system

Abstract

The system according to the embodiment aims to efficiently respond to personnel-related inquiries and to direct them to the appropriate person in charge as necessary. [Solution] A system according to an embodiment includes an information input unit, an inquiry reception unit, an answer generation unit, a provision unit, and a staff member guidance unit. The information input unit inputs personnel-related information. The inquiry reception unit accepts inquiries from users based on the information input by the information input unit. The answer generation unit analyzes the inquiry content accepted by the inquiry reception unit and generates an answer. The provision unit provides the user with the answer generated by the answer generation unit. The staff member guidance unit guides a staff member when the inquiry content meets specific conditions that are beyond the scope of the system.

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, all personnel-related inquiries were concentrated at the organization's personnel department, making it difficult to respond efficiently.

[0005] The system according to the embodiment aims to efficiently respond to personnel-related inquiries and to direct them to the appropriate person in charge as necessary. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an inquiry reception unit, an answer generation unit, a providing unit, and a staff member guidance unit. The information input unit inputs personnel-related information. The inquiry reception unit receives inquiries from users based on the information input by the information input unit. The answer generation unit analyzes the inquiry content received by the inquiry reception unit and generates an answer. The providing unit provides the user with the answer generated by the answer generation unit. The staff member guidance unit guides a staff member when the inquiry content meets specific conditions that are beyond the scope of the system. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently respond to personnel-related inquiries and direct them to the appropriate person in charge as needed. [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) A personnel inquiry response system according to an embodiment of the present invention automatically provides a certain degree of response to inquiries to personnel by importing personnel-related information. This system first includes an "information input unit" that inputs personnel-related information into the system. Next, an "inquiry reception unit" that accepts inquiries from users is also included. The system then includes an "answer generation unit" that analyzes the received inquiry and generates an appropriate response. The generated response is provided to the user. Furthermore, if the inquiry exceeds the scope of the system, a "personnel guidance unit" is also included. This unit indicates the person to whom the inquiry should be directed in the final line of the response text. This mechanism reduces the number of initial inquiries to the organization's personnel and improves the efficiency of inquiry response. For example, an "information input unit" is included that inputs personnel-related information into the system. This information includes details such as salary, benefits, vacation policies, and promotions. For example, it includes information on salary calculation methods and vacation procedures. Next, an "inquiry reception unit" is included that accepts inquiries from users. Users input questions to the system in natural language. Possible questions include, "How many paid vacation days are left this year?" or "What are the conditions for promotion?" The system includes an "answer generation section" that analyzes the received inquiry and generates an appropriate answer. The AI ​​performs this analysis and generates the answer. For example, in response to the question, "How many paid vacation days are left this year?", the system references the user's paid vacation data, calculates the remaining number, and provides the answer. The generated answer is then provided to the user. For example, a specific answer such as, "You have 10 paid vacation days left this year" is provided. Furthermore, if the inquiry exceeds the scope of the system, a "person guidance section" is provided that indicates which person to contact in the final line of the answer. For example, the system may provide guidance such as, "For detailed payroll calculation information, please contact Mr. Tanaka, who is in charge of payroll." This system reduces the number of initial inquiries to the organization's human resources department and improves the efficiency of inquiry response. For example, by having the system automatically answer general questions, the organization's human resources department can focus on more specialized inquiries.This allows the personnel inquiry response system to automatically provide answers to personnel-related inquiries and, if necessary, direct the user to the appropriate person, thereby improving the efficiency of inquiry response.

[0029] A personnel inquiry response system according to an embodiment includes an information input unit, an inquiry reception unit, a response generation unit, a provision unit, and a staff member guidance unit. The information input unit inputs personnel-related information. The personnel-related information includes, but is not limited to, details such as salary, benefits, vacation systems, and promotions. The information input unit can input information such as salary calculation methods and vacation procedures. The information input unit also supports digital information input, allowing users to easily input information. The inquiry reception unit accepts inquiries from users. Users can input questions to the system in natural language. For example, questions such as "How many paid vacation days do I have left this year?" or "What are the conditions for promotion?" are possible. The inquiry reception unit analyzes the user's question and provides information for generating an appropriate response. The response generation unit uses a generation AI to analyze the received inquiry and generate an appropriate response. The generation AI generates an answer to the user's question using, for example, a text generation AI (e.g., LLM). For example, in response to the question, "How many paid vacation days are left this year?", the answer generation unit references the user's paid vacation data, calculates the remaining number of days, and generates an answer. The provision unit provides the generated answer to the user. The provision unit can provide the answer, for example, via email or chat. The provision unit can also provide the answer in an optimal format depending on the user's device. The person in charge guidance unit guides the user to which person in charge to inquire if the inquiry exceeds the scope of the system. For example, guidance such as "For detailed payroll calculations, please contact Tanaka, who is in charge of payroll" is given. As a result, the personnel inquiry response system according to the embodiment automatically provides answers to personnel-related inquiries and guides the user to a person in charge as needed, thereby improving the efficiency of inquiry response.

[0030] The information input unit can input details about salary, employee benefits, vacation systems, and promotions. The information input unit can input information such as the salary calculation method and vacation procedures. For example, the information input unit can input details about the salary calculation method, such as base salary, allowances, and bonuses. The information input unit can also input information about employee benefits, such as health insurance, pension systems, and employee discounts. The information input unit can also input information about vacation systems, such as paid vacation, sick leave, and childcare leave. For example, the information input unit can input information about evaluation criteria and promotion timing as promotion criteria. This input of detailed personnel-related information improves the accuracy of responses to inquiries. Some or all of the above-described processing in the information input unit can be performed using, or without, AI. For example, the information input unit can input the salary calculation method to a generation AI, causing the generation AI to generate a detailed calculation method.

