system

Abstract

We provide the system. [Solution] A terminal device that receives input from test takers, A server device that performs natural language processing based on input from the aforementioned test taker and analyzes personality traits, An information processing device equipped with a generative AI model that generates additional questions based on the aforementioned personality traits and transmits them to the terminal device, It is equipped with a report generation function that generates a final evaluation of the aforementioned personality traits and reports it to the company. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional selective personality test method, it is difficult to fully understand the potential personality characteristics and thinking processes of examinees, and as a result, some excellent examinees have missed the opportunity for an interview with a company. Thus, in a personality evaluation based on selection from limited options, it is impossible to accurately reflect individual diverse characteristics, and there is a need to improve the reliability and accuracy in employment examinations.

Means for Solving the Problems

[0005] This invention relates to a system that enables more detailed and accurate personality assessment by receiving free-form input from test takers, analyzing personality traits using natural language processing technology, and generating questions tailored to the test taker's characteristics using a generative AI model. This system utilizes an information processing device equipped with a generative AI model to enable a multifaceted evaluation of traits based on the test taker's responses, and ultimately achieves appropriate matching through reporting to companies.

[0006] A "terminal device" is a device used by examinees to input information and communicates with a server device to send and receive data.

[0007] A "server device" is a central processing unit that processes the input data of test takers and performs natural language processing and personality trait analysis.

[0008] "Natural language processing" is a technology that analyzes and understands human language and extracts meaning, and includes technologies such as sentiment analysis and intention estimation.

[0009] "Personality traits" refer to characteristics that indicate the individual personality and thinking tendencies of the test taker, and are a concept that includes evaluation items such as cooperativeness and independence.

[0010] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate new questions based on the test taker's input and characteristics.

[0011] An "information processing device" is a computer device used for analyzing input data, executing generated AI models, evaluating personality traits, and generating reports.

[0012] The "report generation function" is a function that generates evaluation results as a report for provision to companies, based on the analyzed personality traits. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface that includes a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system for evaluating the personality of test takers in detail, and operates by combining a terminal device, a server device, a generative AI model, and an information processing device. The user provides free-form input through the terminal, and this input is sent to the server. The server applies natural language processing to the received input data to analyze the test taker's personality traits from multiple perspectives.

[0035] The server uses a generative AI model to dynamically generate additional questions based on the analysis results. These questions are presented to the user via the terminal. The user is free to respond to the presented questions, and their responses are sent back to the server. By repeatedly performing this process, the server collects more detailed information about the test-taker's personality, improving the reliability of the evaluation.

[0036] The server generates a comprehensive personality assessment of the applicant based on all interaction and response data, and outputs this as a report. This report is used by companies as valuable data when matching job seekers with suitable positions.

[0037] For example, if a user answers the question, "How would you respond in a difficult situation?" with, "First, I would break down the problem to understand the overall picture," the server analyzes this response and extracts the problem-solving and logical thinking characteristics of the test-taker. Subsequently, it generates questions such as, "What role would you play if teamwork was required?" to gather further information and evaluate personality traits.

[0038] Thus, the present invention evaluates the diverse personality traits of test-takers that cannot be fully covered by conventional multiple-choice tests, thereby realizing a highly accurate personality test.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user logs into the device and provides initial information such as a free-form self-introduction and personality traits. The device then sends this information to the server.

[0042] Step 2:

[0043] The server applies natural language processing to the user's input data and performs text analysis. This involves basic sentiment analysis and intention estimation to extract initial personality traits.

[0044] Step 3:

[0045] The server uses a generated AI model to create the initial questions based on the analysis results. These questions are designed to gain a detailed understanding of the user's characteristics.

[0046] Step 4:

[0047] The server sends the generated question to the terminal and presents it to the user. The user reviews the question through the terminal and enters their response.

[0048] Step 5:

[0049] The terminal collects user responses and sends them to the server. The server then analyzes the received responses again using natural language processing to identify more detailed personality traits.

[0050] Step 6:

[0051] The server generates the next question based on the new personality traits. This question is designed to obtain more detailed personality information.

[0052] Step 7:

[0053] Repeat steps 4 through 6 as many times as needed to accumulate sufficient data. Through this iterative process, the server evaluates the user's personality in a multifaceted and detailed manner.

[0054] Step 8:

[0055] After all interactions are complete, the server generates a comprehensive personality assessment based on the accumulated data. A report is then created and provided to companies to facilitate matching between job seekers and companies.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Traditional personality assessment methods have the challenge of not being able to adequately evaluate the complex and multifaceted personality traits of test-takers. In particular, multiple-choice question formats have difficulty accurately reflecting the characteristics and individuality of test-takers, making it difficult to gain deep insights. Furthermore, there has been the problem of the time-consuming process of creating reports that companies can use the assessment results to effectively utilize.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes a natural language processing means for analyzing the examinee's free-form input, a generative AI model means for dynamically generating additional questions based on the analysis results, and a report generation means for automatically generating and outputting a report using the evaluation results. This makes it possible to evaluate the examinee's multifaceted personality traits with high accuracy and to quickly obtain a report that can be easily used by companies.

[0061] "Test taker" refers to an individual who undergoes a personality assessment using the system.

[0062] An "information terminal device" refers to an electronic device used by examinees to input data in a free format and transmit it to a server device.

[0063] A "server device" refers to a centralized information processing device that receives input data from test takers, performs analysis, and generates additional questions.

[0064] "Natural language processing" refers to the technology of analyzing free-form input data from test takers and extracting meaning and characteristics from linguistic data.

[0065] A "generative AI model" refers to an artificial intelligence model that dynamically generates additional questions based on analyzed personality traits and utilizes them within the system.

[0066] The "report generation function" refers to a function that automatically creates and outputs reports that companies can use based on the final personality assessment.

[0067] "Analyzing from multiple perspectives" refers to analyzing data from various viewpoints and categories to conduct a comprehensive evaluation.

[0068] The embodiments for carrying out this invention are shown below.

[0069] Users can input text in free form using an information terminal device. This input serves as foundational data for evaluating the user's personality traits. The information terminal device includes input devices such as smartphones, tablets, or personal computers.

[0070] The terminal transmits data entered by the test-taker to the server device. The server device applies natural language processing to the received data and analyzes it. Well-known software libraries such as spaCy and NLTK can be used for natural language processing. This allows the server to comprehensively evaluate the user's personality traits through sentiment analysis and intent estimation.

[0071] Furthermore, the server uses a generative AI model to generate additional questions based on the analyzed personality traits. The generative AI model leverages prompts to form questions that help further evaluate the user's characteristics. For example, it might use a prompt in the format of, "Generate questions to prompt the user to take the next action based on their answers."

[0072] These additional questions are sent to the terminal device via the server and presented to the user. Once the user enters their answers, they are sent back to the server for further analysis. Through this process, the server collects multifaceted and detailed information about the test taker's personality, and ultimately automatically generates and outputs a report that can be used by the company.

[0073] The specific operation of this system can be customized according to the type of personnel a company is seeking, and more precise evaluations can be performed by adjusting new prompts and generative models.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user uses an information terminal device to input text in free form. They input answers to questions about their personality traits, and this data is transferred from the terminal to the server device. The input text data is processed as basic data for initial personality analysis.

[0077] Step 2:

[0078] The server applies natural language processing to the received input data. Specifically, it analyzes the received text data, extracting keywords and understanding the context. This allows for sentiment analysis and intent estimation, and a multifaceted evaluation of the user's personality traits. The analysis results are generated as output and used in the next step.

[0079] Step 3:

[0080] The server uses a generative AI model to generate additional questions based on the analyzed personality traits. Specifically, it utilizes prompts to instruct the generative AI model to generate the next questions. For example, a prompt such as "Create questions to identify new traits to be evaluated based on the user's answers" might be used. The generated additional questions serve as output to prepare for the next user interaction.

[0081] Step 4:

[0082] The server sends the generated additional questions to the terminal device and presents them to the user. The terminal receives these and displays them on the screen or provides notifications to the user. The user provides more specific answers to the presented questions. This answer data is then sent back from the terminal to the server.

[0083] Step 5:

[0084] The server receives new responses from the test takers and performs natural language processing again. In this step, a detailed personality trait assessment is performed by combining the results with the previous analysis. Specifically, the logical consistency and pattern recognition of the responses are performed, and the data is processed to gain deeper insights. Updated personality assessment data is generated as output.

[0085] Step 6:

[0086] The server forms a comprehensive personality assessment and generates a report based on all interactions and responses. The final report is automatically created in PDF or other digital format and output in a format usable by the company. This report comprehensively summarizes the assessment results and visualizes the user's personality traits.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In today's production environment, accurately understanding the personality traits and behavioral patterns of individual workers and achieving efficient team assignments and role allocation is a significant challenge. This can lead to decreased work efficiency and collaborative productivity, potentially negatively impacting overall corporate productivity. To address this challenge, collaborative optimization proposals based on multifaceted personality assessments are needed.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes terminal means for receiving input from test takers, information processing means for performing natural language processing based on the test takers' input and analyzing personality traits, question generation means for generating additional questions based on personality traits and transmitting them to the terminal means, and behavioral analysis means for analyzing worker behavior patterns and making collaborative optimization suggestions. This enables flexible task assignment tailored to the characteristics of individual workers and improves production efficiency.

[0092] A "terminal device" is a device that receives input information from examinees and is responsible for the interface with the user.

[0093] An "information processing device" is a device that performs natural language processing based on input from the examinee and has the function of analyzing personality traits.

[0094] A "question generation means" is a device that generates additional questions based on analyzed personality traits and transmits them to a terminal means.

[0095] A "behavioral analysis tool" is a device that analyzes the behavioral patterns of workers and makes collaborative optimization suggestions based on those patterns.

[0096] "Natural language processing" is a technology that uses computers to process human language and perform sentiment analysis and intention estimation.

[0097] "Personality traits" refer to the characteristics resulting from an analysis of the thinking and behavioral patterns of individual test takers or workers.

[0098] A "collaboration optimization proposal" is a proposal that, based on the results of behavioral analysis, shows improvement measures to enable workers to collaborate most efficiently.

[0099] The system for implementing the present invention consists of a terminal means, an information processing means, a question generation means, and a behavioral analysis means. The terminal means is a device for receiving information entered by examinees or workers, and includes smart glasses and mobile terminals. The data entered by the user is transmitted to the server in real time.

[0100] The server applies natural language processing to the input data using information processing tools to analyze personality traits. In this process, existing natural language processing engines such as Google® Cloud Natural Language API are used to evaluate the input text data from multiple perspectives.

[0101] After personality traits are analyzed, the server uses a question generation mechanism to automatically generate additional questions using a generation AI model (e.g., GPT-3®). These generated questions are intended to further evaluate the user's characteristics and are provided as feedback to the operator via a terminal.

[0102] Furthermore, the server uses behavioral analysis tools to process and analyze worker behavior data. This analysis makes it possible to suggest what roles workers should play in production activities and team collaboration, contributing to the creation of an efficient work environment.

[0103] For example, if a worker possesses problem-solving skills, the generated questions can provide material for considering new strategies, such as how to utilize those skills. This process allows for the optimization of collaborative strategies tailored to the individual personality traits of each worker.

[0104] Example prompts for generative AI models:

[0105] To improve efficiency in teamwork, please generate questions to assess problem-solving skills and team collaboration. For example, a given input might be: "We spend a lot of time adjusting the machine."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal receives user input. The user enters personality information via voice or text. The terminal sends this input data to the server. The input is captured as text data and prepared for transmission to the server.

