System

Abstract

An object of a system according to an embodiment is to quickly and appropriately respond to questions related to pay statements.SOLUTION: A system includes a question reception part, an answer generation part, and an escalation part. The question reception unit receives a question. The answer generation unit generates an answer to the question received by the question reception unit. The escalation unit performs escalation to human support when the answer generated by the answer generation unit exceeds a certain complexity.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to respond quickly and appropriately to questions about pay slips, which can leave employees' doubts and complaints unresolved.

[0005] The system according to the embodiment aims to respond quickly and appropriately to questions about pay slips. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an answer generating unit, and an escalation unit. The question receiving unit receives a question. The answer generating unit generates an answer to the question received by the question receiving unit. The escalation unit escalates to human support if the answer generated by the answer generating unit exceeds a certain level of complexity. [Effects of the Invention]

[0007] The system according to the embodiment can respond quickly and appropriately to questions about pay slips. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pay slip explanation system according to an embodiment of the present invention is a system that solves questions about employees' pay slips and provides explanations about pay calculation elements and fees. As a result, the pay slip explanation system can quickly solve questions about employees' pay and increase transparency.

[0029] A pay slip explanation system according to an embodiment includes a question receiving unit, an answer generating unit, and an escalation unit. The question receiving unit receives questions about pay slips from employees. For example, it receives text-based questions. The question receiving unit can also receive voice questions. The question receiving unit can also receive questions via a chat interface. The answer generating unit generates answers to questions received by the question receiving unit. For example, the generation AI references past similar questions and their answers to generate an optimal answer. The generation AI can also automatically reference the questioner's past question history and related documents to understand the context and generate an optimal answer. The generation AI can also use an emotion estimation function to estimate the questioner's emotional state and generate an answer based on the emotion. The escalation unit escalates to human support if the answer generated by the answer generating unit exceeds a certain level of complexity. For example, a complex question that the generation AI cannot handle is escalated to a human resources representative. The escalation unit can also use the emotion estimation function to adjust the timing of escalation based on the questioner's emotions and provide human support at the appropriate time. As a result, the pay slip explanation system according to the embodiment can quickly resolve questions about employees' salaries and increase transparency.

[0030] The answer generation unit can generate the optimal answer by referring to past similar questions and their answers. For example, when a question is submitted, the generation AI searches for past similar questions and their answers in the database and generates the most appropriate answer. For example, for the question, "How is base salary calculated?", an answer is generated by referring to past similar questions and their answers. The answer generation unit also analyzes the content of the question, and the generation AI provides the optimal answer based on past similar questions and their answers. For example, for the question, "What are the details of allowances?", an answer is generated by referring to past allowance-related questions and their answers. For example, when a question is submitted, the generation AI refers to past similar questions and their answers and generates the optimal answer. For example, for the question, "What are the deductions from my salary?", an answer is generated by referring to past similar questions and their answers. This makes it possible to utilize past data to provide quick and accurate answers.

[0031] The answer generation unit can automatically refer to the asker's past question history and related documents, understand the context, and generate the optimal answer. For example, when a question is submitted, the generation AI refers to the asker's past question history, understands the context, and generates the optimal answer. For example, for the question, "Following the question about my last pay slip, what changes have been made this month?", the answer is generated by referring to the past question history. In addition, to understand the context of the question, the generation AI automatically refers to related documents and provides the optimal answer. For example, for the question, "Please tell me the details of my payroll," the answer is generated by referring to related payroll documents. In addition, when a question is submitted, the answer generation unit refers to the asker's past question history and related documents, understands the context, and generates the optimal answer. For example, for the question, "Please tell me the details of my allowances," the answer is generated by referring to the past question history and related documents. This allows the asker's context to be understood and a more appropriate answer to be provided.

[0032] The answer generation unit can provide answers to questions not only in text but also in video and audio. For example, when a question is submitted, the generation AI provides an answer not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how to read my pay slip." The answer generation unit also adds a function to provide answers to questions not only in text but also in audio. For example, an audio explanation is provided for the question, "Please tell me the details of allowances." The answer generation unit can also provide answers to questions not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how payroll is calculated." This allows for deeper understanding through both visual and auditory senses.

[0033] The answer generation unit can automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, when a question is submitted, the generation AI automatically translates the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "How is base salary calculated?", an answer is provided in English, Chinese, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages. For example, in response to the question, "What are the details of allowances?", an answer is provided in Spanish, French, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, in response to the question, "What are the deductions from my salary?", an answer is provided in German, Italian, etc. This makes it possible to accommodate international employees.