[0031] The inquiry reception unit can accept questions in natural language from a user. The inquiry reception unit accepts questions in natural language from a user. The user can input questions in natural language to the system. For example, questions such as "How many paid vacation days do I have left this year?" or "What are the conditions for promotion?" are possible. The inquiry reception unit analyzes the user's questions and provides information for generating appropriate answers. For example, the inquiry reception unit may analyze the user's questions using text analysis technology to understand the intent of the questions. The inquiry reception unit may also analyze the user's questions using speech recognition technology and convert the voice-input questions into text. For example, when a user inputs a question by voice, the inquiry reception unit analyzes the voice data and converts it into text data. This allows the user to intuitively make inquiries by accepting questions in natural language. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without AI. For example, the inquiry reception unit may input the user's voice data into a generation AI, which then converts the voice data into text data.

[0032] The answer generation unit can refer to the user's paid leave data and calculate the remaining number of paid leave days to provide an answer. The answer generation unit refers to the user's paid leave data and calculates the remaining number of paid leave days to provide an answer. For example, the answer generation unit refers to a database of the user's paid leave and calculates the remaining number of days. The answer generation unit can also analyze the user's paid leave acquisition history and calculate the remaining number of days. For example, the answer generation unit chronologically analyzes the user's paid leave acquisition history and calculates the remaining number of days. The answer generation unit can also update the user's paid leave data in real time and provide the latest remaining number of days. For example, when the user acquires new paid leave, the answer generation unit immediately updates the database and calculates the latest remaining number of days. This allows the answer generation unit to provide an accurate remaining number of days by referring to the paid leave data. Some or all of the above-mentioned processing in the answer generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input the user's paid vacation data into the generation AI and have the generation AI calculate the remaining number of days.

[0033] The providing unit can provide the generated answer to the user. The providing unit provides the generated answer to the user. For example, the providing unit can provide the answer via email or chat. The providing unit can also provide the answer in an optimal format depending on the user's device. For example, if the user is using a smartphone, the providing unit can provide the answer in a mobile-friendly format. If the user is using a desktop, the providing unit can also provide the answer in a format optimized for the desktop. Furthermore, the providing unit can customize the method of providing the answer according to the user's preferences. For example, if the user prefers an answer via email, the providing unit can provide the answer via email. If the user prefers an answer via chat, the providing unit can also provide the answer via chat. This improves the efficiency of responding to inquiries by quickly providing the generated answer to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated answer to a generation AI and have the generation AI provide the answer in an optimal format.

[0034] The staff guidance unit can guide staff members to specific, detailed inquiries. The staff guidance unit guides staff members to specific, detailed inquiries. For example, if the inquiry content exceeds the scope of the system, the staff guidance unit guides the staff member to which staff member to contact. For example, the guidance may include, "For detailed payroll calculations, please contact Tanaka-san, who is in charge of payroll." The staff guidance unit can also automatically select an appropriate staff member based on the inquiry content. For example, if the inquiry content is about employee benefits, the staff guidance unit guides the employee benefits staff member. If the inquiry content is about promotions, the staff guidance unit can also guide the employee promotion staff member. The staff guidance unit can also provide the staff member's contact information. For example, the staff guidance unit may provide the staff member's email address and phone number. This improves the efficiency of inquiry response by guiding the appropriate staff member to detailed inquiries. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the staff guidance unit may input the inquiry content into a generation AI, which then selects an appropriate staff member.

[0035] The information input unit can analyze past information input history and select the optimal input method. The information input unit analyzes past information input history and selects the optimal input method. For example, the information input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The information input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The information input unit can also analyze patterns of information previously input by the user and select the optimal input method. For example, if the user has frequently used voice input in the past, the information input unit preferentially suggests voice input. Also, if the user has frequently used text input in a specific time period, the information input unit can also suggest text input for that time period. In this way, by analyzing past information input history, the optimal input method can be provided to the user. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit inputs past information input history to a generation AI, which can select the optimal input method.

[0036] The information input unit can filter information based on the user's current work situation and areas of interest when inputting information. The information input unit can filter information based on the user's current work situation and areas of interest when inputting information. For example, the information input unit prioritizes input of information related to a project the user is currently working on. The information input unit can also filter and input highly relevant information based on the user's areas of interest. Furthermore, the information input unit can input only necessary information depending on the user's work situation. For example, the information input unit prioritizes input of information related to a project the user is currently working on. The information input unit can also filter and input highly relevant information based on the user's areas of interest. In this way, highly relevant information can be input by filtering information based on the user's work situation and areas of interest. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input data on the user's work situation and areas of interest to a generation AI, and have the generation AI perform filtering.

[0037] When inputting information, the information input unit can prioritize inputting relevant information taking into account the user's geographical location information. When inputting information, the information input unit prioritizes inputting relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the information input unit prioritizes inputting information related to that area. Furthermore, when the user is on a business trip, the information input unit can prioritize inputting information related to the business trip destination. Furthermore, when the user is at home, the information input unit can prioritize inputting information related to the user's home. For example, the information input unit acquires the user's geographical location information from GPS data or an IP address and inputs relevant information based on that information. Furthermore, the information input unit can update the user's geographical location information in real time and input information based on the latest location information. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the user's geographical location information to a generation AI, causing the generation AI to prioritize inputting relevant information.

[0038] The information input unit can analyze the user's social media activity and input related information when inputting information. The information input unit can analyze the user's social media activity and input related information when inputting information. For example, the information input unit can input related information based on information shared by the user on social media. The information input unit can also input related information based on information about accounts the user follows on social media. The information input unit can also input related information based on information about groups the user participates in on social media. For example, the information input unit can analyze posts shared by the user on social media and input information related to the posts. The information input unit can also analyze posts from accounts the user follows and input related information. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the information input unit can be performed using, for example, AI, or can be performed without using AI. For example, the information input unit can input the user's social media activity data to a generation AI, causing the generation AI to input related information.