[0109] Step 2:

[0110] The server performs natural language processing on the transmitted data using information processing tools. Specifically, it uses tools such as the Google Cloud Natural Language API to perform sentiment analysis and intent estimation, and to analyze personality traits from multiple perspectives. The data input is text, and the output is parameter data indicating the personality traits of the test taker.

[0111] Step 3:

[0112] The server utilizes a question generation mechanism to generate additional questions based on the analyzed personality traits. Using a generative AI model (e.g., GPT-3), it generates relevant prompts and creates questions to further evaluate the user's characteristics. The output of this step is text data as additional questions.

[0113] Step 4:

[0114] The terminal displays and presents additional questions sent from the server to the user. The user responds to the additional questions and enters their answers again on the terminal. At this stage, new input data from the user is obtained.

[0115] Step 5:

[0116] The server analyzes the new user response again using information processing tools and comprehensively evaluates the worker's behavioral patterns using behavioral analysis tools. Based on the analysis results, it generates suggestions for achieving efficient teamwork and outputs these suggestions as a report. The output contains suggested information regarding the user's personality traits and optimal roles.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention is a system that combines a terminal device, a server device, a generative AI model, an information processing device, and an emotion engine to evaluate the personality of test takers in detail. The user inputs a free-form self-introduction and responses to arbitrary questions via the terminal. This input data is transmitted from the terminal to the server. The server applies natural language processing to this input data to analyze the test taker's personality traits from multiple perspectives.

[0119] Furthermore, this system incorporates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input, voice, and facial expression data to recognize their emotional state in real time. The server acquires this emotion data and, combined with the results of natural language processing, evaluates personality traits in more detail.

[0120] The generative AI model runs on a server and generates the next question, taking into account not only personality traits but also emotional shifts. These questions are designed to explore more advanced aspects of personality. The server sends the generated questions to the terminal, where the user responds. The user's responses are then analyzed again by natural language processing and an emotion engine to further improve the accuracy of the personality assessment.

[0121] For example, if a user is asked, "What was the biggest challenge you faced in your latest project?" and answers, "It was keeping the team together while we were under tight deadlines," the server analyzes this response and extracts leadership and stress tolerance traits. Furthermore, the emotion engine evaluates how the user felt about that situation and generates follow-up questions such as, "How did you overcome the pressure you felt?"

[0122] Thus, the present invention can evaluate the diverse personality traits of test takers that could not be captured by conventional multiple-choice personality tests, and provide accurate and reliable results.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The user logs into the terminal and inputs responses to the assigned questions and self-expressions as text. The terminal then prepares to send this input data to the server.

[0126] Step 2:

[0127] The terminal sends input data to the server, which then receives the data. Here, a session is initiated to process the data, along with the user's login information.

[0128] Step 3:

[0129] The server applies natural language processing to the received text data and performs analysis. In this process, sentiment analysis and intent estimation are performed to extract the user's initial personality traits.

[0130] Step 4:

[0131] The emotion engine operates on the server side, analyzing the user's text, facial expression data, and voice tone to identify the user's emotional state. This result is then fed back into the personality analysis.

[0132] Step 5:

[0133] The server uses a generative AI model to generate the next question based on the analyzed personality traits and emotional state. This question is designed to deepen the user's understanding of their characteristics.

[0134] Step 6:

[0135] The server sends the generated question to the terminal, which then displays the question to the user. The user reads the new question and enters their response again.

[0136] Step 7:

[0137] The terminal collects the user's responses and sends them back to the server. The server re-analyzes the received responses using natural language processing and an emotion engine to perform a more detailed assessment of personality traits.

[0138] Step 8:

[0139] The server integrates data from multiple interaction cycles to create a comprehensive personality assessment of the user. This assessment is then used to generate a report, which is provided to the company. This process enables a more accurate evaluation of job applicants.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] Conventional personality assessment systems have difficulty evaluating a test-taker's personality traits from multiple perspectives, and in particular, have been unable to provide highly accurate assessments that take into account their emotional state. Therefore, there is a need to accurately understand the test-taker's personality and behavioral characteristics.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes means for performing natural language processing based on input from the test taker and analyzing personality traits from multiple perspectives; means for analyzing voice data and facial expression data using emotion recognition means and recognizing the test taker's emotional state in real time; and information processing means equipped with a generative AI model that generates the next question based on personality traits and emotional state. This makes it possible to evaluate the test taker's personality traits from multiple perspectives with high accuracy.

[0145] A "terminal device" is a device that receives input from test takers and transmits the data to a server.

[0146] The "information processing means" is a system that performs natural language processing on input data from test takers to analyze their personality traits from multiple perspectives.

[0147] "Emotion recognition means" refers to a function or device that analyzes voice data and facial expression data to recognize the emotional state of the test taker in real time.

[0148] A "generative AI model" is an artificial intelligence model that generates the next questions based on the personality traits and emotional state of the test-taker.

[0149] A "report generation means" is a device or system that is responsible for generating a final evaluation of the analyzed personality traits and reporting it to the organization.

[0150] "Personality traits" is a concept that refers to the individual psychological and behavioral characteristics extracted from the behavior and emotional state of the test taker.

[0151] "Natural language processing" refers to the technology used by computers to understand and analyze human language, and includes processes such as morphological analysis and sentiment analysis.

[0152] This invention aims to provide a detailed assessment of the personality of test takers. Users can input self-introductions and answers to questions in a free-form format using a terminal. In addition to text, the input data includes voice and facial expression data.

[0153] The terminal sends input data to the server. Communication technologies such as HTTP and WebSocket are used for this data transfer.

[0154] The server performs natural language processing on the received input data. Programming languages ​​such as Python and R are used, and natural language processing libraries like NLTK and spaCy are utilized. This analyzes the test-taker's personality traits and extracts psychological characteristics such as leadership and stress tolerance.

[0155] Furthermore, the server uses emotion recognition means to evaluate the user's emotional state. For voice data, it utilizes a speech recognition service, and for facial expression data, it uses a facial expression analysis service. This analysis is performed in real time, allowing the server to determine what emotions the user is experiencing.

[0156] The generative AI model generates the following questions based on the user's personality traits and emotional state. For example, based on a prompt such as "We are seeking detailed feedback on the difficulties the user encountered in the project," questions are devised to elicit deeper insights.

[0157] The server sends the generated questions to the terminal, and the next analysis begins when the user answers. This process is looped, improving the accuracy of the personality assessment of the test-taker.

[0158] In this system, in addition to question generation using a generative AI model, emotion recognition-based evaluation supports a detailed analysis of personality traits. Therefore, it becomes possible to evaluate a wide range of personality traits that were overlooked by conventional methods with high accuracy.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users input self-introductions and answers to questions in text format into the device. Voice and facial expression data can also be input into the device via the microphone and camera. This allows raw data to be collected and stored on the device.

[0162] Step 2:

[0163] The device transmits collected text, audio, and facial expression data to the server. Communication is conducted using the HTTP protocol. Input data includes the user's responses, audio files, and facial images.

[0164] Step 3:

[0165] The server performs natural language processing on the received text data. First, it performs morphological analysis to break down the response into individual words. Then, it performs sentiment analysis to extract emotions such as positive, negative, and neutral from the text. The output is personality trait data used for trait analysis.

[0166] Step 4:

[0167] The server converts audio data into text using speech recognition tools, such as Google Cloud Speech-to-Text. The output is the transcribed user's speech. This text data is also processed using natural language processing to evaluate its sentiment, and the results are integrated into personality trait data.

[0168] Step 5:

[0169] The server processes facial expression data using a facial expression analysis tool. Here, the facial expression analysis API identifies the user's basic emotions (such as joy, anger, sadness, etc.). The output is emotion recognition data, which is integrated with text-based personality trait data.

[0170] Step 6:

[0171] The server uses a generative AI model to generate the following questions. The input is integrated personality trait and sentiment data, which is used as prompts for the generative AI model. The output is a question designed to delve deeper into the user's personality traits.

[0172] Step 7:

[0173] The server sends the generated question to the terminal. The user receives the new question and enters their answer again. This allows for new data collection and restarts the analysis process.

[0174] (Application Example 2)

[0175] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0176] In traditional service delivery, accurately understanding customers' personalities and emotional states, and providing personalized responses and suggestions based on that understanding, has been difficult. As a result, improvements in customer satisfaction and service quality have not been fully achieved. This difficulty is particularly pronounced when it is necessary to analyze customer emotions in real time and respond immediately based on that analysis.

[0177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0178] In this invention, the server includes information input / output means for receiving input from the user's facial expressions and voice, means for performing natural language processing and sentiment analysis based on the user's input, and means for generating adaptive questions or customer service responses based on personality traits and emotional states. This enables the provision of personalized services based on the customer's emotions and personality.

[0179] An "information input / output device" is a device that acquires the user's facial expressions and voice and transmits that data to a computing device.

[0180] A "computational device" is a device that performs natural language processing and sentiment analysis based on acquired data to analyze the user's personality traits and emotional state.

[0181] A "question generation model" is an information processing model that generates adaptive questions or customer service responses based on analyzed personality traits and emotional states.

[0182] The "information transmission function" is the function of generating a refined report based on the analysis results and communicating it to the organization.

[0183] This invention is a system that analyzes customer personality traits and emotional states in real time in a physical store and proposes appropriate customer service responses based on that analysis. The system consists of an information input / output device, a computing device, a question generation model, and an information transmission function.

[0184] Smart glasses are used as information input / output devices. These smart glasses are equipped with a camera and microphone to collect customer facial expressions and voice data. The collected data is transmitted via Bluetooth to a computing device within the store.

[0185] The computing device uses the Google Cloud Natural Language API to perform natural language processing on the received data and evaluates the customer's emotional state using OpenAI's (registered trademark) emotion analysis model. This allows for a detailed analysis of personality traits.

[0186] The question generation model generates adaptive customer service responses based on analyzed personality traits and emotional states. These responses are displayed in real time on the smart glasses' screen. For example, if a customer appears somewhat downcast in the store, a prompt such as, "He seems a little down. Let's suggest some products that will help him relax," is generated.

[0187] This allows the server to provide personalized services based on each customer's emotions and personality, thereby improving customer satisfaction.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The smart glasses (device) collect the customer's facial expressions and voice. By capturing the customer's facial features and voice tone using a camera and microphone, it acquires facial expression and voice data. The input is the customer's real-time facial expressions and voice. The output is the collected raw data.

[0191] Step 2:

[0192] The terminal transmits the collected facial expression data and audio data to the computing device (server) via Bluetooth. Since the data is basically transferred in its original format, the input is the raw data collected in step 1, and the output is the data transmitted over the network.

[0193] Step 3:

[0194] The server uses the Google Cloud Natural Language API on a computing device to convert speech data into text and automatically analyzes its sentiment. The input is speech data, and the output is text data and sentiment identification information. Specifically, it evaluates whether the spoken content is positive or negative.

[0195] Step 4:

[0196] Similarly, the server uses OpenAI's emotion analysis model to analyze facial expression data and infer the customer's emotional state from their facial expressions. The input is facial expression data, and the output is the recognized emotional state. For example, information such as a smile or a confused expression is output as an emotion label.

[0197] Step 5:

[0198] The server generates the most appropriate customer service response or question based on the emotions and personality traits analyzed using a generative AI model. The input is the emotional state and personality traits obtained in the previous step, and the output is the generated prompt sentence. For example, "If the customer is feeling down, offer a suggestion that will help them relax."

