and work environment. For example, in the evaluation system, when an examinee takes a personality test, a generation AI asks questions in an interactive format. This dialogue dynamically changes depending on the examinee's answers, allowing for deeper psychological insights. The evaluation system then uses a generation AI to analyze the collected dialogue data and evaluate the examinee's personality traits in detail. For example, the generation AI analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc. expressed during the dialogue. The evaluation system then suggests an appropriate job type and work environment based on the analysis results. For example, if an examinee is evaluated as "good at teamwork," the generation AI suggests a "workplace where teamwork is emphasized." This allows the evaluation system to improve the credibility of personality tests and gain a detailed understanding of the examinee's personality and thought process. This allows the assessment system to gain a detailed understanding of the candidate's personality and thought process, and suggest suitable jobs and work environments, helping candidates find the right work environment without missing out on job interviews, for example.

[0029] The evaluation system according to the embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a proposal unit. The dialogue unit investigates the examinee's personality in a dialogue format. For example, when the examinee takes a personality test, the dialogue unit uses a generation AI to ask questions in a dialogue format. This dialogue dynamically changes depending on the examinee's answers, allowing for deeper psychological state exploration. For example, if the examinee answers "I often feel stressed," the generation AI asks an additional question such as "In what situations do you feel stressed?" to elicit specific situations and emotions. The analysis unit analyzes the dialogue data collected by the dialogue unit. For example, the analysis unit uses the generation AI to analyze the collected dialogue data and evaluate the examinee's personality traits in detail. For example, the analysis unit analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc., expressed by the generation AI during the dialogue. The evaluation unit evaluates the personality traits based on the analysis results obtained by the analysis unit. For example, the evaluation unit uses the generation AI to evaluate the examinee's personality traits based on the analysis results. For example, if an examinee is evaluated as "good at cooperating," the generation AI evaluates the examinee's personality traits in detail based on that evaluation. The suggestion unit proposes an appropriate job type and work environment based on the evaluation results obtained by the evaluation unit. The suggestion unit, for example, uses the generation AI to propose an appropriate job type and work environment based on the evaluation results. For example, if an examinee is evaluated as "good at cooperating in a team," the generation AI proposes a "workplace where teamwork is emphasized." In this way, the evaluation system according to the embodiment can grasp the examinee's personality and thought process in detail and propose an appropriate job type and work environment.

[0030] The dialogue unit can change the questions asked depending on the examinee's answers. The dialogue unit, for example, dynamically changes the questions depending on the examinee's answers. For example, if the examinee answers "I often feel stressed," the generation AI asks an additional question such as "In what situations do you feel stressed?" to elicit specific situations and emotions. The dialogue unit can also adjust the content and order of questions based on the examinee's answers. For example, if the examinee answers "I'm good at cooperating," the generation AI asks a question such as "Please tell me a specific example" to elicit more detailed information. This allows for more detailed personality assessment by flexibly changing the questions depending on the examinee's answers. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the examinee's answer data into the generation AI and have it execute dynamic question generation.

[0031] The analysis unit can analyze the dialogue data and analyze the examinee's emotions, attitudes, and thought patterns. For example, the analysis unit can analyze the dialogue data from multiple angles to analyze the examinee's emotions, attitudes, and thought patterns. For example, the analysis unit can use a generation AI to analyze emotional expressions, attitudes, and thought patterns in the dialogue data to evaluate the examinee's personality traits. The analysis unit can also track changes in the examinee's emotions and attitudes based on the dialogue data. For example, the analysis unit can use a generation AI to analyze emotional changes in the dialogue data to evaluate the examinee's stress level and relaxation level. Furthermore, the analysis unit can analyze the examinee's thought patterns in detail based on the dialogue data. For example, the analysis unit can use a generation AI to analyze logical and intuitive thinking patterns in the dialogue data to evaluate the examinee's thinking characteristics. This allows for a detailed evaluation of the examinee's personality traits by analyzing the dialogue data from multiple angles. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input dialogue data into the generation AI and have the generation AI analyze the emotions, attitudes, and thought patterns.

[0032] The evaluation unit can evaluate the personality traits of the examinee based on the analysis results. The evaluation unit, for example, evaluates the personality traits of the examinee based on the analysis results. For example, the evaluation unit uses a generation AI to evaluate the personality traits of the examinee based on the analysis results. For example, if the examinee is evaluated as "good at cooperation," the generation AI evaluates the personality traits in detail based on that evaluation. The evaluation unit can also evaluate the personality traits of the examinee from multiple angles based on the analysis results. For example, the generation AI can evaluate the examinee's personality traits, such as extroversion, introversion, and agreeableness, based on the analysis results. Furthermore, the evaluation unit can evaluate the personality traits of the examinee using a scoring system or evaluation model based on the analysis results. For example, the generation AI can score the personality traits of the examinee based on the analysis results and evaluate them using an evaluation model. This allows the personality traits of the examinee to be accurately evaluated based on the analysis results. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the analysis results to the generation AI and have the generation AI perform an evaluation of the personality traits.