[0039] The inquiry reception unit can analyze past inquiry histories and select the optimal reception method. The inquiry reception unit analyzes past inquiry histories and selects the optimal reception method. For example, the inquiry reception unit preferentially suggests an inquiry method (voice, text, etc.) that the user has frequently used in the past. The inquiry reception unit can also predict and suggest an reception method to be used during a specific time period based on the user's past inquiry history. The inquiry reception unit can also analyze patterns of the user's past inquiries and select the optimal reception method. For example, if the user has frequently used voice input in the past, the inquiry reception unit preferentially suggests voice input. Also, if the user has frequently used text input during a specific time period, the inquiry reception unit can suggest text input during that time period. In this way, by analyzing past inquiry histories, the optimal reception method can be provided to the user. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit inputs past inquiry histories into a generation AI, which can select the optimal reception method.

[0040] The inquiry reception unit can filter inquiries based on the user's current work situation and areas of interest when receiving an inquiry. The inquiry reception unit can filter inquiries based on the user's current work situation and areas of interest when receiving an inquiry. For example, the inquiry reception unit prioritizes receiving inquiries related to a project the user is currently working on. The inquiry reception unit can also filter and receive highly relevant inquiries based on the user's areas of interest. The inquiry reception unit can also receive only necessary inquiries depending on the user's work situation. For example, the inquiry reception unit prioritizes receiving inquiries related to a project the user is currently working on. The inquiry reception unit can also filter and receive highly relevant inquiries based on the user's areas of interest. In this way, by filtering inquiries based on the user's work situation and areas of interest, highly relevant inquiries can be received. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit can input data on the user's work situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0041] When accepting inquiries, the inquiry reception unit can prioritize accepting highly relevant inquiries by taking into account the user's geographical location information. When accepting inquiries, the inquiry reception unit prioritizes accepting highly relevant inquiries by taking into account the user's geographical location information. For example, when the user is in a specific area, the inquiry reception unit prioritizes accepting inquiries related to that area. Furthermore, when the user is on a business trip, the inquiry reception unit can prioritize accepting inquiries related to the business trip destination. Furthermore, when the user is at home, the inquiry reception unit can prioritize accepting inquiries related to the user's home. For example, the inquiry reception unit acquires the user's geographical location information from GPS data or an IP address and accepts related inquiries based on that information. Furthermore, the inquiry reception unit can update the user's geographical location information in real time and accept inquiries based on the latest location information. In this way, by taking the user's geographical location information into account, highly relevant inquiries can be prioritized. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit can input the user's geographical location information to the generation AI, causing the generation AI to preferentially receive related inquiries.

[0042] The inquiry reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. The inquiry reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. For example, the inquiry reception unit can receive related inquiries based on information shared by the user on social media. The inquiry reception unit can also receive related inquiries based on information about accounts the user follows on social media. The inquiry reception unit can also receive related inquiries based on information about groups the user participates in on social media. For example, the inquiry reception unit can analyze posts shared by the user on social media and receive inquiries related to the content. The inquiry reception unit can also analyze posts from accounts the user follows and receive related inquiries. In this way, by analyzing the user's social media activity, highly relevant inquiries can be received. Some or all of the above-described processing by the inquiry reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the inquiry reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related inquiries.

[0043] The answer generation unit can adjust the level of detail of the answer based on the importance of the inquiry when generating an answer. The answer generation unit adjusts the level of detail of the answer based on the importance of the inquiry when generating an answer. For example, the answer generation unit generates a detailed answer for an inquiry of high importance. The answer generation unit can also generate a concise answer for an inquiry of low importance. Furthermore, the answer generation unit can generate an answer with an appropriate level of detail for an inquiry of medium importance. For example, the answer generation unit analyzes the importance of the inquiry and adjusts the level of detail of the answer according to the importance. The answer generation unit can also analyze the content of the inquiry and adjust the level of detail of the answer based on the content. In this way, adjusting the level of detail of the answer based on the importance of the inquiry enables efficient answer generation. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit inputs inquiry importance data to the generation AI and causes the generation AI to adjust the level of detail of the answer.

[0044] The answer generation unit can apply different answer algorithms depending on the inquiry category when generating an answer. The answer generation unit can apply different answer algorithms depending on the inquiry category when generating an answer. For example, the answer generation unit can apply an algorithm specialized for payroll calculation to an inquiry about salary. The answer generation unit can also apply an algorithm specialized for vacation systems to an inquiry about vacation systems. The answer generation unit can also apply an algorithm specialized for promotions to an inquiry about promotions. For example, the answer generation unit can analyze the inquiry category and select an optimal answer algorithm depending on the category. The answer generation unit can also analyze the content of the inquiry and apply an optimal answer algorithm based on the content. This enables efficient answer generation by applying different answer algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the answer generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answer generation unit can input inquiry category data into a generation AI and have the generation AI apply the optimal answer algorithm.

[0045] The answer generation unit can determine the priority of answers based on the time of submission of the inquiry when generating an answer. The answer generation unit determines the priority of answers based on the time of submission of the inquiry when generating an answer. For example, the answer generation unit generates answers with the highest priority for urgent inquiries. The answer generation unit can also generate answers with a moderate priority for normal inquiries. Furthermore, the answer generation unit can generate answers later for low-priority inquiries. For example, the answer generation unit analyzes the time of submission of an inquiry and determines the priority of answers based on the time of submission. The answer generation unit can also analyze the content of the inquiry and determine the priority of answers based on the content. In this way, efficient answer generation is possible by determining the priority of answers based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input inquiry submission time data into the generation AI and have the generation AI determine the priority of answers.