[0199] Step 6:

[0200] The generated prompt message is sent from the server to the smart glasses and displayed on the glasses' screen. The input is the generated prompt message, and the output is the customer service instruction displayed on the screen. Based on the analysis results, specific countermeasures are presented, and the store staff respond to the customer accordingly.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention is a system for evaluating the personality of test takers in detail, and operates by combining a terminal device, a server device, a generative AI model, and an information processing device. The user provides free-form input through the terminal, and this input is sent to the server. The server applies natural language processing to the received input data to analyze the test taker's personality traits from multiple perspectives.

[0218] The server uses a generative AI model to dynamically generate additional questions based on the analysis results. These questions are presented to the user via the terminal. The user is free to respond to the presented questions, and their responses are sent back to the server. By repeatedly performing this process, the server collects more detailed information about the test-taker's personality, improving the reliability of the evaluation.

[0219] The server generates a comprehensive personality assessment of the applicant based on all interaction and response data, and outputs this as a report. This report is used by companies as valuable data when matching job seekers with suitable positions.

[0220] For example, if a user answers the question, "How would you respond in a difficult situation?" with, "First, I would break down the problem to understand the overall picture," the server analyzes this response and extracts the problem-solving and logical thinking characteristics of the test-taker. Subsequently, it generates questions such as, "What role would you play if teamwork was required?" to gather further information and evaluate personality traits.

[0221] Thus, the present invention evaluates the diverse personality traits of test-takers that cannot be fully covered by conventional multiple-choice tests, thereby realizing a highly accurate personality test.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user logs into the device and provides initial information such as a free-form self-introduction and personality traits. The device then sends this information to the server.

[0225] Step 2:

[0226] The server applies natural language processing to the user's input data and performs text analysis. This involves basic sentiment analysis and intention estimation to extract initial personality traits.

[0227] Step 3:

[0228] The server uses a generated AI model to create the initial questions based on the analysis results. These questions are designed to gain a detailed understanding of the user's characteristics.

[0229] Step 4:

[0230] The server sends the generated question to the terminal and presents it to the user. The user reviews the question through the terminal and enters their response.

[0231] Step 5:

[0232] The terminal collects user responses and sends them to the server. The server then analyzes the received responses again using natural language processing to identify more detailed personality traits.

[0233] Step 6:

[0234] The server generates the next question based on the new personality traits. This question is designed to obtain more detailed personality information.

[0235] Step 7:

[0236] Repeat steps 4 through 6 as many times as needed to accumulate sufficient data. Through this iterative process, the server evaluates the user's personality in a multifaceted and detailed manner.

[0237] Step 8:

[0238] After all interactions are complete, the server generates a comprehensive personality assessment based on the accumulated data. A report is then created and provided to companies to facilitate matching between job seekers and companies.

[0239] (Example 1)

[0240] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0241] Traditional personality assessment methods have the challenge of not being able to adequately evaluate the complex and multifaceted personality traits of test-takers. In particular, multiple-choice question formats have difficulty accurately reflecting the characteristics and individuality of test-takers, making it difficult to gain deep insights. Furthermore, there has been the problem of the time-consuming process of creating reports that companies can use the assessment results to effectively utilize.

[0242] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0243] In this invention, the server includes a natural language processing means for analyzing the examinee's free-form input, a generative AI model means for dynamically generating additional questions based on the analysis results, and a report generation means for automatically generating and outputting a report using the evaluation results. This makes it possible to evaluate the examinee's multifaceted personality traits with high accuracy and to quickly obtain a report that can be easily used by companies.

[0244] "Test taker" refers to an individual who undergoes a personality assessment using the system.

[0245] An "information terminal device" refers to an electronic device used by examinees to input data in a free format and transmit it to a server device.

[0246] A "server device" refers to a centralized information processing device that receives input data from test takers, performs analysis, and generates additional questions.

[0247] "Natural language processing" refers to the technology of analyzing free-form input data from test takers and extracting meaning and characteristics from linguistic data.

[0248] A "generative AI model" refers to an artificial intelligence model that dynamically generates additional questions based on analyzed personality traits and utilizes them within the system.

[0249] The "report generation function" refers to a function that automatically creates and outputs reports that companies can use based on the final personality assessment.

[0250] "Analyzing from multiple perspectives" refers to analyzing data from various viewpoints and categories to conduct a comprehensive evaluation.

[0251] The embodiments for carrying out this invention are shown below.

[0252] Users can input text in free form using an information terminal device. This input serves as foundational data for evaluating the user's personality traits. The information terminal device includes input devices such as smartphones, tablets, or personal computers.

[0253] The terminal transmits data entered by the test-taker to the server device. The server device applies natural language processing to the received data and analyzes it. Well-known software libraries such as spaCy and NLTK can be used for natural language processing. This allows the server to comprehensively evaluate the user's personality traits through sentiment analysis and intent estimation.

[0254] Furthermore, the server uses a generative AI model to generate additional questions based on the analyzed personality traits. The generative AI model leverages prompts to form questions that help further evaluate the user's characteristics. For example, it might use a prompt in the format of, "Generate questions to prompt the user to take the next action based on their answers."

[0255] These additional questions are sent to the terminal device via the server and presented to the user. Once the user enters their answers, they are sent back to the server for further analysis. Through this process, the server collects multifaceted and detailed information about the test taker's personality, and ultimately automatically generates and outputs a report that can be used by the company.

[0256] The specific operation of this system can be customized according to the type of personnel a company is seeking, and more precise evaluations can be performed by adjusting new prompts and generative models.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The user uses an information terminal device to input text in free form. They input answers to questions about their personality traits, and this data is transferred from the terminal to the server device. The input text data is processed as basic data for initial personality analysis.

[0260] Step 2:

[0261] The server applies natural language processing to the received input data. Specifically, it analyzes the received text data, extracting keywords and understanding the context. This allows for sentiment analysis and intent estimation, and a multifaceted evaluation of the user's personality traits. The analysis results are generated as output and used in the next step.

[0262] Step 3:

[0263] The server uses a generative AI model to generate additional questions based on the analyzed personality traits. Specifically, it utilizes prompts to instruct the generative AI model to generate the next questions. For example, a prompt such as "Create questions to identify new traits to be evaluated based on the user's answers" might be used. The generated additional questions serve as output to prepare for the next user interaction.

[0264] Step 4:

[0265] The server sends the generated additional questions to the terminal device and presents them to the user. The terminal receives these and displays them on the screen or provides notifications to the user. The user provides more specific answers to the presented questions. This answer data is then sent back from the terminal to the server.

[0266] Step 5:

[0267] The server receives new responses from the test takers and performs natural language processing again. In this step, a detailed personality trait assessment is performed by combining the results with the previous analysis. Specifically, the logical consistency and pattern recognition of the responses are performed, and the data is processed to gain deeper insights. Updated personality assessment data is generated as output.

[0268] Step 6:

[0269] The server forms a comprehensive personality assessment and generates a report based on all interactions and responses. The final report is automatically created in PDF or other digital format and output in a format usable by the company. This report comprehensively summarizes the assessment results and visualizes the user's personality traits.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In today's production environment, accurately understanding the personality traits and behavioral patterns of individual workers and achieving efficient team assignments and role allocation is a significant challenge. This can lead to decreased work efficiency and collaborative productivity, potentially negatively impacting overall corporate productivity. To address this challenge, collaborative optimization proposals based on multifaceted personality assessments are needed.

[0273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0274] In this invention, the server includes terminal means for receiving input from test takers, information processing means for performing natural language processing based on the test takers' input and analyzing personality traits, question generation means for generating additional questions based on personality traits and transmitting them to the terminal means, and behavioral analysis means for analyzing worker behavior patterns and making collaborative optimization suggestions. This enables flexible task assignment tailored to the characteristics of individual workers and improves production efficiency.

[0275] A "terminal device" is a device that receives input information from examinees and is responsible for the interface with the user.

[0276] An "information processing device" is a device that performs natural language processing based on input from the examinee and has the function of analyzing personality traits.

[0277] A "question generation means" is a device that generates additional questions based on analyzed personality traits and transmits them to a terminal means.

[0278] A "behavioral analysis tool" is a device that analyzes the behavioral patterns of workers and makes collaborative optimization suggestions based on those patterns.

[0279] "Natural language processing" is a technology that uses computers to process human language and perform sentiment analysis and intention estimation.

[0280] "Personality traits" refer to the characteristics resulting from an analysis of the thinking and behavioral patterns of individual test takers or workers.

[0281] A "collaboration optimization proposal" is a proposal that, based on the results of behavioral analysis, shows improvement measures to enable workers to collaborate most efficiently.

[0282] The system for implementing the present invention is composed of terminal means, information processing means, question generation means, and behavior analysis means. The terminal means is a device for receiving information input by examinees or workers, and smart glasses or mobile terminals are applicable. The data input by the user is transmitted to the server in real time.

[0283] The server applies natural language processing to the input data using the information processing means and analyzes personality characteristics. In this process, existing natural language processing engines such as the Google Cloud Natural Language API are used to comprehensively evaluate the input text data.

[0284] After the personality characteristics are analyzed, the server uses the question generation means to automatically generate additional questions by a generation AI model (such as GPT-3, etc.). The generated questions are for further evaluating the user's characteristics and are fed back to the worker through the terminal means.

[0285] Furthermore, the server uses the behavior analysis means to process and analyze the worker's behavior data. Through this analysis, it becomes possible to propose what role the worker should play in production activities or team cooperation scenarios, contributing to the construction of an efficient working environment.

[0286] As a specific example, when a worker has problem-solving thinking, the generated questions can present materials for considering new strategies, such as how to utilize that thinking. Through such a process, the optimization of cooperation strategies according to the personality characteristics of each worker is realized.

[0287] Prompt text examples for the generation AI model:

[0288] Please generate questions for evaluating problem-solving thinking and team coordination when a problem occurs for the purpose of efficiency improvement in team work. As an example, there is an input that a lot of time is spent on machine adjustment.

[0289] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The terminal receives user input. The user enters personality information via voice or text. The terminal sends this input data to the server. The input is captured as text data and prepared for transmission to the server.

[0292] Step 2:

[0293] The server performs natural language processing on the transmitted data using information processing tools. Specifically, it uses tools such as the Google Cloud Natural Language API to perform sentiment analysis and intent estimation, and to analyze personality traits from multiple perspectives. The data input is text, and the output is parameter data indicating the personality traits of the test taker.

[0294] Step 3:

[0295] The server utilizes a question generation mechanism to generate additional questions based on the analyzed personality traits. Using a generative AI model (e.g., GPT-3), it generates relevant prompts and creates questions to further evaluate the user's characteristics. The output of this step is text data as additional questions.

[0296] Step 4:

[0297] The terminal displays and presents additional questions sent from the server to the user. The user responds to the additional questions and enters their answers again on the terminal. At this stage, new input data from the user is obtained.

[0298] Step 5:

[0299] The server analyzes the new user response again using information processing tools and comprehensively evaluates the worker's behavioral patterns using behavioral analysis tools. Based on the analysis results, it generates suggestions for achieving efficient teamwork and outputs these suggestions as a report. The output contains suggested information regarding the user's personality traits and optimal roles.

[0300] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0301] This invention is a system that combines a terminal device, a server device, a generative AI model, an information processing device, and an emotion engine to evaluate the personality of test takers in detail. The user inputs a free-form self-introduction and responses to arbitrary questions via the terminal. This input data is transmitted from the terminal to the server. The server applies natural language processing to this input data to analyze the test taker's personality traits from multiple perspectives.

[0302] Furthermore, this system incorporates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input, voice, and facial expression data to recognize their emotional state in real time. The server acquires this emotion data and, combined with the results of natural language processing, evaluates personality traits in more detail.