[0033] The suggestion unit can suggest a job type and a work environment based on the evaluation results. For example, the suggestion unit suggests an appropriate job type and a work environment based on the evaluation results. For example, the suggestion unit uses a generation AI to suggest an appropriate job type and a work environment based on the evaluation results. For example, if an examinee is evaluated as "good at working in teams," the generation AI suggests a "workplace where teamwork is emphasized." The suggestion unit can also suggest a work environment that is optimal for the examinee's personality traits based on the evaluation results. For example, the generation AI can suggest a work environment that is optimal for the examinee's personality traits based on the evaluation results. The suggestion unit can also suggest a job type and a work environment based on the examinee's personality traits based on the evaluation results. For example, the generation AI can suggest a job type and a work environment based on the examinee's personality traits based on the evaluation results. This makes it possible to suggest a job type and a work environment that is optimal for the examinee based on the evaluation results. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the evaluation results into the generation AI and cause the generation AI to suggest a job type and a work environment.

[0034] The dialogue unit can analyze the examinee's past dialogue history and select the order of questions. The dialogue unit, for example, analyzes the examinee's past dialogue history and selects the optimal order of questions. For example, the generation AI determines the optimal order of questions based on the patterns of questions the examinee has answered in the past. The generation AI can also analyze the content of the examinee's past answers and prioritize related questions. The generation AI can also focus on questions related to a specific topic based on the examinee's past dialogue history. This enables efficient personality evaluation by selecting the optimal order of questions based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's past dialogue history data into the generation AI and have the generation AI select the order of questions.

[0035] The dialogue unit can analyze the examinee's nonverbal responses during the dialogue and dynamically change the content of the questions. For example, the dialogue unit can analyze the examinee's nonverbal responses (facial expressions, gestures, etc.) during the dialogue and dynamically change the content of the questions. For example, if the examinee looks confused, the generation AI can simplify the questions. Also, if the examinee shows interest, the generation AI can ask additional questions related to that topic. Also, if the examinee looks tired, the generation AI can suggest taking a break. In this way, by analyzing nonverbal responses, more appropriate questions can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's nonverbal response data into the generation AI and cause the generation AI to dynamically change the content of the questions.

[0036] The dialogue unit can generate follow-up questions to dig deeper into specific personality traits based on the examinee's answers. The dialogue unit generates follow-up questions to dig deeper into specific personality traits based on the examinee's answers, for example. For example, if the examinee answers "I'm good at cooperating," the generation AI can ask about specific episodes. Also, if the examinee answers "I'm resistant to stress," the generation AI can ask about specific situations. Also, if the examinee answers "I have leadership skills," the generation AI can ask about specific experiences. This allows for more detailed personality evaluation by digging deeper into specific personality traits. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's answer data into the generation AI and cause the generation AI to generate follow-up questions.

[0037] The dialogue unit can customize the questions during the dialogue, taking into account the examinee's geographical and cultural background. The dialogue unit, for example, customizes the questions during the dialogue, taking into account the examinee's geographical and cultural background. For example, if the examinee is from a particular region, the generation AI asks questions related to that region. The generation AI can also use appropriate expressions taking into account the examinee's cultural background. The generation AI can also provide relevant examples based on the examinee's geographical background. This allows for more appropriate questions to be provided by taking into account the examinee's geographical and cultural background. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's geographical and cultural background data into the generation AI and have the generation AI customize the questions.

[0038] The dialogue unit can adjust the content of questions during the dialogue, taking into account the work experience and educational background of the examinee. The dialogue unit, for example, adjusts the content of questions during the dialogue, taking into account the work experience and educational background of the examinee. For example, the generation AI asks relevant questions based on the work experience of the examinee. The generation AI can also ask questions of an appropriate level of difficulty based on the examinee's educational background. The generation AI can also ask specific examples, taking into account the work experience and educational background of the examinee. In this way, more appropriate questions can be provided by taking into account the work experience and educational background. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the work experience and educational background data of the examinee into the generation AI, and have the generation AI adjust the content of the questions.

[0039] The dialogue unit can add relevant questions based on the examinee's interests during the dialogue. For example, if the examinee is interested in a particular field, the generation AI can ask questions related to that field. The generation AI can also suggest related topics based on the examinee's interests. The generation AI can also ask more in-depth questions based on the examinee's interests. This allows for a more detailed personality assessment by adding questions based on the examinee's interests. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's interest data into the generation AI and have the generation AI add relevant questions.