[0046] The answer generation unit can adjust the order of answers based on the relevance of the inquiry when generating an answer. The answer generation unit adjusts the order of answers based on the relevance of the inquiry when generating an answer. For example, the answer generation unit generates an answer with the highest priority for a highly relevant inquiry. The answer generation unit can also generate an answer with a moderate priority for a moderately relevant inquiry. Furthermore, the answer generation unit can postpone generating an answer for a lowly relevant inquiry. For example, the answer generation unit analyzes the relevance of the inquiry and adjusts the order of answers based on the relevance. The answer generation unit can also analyze the content of the inquiry and adjust the order of answers based on the content. In this way, adjusting the order of answers based on the relevance of the inquiry enables efficient answer generation. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input query relevance data to the generation AI and have the generation AI adjust the order of the answers.

[0047] The providing unit can select the optimal delivery method by referring to the user's past inquiry history when providing the information. The providing unit can select the optimal delivery method by referring to the user's past inquiry history when providing the information. For example, the providing unit preferentially suggests delivery methods (email, chat, etc.) that the user has used in the past. The providing unit can also predict and suggest a delivery method to be used during a specific time period based on the user's past inquiry history. The providing unit can also analyze the content of the user's past inquiries and select the optimal delivery method. For example, if the user has frequently requested replies via email in the past, the providing unit preferentially suggests delivery via email. Also, if the user has frequently requested replies via chat during a specific time period, the providing unit can suggest delivery via chat during that time period. In this way, the optimal delivery method can be selected by referring to the user's past inquiry history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal delivery method.

[0048] The providing unit can customize the means of delivery based on the user's current work status at the time of delivery. The providing unit customizes the means of delivery based on the user's current work status at the time of delivery. For example, if the user is in a meeting, the providing unit sends a notification after the meeting ends. Furthermore, if the user is out and about, the providing unit can use a delivery method optimized for mobile devices. Furthermore, if the user is doing desk work, the providing unit can prioritize desktop notifications. For example, the providing unit analyzes the user's work status and selects the optimal delivery means based on the work status. Furthermore, the providing unit can update the user's work status in real time and customize the delivery means based on the latest work status. This enables efficient answer provision by customizing the delivery means based on the user's work status. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit inputs the user's work status data into a generating AI and allows the generating AI to customize the delivery means.

[0049] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing information. The providing unit selects the optimal delivery method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is on a business trip, the providing unit can prioritize providing information related to the business trip destination. Furthermore, if the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit can obtain the user's geographical location information from GPS data or an IP address and provide related information based on that information. Furthermore, the providing unit can update the user's geographical location information in real time and provide information based on the latest location information. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal delivery method.

[0050] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of providing the information. The providing unit can analyze the user's social media activity and suggest a means of provision at the time of providing the information. For example, the providing unit can provide related information based on information shared by the user on social media. The providing unit can also provide related information based on information about accounts the user follows on social media. The providing unit can also provide related information based on information about groups the user participates in on social media. For example, the providing unit can analyze posts shared by the user on social media and provide information related to the content. The providing unit can also analyze posts from accounts the user follows and provide related information. In this way, the optimal means of provision can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal means of provision.

[0051] When providing guidance to a representative, the representative guidance unit can select the optimal guidance method by referring to the user's past inquiry history. When providing guidance to a representative, the representative guidance unit selects the optimal guidance method by referring to the user's past inquiry history. For example, the representative guidance unit prioritizes suggesting guidance methods (email, chat, etc.) that the user has used in the past. The representative guidance unit can also predict and suggest a guidance method to use during a specific time period based on the user's past inquiry history. The representative guidance unit can also analyze the content of the user's past inquiries and select the optimal guidance method. For example, if the user has frequently requested guidance via email in the past, the representative guidance unit prioritizes suggesting guidance via email. Also, if the user has frequently requested guidance via chat during a specific time period, the representative guidance unit can suggest guidance via chat during that time period. In this way, the optimal guidance method can be selected by referring to the user's past inquiry history. Some or all of the above-described processing in the representative guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the representative guidance unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal guidance method.

[0052] The staff guidance unit can customize the guidance method based on the user's current work status when providing staff guidance. The staff guidance unit customizes the guidance method based on the user's current work status when providing staff guidance. For example, if the user is in a meeting, the staff guidance unit sends a notification after the meeting ends. Furthermore, if the user is out, the staff guidance unit can use a guidance method optimized for mobile devices. Furthermore, if the user is doing desk work, the staff guidance unit can prioritize desktop notifications. For example, the staff guidance unit analyzes the user's work status and selects the optimal guidance method based on the work status. Furthermore, the staff guidance unit can update the user's work status in real time and customize the guidance method based on the latest work status. This enables efficient staff guidance by customizing the guidance method based on the user's work status. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the staff guidance unit inputs the user's work status data into a generation AI and allows the generation AI to customize the guidance method.

[0053] The staff guidance unit can select the optimal guidance method by taking into account the user's geographical location information when providing staff guidance. The staff guidance unit selects the optimal guidance method by taking into account the user's geographical location information when providing staff guidance. For example, if the user is in a specific area, the staff guidance unit can prioritize guidance to staff related to that area. Furthermore, if the user is on a business trip, the staff guidance unit can prioritize guidance to staff related to the business trip destination. Furthermore, if the user is at home, the staff guidance unit can prioritize guidance to staff related to the user's home. For example, the staff guidance unit can obtain the user's geographical location information from GPS data or IP address and guide related staff based on that information. Furthermore, the staff guidance unit can update the user's geographical location information in real time and guide staff based on the latest location information. In this way, the optimal guidance method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the staff guidance unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal guidance method.