[0034] The personalized information providing unit can analyze an employee's individual pay slip data in real time and provide a personalized answer based on the latest information. For example, when a question is submitted, the generation AI analyzes the employee's individual pay slip data in real time and provides a personalized answer based on the latest information. For example, in response to the question, "What allowances are included in my pay this month?", an answer is generated by referring to the latest pay slip. The personalized information providing unit can also analyze an employee's individual pay slip data in real time and provide a personalized answer. For example, in response to the question, "What deductions are included in my pay?", an answer is generated by referring to the latest pay slip. The personalized information providing unit can also analyze an employee's individual pay slip data in real time and provide a personalized answer based on the latest information. For example, in response to the question, "Please tell me why my pay is low this month?", an answer is generated by referring to the latest pay slip. This makes it possible to provide a personalized answer based on the latest information.

[0035] The personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, when a question is submitted, the personalized information provision unit has the generation AI refer to the employee's past pay slips and work history to provide information on future salary predictions. For example, in response to the question, "How much will you be making next year?", a prediction is provided based on past data. The personalized information provision unit also refers to an employee's past pay slips and work history to provide information on career paths. For example, in response to the question, "How much will your salary be if you get promoted?", a prediction is provided based on past data. In addition, when a question is submitted, the personalized information provision unit has the generation AI refer to the employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, in response to the question, "When will your next raise be?", a prediction is provided based on past data. This makes it possible to provide information on future salary predictions and career paths.

[0036] The personalized information provision unit can provide personalized information in a visual format such as a graph or chart to promote understanding. In the personalized information provision unit, for example, when a question is submitted, the generation AI provides personalized information in the form of a graph or chart. For example, in response to the question, "Please tell me the breakdown of my salary," the breakdown is displayed in a graph. The personalized information provision unit also adds a function to provide personalized information in a chart format. For example, in response to the question, "Please tell me the details of my allowances," the details are displayed in a chart. In addition, when a question is submitted, the personalized information provision unit can provide personalized information in the form of a graph or chart. For example, in response to the question, "Please tell me the breakdown of my deductions," the breakdown is displayed in a graph. This makes it possible to provide information in a visual format and promote understanding.

[0037] The personalized information provision unit can provide personalized information to employees' smartphones or tablets via push notifications, allowing them to access the information at any time. For example, when a question is submitted, the generation AI provides personalized information to the employee's smartphone via push notification. For example, in response to the question, "Please tell me the details of my pay slip," a notification is sent to the smartphone. The personalized information provision unit also adds a function to provide personalized information to employees' tablets via push notifications. For example, in response to the question, "Please tell me the details of my allowances," a notification is sent to the tablet. The personalized information provision unit also adds a function to provide personalized information to employees' smartphones or tablets via push notifications. For example, in response to the question, "Please tell me the breakdown of my deductions," a notification is sent to the smartphone or tablet. This allows employees to access the information at any time.

[0038] The change / adjustment explanation section can perform a detailed simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, when a question is submitted, the generation AI performs a detailed simulation regarding changes or adjustments to the salary system and provides the results to employees. For example, in response to the question, "What is the impact of the new salary system?", the generation AI provides the simulation results. Furthermore, the change / adjustment explanation section can perform a simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, in response to the question, "What is the impact of the new tax system?", the generation AI provides the simulation results. Furthermore, when a question is submitted, the change / adjustment explanation section can perform a detailed simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, in response to the question, "What is the impact of the new allowance?", the generation AI provides the simulation results. In this way, detailed simulation results regarding changes or adjustments to the salary system can be provided.

[0039] The change / adjustment explanation section automatically analyzes the legal requirements and company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, when a question is submitted, the generation AI automatically analyzes the legal requirements behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new tax system?", an explanation based on the legal requirements is provided. The change / adjustment explanation section also automatically analyzes the company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new salary system?", an explanation based on company policies is provided. The change / adjustment explanation section also automatically analyzes the legal requirements and company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new allowance?", an explanation based on legal requirements and company policies is provided. This makes it possible to provide an easy-to-understand explanation of the background to the change or adjustment.

[0040] The change / adjustment explanation section provides explanations of changes and adjustments in an interactive FAQ format, making it easier for employees to find the information they need on their own. For example, when a question is submitted, the generation AI provides an explanation of the changes or adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new salary system," an answer is provided in FAQ format. The change / adjustment explanation section also adds a function to provide explanations of changes and adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new allowances," an answer is provided in FAQ format. The change / adjustment explanation section also adds a function to provide explanations of changes and adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new tax system," an answer is provided in FAQ format. This makes it easier for employees to find the information they need on their own.