[0040] The analysis unit can extract specific keywords and phrases from the dialogue data and analyze personality traits in detail. The analysis unit, for example, extracts specific keywords and phrases from the dialogue data and analyzes personality traits in detail. For example, the generation AI extracts keywords frequently used by the test taker and analyzes personality traits. The generation AI can also extract specific phrases from the test taker's answers and evaluate personality traits. The generation AI can also analyze the test taker's dialogue data and evaluate personality traits from multiple angles. In this way, personality traits can be analyzed in detail by extracting specific keywords and phrases. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input dialogue data to the generation AI and have the generation AI extract keywords and phrases and analyze personality traits.

[0041] The analysis unit can analyze the dialogue data along a time axis and track changes in the examinee's emotions. The analysis unit, for example, analyzes the dialogue data along a time axis and tracks changes in the examinee's emotions. For example, the generation AI analyzes the examinee's dialogue data along a time axis and tracks changes in emotions. The generation AI can also analyze changes in the examinee's emotions and identify timing of stress and relaxation. The generation AI can also analyze the examinee's dialogue data along a time axis and visualize changes in emotions. This enables more detailed personality evaluation by tracking changes in emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input dialogue data to the generation AI and have the generation AI track changes in emotions.

[0042] The analysis unit can analyze the dialogue data from multiple perspectives and evaluate the personality traits of the examinee from multiple angles. The analysis unit, for example, analyzes the dialogue data from multiple perspectives and evaluates the personality traits of the examinee from multiple angles. For example, the generation AI analyzes the dialogue data of the examinee from multiple perspectives and evaluates the personality traits. The generation AI can also analyze the emotions, attitudes, and thought patterns of the examinee from multiple angles. The generation AI can also analyze the dialogue data of the examinee from multiple angles and evaluate the personality traits in detail. In this way, the personality traits can be evaluated from multiple angles by analyzing from multiple perspectives. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the dialogue data to the generation AI and have the generation AI perform analysis from multiple perspectives.

[0043] The analysis unit can integrate the dialogue data with other data sources and analyze it. For example, the analysis unit integrates the dialogue data with other data sources (social media, resume, etc.) and analyzes it. For example, the generation AI integrates the examinee's social media data and analyzes personality traits. The generation AI can also integrate the examinee's resume data and analyze personality traits. The generation AI can also integrate the examinee's dialogue data with other data sources and analyze personality traits from multiple angles. In this way, personality traits can be analyzed from multiple angles by integrating them with other data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the dialogue data and other data sources into the generation AI and have the generation AI perform the integrated analysis.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The evaluation system can take into account the examinee's past work experience and educational background when evaluating the examinee's personality traits. For example, the analysis unit analyzes the examinee's resume data and evaluates the personality traits based on the examinee's past work experience and educational background. The evaluation unit can also evaluate the examinee's aptitude in more detail based on the examinee's past work experience and educational background. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on the examinee's past work experience and educational background. This makes it possible to suggest a more appropriate job type and work environment by taking the examinee's past work experience and educational background into consideration.

[0046] The evaluation system can take into account the examinee's hobbies and interests when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their hobbies and interests, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on the hobbies and interests. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on the hobbies and interests. This makes it possible to suggest more appropriate job types and work environments by taking the examinee's hobbies and interests into consideration.

[0047] The evaluation system can take into account the examinee's lifestyle and habits when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their lifestyle and habits, and the analysis unit evaluates the examinee's personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their lifestyle and habits. Furthermore, the suggestion unit can suggest the most suitable occupation and work environment for the examinee based on their lifestyle and habits. This makes it possible to suggest more appropriate occupations and work environments by taking the examinee's lifestyle and habits into consideration.

[0048] The evaluation system can take into account the examinee's health condition and fitness level when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their health condition and fitness level, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their health condition and fitness level. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on their health condition and fitness level. This makes it possible to suggest more appropriate job types and work environments by taking the examinee's health condition and fitness level into consideration.

[0049] The evaluation system can take into account the examinee's family structure and home environment when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their family structure and home environment, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their family structure and home environment. Furthermore, the suggestion unit can suggest the most suitable occupation and work environment for the examinee based on their family structure and home environment. This makes it possible to suggest more appropriate occupations and work environments by taking the examinee's family structure and home environment into consideration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The dialogue section investigates the test taker's personality in a dialogue format. For example, when a test taker takes a personality test, the generation AI asks questions in a dialogue format. This dialogue changes dynamically depending on the test taker's answers, allowing for deeper psychological state exploration. For example, if a test taker answers "I often feel stressed," the generation AI asks additional questions such as "In what situations do you feel stressed?" to elicit specific situations and emotions. Step 2: The analysis unit analyzes the dialogue data collected by the dialogue unit. For example, using a generation AI, the analysis unit analyzes the collected dialogue data and evaluates the examinee's personality traits in detail. For example, the analysis unit analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc. shown by the generation AI during the dialogue. Step 3: The evaluation unit evaluates the personality traits based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the personality traits of the examinee based on the analysis results, for example, using a generation AI. For example, if the examinee is evaluated as "good at cooperating," the generation AI will evaluate the personality traits in detail based on that evaluation. Step 4: The proposal unit proposes appropriate job types and work environments based on the evaluation results obtained by the evaluation unit. The proposal unit, for example, uses a generation AI to propose appropriate job types and work environments based on the evaluation results. For example, if the candidate is evaluated as "good at working together in a team," the generation AI will propose a "workplace where teamwork is emphasized."