[0054] When providing assistance to a user, the agent guidance unit can analyze the user's social media activity and suggest a means of guidance. When providing assistance to a user, the agent guidance unit analyzes the user's social media activity and suggest a means of guidance. For example, the agent guidance unit guides the user to relevant agents based on information shared by the user on social media. The agent guidance unit can also guide the user to relevant agents based on information about accounts the user follows on social media. The agent guidance unit can also guide the user to relevant agents based on information about groups the user participates in on social media. For example, the agent guidance unit analyzes posts shared by the user on social media and suggests agents related to the posts. The agent guidance unit can also analyze posts from accounts the user follows and suggest relevant agents. In this way, the user's social media activity can be analyzed to suggest the optimal means of guidance. Some or all of the above-described processing in the agent guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the agent guidance unit inputs the user's social media activity data into a generation AI, which can then suggest the optimal means of guidance.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The inquiry reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, it can prioritize and suggest inquiry methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. It can also analyze patterns of the content of inquiries made by the user in the past and select the optimal reception method. In this way, it is possible to provide the optimal reception method to the user by analyzing the past inquiry history.

[0057] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the user. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is on a business trip, information related to the business trip destination can be provided preferentially. Furthermore, if the user is at home, information related to the home can be provided preferentially. In this way, the optimal providing method can be selected by taking into consideration the geographical location information of the user.

[0058] The information input unit can analyze the user's social media activity and input relevant information. For example, the information input unit can input relevant information based on information shared by the user on social media. The information input unit can also input relevant information based on information about accounts the user follows on social media. Furthermore, the information input unit can also input relevant information based on information about groups the user participates in on social media. In this way, highly relevant information can be input by analyzing the user's social media activity.

[0059] The answer generation unit can apply different answer algorithms depending on the category of the inquiry. For example, an algorithm specialized for payroll calculations can be applied to an inquiry about salary. Also, an algorithm specialized for vacation systems can be applied to an inquiry about vacation systems. Furthermore, an algorithm specialized for promotions can be applied to an inquiry about promotions. This allows for efficient answer generation by applying different answer algorithms depending on the category of the inquiry.

[0060] When guiding a person to a person, the person guidance unit can select the optimal guidance method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, it can prioritize guidance to a person related to that area. Also, if the user is on a business trip, it can prioritize guidance to a person related to the business trip destination. Furthermore, if the user is at home, it can prioritize guidance to a person related to the user's home. In this way, it is possible to select the optimal guidance method by taking into consideration the user's geographical location information.

[0061] The information input unit can analyze the past information input history and select the optimal input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze the patterns of information that the user has input in the past and select the optimal input method. In this way, it is possible to provide the optimal input method to the user by analyzing the past information input history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The information input section inputs personnel-related information. Personnel-related information includes details such as salary, benefits, vacation policy, promotion, etc. The information input section supports information input in digital form, making it easy for users to input information. Step 2: The inquiry reception unit receives inquiries from users. Users can input questions to the system in natural language. The inquiry reception unit analyzes the user's questions and provides information to generate appropriate answers. Step 3: The answer generation unit uses the generation AI to analyze the received inquiry and generate an appropriate answer. For example, it references the user's paid vacation data, calculates the number of days remaining, and generates an answer. Step 4: The providing unit provides the generated answer to the user. The providing unit provides the answer via email or chat, and provides the answer in the optimal format depending on the user's device. Step 5: If the inquiry exceeds the scope of the system, the person in charge guidance section will guide the person to whom the inquiry should be made. For example, the guidance may say, "For detailed payroll calculation information, please contact Tanaka-san, who is in charge of payroll."

[0064] (Example 2) A personnel inquiry response system according to an embodiment of the present invention automatically provides a certain degree of response to inquiries to personnel by importing personnel-related information. This system first includes an "information input unit" that inputs personnel-related information into the system. Next, an "inquiry reception unit" that accepts inquiries from users is also included. The system then includes an "answer generation unit" that analyzes the received inquiry and generates an appropriate response. The generated response is provided to the user. Furthermore, if the inquiry exceeds the scope of the system, a "personnel guidance unit" is also included. This unit indicates the person to whom the inquiry should be directed in the final line of the response text. This mechanism reduces the number of initial inquiries to the organization's personnel and improves the efficiency of inquiry response. For example, an "information input unit" is included that inputs personnel-related information into the system. This information includes details such as salary, benefits, vacation policies, and promotions. For example, it includes information on salary calculation methods and vacation procedures. Next, an "inquiry reception unit" is included that accepts inquiries from users. Users input questions to the system in natural language. Possible questions include, "How many paid vacation days are left this year?" or "What are the conditions for promotion?" The system includes an "answer generation section" that analyzes the received inquiry and generates an appropriate answer. The AI ​​performs this analysis and generates the answer. For example, in response to the question, "How many paid vacation days are left this year?", the system references the user's paid vacation data, calculates the remaining number, and provides the answer. The generated answer is then provided to the user. For example, a specific answer such as, "You have 10 paid vacation days left this year" is provided. Furthermore, if the inquiry exceeds the scope of the system, a "person guidance section" is provided that indicates which person to contact in the final line of the answer. For example, the system may provide guidance such as, "For detailed payroll calculation information, please contact Mr. Tanaka, who is in charge of payroll." This system reduces the number of initial inquiries to the organization's human resources department and improves the efficiency of inquiry response. For example, by having the system automatically answer general questions, the organization's human resources department can focus on more specialized inquiries.This allows the personnel inquiry response system to automatically provide answers to personnel-related inquiries and, if necessary, direct the user to the appropriate person, thereby improving the efficiency of inquiry response.