[0041] The change / adjustment explanation section can provide explanations of the changes and adjustments using videos or animations, making them easier to understand visually. For example, when a question is submitted, the generation AI provides a video explanation of the changes and adjustments. For example, a video explanation is provided for the question, "Please tell me the details of the new salary system." The change / adjustment explanation section also adds a function to provide explanations of the changes and adjustments using animations. For example, an animated explanation is provided for the question, "Please tell me the details of the new allowances." The change / adjustment explanation section can provide explanations of the changes and adjustments using videos or animations, making them easier to understand visually. For example, a video explanation is provided for the question, "Please tell me the details of the new tax system." This makes it easier to understand visually.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The answer generation unit can refer to past similar questions and their answers to generate the optimal answer. For example, when a question is submitted, the generation AI searches the database for past similar questions and their answers to generate the most appropriate answer. For example, for the question, "How is base salary calculated?", an answer is generated by referring to past similar questions and their answers. The answer generation unit also analyzes the content of the question, and the generation AI provides the optimal answer based on past similar questions and their answers. For example, for the question, "What are the details of allowances?", an answer is generated by referring to past allowance-related questions and their answers. Also, when a question is submitted, the answer generation unit refers to past similar questions and their answers to generate the optimal answer. For example, for the question, "What are the deductions from my salary?", an answer is generated by referring to past similar questions and their answers. This enables the generation AI to utilize past data to provide quick and accurate answers.

[0044] The answer generation unit can automatically refer to the asker's past question history and related documents, understand the context, and generate the optimal answer. For example, when a question is submitted, the generation AI refers to the asker's past question history, understands the context, and generates the optimal answer. For example, for the question, "Following the question about my last pay slip, what changes have been made this month?", it generates an answer by referring to the past question history. In addition, to understand the context of the question, the generation AI automatically refers to related documents and provides the optimal answer. For example, for the question, "Please tell me the details of my payroll," it generates an answer by referring to related payroll documents. In addition, when a question is submitted, the answer generation unit refers to the asker's past question history and related documents, understands the context, and generates the optimal answer. For example, for the question, "Please tell me the details of my allowances," it generates an answer by referring to the past question history and related documents. This allows the asker's context to be understood and a more appropriate answer to be provided.

[0045] The answer generation unit can provide answers to questions not only in text but also in video and audio. For example, when a question is submitted, the generation AI provides answers not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how to read my pay slip." The answer generation unit also adds a function to provide answers to questions not only in text but also in audio. For example, an audio explanation is provided for the question, "Please tell me the details of allowances." The answer generation unit can provide answers not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how payroll is calculated." This allows for deeper understanding through both visual and auditory means.

[0046] The answer generation unit can automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, when a question is submitted, the generation AI can automatically translate the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "How is my base salary calculated?", an answer is provided in English, Chinese, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages. For example, in response to the question, "What are the details of my allowances?", an answer is provided in Spanish, French, etc. In addition, when a question is submitted, the answer generation unit can automatically translate the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "What are the deductions from my salary?", an answer is provided in German, Italian, etc. This makes it possible to accommodate international employees.

[0047] The personalized information provision unit analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, when a question is submitted, the generation AI analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, for the question, "What allowances are included in my pay this month?", an answer is generated by referring to the latest pay slip. The personalized information provision unit also analyzes each employee's individual pay slip data in real time and can provide a personalized answer. For example, for the question, "What deductions are included in my pay?", an answer is generated by referring to the latest pay slip. The personalized information provision unit also analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, for the question, "Please tell me why my pay is low this month?", an answer is generated by referring to the latest pay slip. This makes it possible to provide a personalized answer based on the latest information.

[0048] The personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, when a question is submitted, the generation AI refers to the employee's past pay slips and work history to provide information on future salary predictions. For example, in response to the question, "How much will you be making next year?", a prediction is provided based on past data. The personalized information provision unit also refers to an employee's past pay slips and work history to provide information on career paths. For example, in response to the question, "How much will your salary be if you get promoted?", a prediction is provided based on past data. In addition, when a question is submitted, the personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, in response to the question, "When will your next raise be?", a prediction is provided based on past data. This makes it possible to provide information on future salary predictions and career paths.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The question reception unit receives questions about pay slips from employees. For example, questions can be submitted via text, voice, or chat. Step 2: The answer generation unit generates an answer to the question received by the question reception unit. For example, the generation AI refers to past similar questions and their answers, automatically references the questioner's past question history and related documents to understand the context, and uses an emotion estimation function to estimate the questioner's emotional state and generate the optimal answer. Step 3: The escalation unit escalates to human support if the answer generated by the answer generation unit exceeds a certain level of complexity. For example, a question that is too complex for the generation AI to handle will be escalated to a human resources representative. The emotion estimation function also adjusts the timing of escalation based on the questioner's emotions, providing human support at the appropriate time.