[0052] (Example 2) An evaluation system according to an embodiment of the present invention interactively surveys an examinee's personality, and a generation AI analyzes the dialogue data, evaluates the examinee's personality traits, and suggests an appropriate job type and work environment. The evaluation system interactively surveys an examinee's personality, and a generation AI analyzes the dialogue data and evaluates the examinee's personality traits, thereby suggesting an appropriate job type and work environment. For example, in the evaluation system, when an examinee takes a personality test, a generation AI asks questions in an interactive format. This dialogue dynamically changes depending on the examinee's answers, allowing for deeper psychological insights. The evaluation system then uses a generation AI to analyze the collected dialogue data and evaluate the examinee's personality traits in detail. For example, the generation AI analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc. expressed during the dialogue. The evaluation system then suggests an appropriate job type and work environment based on the analysis results. For example, if an examinee is evaluated as "good at teamwork," the generation AI suggests a "workplace where teamwork is emphasized." This allows the evaluation system to improve the credibility of personality tests and gain a detailed understanding of the examinee's personality and thought process. This allows the assessment system to gain a detailed understanding of the candidate's personality and thought process, and suggest suitable jobs and work environments, helping candidates find the right work environment without missing out on job interviews, for example.

[0053] The evaluation system according to the embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a proposal unit. The dialogue unit investigates the examinee's personality in a dialogue format. For example, when the examinee takes a personality test, the dialogue unit uses a generation AI to ask questions in a dialogue format. This dialogue dynamically changes depending on the examinee's answers, allowing for deeper psychological state exploration. For example, if the examinee answers "I often feel stressed," the generation AI asks an additional question such as "In what situations do you feel stressed?" to elicit specific situations and emotions. The analysis unit analyzes the dialogue data collected by the dialogue unit. For example, the analysis unit uses the generation AI to analyze the collected dialogue data and evaluate the examinee's personality traits in detail. For example, the analysis unit analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc., expressed by the generation AI during the dialogue. The evaluation unit evaluates the personality traits based on the analysis results obtained by the analysis unit. For example, the evaluation unit uses the generation AI to evaluate the examinee's personality traits based on the analysis results. For example, if an examinee is evaluated as "good at cooperating," the generation AI evaluates the examinee's personality traits in detail based on that evaluation. The suggestion unit proposes an appropriate job type and work environment based on the evaluation results obtained by the evaluation unit. The suggestion unit, for example, uses the generation AI to propose an appropriate job type and work environment based on the evaluation results. For example, if an examinee is evaluated as "good at cooperating in a team," the generation AI proposes a "workplace where teamwork is emphasized." In this way, the evaluation system according to the embodiment can grasp the examinee's personality and thought process in detail and propose an appropriate job type and work environment.

[0054] The dialogue unit can change the questions asked depending on the examinee's answers. The dialogue unit, for example, dynamically changes the questions depending on the examinee's answers. For example, if the examinee answers "I often feel stressed," the generation AI asks an additional question such as "In what situations do you feel stressed?" to elicit specific situations and emotions. The dialogue unit can also adjust the content and order of questions based on the examinee's answers. For example, if the examinee answers "I'm good at cooperating," the generation AI asks a question such as "Please tell me a specific example" to elicit more detailed information. This allows for more detailed personality assessment by flexibly changing the questions depending on the examinee's answers. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the examinee's answer data into the generation AI and have it execute dynamic question generation.

[0055] The analysis unit can analyze the dialogue data and analyze the examinee's emotions, attitudes, and thought patterns. For example, the analysis unit can analyze the dialogue data from multiple angles to analyze the examinee's emotions, attitudes, and thought patterns. For example, the analysis unit can use a generation AI to analyze emotional expressions, attitudes, and thought patterns in the dialogue data to evaluate the examinee's personality traits. The analysis unit can also track changes in the examinee's emotions and attitudes based on the dialogue data. For example, the analysis unit can use a generation AI to analyze emotional changes in the dialogue data to evaluate the examinee's stress level and relaxation level. Furthermore, the analysis unit can analyze the examinee's thought patterns in detail based on the dialogue data. For example, the analysis unit can use a generation AI to analyze logical and intuitive thinking patterns in the dialogue data to evaluate the examinee's thinking characteristics. This allows for a detailed evaluation of the examinee's personality traits by analyzing the dialogue data from multiple angles. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input dialogue data into the generation AI and have the generation AI analyze the emotions, attitudes, and thought patterns.