[0065] A personnel inquiry response system according to an embodiment includes an information input unit, an inquiry reception unit, a response generation unit, a provision unit, and a staff member guidance unit. The information input unit inputs personnel-related information. The personnel-related information includes, but is not limited to, details such as salary, benefits, vacation systems, and promotions. The information input unit can input information such as salary calculation methods and vacation procedures. The information input unit also supports digital information input, allowing users to easily input information. The inquiry reception unit accepts inquiries from users. Users can input questions to the system in natural language. For example, questions such as "How many paid vacation days do I have left this year?" or "What are the conditions for promotion?" are possible. The inquiry reception unit analyzes the user's question and provides information for generating an appropriate response. The response generation unit uses a generation AI to analyze the received inquiry and generate an appropriate response. The generation AI generates an answer to the user's question using, for example, a text generation AI (e.g., LLM). For example, in response to the question, "How many paid vacation days are left this year?", the answer generation unit references the user's paid vacation data, calculates the remaining number of days, and generates an answer. The provision unit provides the generated answer to the user. The provision unit can provide the answer, for example, via email or chat. The provision unit can also provide the answer in an optimal format depending on the user's device. The person in charge guidance unit guides the user to which person in charge to inquire if the inquiry exceeds the scope of the system. For example, guidance such as "For detailed payroll calculations, please contact Tanaka, who is in charge of payroll" is given. As a result, the personnel inquiry response system according to the embodiment automatically provides answers to personnel-related inquiries and guides the user to a person in charge as needed, thereby improving the efficiency of inquiry response.

[0066] The information input unit can input details about salary, employee benefits, vacation systems, and promotions. The information input unit can input information such as the salary calculation method and vacation procedures. For example, the information input unit can input details about the salary calculation method, such as base salary, allowances, and bonuses. The information input unit can also input information about employee benefits, such as health insurance, pension systems, and employee discounts. The information input unit can also input information about vacation systems, such as paid vacation, sick leave, and childcare leave. For example, the information input unit can input information about evaluation criteria and promotion timing as promotion criteria. This input of detailed personnel-related information improves the accuracy of responses to inquiries. Some or all of the above-described processing in the information input unit can be performed using, or without, AI. For example, the information input unit can input the salary calculation method to a generation AI, causing the generation AI to generate a detailed calculation method.

[0067] The inquiry reception unit can accept questions in natural language from a user. The inquiry reception unit accepts questions in natural language from a user. The user can input questions in natural language to the system. For example, questions such as "How many paid vacation days do I have left this year?" or "What are the conditions for promotion?" are possible. The inquiry reception unit analyzes the user's questions and provides information for generating appropriate answers. For example, the inquiry reception unit may analyze the user's questions using text analysis technology to understand the intent of the questions. The inquiry reception unit may also analyze the user's questions using speech recognition technology and convert the voice-input questions into text. For example, when a user inputs a question by voice, the inquiry reception unit analyzes the voice data and converts it into text data. This allows the user to intuitively make inquiries by accepting questions in natural language. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without AI. For example, the inquiry reception unit may input the user's voice data into a generation AI, which then converts the voice data into text data.

[0068] The answer generation unit can refer to the user's paid leave data and calculate the remaining number of paid leave days to provide an answer. The answer generation unit refers to the user's paid leave data and calculates the remaining number of paid leave days to provide an answer. For example, the answer generation unit refers to a database of the user's paid leave and calculates the remaining number of days. The answer generation unit can also analyze the user's paid leave acquisition history and calculate the remaining number of days. For example, the answer generation unit chronologically analyzes the user's paid leave acquisition history and calculates the remaining number of days. The answer generation unit can also update the user's paid leave data in real time and provide the latest remaining number of days. For example, when the user acquires new paid leave, the answer generation unit immediately updates the database and calculates the latest remaining number of days. This allows the answer generation unit to provide an accurate remaining number of days by referring to the paid leave data. Some or all of the above-mentioned processing in the answer generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input the user's paid vacation data into the generation AI and have the generation AI calculate the remaining number of days.

[0069] The providing unit can provide the generated answer to the user. The providing unit provides the generated answer to the user. For example, the providing unit can provide the answer via email or chat. The providing unit can also provide the answer in an optimal format depending on the user's device. For example, if the user is using a smartphone, the providing unit can provide the answer in a mobile-friendly format. If the user is using a desktop, the providing unit can also provide the answer in a format optimized for the desktop. Furthermore, the providing unit can customize the method of providing the answer according to the user's preferences. For example, if the user prefers an answer via email, the providing unit can provide the answer via email. If the user prefers an answer via chat, the providing unit can also provide the answer via chat. This improves the efficiency of responding to inquiries by quickly providing the generated answer to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated answer to a generation AI and have the generation AI provide the answer in an optimal format.

[0070] The staff guidance unit can guide staff members to specific, detailed inquiries. The staff guidance unit guides staff members to specific, detailed inquiries. For example, if the inquiry content exceeds the scope of the system, the staff guidance unit guides the staff member to which staff member to contact. For example, the guidance may include, "For detailed payroll calculations, please contact Tanaka-san, who is in charge of payroll." The staff guidance unit can also automatically select an appropriate staff member based on the inquiry content. For example, if the inquiry content is about employee benefits, the staff guidance unit guides the employee benefits staff member. If the inquiry content is about promotions, the staff guidance unit can also guide the employee promotion staff member. The staff guidance unit can also provide the staff member's contact information. For example, the staff guidance unit may provide the staff member's email address and phone number. This improves the efficiency of inquiry response by guiding the appropriate staff member to detailed inquiries. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the staff guidance unit may input the inquiry content into a generation AI, which then selects an appropriate staff member.

[0071] The information input unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. The information input unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. For example, if the user is feeling stressed, the information input unit can delay the timing of information input and encourage the user to input in a relaxed state. Furthermore, if the user is relaxed, the information input unit can accelerate the timing of information input to allow the user to input information efficiently. Furthermore, if the user is in a hurry, the information input unit can optimize the timing of information input to allow the user to input information quickly. For example, the information input unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the information input unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the information input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient information input by adjusting the timing of information input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the information input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information input unit may input user emotion data to the generation AI, and have the generation AI adjust the timing of information input.