[0303] The generative AI model runs on a server and generates the next question, taking into account not only personality traits but also emotional shifts. These questions are designed to explore more advanced aspects of personality. The server sends the generated questions to the terminal, where the user responds. The user's responses are then analyzed again by natural language processing and an emotion engine to further improve the accuracy of the personality assessment.

[0304] For example, when a user receives a question "What was the biggest challenge you faced in the latest project?" and answers "It was to bring the team together while being chased by the deadline", the server analyzes this response and extracts characteristics such as leadership and stress tolerance. Furthermore, through the emotion engine, it evaluates what emotions the user had about that scenario and generates a follow-up question such as "How did you overcome the situation where you felt pressured?".

[0305] In this way, the present invention can evaluate various personality characteristics of the examinee that could not be fully grasped by the conventional selective personality test and provide accurate and reliable results.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] The user logs in to the terminal and enters, as text, responses to the instructed questions and self-expression. The terminal prepares to send this input data to the server.

[0309] Step 2:

[0310] The terminal sends the input data to the server, and the server receives the data. Here, a session for processing the data together with the user's login information is started.

[0311] Step 3:

[0312] The server applies natural language processing to the received text data for analysis. In this process, emotion analysis and intention estimation are performed to extract the user's initial personality characteristics.

[0313] Step 4:

[0314] The emotion engine operates on the server side, analyzing the user's text, facial expression data, and voice tone to identify the user's emotional state. This result is then fed back into the personality analysis.

[0315] Step 5:

[0316] The server uses a generative AI model to generate the next question based on the analyzed personality traits and emotional state. This question is designed to deepen the user's understanding of their characteristics.

[0317] Step 6:

[0318] The server sends the generated question to the terminal, which then displays the question to the user. The user reads the new question and enters their response again.

[0319] Step 7:

[0320] The terminal collects the user's responses and sends them back to the server. The server re-analyzes the received responses using natural language processing and an emotion engine to perform a more detailed assessment of personality traits.

[0321] Step 8:

[0322] The server integrates data from multiple interaction cycles to create a comprehensive personality assessment of the user. This assessment is then used to generate a report, which is provided to the company. This process enables a more accurate evaluation of job applicants.

[0323] (Example 2)

[0324] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0325] Conventional personality assessment systems have difficulty evaluating a test-taker's personality traits from multiple perspectives, and in particular, have been unable to provide highly accurate assessments that take into account their emotional state. Therefore, there is a need to accurately understand the test-taker's personality and behavioral characteristics.

[0326] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0327] In this invention, the server includes means for performing natural language processing based on input from the test taker and analyzing personality traits from multiple perspectives; means for analyzing voice data and facial expression data using emotion recognition means and recognizing the test taker's emotional state in real time; and information processing means equipped with a generative AI model that generates the next question based on personality traits and emotional state. This makes it possible to evaluate the test taker's personality traits from multiple perspectives with high accuracy.

[0328] A "terminal device" is a device that receives input from test takers and transmits the data to a server.

[0329] The "information processing means" is a system that performs natural language processing on input data from test takers to analyze their personality traits from multiple perspectives.

[0330] "Emotion recognition means" refers to a function or device that analyzes voice data and facial expression data to recognize the emotional state of the test taker in real time.

[0331] A "generative AI model" is an artificial intelligence model that generates the next questions based on the personality traits and emotional state of the test-taker.

[0332] A "report generation means" is a device or system that is responsible for generating a final evaluation of the analyzed personality traits and reporting it to the organization.

[0333] "Personality traits" is a concept that refers to the individual psychological and behavioral characteristics extracted from the behavior and emotional state of the test taker.

[0334] "Natural language processing" refers to the technology used by computers to understand and analyze human language, and includes processes such as morphological analysis and sentiment analysis.

[0335] This invention aims to provide a detailed assessment of the personality of test takers. Users can input self-introductions and answers to questions in a free-form format using a terminal. In addition to text, the input data includes voice and facial expression data.

[0336] The terminal sends input data to the server. Communication technologies such as HTTP and WebSocket are used for this data transfer.

[0337] The server performs natural language processing on the received input data. Programming languages ​​such as Python and R are used, and natural language processing libraries like NLTK and spaCy are utilized. This analyzes the test-taker's personality traits and extracts psychological characteristics such as leadership and stress tolerance.

[0338] Furthermore, the server uses emotion recognition means to evaluate the user's emotional state. For voice data, it utilizes a speech recognition service, and for facial expression data, it uses a facial expression analysis service. This analysis is performed in real time, allowing the server to determine what emotions the user is experiencing.

[0339] The generative AI model generates the following questions based on the user's personality traits and emotional state. For example, based on a prompt such as "We are seeking detailed feedback on the difficulties the user encountered in the project," questions are devised to elicit deeper insights.

[0340] The server sends the generated questions to the terminal, and the next analysis begins when the user answers. This process is looped, improving the accuracy of the personality assessment of the test-taker.

[0341] In this system, in addition to question generation using a generative AI model, emotion recognition-based evaluation supports a detailed analysis of personality traits. Therefore, it becomes possible to evaluate a wide range of personality traits that were overlooked by conventional methods with high accuracy.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] Users input self-introductions and answers to questions in text format into the device. Voice and facial expression data can also be input into the device via the microphone and camera. This allows raw data to be collected and stored on the device.

[0345] Step 2:

[0346] The device transmits collected text, audio, and facial expression data to the server. Communication is conducted using the HTTP protocol. Input data includes the user's responses, audio files, and facial images.

[0347] Step 3:

[0348] The server performs natural language processing on the received text data. First, it performs morphological analysis to break down the response into individual words. Then, it performs sentiment analysis to extract emotions such as positive, negative, and neutral from the text. The output is personality trait data used for trait analysis.

[0349] Step 4:

[0350] The server converts audio data into text using speech recognition tools, such as Google Cloud Speech-to-Text. The output is the transcribed user's speech. This text data is also processed using natural language processing to evaluate its sentiment, and the results are integrated into personality trait data.

[0351] Step 5:

[0352] The server processes facial expression data using a facial expression analysis tool. Here, the facial expression analysis API identifies the user's basic emotions (such as joy, anger, sadness, etc.). The output is emotion recognition data, which is integrated with text-based personality trait data.

[0353] Step 6:

[0354] The server uses a generative AI model to generate the following questions. The input is integrated personality trait and sentiment data, which is used as prompts for the generative AI model. The output is a question designed to delve deeper into the user's personality traits.

[0355] Step 7:

[0356] The server sends the generated question to the terminal. The user receives the new question and enters their answer again. This allows for new data collection and restarts the analysis process.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] In traditional service delivery, accurately understanding customers' personalities and emotional states, and providing personalized responses and suggestions based on that understanding, has been difficult. As a result, improvements in customer satisfaction and service quality have not been fully achieved. This difficulty is particularly pronounced when it is necessary to analyze customer emotions in real time and respond immediately based on that analysis.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes information input / output means for receiving input from the user's facial expressions and voice, means for performing natural language processing and sentiment analysis based on the user's input, and means for generating adaptive questions or customer service responses based on personality traits and emotional states. This enables the provision of personalized services based on the customer's emotions and personality.

[0362] An "information input / output device" is a device that acquires the user's facial expressions and voice and transmits that data to a computing device.

[0363] A "computational device" is a device that performs natural language processing and sentiment analysis based on acquired data to analyze the user's personality traits and emotional state.

[0364] A "question generation model" is an information processing model that generates adaptive questions or customer service responses based on analyzed personality traits and emotional states.

[0365] The "information transmission function" is the function of generating a refined report based on the analysis results and communicating it to the organization.

[0366] This invention is a system that analyzes customer personality traits and emotional states in real time in a physical store and proposes appropriate customer service responses based on that analysis. The system consists of an information input / output device, a computing device, a question generation model, and an information transmission function.

[0367] Smart glasses are used as information input / output devices. These smart glasses are equipped with a camera and microphone to collect customer facial expressions and voice data. The collected data is transmitted via Bluetooth to a computing device within the store.

[0368] The computing system uses the Google Cloud Natural Language API to perform natural language processing on the received data and uses OpenAI's emotion analysis model to evaluate the customer's emotional state. This allows for a detailed analysis of personality traits.

[0369] The question generation model generates adaptive customer service responses based on analyzed personality traits and emotional states. These responses are displayed in real time on the smart glasses' screen. For example, if a customer appears somewhat downcast in the store, a prompt such as, "He seems a little down. Let's suggest some products that will help him relax," is generated.

[0370] This allows the server to provide personalized services based on each customer's emotions and personality, thereby improving customer satisfaction.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The smart glasses (device) collect the customer's facial expressions and voice. By capturing the customer's facial features and voice tone using a camera and microphone, it acquires facial expression and voice data. The input is the customer's real-time facial expressions and voice. The output is the collected raw data.

[0374] Step 2:

[0375] The terminal transmits the collected facial expression data and audio data to the computing device (server) via Bluetooth. Since the data is basically transferred in its original format, the input is the raw data collected in step 1, and the output is the data transmitted over the network.

[0376] Step 3:

[0377] The server uses the Google Cloud Natural Language API on a computing device to convert speech data into text and automatically analyzes its sentiment. The input is speech data, and the output is text data and sentiment identification information. Specifically, it evaluates whether the spoken content is positive or negative.

[0378] Step 4:

[0379] Similarly, the server uses OpenAI's emotion analysis model to analyze facial expression data and infer the customer's emotional state from their facial expressions. The input is facial expression data, and the output is the recognized emotional state. For example, information such as a smile or a confused expression is output as an emotion label.

[0380] Step 5:

[0381] The server generates the most appropriate customer service response or question based on the emotions and personality traits analyzed using a generative AI model. The input is the emotional state and personality traits obtained in the previous step, and the output is the generated prompt sentence. For example, "If the customer is feeling down, offer a suggestion that will help them relax."

[0382] Step 6:

[0383] The generated prompt message is sent from the server to the smart glasses and displayed on the glasses' screen. The input is the generated prompt message, and the output is the customer service instruction displayed on the screen. Based on the analysis results, specific countermeasures are presented, and the store staff respond to the customer accordingly.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention is a system for evaluating the personality of test takers in detail, and operates by combining a terminal device, a server device, a generative AI model, and an information processing device. The user provides free-form input through the terminal, and this input is sent to the server. The server applies natural language processing to the received input data to analyze the test taker's personality traits from multiple perspectives.

[0401] The server uses a generative AI model to dynamically generate additional questions based on the analysis results. These questions are presented to the user via the terminal. The user is free to respond to the presented questions, and their responses are sent back to the server. By repeatedly performing this process, the server collects more detailed information about the test-taker's personality, improving the reliability of the evaluation.

[0402] The server generates a comprehensive personality assessment of the applicant based on all interaction and response data, and outputs this as a report. This report is used by companies as valuable data when matching job seekers with suitable positions.

[0403] For example, if a user answers the question, "How would you respond in a difficult situation?" with, "First, I would break down the problem to understand the overall picture," the server analyzes this response and extracts the problem-solving and logical thinking characteristics of the test-taker. Subsequently, it generates questions such as, "What role would you play if teamwork was required?" to gather further information and evaluate personality traits.

[0404] Thus, the present invention evaluates the diverse personality traits of test-takers that cannot be fully covered by conventional multiple-choice tests, thereby realizing a highly accurate personality test.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The user logs into the device and provides initial information such as a free-form self-introduction and personality traits. The device then sends this information to the server.

[0408] Step 2:

[0409] The server applies natural language processing to the user's input data and performs text analysis. This involves basic sentiment analysis and intention estimation to extract initial personality traits.

[0410] Step 3:

[0411] The server uses a generated AI model to create the initial questions based on the analysis results. These questions are designed to gain a detailed understanding of the user's characteristics.