[0056] The evaluation unit can evaluate the personality traits of the examinee based on the analysis results. The evaluation unit, for example, evaluates the personality traits of the examinee based on the analysis results. For example, the evaluation unit uses a generation AI to evaluate the personality traits of the examinee based on the analysis results. For example, if the examinee is evaluated as "good at cooperation," the generation AI evaluates the personality traits in detail based on that evaluation. The evaluation unit can also evaluate the personality traits of the examinee from multiple angles based on the analysis results. For example, the generation AI can evaluate the examinee's personality traits, such as extroversion, introversion, and agreeableness, based on the analysis results. Furthermore, the evaluation unit can evaluate the personality traits of the examinee using a scoring system or evaluation model based on the analysis results. For example, the generation AI can score the personality traits of the examinee based on the analysis results and evaluate them using an evaluation model. This allows the personality traits of the examinee to be accurately evaluated based on the analysis results. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the analysis results to the generation AI and have the generation AI perform an evaluation of the personality traits.

[0057] The suggestion unit can suggest a job type and a work environment based on the evaluation results. For example, the suggestion unit suggests an appropriate job type and a work environment based on the evaluation results. For example, the suggestion unit uses a generation AI to suggest an appropriate job type and a work environment based on the evaluation results. For example, if an examinee is evaluated as "good at working in teams," the generation AI suggests a "workplace where teamwork is emphasized." The suggestion unit can also suggest a work environment that is optimal for the examinee's personality traits based on the evaluation results. For example, the generation AI can suggest a work environment that is optimal for the examinee's personality traits based on the evaluation results. The suggestion unit can also suggest a job type and a work environment based on the examinee's personality traits based on the evaluation results. For example, the generation AI can suggest a job type and a work environment based on the examinee's personality traits based on the evaluation results. This makes it possible to suggest a job type and a work environment that is optimal for the examinee based on the evaluation results. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the evaluation results into the generation AI and cause the generation AI to suggest a job type and a work environment.

[0058] The dialogue unit can estimate the examinee's emotions and adjust the difficulty and content of questions based on the estimated emotions. The dialogue unit, for example, estimates the examinee's emotions and adjusts the difficulty and content of questions based on the estimated emotions. For example, if the examinee is nervous, the generation AI can start with easy questions and gradually increase the difficulty. Also, if the examinee is relaxed, the generation AI can ask more in-depth questions early on. Also, if the examinee is feeling stressed, the generation AI can interject questions to relax the examinee. This allows for more accurate personality assessment by adjusting the difficulty and content of questions based on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without the generation AI. For example, the dialogue unit can input the examinee's emotional data into the generation AI and have the generation AI adjust the difficulty and content of questions.

[0059] The dialogue unit can analyze the examinee's past dialogue history and select the order of questions. The dialogue unit, for example, analyzes the examinee's past dialogue history and selects the optimal order of questions. For example, the generation AI determines the optimal order of questions based on the patterns of questions the examinee has answered in the past. The generation AI can also analyze the content of the examinee's past answers and prioritize related questions. The generation AI can also focus on questions related to a specific topic based on the examinee's past dialogue history. This enables efficient personality evaluation by selecting the optimal order of questions based on the past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's past dialogue history data into the generation AI and have the generation AI select the order of questions.

[0060] The dialogue unit can analyze the examinee's nonverbal responses during the dialogue and dynamically change the content of the questions. For example, the dialogue unit can analyze the examinee's nonverbal responses (facial expressions, gestures, etc.) during the dialogue and dynamically change the content of the questions. For example, if the examinee looks confused, the generation AI can simplify the questions. Also, if the examinee shows interest, the generation AI can ask additional questions related to that topic. Also, if the examinee looks tired, the generation AI can suggest taking a break. In this way, by analyzing nonverbal responses, more appropriate questions can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's nonverbal response data into the generation AI and cause the generation AI to dynamically change the content of the questions.

[0061] The dialogue unit can generate follow-up questions to dig deeper into specific personality traits based on the examinee's answers. The dialogue unit generates follow-up questions to dig deeper into specific personality traits based on the examinee's answers, for example. For example, if the examinee answers "I'm good at cooperating," the generation AI can ask about specific episodes. Also, if the examinee answers "I'm resistant to stress," the generation AI can ask about specific situations. Also, if the examinee answers "I have leadership skills," the generation AI can ask about specific experiences. This allows for more detailed personality evaluation by digging deeper into specific personality traits. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's answer data into the generation AI and cause the generation AI to generate follow-up questions.