[0072] The information input unit can analyze past information input history and select the optimal input method. The information input unit analyzes past information input history and selects the optimal input method. For example, the information input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The information input unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The information input unit can also analyze patterns of information previously input by the user and select the optimal input method. For example, if the user has frequently used voice input in the past, the information input unit preferentially suggests voice input. Also, if the user has frequently used text input in a specific time period, the information input unit can also suggest text input for that time period. In this way, by analyzing past information input history, the optimal input method can be provided to the user. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit inputs past information input history to a generation AI, which can select the optimal input method.

[0073] The information input unit can filter information based on the user's current work situation and areas of interest when inputting information. The information input unit can filter information based on the user's current work situation and areas of interest when inputting information. For example, the information input unit prioritizes input of information related to a project the user is currently working on. The information input unit can also filter and input highly relevant information based on the user's areas of interest. Furthermore, the information input unit can input only necessary information depending on the user's work situation. For example, the information input unit prioritizes input of information related to a project the user is currently working on. The information input unit can also filter and input highly relevant information based on the user's areas of interest. In this way, highly relevant information can be input by filtering information based on the user's work situation and areas of interest. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input data on the user's work situation and areas of interest to a generation AI, and have the generation AI perform filtering.

[0074] The information input unit can estimate the user's emotions and prioritize the information to be input based on the estimated user emotions. The information input unit can estimate the user's emotions and prioritize the information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the information input unit can postpone input of less important information and prioritize input of more important information. Furthermore, if the user is relaxed, the information input unit can prioritize input of detailed information. Furthermore, if the user is in a hurry, the information input unit can prioritize input of the most important information. For example, the information input unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The information input unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the information input unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient information input by prioritizing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information input unit may be performed using AI, or may be performed without using AI. For example, the information input unit may input user emotion data to the generation AI, causing the generation AI to determine the priority of the information.

[0075] When inputting information, the information input unit can prioritize inputting relevant information taking into account the user's geographical location information. When inputting information, the information input unit prioritizes inputting relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the information input unit prioritizes inputting information related to that area. Furthermore, when the user is on a business trip, the information input unit can prioritize inputting information related to the business trip destination. Furthermore, when the user is at home, the information input unit can prioritize inputting information related to the user's home. For example, the information input unit acquires the user's geographical location information from GPS data or an IP address and inputs relevant information based on that information. Furthermore, the information input unit can update the user's geographical location information in real time and input information based on the latest location information. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information input unit may be performed using, for example, AI, or may be performed without using AI. For example, the information input unit can input the user's geographical location information to a generation AI, causing the generation AI to prioritize inputting relevant information.

[0076] The information input unit can analyze the user's social media activity and input related information when inputting information. The information input unit can analyze the user's social media activity and input related information when inputting information. For example, the information input unit can input related information based on information shared by the user on social media. The information input unit can also input related information based on information about accounts the user follows on social media. The information input unit can also input related information based on information about groups the user participates in on social media. For example, the information input unit can analyze posts shared by the user on social media and input information related to the posts. The information input unit can also analyze posts from accounts the user follows and input related information. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the information input unit can be performed using, for example, AI, or can be performed without using AI. For example, the information input unit can input the user's social media activity data to a generation AI, causing the generation AI to input related information.

[0077] The inquiry reception unit can estimate a user's emotions and adjust the inquiry reception method based on the estimated user emotions. The inquiry reception unit can estimate a user's emotions and adjust the inquiry reception method based on the estimated user emotions. For example, if a user is stressed, the inquiry reception unit can provide a simple interface and minimize the inquiry procedure. Furthermore, if a user is relaxed, the inquiry reception unit can provide detailed inquiry options and suggest a customizable reception method. Furthermore, if a user is in a hurry, the inquiry reception unit can prioritize voice input and quickly accept inquiries. For example, the inquiry reception unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The inquiry reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the inquiry reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient inquiry reception by adjusting the inquiry reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the inquiry reception unit may be performed using, or without, an AI. For example, the inquiry reception unit may input user emotion data into the generation AI, causing the generation AI to adjust the method of receiving the inquiry.

[0078] The inquiry reception unit can analyze past inquiry histories and select the optimal reception method. The inquiry reception unit analyzes past inquiry histories and selects the optimal reception method. For example, the inquiry reception unit preferentially suggests an inquiry method (voice, text, etc.) that the user has frequently used in the past. The inquiry reception unit can also predict and suggest an reception method to be used during a specific time period based on the user's past inquiry history. The inquiry reception unit can also analyze patterns of the user's past inquiries and select the optimal reception method. For example, if the user has frequently used voice input in the past, the inquiry reception unit preferentially suggests voice input. Also, if the user has frequently used text input during a specific time period, the inquiry reception unit can suggest text input during that time period. In this way, by analyzing past inquiry histories, the optimal reception method can be provided to the user. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit inputs past inquiry histories into a generation AI, which can select the optimal reception method.

[0079] The inquiry reception unit can filter inquiries based on the user's current work situation and areas of interest when receiving an inquiry. The inquiry reception unit can filter inquiries based on the user's current work situation and areas of interest when receiving an inquiry. For example, the inquiry reception unit prioritizes receiving inquiries related to a project the user is currently working on. The inquiry reception unit can also filter and receive highly relevant inquiries based on the user's areas of interest. The inquiry reception unit can also receive only necessary inquiries depending on the user's work situation. For example, the inquiry reception unit prioritizes receiving inquiries related to a project the user is currently working on. The inquiry reception unit can also filter and receive highly relevant inquiries based on the user's areas of interest. In this way, by filtering inquiries based on the user's work situation and areas of interest, highly relevant inquiries can be received. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit can input data on the user's work situation and areas of interest into a generation AI and have the generation AI perform filtering.