[0412] Step 4:

[0413] The server sends the generated question to the terminal and presents it to the user. The user reviews the question through the terminal and enters their response.

[0414] Step 5:

[0415] The terminal collects user responses and sends them to the server. The server then analyzes the received responses again using natural language processing to identify more detailed personality traits.

[0416] Step 6:

[0417] The server generates the next question based on the new personality traits. This question is designed to obtain more detailed personality information.

[0418] Step 7:

[0419] Repeat steps 4 through 6 as many times as needed to accumulate sufficient data. Through this iterative process, the server evaluates the user's personality in a multifaceted and detailed manner.

[0420] Step 8:

[0421] After all interactions are complete, the server generates a comprehensive personality assessment based on the accumulated data. A report is then created and provided to companies to facilitate matching between job seekers and companies.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] Traditional personality assessment methods have the challenge of not being able to adequately evaluate the complex and multifaceted personality traits of test-takers. In particular, multiple-choice question formats have difficulty accurately reflecting the characteristics and individuality of test-takers, making it difficult to gain deep insights. Furthermore, there has been the problem of the time-consuming process of creating reports that companies can use the assessment results to effectively utilize.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes a natural language processing means for analyzing the examinee's free-form input, a generative AI model means for dynamically generating additional questions based on the analysis results, and a report generation means for automatically generating and outputting a report using the evaluation results. This makes it possible to evaluate the examinee's multifaceted personality traits with high accuracy and to quickly obtain a report that can be easily used by companies.

[0427] "Test taker" refers to an individual who undergoes a personality assessment using the system.

[0428] An "information terminal device" refers to an electronic device used by examinees to input data in a free format and transmit it to a server device.

[0429] A "server device" refers to a centralized information processing device that receives input data from test takers, performs analysis, and generates additional questions.

[0430] "Natural language processing" refers to the technology of analyzing free-form input data from test takers and extracting meaning and characteristics from linguistic data.

[0431] A "generative AI model" refers to an artificial intelligence model that dynamically generates additional questions based on analyzed personality traits and utilizes them within the system.

[0432] The "report generation function" refers to a function that automatically creates and outputs reports that companies can use based on the final personality assessment.

[0433] "Analyzing from multiple perspectives" refers to analyzing data from various viewpoints and categories to conduct a comprehensive evaluation.

[0434] The embodiments for carrying out this invention are shown below.

[0435] Users can input text in free form using an information terminal device. This input serves as foundational data for evaluating the user's personality traits. The information terminal device includes input devices such as smartphones, tablets, or personal computers.

[0436] The terminal transmits data entered by the test-taker to the server device. The server device applies natural language processing to the received data and analyzes it. Well-known software libraries such as spaCy and NLTK can be used for natural language processing. This allows the server to comprehensively evaluate the user's personality traits through sentiment analysis and intent estimation.

[0437] Furthermore, the server uses a generative AI model to generate additional questions based on the analyzed personality traits. The generative AI model leverages prompts to form questions that help further evaluate the user's characteristics. For example, it might use a prompt in the format of, "Generate questions to prompt the user to take the next action based on their answers."

[0438] These additional questions are sent to the terminal device via the server and presented to the user. Once the user enters their answers, they are sent back to the server for further analysis. Through this process, the server collects multifaceted and detailed information about the test taker's personality, and ultimately automatically generates and outputs a report that can be used by the company.

[0439] The specific operation of this system can be customized according to the type of personnel a company is seeking, and more precise evaluations can be performed by adjusting new prompts and generative models.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The user uses an information terminal device to input text in free form. They input answers to questions about their personality traits, and this data is transferred from the terminal to the server device. The input text data is processed as basic data for initial personality analysis.

[0443] Step 2:

[0444] The server applies natural language processing to the received input data. Specifically, it analyzes the received text data, extracting keywords and understanding the context. This allows for sentiment analysis and intent estimation, and a multifaceted evaluation of the user's personality traits. The analysis results are generated as output and used in the next step.

[0445] Step 3:

[0446] The server uses a generative AI model to generate additional questions based on the analyzed personality traits. Specifically, it utilizes prompts to instruct the generative AI model to generate the next questions. For example, a prompt such as "Create questions to identify new traits to be evaluated based on the user's answers" might be used. The generated additional questions serve as output to prepare for the next user interaction.

[0447] Step 4:

[0448] The server sends the generated additional questions to the terminal device and presents them to the user. The terminal receives these and displays them on the screen or provides notifications to the user. The user provides more specific answers to the presented questions. This answer data is then sent back from the terminal to the server.

[0449] Step 5:

[0450] The server receives new responses from the test takers and performs natural language processing again. In this step, a detailed personality trait assessment is performed by combining the results with the previous analysis. Specifically, the logical consistency and pattern recognition of the responses are performed, and the data is processed to gain deeper insights. Updated personality assessment data is generated as output.

[0451] Step 6:

[0452] The server forms a comprehensive personality assessment and generates a report based on all interactions and responses. The final report is automatically created in PDF or other digital format and output in a format usable by the company. This report comprehensively summarizes the assessment results and visualizes the user's personality traits.

[0453] (Application Example 1)

[0454] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] In today's production environment, accurately understanding the personality traits and behavioral patterns of individual workers and achieving efficient team assignments and role allocation is a significant challenge. This can lead to decreased work efficiency and collaborative productivity, potentially negatively impacting overall corporate productivity. To address this challenge, collaborative optimization proposals based on multifaceted personality assessments are needed.

[0456] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0457] In this invention, the server includes terminal means for receiving input from test takers, information processing means for performing natural language processing based on the test takers' input and analyzing personality traits, question generation means for generating additional questions based on personality traits and transmitting them to the terminal means, and behavioral analysis means for analyzing worker behavior patterns and making collaborative optimization suggestions. This enables flexible task assignment tailored to the characteristics of individual workers and improves production efficiency.

[0458] A "terminal device" is a device that receives input information from examinees and is responsible for the interface with the user.

[0459] An "information processing device" is a device that performs natural language processing based on input from the examinee and has the function of analyzing personality traits.

[0460] A "question generation means" is a device that generates additional questions based on analyzed personality traits and transmits them to a terminal means.

[0461] A "behavioral analysis tool" is a device that analyzes the behavioral patterns of workers and makes collaborative optimization suggestions based on those patterns.

[0462] "Natural language processing" is a technology that uses computers to process human language and perform sentiment analysis and intention estimation.

[0463] "Personality traits" refer to the characteristics resulting from an analysis of the thinking and behavioral patterns of individual test takers or workers.

[0464] A "collaboration optimization proposal" is a proposal that, based on the results of behavioral analysis, shows improvement measures to enable workers to collaborate most efficiently.

[0465] The system for implementing the present invention consists of a terminal means, an information processing means, a question generation means, and a behavioral analysis means. The terminal means is a device for receiving information entered by examinees or workers, and includes smart glasses and mobile terminals. The data entered by the user is transmitted to the server in real time.

[0466] The server applies natural language processing to the input data using information processing tools to analyze personality traits. In this process, existing natural language processing engines such as the Google Cloud Natural Language API are used to evaluate the input text data from multiple perspectives.

[0467] After personality traits are analyzed, the server uses a question generation mechanism to automatically generate additional questions using a generation AI model (e.g., GPT-3). These generated questions are designed to further evaluate the user's characteristics and are provided as feedback to the operator via a terminal.

[0468] Furthermore, the server uses behavioral analysis tools to process and analyze worker behavior data. This analysis makes it possible to suggest what roles workers should play in production activities and team collaboration, contributing to the creation of an efficient work environment.

[0469] For example, if a worker possesses problem-solving skills, the generated questions can provide material for considering new strategies, such as how to utilize those skills. This process allows for the optimization of collaborative strategies tailored to the individual personality traits of each worker.

[0470] Example prompts for generative AI models:

[0471] To improve efficiency in teamwork, please generate questions to assess problem-solving skills and team collaboration. For example, a given input might be: "We spend a lot of time adjusting the machine."

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The terminal receives user input. The user enters personality information via voice or text. The terminal sends this input data to the server. The input is captured as text data and prepared for transmission to the server.

[0475] Step 2:

[0476] The server performs natural language processing on the transmitted data using information processing tools. Specifically, it uses tools such as the Google Cloud Natural Language API to perform sentiment analysis and intent estimation, and to analyze personality traits from multiple perspectives. The data input is text, and the output is parameter data indicating the personality traits of the test taker.

[0477] Step 3:

[0478] The server utilizes a question generation mechanism to generate additional questions based on the analyzed personality traits. Using a generative AI model (e.g., GPT-3), it generates relevant prompts and creates questions to further evaluate the user's characteristics. The output of this step is text data as additional questions.

[0479] Step 4:

[0480] The terminal displays and presents additional questions sent from the server to the user. The user responds to the additional questions and enters their answers again on the terminal. At this stage, new input data from the user is obtained.

[0481] Step 5:

[0482] The server analyzes the new user response again using information processing tools and comprehensively evaluates the worker's behavioral patterns using behavioral analysis tools. Based on the analysis results, it generates suggestions for achieving efficient teamwork and outputs these suggestions as a report. The output contains suggested information regarding the user's personality traits and optimal roles.

[0483] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0484] This invention is a system that combines a terminal device, a server device, a generative AI model, an information processing device, and an emotion engine to evaluate the personality of test takers in detail. The user inputs a free-form self-introduction and responses to arbitrary questions via the terminal. This input data is transmitted from the terminal to the server. The server applies natural language processing to this input data to analyze the test taker's personality traits from multiple perspectives.

[0485] Furthermore, this system incorporates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input, voice, and facial expression data to recognize their emotional state in real time. The server acquires this emotion data and, combined with the results of natural language processing, evaluates personality traits in more detail.

[0486] The generative AI model runs on a server and generates the next question, taking into account not only personality traits but also emotional shifts. These questions are designed to explore more advanced aspects of personality. The server sends the generated questions to the terminal, where the user responds. The user's responses are then analyzed again by natural language processing and an emotion engine to further improve the accuracy of the personality assessment.

[0487] For example, if a user is asked, "What was the biggest challenge you faced in your latest project?" and answers, "It was keeping the team together while we were under tight deadlines," the server analyzes this response and extracts leadership and stress tolerance traits. Furthermore, the emotion engine evaluates how the user felt about that situation and generates follow-up questions such as, "How did you overcome the pressure you felt?"

[0488] Thus, the present invention can evaluate the diverse personality traits of test takers that could not be captured by conventional multiple-choice personality tests, and provide accurate and reliable results.

[0489] The following describes the processing flow.

[0490] Step 1:

[0491] The user logs into the terminal and inputs responses to the assigned questions and self-expressions as text. The terminal then prepares to send this input data to the server.

[0492] Step 2:

[0493] The terminal sends input data to the server, which then receives the data. Here, a session is initiated to process the data, along with the user's login information.

[0494] Step 3:

[0495] The server applies natural language processing to the received text data and performs analysis. In this process, sentiment analysis and intent estimation are performed to extract the user's initial personality traits.

[0496] Step 4:

[0497] The emotion engine operates on the server side, analyzing the user's text, facial expression data, and voice tone to identify the user's emotional state. This result is then fed back into the personality analysis.

[0498] Step 5:

[0499] The server uses a generative AI model to generate the next question based on the analyzed personality traits and emotional state. This question is designed to deepen the user's understanding of their characteristics.

[0500] Step 6:

[0501] The server sends the generated question to the terminal, which then displays the question to the user. The user reads the new question and enters their response again.