[0051] (Example 2) The pay slip explanation system according to an embodiment of the present invention is a system that solves questions about employees' pay slips and provides explanations about pay calculation elements and fees. As a result, the pay slip explanation system can quickly solve questions about employees' pay and increase transparency.

[0052] A pay slip explanation system according to an embodiment includes a question receiving unit, an answer generating unit, and an escalation unit. The question receiving unit receives questions about pay slips from employees. For example, it receives text-based questions. The question receiving unit can also receive voice questions. The question receiving unit can also receive questions via a chat interface. The answer generating unit generates answers to questions received by the question receiving unit. For example, the generation AI references past similar questions and their answers to generate an optimal answer. The generation AI can also automatically reference the questioner's past question history and related documents to understand the context and generate an optimal answer. The generation AI can also use an emotion estimation function to estimate the questioner's emotional state and generate an answer based on the emotion. The escalation unit escalates to human support if the answer generated by the answer generating unit exceeds a certain level of complexity. For example, a complex question that the generation AI cannot handle is escalated to a human resources representative. The escalation unit can also use the emotion estimation function to adjust the timing of escalation based on the questioner's emotions and provide human support at the appropriate time. As a result, the pay slip explanation system according to the embodiment can quickly resolve questions about employees' salaries and increase transparency.

[0053] The answer generation unit can generate the optimal answer by referring to past similar questions and their answers. For example, when a question is submitted, the generation AI searches for past similar questions and their answers in the database and generates the most appropriate answer. For example, for the question, "How is base salary calculated?", an answer is generated by referring to past similar questions and their answers. The answer generation unit also analyzes the content of the question, and the generation AI provides the optimal answer based on past similar questions and their answers. For example, for the question, "What are the details of allowances?", an answer is generated by referring to past allowance-related questions and their answers. For example, when a question is submitted, the generation AI refers to past similar questions and their answers and generates the optimal answer. For example, for the question, "What are the deductions from my salary?", an answer is generated by referring to past similar questions and their answers. This makes it possible to utilize past data to provide quick and accurate answers.

[0054] The answer generation unit can automatically refer to the asker's past question history and related documents, understand the context, and generate the optimal answer. For example, when a question is submitted, the generation AI refers to the asker's past question history, understands the context, and generates the optimal answer. For example, for the question, "Following the question about my last pay slip, what changes have been made this month?", the answer is generated by referring to the past question history. In addition, to understand the context of the question, the generation AI automatically refers to related documents and provides the optimal answer. For example, for the question, "Please tell me the details of my payroll," the answer is generated by referring to related payroll documents. In addition, when a question is submitted, the answer generation unit refers to the asker's past question history and related documents, understands the context, and generates the optimal answer. For example, for the question, "Please tell me the details of my allowances," the answer is generated by referring to the past question history and related documents. This allows the asker's context to be understood and a more appropriate answer to be provided.

[0055] The answer generation unit can estimate the emotional state of the questioner using the emotion estimation function and generate an answer corresponding to the emotion. For example, when a question is submitted, the answer generation unit uses the emotion estimation function to estimate the emotional state of the questioner and generate an answer corresponding to the emotion. For example, if the questioner is feeling anxious, an answer that gives a sense of security is provided. The answer generation unit also uses the emotion estimation function to analyze the emotional state of the questioner in real time and generate an answer corresponding to the emotion. For example, if the questioner is feeling angry, an answer that responds calmly is provided. Also, when a question is submitted, the answer generation unit uses the emotion estimation function to estimate the emotional state of the questioner and generate an answer corresponding to the emotion. For example, if the questioner is confused, an answer that explains the question in an easy-to-understand manner is provided. This makes it possible to provide an appropriate answer corresponding to the questioner's emotion.

[0056] The answer generation unit can provide answers to questions not only in text but also in video and audio. For example, when a question is submitted, the generation AI provides an answer not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how to read my pay slip." The answer generation unit also adds a function to provide answers to questions not only in text but also in audio. For example, an audio explanation is provided for the question, "Please tell me the details of allowances." The answer generation unit can also provide answers to questions not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how payroll is calculated." This allows for deeper understanding through both visual and auditory senses.

[0057] The answer generation unit can automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, when a question is submitted, the generation AI automatically translates the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "How is base salary calculated?", an answer is provided in English, Chinese, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages. For example, in response to the question, "What are the details of allowances?", an answer is provided in Spanish, French, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, in response to the question, "What are the deductions from my salary?", an answer is provided in German, Italian, etc. This makes it possible to accommodate international employees.