[0062] The dialogue unit can estimate the examinee's emotions and adjust the tone and wording of questions based on the estimated emotions. The dialogue unit, for example, estimates the examinee's emotions and adjusts the tone and wording of questions based on the estimated emotions. For example, if the examinee is nervous, the generation AI can ask questions in a gentle tone. If the examinee is relaxed, the generation AI can ask questions in a friendly tone. If the examinee is stressed, the generation AI can ask questions in a calm tone. This allows for more accurate personality assessment by adjusting the tone and wording of questions according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the dialogue unit may be performed using the generation AI, or may be performed without the generation AI. For example, the dialogue unit can input the examinee's emotion data into the generation AI and have the generation AI adjust the tone and wording of questions.

[0063] The dialogue unit can customize the questions during the dialogue, taking into account the examinee's geographical and cultural background. The dialogue unit, for example, customizes the questions during the dialogue, taking into account the examinee's geographical and cultural background. For example, if the examinee is from a particular region, the generation AI asks questions related to that region. The generation AI can also use appropriate expressions taking into account the examinee's cultural background. The generation AI can also provide relevant examples based on the examinee's geographical background. This allows for more appropriate questions to be provided by taking into account the examinee's geographical and cultural background. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's geographical and cultural background data into the generation AI and have the generation AI customize the questions.

[0064] The dialogue unit can adjust the content of questions during the dialogue, taking into account the work experience and educational background of the examinee. The dialogue unit, for example, adjusts the content of questions during the dialogue, taking into account the work experience and educational background of the examinee. For example, the generation AI asks relevant questions based on the work experience of the examinee. The generation AI can also ask questions of an appropriate level of difficulty based on the examinee's educational background. The generation AI can also ask specific examples, taking into account the work experience and educational background of the examinee. In this way, more appropriate questions can be provided by taking into account the work experience and educational background. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the work experience and educational background data of the examinee into the generation AI, and have the generation AI adjust the content of the questions.

[0065] The dialogue unit can add relevant questions based on the examinee's interests during the dialogue. For example, if the examinee is interested in a particular field, the generation AI can ask questions related to that field. The generation AI can also suggest related topics based on the examinee's interests. The generation AI can also ask more in-depth questions based on the examinee's interests. This allows for a more detailed personality assessment by adding questions based on the examinee's interests. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can input the examinee's interest data into the generation AI and have the generation AI add relevant questions.

[0066] The analysis unit can estimate the examinee's emotions and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit estimates the examinee's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the examinee is nervous, the generation AI can improve the accuracy of emotion analysis. Furthermore, if the examinee is relaxed, the generation AI can perform detailed emotion analysis. Furthermore, if the examinee is stressed, the generation AI can perform analysis to identify the stress factors. This enables more accurate personality assessment by adjusting the analysis algorithm according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit can input the examinee's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0067] The analysis unit can extract specific keywords and phrases from the dialogue data and analyze personality traits in detail. The analysis unit, for example, extracts specific keywords and phrases from the dialogue data and analyzes personality traits in detail. For example, the generation AI extracts keywords frequently used by the test taker and analyzes personality traits. The generation AI can also extract specific phrases from the test taker's answers and evaluate personality traits. The generation AI can also analyze the test taker's dialogue data and evaluate personality traits from multiple angles. In this way, personality traits can be analyzed in detail by extracting specific keywords and phrases. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input dialogue data to the generation AI and have the generation AI extract keywords and phrases and analyze personality traits.

[0068] The analysis unit can analyze the dialogue data along a time axis and track changes in the examinee's emotions. The analysis unit, for example, analyzes the dialogue data along a time axis and tracks changes in the examinee's emotions. For example, the generation AI analyzes the examinee's dialogue data along a time axis and tracks changes in emotions. The generation AI can also analyze changes in the examinee's emotions and identify timing of stress and relaxation. The generation AI can also analyze the examinee's dialogue data along a time axis and visualize changes in emotions. This enables more detailed personality evaluation by tracking changes in emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input dialogue data to the generation AI and have the generation AI track changes in emotions.

[0069] The analysis unit can analyze the dialogue data from multiple perspectives and evaluate the personality traits of the examinee from multiple angles. The analysis unit, for example, analyzes the dialogue data from multiple perspectives and evaluates the personality traits of the examinee from multiple angles. For example, the generation AI analyzes the dialogue data of the examinee from multiple perspectives and evaluates the personality traits. The generation AI can also analyze the emotions, attitudes, and thought patterns of the examinee from multiple angles. The generation AI can also analyze the dialogue data of the examinee from multiple angles and evaluate the personality traits in detail. In this way, the personality traits can be evaluated from multiple angles by analyzing from multiple perspectives. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the dialogue data to the generation AI and have the generation AI perform analysis from multiple perspectives.