[0502] Step 7:

[0503] The terminal collects the user's responses and sends them back to the server. The server re-analyzes the received responses using natural language processing and an emotion engine to perform a more detailed assessment of personality traits.

[0504] Step 8:

[0505] The server integrates data from multiple interaction cycles to create a comprehensive personality assessment of the user. This assessment is then used to generate a report, which is provided to the company. This process enables a more accurate evaluation of job applicants.

[0506] (Example 2)

[0507] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] Conventional personality assessment systems have difficulty evaluating a test-taker's personality traits from multiple perspectives, and in particular, have been unable to provide highly accurate assessments that take into account their emotional state. Therefore, there is a need to accurately understand the test-taker's personality and behavioral characteristics.

[0509] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0510] In this invention, the server includes means for performing natural language processing based on input from the test taker and analyzing personality traits from multiple perspectives; means for analyzing voice data and facial expression data using emotion recognition means and recognizing the test taker's emotional state in real time; and information processing means equipped with a generative AI model that generates the next question based on personality traits and emotional state. This makes it possible to evaluate the test taker's personality traits from multiple perspectives with high accuracy.

[0511] A "terminal device" is a device that receives input from test takers and transmits the data to a server.

[0512] The "information processing means" is a system that performs natural language processing on input data from test takers to analyze their personality traits from multiple perspectives.

[0513] "Emotion recognition means" refers to a function or device that analyzes voice data and facial expression data to recognize the emotional state of the test taker in real time.

[0514] A "generative AI model" is an artificial intelligence model that generates the next questions based on the personality traits and emotional state of the test-taker.

[0515] A "report generation means" is a device or system that is responsible for generating a final evaluation of the analyzed personality traits and reporting it to the organization.

[0516] "Personality traits" is a concept that refers to the individual psychological and behavioral characteristics extracted from the behavior and emotional state of the test taker.

[0517] "Natural language processing" refers to the technology used by computers to understand and analyze human language, and includes processes such as morphological analysis and sentiment analysis.

[0518] This invention aims to provide a detailed assessment of the personality of test takers. Users can input self-introductions and answers to questions in a free-form format using a terminal. In addition to text, the input data includes voice and facial expression data.

[0519] The terminal sends input data to the server. Communication technologies such as HTTP and WebSocket are used for this data transfer.

[0520] The server performs natural language processing on the received input data. Programming languages ​​such as Python and R are used, and natural language processing libraries like NLTK and spaCy are utilized. This analyzes the test-taker's personality traits and extracts psychological characteristics such as leadership and stress tolerance.

[0521] Furthermore, the server uses emotion recognition means to evaluate the user's emotional state. For voice data, it utilizes a speech recognition service, and for facial expression data, it uses a facial expression analysis service. This analysis is performed in real time, allowing the server to determine what emotions the user is experiencing.

[0522] The generative AI model generates the following questions based on the user's personality traits and emotional state. For example, based on a prompt such as "We are seeking detailed feedback on the difficulties the user encountered in the project," questions are devised to elicit deeper insights.

[0523] The server sends the generated questions to the terminal, and the next analysis begins when the user answers. This process is looped, improving the accuracy of the personality assessment of the test-taker.

[0524] In this system, in addition to question generation using a generative AI model, emotion recognition-based evaluation supports a detailed analysis of personality traits. Therefore, it becomes possible to evaluate a wide range of personality traits that were overlooked by conventional methods with high accuracy.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] Users input self-introductions and answers to questions in text format into the device. Voice and facial expression data can also be input into the device via the microphone and camera. This allows raw data to be collected and stored on the device.

[0528] Step 2:

[0529] The device transmits collected text, audio, and facial expression data to the server. Communication is conducted using the HTTP protocol. Input data includes the user's responses, audio files, and facial images.

[0530] Step 3:

[0531] The server performs natural language processing on the received text data. First, it performs morphological analysis to break down the response into individual words. Then, it performs sentiment analysis to extract emotions such as positive, negative, and neutral from the text. The output is personality trait data used for trait analysis.

[0532] Step 4:

[0533] The server converts audio data into text using speech recognition tools, such as Google Cloud Speech-to-Text. The output is the transcribed user's speech. This text data is also processed using natural language processing to evaluate its sentiment, and the results are integrated into personality trait data.

[0534] Step 5:

[0535] The server processes facial expression data using a facial expression analysis tool. Here, the facial expression analysis API identifies the user's basic emotions (such as joy, anger, sadness, etc.). The output is emotion recognition data, which is integrated with text-based personality trait data.

[0536] Step 6:

[0537] The server uses a generative AI model to generate the following questions. The input is integrated personality trait and sentiment data, which is used as prompts for the generative AI model. The output is a question designed to delve deeper into the user's personality traits.

[0538] Step 7:

[0539] The server sends the generated question to the terminal. The user receives the new question and enters their answer again. This allows for new data collection and restarts the analysis process.

[0540] (Application Example 2)

[0541] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In traditional service delivery, accurately understanding customers' personalities and emotional states, and providing personalized responses and suggestions based on that understanding, has been difficult. As a result, improvements in customer satisfaction and service quality have not been fully achieved. This difficulty is particularly pronounced when it is necessary to analyze customer emotions in real time and respond immediately based on that analysis.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes information input / output means for receiving input from the user's facial expressions and voice, means for performing natural language processing and sentiment analysis based on the user's input, and means for generating adaptive questions or customer service responses based on personality traits and emotional states. This enables the provision of personalized services based on the customer's emotions and personality.

[0545] An "information input / output device" is a device that acquires the user's facial expressions and voice and transmits that data to a computing device.

[0546] A "computational device" is a device that performs natural language processing and sentiment analysis based on acquired data to analyze the user's personality traits and emotional state.

[0547] A "question generation model" is an information processing model that generates adaptive questions or customer service responses based on analyzed personality traits and emotional states.

[0548] The "information transmission function" is the function of generating a refined report based on the analysis results and communicating it to the organization.

[0549] This invention is a system that analyzes customer personality traits and emotional states in real time in a physical store and proposes appropriate customer service responses based on that analysis. The system consists of an information input / output device, a computing device, a question generation model, and an information transmission function.

[0550] Smart glasses are used as information input / output devices. These smart glasses are equipped with a camera and microphone to collect customer facial expressions and voice data. The collected data is transmitted via Bluetooth to a computing device within the store.

[0551] The computing system uses the Google Cloud Natural Language API to perform natural language processing on the received data and uses OpenAI's emotion analysis model to evaluate the customer's emotional state. This allows for a detailed analysis of personality traits.

[0552] The question generation model generates adaptive customer service responses based on analyzed personality traits and emotional states. These responses are displayed in real time on the smart glasses' screen. For example, if a customer appears somewhat downcast in the store, a prompt such as, "He seems a little down. Let's suggest some products that will help him relax," is generated.

[0553] This allows the server to provide personalized services based on each customer's emotions and personality, thereby improving customer satisfaction.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The smart glasses (device) collect the customer's facial expressions and voice. By capturing the customer's facial features and voice tone using a camera and microphone, it acquires facial expression and voice data. The input is the customer's real-time facial expressions and voice. The output is the collected raw data.

[0557] Step 2:

[0558] The terminal transmits the collected facial expression data and audio data to the computing device (server) via Bluetooth. Since the data is basically transferred in its original format, the input is the raw data collected in step 1, and the output is the data transmitted over the network.

[0559] Step 3:

[0560] The server uses the Google Cloud Natural Language API on a computing device to convert speech data into text and automatically analyzes its sentiment. The input is speech data, and the output is text data and sentiment identification information. Specifically, it evaluates whether the spoken content is positive or negative.

[0561] Step 4:

[0562] Similarly, the server uses OpenAI's emotion analysis model to analyze facial expression data and infer the customer's emotional state from their facial expressions. The input is facial expression data, and the output is the recognized emotional state. For example, information such as a smile or a confused expression is output as an emotion label.

[0563] Step 5:

[0564] The server generates the most appropriate customer service response or question based on the emotions and personality traits analyzed using a generative AI model. The input is the emotional state and personality traits obtained in the previous step, and the output is the generated prompt sentence. For example, "If the customer is feeling down, offer a suggestion that will help them relax."

[0565] Step 6:

[0566] The generated prompt message is sent from the server to the smart glasses and displayed on the glasses' screen. The input is the generated prompt message, and the output is the customer service instruction displayed on the screen. Based on the analysis results, specific countermeasures are presented, and the store staff respond to the customer accordingly.

[0567] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] This invention is a system for evaluating the personality of test takers in detail, and operates by combining a terminal device, a server device, a generative AI model, and an information processing device. The user provides free-form input through the terminal, and this input is sent to the server. The server applies natural language processing to the received input data to analyze the test taker's personality traits from multiple perspectives.

[0585] The server uses a generative AI model to dynamically generate additional questions based on the analysis results. These questions are presented to the user via the terminal. The user is free to respond to the presented questions, and their responses are sent back to the server. By repeatedly performing this process, the server collects more detailed information about the test-taker's personality, improving the reliability of the evaluation.

[0586] The server generates a comprehensive personality assessment of the applicant based on all interaction and response data, and outputs this as a report. This report is used by companies as valuable data when matching job seekers with suitable positions.

[0587] For example, if a user answers the question, "How would you respond in a difficult situation?" with, "First, I would break down the problem to understand the overall picture," the server analyzes this response and extracts the problem-solving and logical thinking characteristics of the test-taker. Subsequently, it generates questions such as, "What role would you play if teamwork was required?" to gather further information and evaluate personality traits.

[0588] Thus, the present invention evaluates the diverse personality traits of test-takers that cannot be fully covered by conventional multiple-choice tests, thereby realizing a highly accurate personality test.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user logs into the device and provides initial information such as a free-form self-introduction and personality traits. The device then sends this information to the server.

[0592] Step 2:

[0593] The server applies natural language processing to the user's input data and performs text analysis. This involves basic sentiment analysis and intention estimation to extract initial personality traits.

[0594] Step 3:

[0595] The server uses a generated AI model to create the initial questions based on the analysis results. These questions are designed to gain a detailed understanding of the user's characteristics.

[0596] Step 4:

[0597] The server sends the generated question to the terminal and presents it to the user. The user reviews the question through the terminal and enters their response.

[0598] Step 5:

[0599] The terminal collects user responses and sends them to the server. The server then analyzes the received responses again using natural language processing to identify more detailed personality traits.

[0600] Step 6:

[0601] The server generates the next question based on the new personality traits. This question is designed to obtain more detailed personality information.

[0602] Step 7:

[0603] Repeat steps 4 through 6 as many times as needed to accumulate sufficient data. Through this iterative process, the server evaluates the user's personality in a multifaceted and detailed manner.

[0604] Step 8:

[0605] After all interactions are complete, the server generates a comprehensive personality assessment based on the accumulated data. A report is then created and provided to companies to facilitate matching between job seekers and companies.

[0606] (Example 1)

[0607] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] Traditional personality assessment methods have the challenge of not being able to adequately evaluate the complex and multifaceted personality traits of test-takers. In particular, multiple-choice question formats have difficulty accurately reflecting the characteristics and individuality of test-takers, making it difficult to gain deep insights. Furthermore, there has been the problem of the time-consuming process of creating reports that companies can use the assessment results to effectively utilize.

[0609] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0610] In this invention, the server includes a natural language processing means for analyzing the examinee's free-form input, a generative AI model means for dynamically generating additional questions based on the analysis results, and a report generation means for automatically generating and outputting a report using the evaluation results. This makes it possible to evaluate the examinee's multifaceted personality traits with high accuracy and to quickly obtain a report that can be easily used by companies.