[0058] The escalation unit uses the emotion estimation function to adjust the timing of escalation according to the questioner's emotions, allowing the system to provide human support at the appropriate time. For example, when a question is submitted, the generation AI uses the emotion estimation function to analyze the questioner's emotional state and provide human support at the appropriate time. For example, if the questioner is feeling strong anxiety, the system immediately escalates to human support. The escalation unit also uses the emotion estimation function to analyze the questioner's emotional state in real time and adjust the timing of escalation. For example, if the questioner is feeling angry, the system quickly escalates to human support. Also, when a question is submitted, the generation AI uses the emotion estimation function to analyze the questioner's emotional state and provide human support at the appropriate time. For example, if the questioner is confused, the system escalates to human support early. This allows the system to provide human support at the appropriate time according to the questioner's emotions.

[0059] The personalized information providing unit can analyze an employee's individual pay slip data in real time and provide a personalized answer based on the latest information. For example, when a question is submitted, the generation AI analyzes the employee's individual pay slip data in real time and provides a personalized answer based on the latest information. For example, in response to the question, "What allowances are included in my pay this month?", an answer is generated by referring to the latest pay slip. The personalized information providing unit can also analyze an employee's individual pay slip data in real time and provide a personalized answer. For example, in response to the question, "What deductions are included in my pay?", an answer is generated by referring to the latest pay slip. The personalized information providing unit can also analyze an employee's individual pay slip data in real time and provide a personalized answer based on the latest information. For example, in response to the question, "Please tell me why my pay is low this month?", an answer is generated by referring to the latest pay slip. This makes it possible to provide a personalized answer based on the latest information.

[0060] The personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, when a question is submitted, the personalized information provision unit has the generation AI refer to the employee's past pay slips and work history to provide information on future salary predictions. For example, in response to the question, "How much will you be making next year?", a prediction is provided based on past data. The personalized information provision unit also refers to an employee's past pay slips and work history to provide information on career paths. For example, in response to the question, "How much will your salary be if you get promoted?", a prediction is provided based on past data. In addition, when a question is submitted, the personalized information provision unit has the generation AI refer to the employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, in response to the question, "When will your next raise be?", a prediction is provided based on past data. This makes it possible to provide information on future salary predictions and career paths.

[0061] The personalized information provision unit uses the emotion estimation function to take into account the emotional state of the employee and provide information that elicits positive emotions. For example, when a question is submitted, the personalized information provision unit uses the emotion estimation function to analyze the emotional state of the employee and provide information that elicits positive emotions. For example, the personalized information provision unit provides information about the possibility of a salary increase. The personalized information provision unit also uses the emotion estimation function to analyze the emotional state of the employee in real time and provide information that elicits positive emotions. For example, the personalized information provision unit provides information about upcoming bonus payments. The personalized information provision unit also uses the emotion estimation function to analyze the emotional state of the employee and provide information that elicits positive emotions. For example, the personalized information provision unit provides information about details of employee benefits. This makes it possible to provide information that elicits positive emotions in employees.

[0062] The personalized information provision unit can provide personalized information in a visual format such as a graph or chart to promote understanding. In the personalized information provision unit, for example, when a question is submitted, the generation AI provides personalized information in the form of a graph or chart. For example, in response to the question, "Please tell me the breakdown of my salary," the breakdown is displayed in a graph. The personalized information provision unit also adds a function to provide personalized information in a chart format. For example, in response to the question, "Please tell me the details of my allowances," the details are displayed in a chart. In addition, when a question is submitted, the personalized information provision unit can provide personalized information in the form of a graph or chart. For example, in response to the question, "Please tell me the breakdown of my deductions," the breakdown is displayed in a graph. This makes it possible to provide information in a visual format and promote understanding.

[0063] The personalized information provision unit can provide personalized information to employees' smartphones or tablets via push notifications, allowing them to access the information at any time. For example, when a question is submitted, the generation AI provides personalized information to the employee's smartphone via push notification. For example, in response to the question, "Please tell me the details of my pay slip," a notification is sent to the smartphone. The personalized information provision unit also adds a function to provide personalized information to employees' tablets via push notifications. For example, in response to the question, "Please tell me the details of my allowances," a notification is sent to the tablet. The personalized information provision unit also adds a function to provide personalized information to employees' smartphones or tablets via push notifications. For example, in response to the question, "Please tell me the breakdown of my deductions," a notification is sent to the smartphone or tablet. This allows employees to access the information at any time.