[0070] The analysis unit can estimate the examinee's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the examinee's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the examinee is nervous, the generation AI can provide a simple display method. Alternatively, if the examinee is relaxed, the generation AI can provide a detailed display method. Alternatively, if the examinee is stressed, the generation AI can provide a display method that visually relaxes the examinee. This allows for more appropriate feedback by adjusting the display method according to the examinee's emotions. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the examinee's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0071] The analysis unit can integrate the dialogue data with other data sources and analyze it. For example, the analysis unit integrates the dialogue data with other data sources (social media, resume, etc.) and analyzes it. For example, the generation AI integrates the examinee's social media data and analyzes personality traits. The generation AI can also integrate the examinee's resume data and analyze personality traits. The generation AI can also integrate the examinee's dialogue data with other data sources and analyze personality traits from multiple angles. In this way, personality traits can be analyzed from multiple angles by integrating them with other data sources. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the dialogue data and other data sources into the generation AI and have the generation AI perform the integrated analysis. === Hard Collateral 1-1 === Each of the multiple elements, including the dialogue unit, analysis unit, evaluation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, when an examinee takes a personality test, questions are asked in an interactive format using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected dialogue data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates personality traits based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate job types and work environments based on the evaluation results. === Hard Collateral 1-2 === Each of the multiple elements, including the dialogue unit, analysis unit, evaluation unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, when an examinee takes a personality test, questions are asked in an interactive format using the microphone 238 or camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected dialogue data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates personality traits based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate job types and work environments based on the evaluation results. === Hard Collateral 1-3 === Each of the multiple elements, including the dialogue unit, analysis unit, evaluation unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, when an examinee takes a personality test, questions are asked in an interactive format using the microphone 238 or camera 42 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected dialogue data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates personality traits based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate job types and work environments based on the evaluation results. === Hard Collateral 1-4 === Each of the multiple elements, including the dialogue unit, analysis unit, evaluation unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, when an examinee takes a personality test, questions are asked in an interactive format using the microphone 238 or camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes collected dialogue data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates personality traits based on the analysis results. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an appropriate job type or work environment based on the evaluation results.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The evaluation system can take into account the examinee's past work experience and educational background when evaluating the examinee's personality traits. For example, the analysis unit analyzes the examinee's resume data and evaluates the personality traits based on the examinee's past work experience and educational background. The evaluation unit can also evaluate the examinee's aptitude in more detail based on the examinee's past work experience and educational background. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on the examinee's past work experience and educational background. This makes it possible to suggest a more appropriate job type and work environment by taking the examinee's past work experience and educational background into consideration.

[0074] The evaluation system can take into account the examinee's hobbies and interests when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their hobbies and interests, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on the hobbies and interests. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on the hobbies and interests. This makes it possible to suggest more appropriate job types and work environments by taking the examinee's hobbies and interests into consideration.

[0075] The evaluation system can take into account the examinee's lifestyle and habits when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their lifestyle and habits, and the analysis unit evaluates the examinee's personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their lifestyle and habits. Furthermore, the suggestion unit can suggest the most suitable occupation and work environment for the examinee based on their lifestyle and habits. This makes it possible to suggest more appropriate occupations and work environments by taking the examinee's lifestyle and habits into consideration.

[0076] The evaluation system can take into account the examinee's health condition and fitness level when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their health condition and fitness level, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their health condition and fitness level. Furthermore, the suggestion unit can suggest the most suitable job type and work environment for the examinee based on their health condition and fitness level. This makes it possible to suggest more appropriate job types and work environments by taking the examinee's health condition and fitness level into consideration.

[0077] The evaluation system can take into account the examinee's family structure and home environment when evaluating the examinee's personality traits. For example, the dialogue unit asks the examinee questions about their family structure and home environment, and the analysis unit evaluates the personality traits based on the answers. The evaluation unit can also evaluate the examinee's aptitude in more detail based on their family structure and home environment. Furthermore, the suggestion unit can suggest the most suitable occupation and work environment for the examinee based on their family structure and home environment. This makes it possible to suggest more appropriate occupations and work environments by taking the examinee's family structure and home environment into consideration.

[0078] When evaluating the personality traits of an examinee, the evaluation system can estimate the examinee's emotions and adjust the evaluation results based on the estimated emotions. For example, the analysis unit analyzes dialogue data and estimates the examinee's emotions. Then, the evaluation unit can adjust the evaluation results of the personality traits based on the estimated emotions. For example, if the examinee is nervous, the evaluation results can be relaxed. Also, if the examinee is relaxed, the evaluation results can be made more detailed. This allows for more accurate personality evaluation by adjusting the evaluation results according to the examinee's emotions.