[0611] "Test taker" refers to an individual who undergoes a personality assessment using the system.

[0612] An "information terminal device" refers to an electronic device used by examinees to input data in a free format and transmit it to a server device.

[0613] A "server device" refers to a centralized information processing device that receives input data from test takers, performs analysis, and generates additional questions.

[0614] "Natural language processing" refers to the technology of analyzing free-form input data from test takers and extracting meaning and characteristics from linguistic data.

[0615] A "generative AI model" refers to an artificial intelligence model that dynamically generates additional questions based on analyzed personality traits and utilizes them within the system.

[0616] The "report generation function" refers to a function that automatically creates and outputs reports that companies can use based on the final personality assessment.

[0617] "Analyzing from multiple perspectives" refers to analyzing data from various viewpoints and categories to conduct a comprehensive evaluation.

[0618] The embodiments for carrying out this invention are shown below.

[0619] Users can input text in free form using an information terminal device. This input serves as foundational data for evaluating the user's personality traits. The information terminal device includes input devices such as smartphones, tablets, or personal computers.

[0620] The terminal transmits data entered by the test-taker to the server device. The server device applies natural language processing to the received data and analyzes it. Well-known software libraries such as spaCy and NLTK can be used for natural language processing. This allows the server to comprehensively evaluate the user's personality traits through sentiment analysis and intent estimation.

[0621] Furthermore, the server uses a generative AI model to generate additional questions based on the analyzed personality traits. The generative AI model leverages prompts to form questions that help further evaluate the user's characteristics. For example, it might use a prompt in the format of, "Generate questions to prompt the user to take the next action based on their answers."

[0622] These additional questions are sent to the terminal device via the server and presented to the user. Once the user enters their answers, they are sent back to the server for further analysis. Through this process, the server collects multifaceted and detailed information about the test taker's personality, and ultimately automatically generates and outputs a report that can be used by the company.

[0623] The specific operation of this system can be customized according to the type of personnel a company is seeking, and more precise evaluations can be performed by adjusting new prompts and generative models.

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] The user uses an information terminal device to input text in free form. They input answers to questions about their personality traits, and this data is transferred from the terminal to the server device. The input text data is processed as basic data for initial personality analysis.

[0627] Step 2:

[0628] The server applies natural language processing to the received input data. Specifically, it analyzes the received text data, extracting keywords and understanding the context. This allows for sentiment analysis and intent estimation, and a multifaceted evaluation of the user's personality traits. The analysis results are generated as output and used in the next step.

[0629] Step 3:

[0630] The server uses a generative AI model to generate additional questions based on the analyzed personality traits. Specifically, it utilizes prompts to instruct the generative AI model to generate the next questions. For example, a prompt such as "Create questions to identify new traits to be evaluated based on the user's answers" might be used. The generated additional questions serve as output to prepare for the next user interaction.

[0631] Step 4:

[0632] The server sends the generated additional questions to the terminal device and presents them to the user. The terminal receives these and displays them on the screen or provides notifications to the user. The user provides more specific answers to the presented questions. This answer data is then sent back from the terminal to the server.

[0633] Step 5:

[0634] The server receives new responses from the test takers and performs natural language processing again. In this step, a detailed personality trait assessment is performed by combining the results with the previous analysis. Specifically, the logical consistency and pattern recognition of the responses are performed, and the data is processed to gain deeper insights. Updated personality assessment data is generated as output.

[0635] Step 6:

[0636] The server forms a comprehensive personality assessment and generates a report based on all interactions and responses. The final report is automatically created in PDF or other digital format and output in a format usable by the company. This report comprehensively summarizes the assessment results and visualizes the user's personality traits.

[0637] (Application Example 1)

[0638] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In today's production environment, accurately understanding the personality traits and behavioral patterns of individual workers and achieving efficient team assignments and role allocation is a significant challenge. This can lead to decreased work efficiency and collaborative productivity, potentially negatively impacting overall corporate productivity. To address this challenge, collaborative optimization proposals based on multifaceted personality assessments are needed.

[0640] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0641] In this invention, the server includes terminal means for receiving input from test takers, information processing means for performing natural language processing based on the test takers' input and analyzing personality traits, question generation means for generating additional questions based on personality traits and transmitting them to the terminal means, and behavioral analysis means for analyzing worker behavior patterns and making collaborative optimization suggestions. This enables flexible task assignment tailored to the characteristics of individual workers and improves production efficiency.

[0642] A "terminal device" is a device that receives input information from examinees and is responsible for the interface with the user.

[0643] An "information processing device" is a device that performs natural language processing based on input from the examinee and has the function of analyzing personality traits.

[0644] A "question generation means" is a device that generates additional questions based on analyzed personality traits and transmits them to a terminal means.

[0645] A "behavioral analysis tool" is a device that analyzes the behavioral patterns of workers and makes collaborative optimization suggestions based on those patterns.

[0646] "Natural language processing" is a technology that uses computers to process human language and perform sentiment analysis and intention estimation.

[0647] "Personality traits" refer to the characteristics resulting from an analysis of the thinking and behavioral patterns of individual test takers or workers.

[0648] A "collaboration optimization proposal" is a proposal that, based on the results of behavioral analysis, shows improvement measures to enable workers to collaborate most efficiently.

[0649] The system for implementing the present invention consists of a terminal means, an information processing means, a question generation means, and a behavioral analysis means. The terminal means is a device for receiving information entered by examinees or workers, and includes smart glasses and mobile terminals. The data entered by the user is transmitted to the server in real time.

[0650] The server applies natural language processing to the input data using information processing tools to analyze personality traits. In this process, existing natural language processing engines such as the Google Cloud Natural Language API are used to evaluate the input text data from multiple perspectives.

[0651] After personality traits are analyzed, the server uses a question generation mechanism to automatically generate additional questions using a generation AI model (e.g., GPT-3). These generated questions are designed to further evaluate the user's characteristics and are provided as feedback to the operator via a terminal.

[0652] Furthermore, the server uses behavioral analysis tools to process and analyze worker behavior data. This analysis makes it possible to suggest what roles workers should play in production activities and team collaboration, contributing to the creation of an efficient work environment.

[0653] For example, if a worker possesses problem-solving skills, the generated questions can provide material for considering new strategies, such as how to utilize those skills. This process allows for the optimization of collaborative strategies tailored to the individual personality traits of each worker.

[0654] Example prompts for generative AI models:

[0655] To improve efficiency in teamwork, please generate questions to assess problem-solving skills and team collaboration. For example, a given input might be: "We spend a lot of time adjusting the machine."

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The terminal receives user input. The user enters personality information via voice or text. The terminal sends this input data to the server. The input is captured as text data and prepared for transmission to the server.

[0659] Step 2:

[0660] The server performs natural language processing on the transmitted data using information processing tools. Specifically, it uses tools such as the Google Cloud Natural Language API to perform sentiment analysis and intent estimation, and to analyze personality traits from multiple perspectives. The data input is text, and the output is parameter data indicating the personality traits of the test taker.

[0661] Step 3:

[0662] The server utilizes a question generation mechanism to generate additional questions based on the analyzed personality traits. Using a generative AI model (e.g., GPT-3), it generates relevant prompts and creates questions to further evaluate the user's characteristics. The output of this step is text data as additional questions.

[0663] Step 4:

[0664] The terminal displays and presents additional questions sent from the server to the user. The user responds to the additional questions and enters their answers again on the terminal. At this stage, new input data from the user is obtained.

[0665] Step 5:

[0666] The server analyzes the new user response again using information processing tools and comprehensively evaluates the worker's behavioral patterns using behavioral analysis tools. Based on the analysis results, it generates suggestions for achieving efficient teamwork and outputs these suggestions as a report. The output contains suggested information regarding the user's personality traits and optimal roles.

[0667] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0668] This invention is a system that combines a terminal device, a server device, a generative AI model, an information processing device, and an emotion engine to evaluate the personality of test takers in detail. The user inputs a free-form self-introduction and responses to arbitrary questions via the terminal. This input data is transmitted from the terminal to the server. The server applies natural language processing to this input data to analyze the test taker's personality traits from multiple perspectives.

[0669] Furthermore, this system incorporates an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input, voice, and facial expression data to recognize their emotional state in real time. The server acquires this emotion data and, combined with the results of natural language processing, evaluates personality traits in more detail.

[0670] The generative AI model runs on a server and generates the next question, taking into account not only personality traits but also emotional shifts. These questions are designed to explore more advanced aspects of personality. The server sends the generated questions to the terminal, where the user responds. The user's responses are then analyzed again by natural language processing and an emotion engine to further improve the accuracy of the personality assessment.

[0671] For example, if a user is asked, "What was the biggest challenge you faced in your latest project?" and answers, "It was keeping the team together while we were under tight deadlines," the server analyzes this response and extracts leadership and stress tolerance traits. Furthermore, the emotion engine evaluates how the user felt about that situation and generates follow-up questions such as, "How did you overcome the pressure you felt?"

[0672] Thus, the present invention can evaluate the diverse personality traits of test takers that could not be captured by conventional multiple-choice personality tests, and provide accurate and reliable results.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The user logs into the terminal and inputs responses to the assigned questions and self-expressions as text. The terminal then prepares to send this input data to the server.

[0676] Step 2:

[0677] The terminal sends input data to the server, which then receives the data. Here, a session is initiated to process the data, along with the user's login information.

[0678] Step 3:

[0679] The server applies natural language processing to the received text data and performs analysis. In this process, sentiment analysis and intent estimation are performed to extract the user's initial personality traits.

[0680] Step 4:

[0681] The emotion engine operates on the server side, analyzing the user's text, facial expression data, and voice tone to identify the user's emotional state. This result is then fed back into the personality analysis.

[0682] Step 5:

[0683] The server uses a generative AI model to generate the next question based on the analyzed personality traits and emotional state. This question is designed to deepen the user's understanding of their characteristics.

[0684] Step 6:

[0685] The server sends the generated question to the terminal, which then displays the question to the user. The user reads the new question and enters their response again.

[0686] Step 7:

[0687] The terminal collects the user's responses and sends them back to the server. The server re-analyzes the received responses using natural language processing and an emotion engine to perform a more detailed assessment of personality traits.

[0688] Step 8:

[0689] The server integrates data from multiple interaction cycles to create a comprehensive personality assessment of the user. This assessment is then used to generate a report, which is provided to the company. This process enables a more accurate evaluation of job applicants.

[0690] (Example 2)

[0691] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] Conventional personality assessment systems have difficulty evaluating a test-taker's personality traits from multiple perspectives, and in particular, have been unable to provide highly accurate assessments that take into account their emotional state. Therefore, there is a need to accurately understand the test-taker's personality and behavioral characteristics.

[0693] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0694] In this invention, the server includes means for performing natural language processing based on input from the test taker and analyzing personality traits from multiple perspectives; means for analyzing voice data and facial expression data using emotion recognition means and recognizing the test taker's emotional state in real time; and information processing means equipped with a generative AI model that generates the next question based on personality traits and emotional state. This makes it possible to evaluate the test taker's personality traits from multiple perspectives with high accuracy.

[0695] A "terminal device" is a device that receives input from test takers and transmits the data to a server.

[0696] The "information processing means" is a system that performs natural language processing on input data from test takers to analyze their personality traits from multiple perspectives.

[0697] "Emotion recognition means" refers to a function or device that analyzes voice data and facial expression data to recognize the emotional state of the test taker in real time.

[0698] A "generative AI model" is an artificial intelligence model that generates the next questions based on the personality traits and emotional state of the test-taker.

[0699] A "report generation means" is a device or system that is responsible for generating a final evaluation of the analyzed personality traits and reporting it to the organization.