[0064] The personalized information provision unit uses an emotion estimation function to prioritize providing information that employees are most interested in, thereby optimizing how information is received. For example, when a question is submitted, the personalized information provision unit uses the emotion estimation function to prioritize providing the information that employees are most interested in. For example, information with a high emotion score is displayed preferentially. The personalized information provision unit also uses the emotion estimation function to analyze in real time the information that employees are most interested in and provide it preferentially. For example, information with a strong positive emotion is displayed preferentially. The personalized information provision unit also uses the emotion estimation function to prioritize providing the information that employees are most interested in, thereby optimizing how information is received. For example, information with a high emotion score is displayed preferentially. This makes it possible to provide the information that employees are most interested in preferentially.

[0065] The change / adjustment explanation section can perform a detailed simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, when a question is submitted, the generation AI performs a detailed simulation regarding changes or adjustments to the salary system and provides the results to employees. For example, in response to the question, "What is the impact of the new salary system?", the generation AI provides the simulation results. Furthermore, the change / adjustment explanation section can perform a simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, in response to the question, "What is the impact of the new tax system?", the generation AI provides the simulation results. Furthermore, when a question is submitted, the change / adjustment explanation section can perform a detailed simulation regarding changes or adjustments to the salary system and provide the results to employees. For example, in response to the question, "What is the impact of the new allowance?", the generation AI provides the simulation results. In this way, detailed simulation results regarding changes or adjustments to the salary system can be provided.

[0066] The change / adjustment explanation section automatically analyzes the legal requirements and company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, when a question is submitted, the generation AI automatically analyzes the legal requirements behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new tax system?", an explanation based on the legal requirements is provided. The change / adjustment explanation section also automatically analyzes the company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new salary system?", an explanation based on company policies is provided. The change / adjustment explanation section also automatically analyzes the legal requirements and company policies behind the change or adjustment and provides an easy-to-understand explanation. For example, in response to the question, "What is the background to the new allowance?", an explanation based on legal requirements and company policies is provided. This makes it possible to provide an easy-to-understand explanation of the background to the change or adjustment.

[0067] The change or adjustment explanation section uses the emotion estimation function to analyze employees' emotional reactions to the change or adjustment, and can provide additional information to alleviate negative emotions. For example, when a question is submitted, the generation AI uses the emotion estimation function to analyze employees' emotional reactions to the change or adjustment, and can provide additional information to alleviate negative emotions. For example, the reasons for the change and its benefits are explained in detail. The change or adjustment explanation section also uses the emotion estimation function to analyze employees' emotional reactions to the change or adjustment in real time, and can provide additional information to alleviate negative emotions. For example, the impact of the change is explained in detail. The change or adjustment explanation section also uses the emotion estimation function to analyze employees' emotional reactions to the change or adjustment, and can provide additional information to alleviate negative emotions. For example, the background and purpose of the change are explained in detail. This can provide additional information to alleviate negative emotions.

[0068] The change / adjustment explanation section provides explanations of changes and adjustments in an interactive FAQ format, making it easier for employees to find the information they need on their own. For example, when a question is submitted, the generation AI provides an explanation of the changes or adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new salary system," an answer is provided in FAQ format. The change / adjustment explanation section also adds a function to provide explanations of changes and adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new allowances," an answer is provided in FAQ format. The change / adjustment explanation section also adds a function to provide explanations of changes and adjustments in an interactive FAQ format. For example, to the question, "Please tell me the details of the new tax system," an answer is provided in FAQ format. This makes it easier for employees to find the information they need on their own.

[0069] The change / adjustment explanation section can provide explanations of the changes and adjustments using videos or animations, making them easier to understand visually. For example, when a question is submitted, the generation AI provides a video explanation of the changes and adjustments. For example, a video explanation is provided for the question, "Please tell me the details of the new salary system." The change / adjustment explanation section also adds a function to provide explanations of the changes and adjustments using animations. For example, an animated explanation is provided for the question, "Please tell me the details of the new allowances." The change / adjustment explanation section can provide explanations of the changes and adjustments using videos or animations, making them easier to understand visually. For example, a video explanation is provided for the question, "Please tell me the details of the new tax system." This makes it easier to understand visually.