[0079] When evaluating the personality traits of an examinee, the evaluation system can estimate the examinee's emotions and provide feedback based on the estimated emotions. For example, the analysis unit analyzes dialogue data and estimates the examinee's emotions. Next, the suggestion unit can adjust the feedback to the examinee based on the estimated emotions. For example, if the examinee is nervous, feedback to help the examinee relax can be provided. Also, if the examinee is relaxed, detailed feedback can be provided. This allows for more appropriate feedback to be provided by adjusting the feedback according to the examinee's emotions.

[0080] When evaluating a test taker's personality traits, the evaluation system can estimate the test taker's emotions and adjust the order of questions based on the estimated emotions. For example, the dialogue unit analyzes dialogue data to estimate the test taker's emotions. Then, the dialogue unit can adjust the order of questions based on the estimated emotions. For example, if the test taker is nervous, the dialogue unit can start with simple questions. On the other hand, if the test taker is relaxed, the dialogue unit can ask more in-depth questions at an early stage. This allows for a more appropriate personality evaluation by adjusting the order of questions according to the test taker's emotions.

[0081] When evaluating the personality traits of an examinee, the evaluation system can estimate the examinee's emotions and adjust the content of questions based on the estimated emotions. For example, the dialogue unit analyzes dialogue data and estimates the examinee's emotions. Next, the dialogue unit can adjust the content of questions based on the estimated emotions. For example, if the examinee is nervous, the dialogue unit can ask questions to relax the examinee. Also, if the examinee is relaxed, the dialogue unit can ask more in-depth questions. This allows for more appropriate personality evaluation by adjusting the content of questions according to the examinee's emotions.

[0082] When evaluating the personality traits of an examinee, the evaluation system can estimate the examinee's emotions and adjust the display method of the evaluation results based on the estimated emotions. For example, the analysis unit analyzes dialogue data and estimates the examinee's emotions. Then, the evaluation unit can adjust the display method of the evaluation results based on the estimated emotions. For example, if the examinee is nervous, a simple display method can be provided. On the other hand, if the examinee is relaxed, a detailed display method can be provided. This allows for more appropriate feedback by adjusting the display method of the evaluation results according to the examinee's emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The dialogue section investigates the test taker's personality in a dialogue format. For example, when a test taker takes a personality test, the generation AI asks questions in a dialogue format. This dialogue changes dynamically depending on the test taker's answers, allowing for deeper psychological state exploration. For example, if a test taker answers "I often feel stressed," the generation AI asks additional questions such as "In what situations do you feel stressed?" to elicit specific situations and emotions. Step 2: The analysis unit analyzes the dialogue data collected by the dialogue unit. For example, using a generation AI, the analysis unit analyzes the collected dialogue data and evaluates the examinee's personality traits in detail. For example, the analysis unit analyzes the examinee's personality traits from multiple angles based on the emotions, attitudes, thought patterns, etc. shown by the generation AI during the dialogue. Step 3: The evaluation unit evaluates the personality traits based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the personality traits of the examinee based on the analysis results, for example, using a generation AI. For example, if the examinee is evaluated as "good at cooperating," the generation AI will evaluate the personality traits in detail based on that evaluation. Step 4: The proposal unit proposes appropriate job types and work environments based on the evaluation results obtained by the evaluation unit. The proposal unit, for example, uses a generation AI to propose appropriate job types and work environments based on the evaluation results. For example, if the candidate is evaluated as "good at working together in a team," the generation AI will propose a "workplace where teamwork is emphasized."

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] [Explanation of symbols]

[0157] 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 dialogue section in which the examinee's personality is investigated in a dialogue format; an analysis unit that analyzes the dialogue data collected by the dialogue unit; an evaluation unit that evaluates personality traits based on the analysis results obtained by the analysis unit; a proposal unit that proposes a job type and a work environment based on the evaluation results obtained by the evaluation unit; Equipped with A system characterized by:

2. The dialogue unit Change questions based on test-taker answers 2. The system of claim 1.

3. The analysis unit Analyzing conversation data to analyze test takers' emotions, attitudes, and thought patterns 2. The system of claim 1.

4. The evaluation unit Evaluate the examinee's personality traits based on the analysis results 2. The system of claim 1.

5. The proposal unit Propose job types and work environments based on the evaluation results 2. The system of claim 1.

6. The dialogue unit Estimate the test taker's emotions and adjust the difficulty and content of questions based on the estimated emotions of the test taker 2. The system of claim 1.

7. The dialogue unit Analyze the test taker's past dialogue history and select the order of questions 2. The system of claim 1.

8. The dialogue unit Analyze the test taker's non-verbal responses during the conversation and dynamically change the questions asked 2. The system of claim 1.

9. The dialogue unit Generate follow-up questions based on test-taker answers to explore specific personality traits 2. The system of claim 1.

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