[0700] "Personality traits" is a concept that refers to the individual psychological and behavioral characteristics extracted from the behavior and emotional state of the test taker.

[0701] "Natural language processing" refers to the technology used by computers to understand and analyze human language, and includes processes such as morphological analysis and sentiment analysis.

[0702] This invention aims to provide a detailed assessment of the personality of test takers. Users can input self-introductions and answers to questions in a free-form format using a terminal. In addition to text, the input data includes voice and facial expression data.

[0703] The terminal sends input data to the server. Communication technologies such as HTTP and WebSocket are used for this data transfer.

[0704] The server performs natural language processing on the received input data. Programming languages ​​such as Python and R are used, and natural language processing libraries like NLTK and spaCy are utilized. This analyzes the test-taker's personality traits and extracts psychological characteristics such as leadership and stress tolerance.

[0705] Furthermore, the server uses emotion recognition means to evaluate the user's emotional state. For voice data, it utilizes a speech recognition service, and for facial expression data, it uses a facial expression analysis service. This analysis is performed in real time, allowing the server to determine what emotions the user is experiencing.

[0706] The generative AI model generates the following questions based on the user's personality traits and emotional state. For example, based on a prompt such as "We are seeking detailed feedback on the difficulties the user encountered in the project," questions are devised to elicit deeper insights.

[0707] The server sends the generated questions to the terminal, and the next analysis begins when the user answers. This process is looped, improving the accuracy of the personality assessment of the test-taker.

[0708] In this system, in addition to question generation using a generative AI model, emotion recognition-based evaluation supports a detailed analysis of personality traits. Therefore, it becomes possible to evaluate a wide range of personality traits that were overlooked by conventional methods with high accuracy.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] Users input self-introductions and answers to questions in text format into the device. Voice and facial expression data can also be input into the device via the microphone and camera. This allows raw data to be collected and stored on the device.

[0712] Step 2:

[0713] The device transmits collected text, audio, and facial expression data to the server. Communication is conducted using the HTTP protocol. Input data includes the user's responses, audio files, and facial images.

[0714] Step 3:

[0715] The server performs natural language processing on the received text data. First, it performs morphological analysis to break down the response into individual words. Then, it performs sentiment analysis to extract emotions such as positive, negative, and neutral from the text. The output is personality trait data used for trait analysis.

[0716] Step 4:

[0717] The server converts audio data into text using speech recognition tools, such as Google Cloud Speech-to-Text. The output is the transcribed user's speech. This text data is also processed using natural language processing to evaluate its sentiment, and the results are integrated into personality trait data.

[0718] Step 5:

[0719] The server processes facial expression data using a facial expression analysis tool. Here, the facial expression analysis API identifies the user's basic emotions (such as joy, anger, sadness, etc.). The output is emotion recognition data, which is integrated with text-based personality trait data.

[0720] Step 6:

[0721] The server uses a generative AI model to generate the following questions. The input is integrated personality trait and sentiment data, which is used as prompts for the generative AI model. The output is a question designed to delve deeper into the user's personality traits.

[0722] Step 7:

[0723] The server sends the generated question to the terminal. The user receives the new question and enters their answer again. This allows for new data collection and restarts the analysis process.

[0724] (Application Example 2)

[0725] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0726] In traditional service delivery, accurately understanding customers' personalities and emotional states, and providing personalized responses and suggestions based on that understanding, has been difficult. As a result, improvements in customer satisfaction and service quality have not been fully achieved. This difficulty is particularly pronounced when it is necessary to analyze customer emotions in real time and respond immediately based on that analysis.

[0727] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0728] In this invention, the server includes information input / output means for receiving input from the user's facial expressions and voice, means for performing natural language processing and sentiment analysis based on the user's input, and means for generating adaptive questions or customer service responses based on personality traits and emotional states. This enables the provision of personalized services based on the customer's emotions and personality.

[0729] An "information input / output device" is a device that acquires the user's facial expressions and voice and transmits that data to a computing device.

[0730] A "computational device" is a device that performs natural language processing and sentiment analysis based on acquired data to analyze the user's personality traits and emotional state.

[0731] A "question generation model" is an information processing model that generates adaptive questions or customer service responses based on analyzed personality traits and emotional states.

[0732] The "information transmission function" is the function of generating a refined report based on the analysis results and communicating it to the organization.

[0733] This invention is a system that analyzes customer personality traits and emotional states in real time in a physical store and proposes appropriate customer service responses based on that analysis. The system consists of an information input / output device, a computing device, a question generation model, and an information transmission function.

[0734] Smart glasses are used as information input / output devices. These smart glasses are equipped with a camera and microphone to collect customer facial expressions and voice data. The collected data is transmitted via Bluetooth to a computing device within the store.

[0735] The computing system uses the Google Cloud Natural Language API to perform natural language processing on the received data and uses OpenAI's emotion analysis model to evaluate the customer's emotional state. This allows for a detailed analysis of personality traits.

[0736] The question generation model generates adaptive customer service responses based on analyzed personality traits and emotional states. These responses are displayed in real time on the smart glasses' screen. For example, if a customer appears somewhat downcast in the store, a prompt such as, "He seems a little down. Let's suggest some products that will help him relax," is generated.

[0737] This allows the server to provide personalized services based on each customer's emotions and personality, thereby improving customer satisfaction.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The smart glasses (device) collect the customer's facial expressions and voice. By capturing the customer's facial features and voice tone using a camera and microphone, it acquires facial expression and voice data. The input is the customer's real-time facial expressions and voice. The output is the collected raw data.

[0741] Step 2:

[0742] The terminal transmits the collected facial expression data and audio data to the computing device (server) via Bluetooth. Since the data is basically transferred in its original format, the input is the raw data collected in step 1, and the output is the data transmitted over the network.

[0743] Step 3:

[0744] The server uses the Google Cloud Natural Language API on a computing device to convert speech data into text and automatically analyzes its sentiment. The input is speech data, and the output is text data and sentiment identification information. Specifically, it evaluates whether the spoken content is positive or negative.

[0745] Step 4:

[0746] Similarly, the server uses OpenAI's emotion analysis model to analyze facial expression data and infer the customer's emotional state from their facial expressions. The input is facial expression data, and the output is the recognized emotional state. For example, information such as a smile or a confused expression is output as an emotion label.

[0747] Step 5:

[0748] The server generates the most appropriate customer service response or question based on the emotions and personality traits analyzed using a generative AI model. The input is the emotional state and personality traits obtained in the previous step, and the output is the generated prompt sentence. For example, "If the customer is feeling down, offer a suggestion that will help them relax."

[0749] Step 6:

[0750] The generated prompt message is sent from the server to the smart glasses and displayed on the glasses' screen. The input is the generated prompt message, and the output is the customer service instruction displayed on the screen. Based on the analysis results, specific countermeasures are presented, and the store staff respond to the customer accordingly.

[0751] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0752] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0754] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0755] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0756] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0757] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0758] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0759] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0760] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0761] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0762] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0763] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0764] 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.

[0765] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0766] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0767] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0768] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0769] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0770] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0771] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] A terminal device that receives input from test takers,

[0775] A server device that performs natural language processing based on input from the aforementioned test taker and analyzes personality traits,

[0776] An information processing device equipped with a generative AI model that generates additional questions based on the aforementioned personality traits and transmits them to the terminal device,

[0777] It is equipped with a report generation function that generates a final evaluation of the aforementioned personality traits and reports it to the company.

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, characterized in that the generation AI model has a function to generate an initial question and a function to generate more detailed questions based on the test taker's response.

[0781] (Claim 3)

[0782] The system according to claim 1, characterized in that the natural language processing includes sentiment analysis and intention estimation, and has a function to evaluate the characteristics of the test taker from multiple perspectives.

[0783] "Example 1"

[0784] (Claim 1)

[0785] An information terminal device that accepts free-form input from test takers and transmits the data to a server device,

[0786] The server device has an analysis function that uses natural language processing to comprehensively analyze the personality traits of the test taker, and a processing means that dynamically generates additional questions using a generative AI model and transmits them to the terminal device.

[0787] Equipped with a report generation function that generates and outputs reports usable by companies based on the aforementioned analysis results,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, characterized in that the generating AI model has the function of generating an initial question and detailed questions based on the examinee's response based on a prompt sentence.

[0791] (Claim 3)

[0792] The system according to claim 1, characterized in that the natural language processing includes sentiment analysis and intention estimation, and includes a function to evaluate the characteristics of the test taker from multiple perspectives.

[0793] "Application Example 1"

[0794] (Claim 1)

[0795] A terminal device for receiving input from test takers,

[0796] An information processing means that performs natural language processing based on the input from the examinee and analyzes personality traits,

[0797] A question generation means that generates additional questions based on the aforementioned personality traits and transmits them to the terminal means,

[0798] A report generation means for generating the final evaluation of the aforementioned personality traits and reporting it to the organization,

[0799] A behavioral analysis tool that analyzes workers' behavioral patterns and proposes collaborative optimization,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, characterized in that the question generation means includes a function for generating an initial question and a function for generating more detailed questions based on the examinee's response.

[0803] (Claim 3)

[0804] The system according to claim 1, characterized in that the natural language processing includes sentiment analysis and intent estimation, has a function to evaluate the characteristics of the test taker from multiple perspectives, and further makes suggestions to optimize the worker's performance.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] A terminal device for receiving input from test takers,

[0808] An information processing means that performs natural language processing based on the input from the examinee and analyzes personality traits from multiple perspectives,

[0809] The aforementioned natural language processing includes a means that uses emotion recognition means to analyze voice data and facial expression data and recognize the emotional state of the test taker in real time.

[0810] Information processing means equipped with a generative AI model that generates the following questions based on the aforementioned personality traits and emotional state and transmits them to the terminal means,

[0811] The system includes a report generation means for generating a final evaluation of the aforementioned personality traits and reporting it to the organization.

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, characterized in that the generating AI model has a function to generate an initial question and a function to generate more detailed questions based on the test taker's responses and emotional state.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the natural language processing includes sentiment analysis and intention estimation, has a function to evaluate the characteristics of the test taker from multiple perspectives, and further takes into account the emotional state of the test taker.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] An information input / output device that accepts input from the user's facial expressions and voice,

[0820] A computing device that performs natural language processing and sentiment analysis based on the user's input, and analyzes personality traits and emotional states.

[0821] An information processing device equipped with a question generation model that generates adaptive questions or customer service responses based on the aforementioned personality traits and emotional states and transmits them to the information input / output device,

[0822] It has an information transmission function that generates a report adjusted based on the aforementioned analysis results and transmits it to the organization.

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, characterized in that the question generation model includes a function to generate an initial question or initial response, and a function to generate more detailed questions or responses based on the user's response.

[0826] (Claim 3)

[0827] The system according to claim 1, characterized in that the natural language processing and sentiment analysis include intention estimation and sentiment recognition, and have a function to evaluate the user's characteristics from multiple perspectives. [Explanation of Symbols]

[0828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal device that receives input from test takers, A server device that performs natural language processing based on input from the aforementioned test taker and analyzes personality traits, An information processing device equipped with a generative AI model that generates additional questions based on the aforementioned personality traits and transmits them to the terminal device, It is equipped with a report generation function that generates a final evaluation of the aforementioned personality traits and reports it to the company. A system that includes this.

2. The system according to claim 1, characterized in that the generation AI model has a function to generate initial questions and a function to generate more detailed questions based on the examinee's responses.

3. The system according to claim 1, characterized in that the natural language processing includes sentiment analysis and intention estimation, and has a function to evaluate the characteristics of the test taker from multiple perspectives.

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