[0070] The change / adjustment explanation section uses the emotion estimation function to monitor employees' emotional reactions to the change or adjustment in real time, and can provide additional support as needed. For example, when a question is submitted, the generation AI uses the emotion estimation function to monitor employees' emotional reactions to the change or adjustment in real time, and can provide additional support as needed. For example, if negative emotions are strong, a detailed explanation is added. The change / adjustment explanation section also uses the emotion estimation function to analyze employees' emotional reactions to the change or adjustment in real time, and can provide additional support as needed. For example, if positive emotions are low, the benefits are emphasized. The change / adjustment explanation section also uses the emotion estimation function to monitor employees' emotional reactions to the change or adjustment in real time, and can provide additional support as needed. For example, if an employee seems confused, an easy-to-understand explanation is added. This allows for emotional reactions to be monitored in real time, and can provide additional support as needed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The question reception unit receives questions about pay slips from employees. For example, it receives text-based questions. The question reception unit can also receive voice questions. The question reception unit can also receive questions via a chat interface. The answer generation unit generates answers to questions received by the question reception unit. For example, the generation AI references past similar questions and their answers to generate an optimal answer. The generation AI can also automatically reference the questioner's past question history and related documents to understand the context and generate an optimal answer. The generation AI can also use an emotion estimation function to estimate the questioner's emotional state and generate an answer based on that emotion. The escalation unit escalates to human support if the answer generated by the answer generation unit exceeds a certain level of complexity. For example, a complex question that the generation AI cannot handle is escalated to a human resources representative. The escalation unit can also adjust the timing of escalation based on the questioner's emotions using the emotion estimation function to provide human support at the appropriate time. As a result, the pay slip explanation system according to the embodiment can quickly resolve questions about employees' salaries and increase transparency.

[0073] The answer generation unit can refer to past similar questions and their answers to generate the optimal answer. For example, when a question is submitted, the generation AI searches the database for past similar questions and their answers to generate the most appropriate answer. For example, for the question, "How is base salary calculated?", an answer is generated by referring to past similar questions and their answers. The answer generation unit also analyzes the content of the question, and the generation AI provides the optimal answer based on past similar questions and their answers. For example, for the question, "What are the details of allowances?", an answer is generated by referring to past allowance-related questions and their answers. Also, when a question is submitted, the answer generation unit refers to past similar questions and their answers to generate the optimal answer. For example, for the question, "What are the deductions from my salary?", an answer is generated by referring to past similar questions and their answers. This enables the generation AI to utilize past data to provide quick and accurate answers.

[0074] The answer generation unit can automatically refer to the asker's past question history and related documents, understand the context, and generate the optimal answer. For example, when a question is submitted, the generation AI refers to the asker's past question history, understands the context, and generates the optimal answer. For example, for the question, "Following the question about my last pay slip, what changes have been made this month?", it generates an answer by referring to the past question history. In addition, to understand the context of the question, the generation AI automatically refers to related documents and provides the optimal answer. For example, for the question, "Please tell me the details of my payroll," it generates an answer by referring to related payroll documents. In addition, when a question is submitted, the answer generation unit refers to the asker's past question history and related documents, understands the context, and generates the optimal answer. For example, for the question, "Please tell me the details of my allowances," it generates an answer by referring to the past question history and related documents. This allows the asker's context to be understood and a more appropriate answer to be provided.

[0075] The answer generation unit can use the emotion estimation function to estimate the emotional state of the questioner and generate an answer that corresponds to the emotion. For example, when a question is submitted, the generation AI uses the emotion estimation function to estimate the emotional state of the questioner and generate an answer that corresponds to the emotion. For example, if the questioner is feeling anxious, an answer that gives a sense of security is provided. The answer generation unit also uses the emotion estimation function to analyze the emotional state of the questioner in real time and generate an answer that corresponds to the emotion. For example, if the questioner is feeling angry, an answer that responds calmly is provided. Also, when a question is submitted, the answer generation unit uses the emotion estimation function to estimate the emotional state of the questioner and generate an answer that corresponds to the emotion. For example, if the questioner is confused, an answer that explains the question in an easy-to-understand manner is provided. This makes it possible to provide an appropriate answer that corresponds to the questioner's emotion.

[0076] The answer generation unit can provide answers to questions not only in text but also in video and audio. For example, when a question is submitted, the generation AI provides answers not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how to read my pay slip." The answer generation unit also adds a function to provide answers to questions not only in text but also in audio. For example, an audio explanation is provided for the question, "Please tell me the details of allowances." The answer generation unit can provide answers not only in text but also in video and audio. For example, a video explanation is provided for the question, "Please tell me how payroll is calculated." This allows for deeper understanding through both visual and auditory means.

[0077] The answer generation unit can automatically translate answers to questions into different languages, making it possible to accommodate international employees. For example, when a question is submitted, the generation AI can automatically translate the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "How is my base salary calculated?", an answer is provided in English, Chinese, etc. The answer generation unit also adds a function to automatically translate answers to questions into different languages. For example, in response to the question, "What are the details of my allowances?", an answer is provided in Spanish, French, etc. In addition, when a question is submitted, the answer generation unit can automatically translate the answer into different languages, making it possible to accommodate international employees. For example, in response to the question, "What are the deductions from my salary?", an answer is provided in German, Italian, etc. This makes it possible to accommodate international employees.

[0078] The escalation unit uses the emotion estimation function to adjust the timing of escalation according to the questioner's emotions, allowing the system to provide human support at the appropriate time. For example, when a question is submitted, the generation AI uses the emotion estimation function to analyze the questioner's emotional state and provide human support at the appropriate time. For example, if the questioner is feeling very anxious, the system will immediately escalate to human support. The escalation unit also uses the emotion estimation function to analyze the questioner's emotional state in real time and adjust the timing of escalation. For example, if the questioner is feeling angry, the system will quickly escalate to human support. When a question is submitted, the generation AI uses the emotion estimation function to analyze the questioner's emotional state and provide human support at the appropriate time. For example, if the questioner is confused, the system will escalate to human support early. This allows the system to provide human support at the appropriate time according to the questioner's emotions.

[0079] The personalized information provision unit analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, when a question is submitted, the generation AI analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, for the question, "What allowances are included in my pay this month?", an answer is generated by referring to the latest pay slip. The personalized information provision unit also analyzes each employee's individual pay slip data in real time and can provide a personalized answer. For example, for the question, "What deductions are included in my pay?", an answer is generated by referring to the latest pay slip. The personalized information provision unit also analyzes each employee's individual pay slip data in real time and can provide a personalized answer based on the latest information. For example, for the question, "Please tell me why my pay is low this month?", an answer is generated by referring to the latest pay slip. This makes it possible to provide a personalized answer based on the latest information.

[0080] The personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, when a question is submitted, the generation AI refers to the employee's past pay slips and work history to provide information on future salary predictions. For example, in response to the question, "How much will you be making next year?", a prediction is provided based on past data. The personalized information provision unit also refers to an employee's past pay slips and work history to provide information on career paths. For example, in response to the question, "How much will your salary be if you get promoted?", a prediction is provided based on past data. In addition, when a question is submitted, the personalized information provision unit can refer to an employee's past pay slips and work history to provide information on future salary predictions and career paths. For example, in response to the question, "When will your next raise be?", a prediction is provided based on past data. This makes it possible to provide information on future salary predictions and career paths.

[0081] The personalized information provision unit uses the emotion estimation function to take into account the emotional state of the employee and provide information that elicits positive emotions. For example, when a question is submitted, the generation AI uses the emotion estimation function to analyze the employee's emotional state and provide information that elicits positive emotions. For example, it provides information about the possibility of a salary increase. The personalized information provision unit also uses the emotion estimation function to analyze the employee's emotional state in real time and provide information that elicits positive emotions. For example, it provides information about upcoming bonus payments. The personalized information provision unit also uses the emotion estimation function to analyze the employee's emotional state when a question is submitted, and provides information that elicits positive emotions. For example, it provides information about details of employee benefits. This makes it possible to provide information that elicits positive emotions in employees.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The question reception unit receives questions about pay slips from employees. For example, questions can be submitted via text, voice, or chat. Step 2: The answer generation unit generates an answer to the question received by the question reception unit. For example, the generation AI refers to past similar questions and their answers, automatically references the questioner's past question history and related documents to understand the context, and uses an emotion estimation function to estimate the questioner's emotional state and generate the optimal answer. Step 3: The escalation unit escalates to human support if the answer generated by the answer generation unit exceeds a certain level of complexity. For example, a question that is too complex for the generation AI to handle will be escalated to a human resources representative. The emotion estimation function also adjusts the timing of escalation based on the questioner's emotions, providing human support at the appropriate time.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question reception unit that receives questions; an answer generation unit that generates an answer to the question received by the question receiving unit; an escalation unit that escalates to human support when the answer generated by the answer generation unit exceeds a certain level of complexity. A system characterized by:

2. The answer generation unit Generate the best answer by looking at similar questions and their answers 2. The system of claim 1.

3. The answer generation unit Automatically refer to the questioner's past question history and related documents to understand the context and generate the best answer 2. The system of claim 1.

4. The answer generation unit Estimate the emotional state of the questioner and generate an answer that matches that emotion 2. The system of claim 1.

5. The answer generation unit Answer questions not only in text but also in video and audio formats 2. The system of claim 1.

6. The answer generation unit Automatically translate answers to questions into different languages ​​to accommodate an international workforce 2. The system of claim 1.

7. The escalation unit Adjust the timing of escalation based on the questioner's emotions and provide human support at the appropriate time.

2. The system of claim 1.

8. The personalized information section Analyzes individual employee payslip data in real time to provide personalized answers based on the latest information 2. The system of claim 1.

Images