[0080] The inquiry reception unit can estimate the user's emotions and determine the priority of inquiries to be received based on the estimated user emotions. The inquiry reception unit can estimate the user's emotions and determine the priority of inquiries to be received based on the estimated user emotions. For example, when the user is feeling stressed, the inquiry reception unit can postpone less important inquiries and prioritize more important inquiries. Furthermore, when the user is relaxed, the inquiry reception unit can prioritize detailed inquiries. Furthermore, when the user is in a hurry, the inquiry reception unit can prioritize the most important inquiries. For example, the inquiry reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the inquiry reception unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the inquiry reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient inquiry reception by determining the priority of inquiries according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the inquiry reception unit may be performed using, or without, an AI. For example, the inquiry reception unit may input user emotion data into the generation AI, causing the generation AI to determine the priority of inquiries.

[0081] When accepting inquiries, the inquiry reception unit can prioritize accepting highly relevant inquiries by taking into account the user's geographical location information. When accepting inquiries, the inquiry reception unit prioritizes accepting highly relevant inquiries by taking into account the user's geographical location information. For example, when the user is in a specific area, the inquiry reception unit prioritizes accepting inquiries related to that area. Furthermore, when the user is on a business trip, the inquiry reception unit can prioritize accepting inquiries related to the business trip destination. Furthermore, when the user is at home, the inquiry reception unit can prioritize accepting inquiries related to the user's home. For example, the inquiry reception unit acquires the user's geographical location information from GPS data or an IP address and accepts related inquiries based on that information. Furthermore, the inquiry reception unit can update the user's geographical location information in real time and accept inquiries based on the latest location information. In this way, by taking the user's geographical location information into account, highly relevant inquiries can be prioritized. Some or all of the above-described processing in the inquiry reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the inquiry reception unit can input the user's geographical location information to the generation AI, causing the generation AI to preferentially receive related inquiries.

[0082] The inquiry reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. The inquiry reception unit can analyze the user's social media activity and receive related inquiries when receiving an inquiry. For example, the inquiry reception unit can receive related inquiries based on information shared by the user on social media. The inquiry reception unit can also receive related inquiries based on information about accounts the user follows on social media. The inquiry reception unit can also receive related inquiries based on information about groups the user participates in on social media. For example, the inquiry reception unit can analyze posts shared by the user on social media and receive inquiries related to the content. The inquiry reception unit can also analyze posts from accounts the user follows and receive related inquiries. In this way, by analyzing the user's social media activity, highly relevant inquiries can be received. Some or all of the above-described processing by the inquiry reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the inquiry reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related inquiries.

[0083] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is stressed, the answer generation unit uses a simple and easy-to-understand expression. Furthermore, if the user is relaxed, the answer generation unit can use an expression that includes detailed information. Furthermore, if the user is in a hurry, the answer generation unit can use a concise expression that focuses on the main points. For example, the answer generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The answer generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the answer generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient answer generation by adjusting the way the answer is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answer generation unit may be performed using, or without, the generation AI. For example, the answer generation unit may input user emotion data into the generation AI, causing the generation AI to adjust the way the answer is expressed.

[0084] The answer generation unit can adjust the level of detail of the answer based on the importance of the inquiry when generating an answer. The answer generation unit adjusts the level of detail of the answer based on the importance of the inquiry when generating an answer. For example, the answer generation unit generates a detailed answer for an inquiry of high importance. The answer generation unit can also generate a concise answer for an inquiry of low importance. Furthermore, the answer generation unit can generate an answer with an appropriate level of detail for an inquiry of medium importance. For example, the answer generation unit analyzes the importance of the inquiry and adjusts the level of detail of the answer according to the importance. The answer generation unit can also analyze the content of the inquiry and adjust the level of detail of the answer based on the content. In this way, adjusting the level of detail of the answer based on the importance of the inquiry enables efficient answer generation. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit inputs inquiry importance data to the generation AI and causes the generation AI to adjust the level of detail of the answer.

[0085] The answer generation unit can apply different answer algorithms depending on the inquiry category when generating an answer. The answer generation unit can apply different answer algorithms depending on the inquiry category when generating an answer. For example, the answer generation unit can apply an algorithm specialized for payroll calculation to an inquiry about salary. The answer generation unit can also apply an algorithm specialized for vacation systems to an inquiry about vacation systems. The answer generation unit can also apply an algorithm specialized for promotions to an inquiry about promotions. For example, the answer generation unit can analyze the inquiry category and select an optimal answer algorithm depending on the category. The answer generation unit can also analyze the content of the inquiry and apply an optimal answer algorithm based on the content. This enables efficient answer generation by applying different answer algorithms depending on the inquiry category. Some or all of the above-mentioned processing in the answer generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answer generation unit can input inquiry category data into a generation AI and have the generation AI apply the optimal answer algorithm.

[0086] The answer generation unit can estimate the user's emotions and adjust the length of the answers based on the estimated user emotions. The answer generation unit can estimate the user's emotions and adjust the length of the answers based on the estimated user emotions. For example, if the user is stressed, the answer generation unit generates a short, to-the-point answer. Furthermore, if the user is relaxed, the answer generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is in a hurry, the answer generation unit can generate a quick, concise answer. For example, the answer generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The answer generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the answer generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for efficient answer generation by adjusting the length of the answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answer generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the answer generation unit may input user emotion data into the generation AI, causing the generation AI to adjust the length of the answer.

[0087] The answer generation unit can determine the priority of answers based on the time of submission of the inquiry when generating an answer. The answer generation unit determines the priority of answers based on the time of submission of the inquiry when generating an answer. For example, the answer generation unit generates answers with the highest priority for urgent inquiries. The answer generation unit can also generate answers with a moderate priority for normal inquiries. Furthermore, the answer generation unit can generate answers later for low-priority inquiries. For example, the answer generation unit analyzes the time of submission of an inquiry and determines the priority of answers based on the time of submission. The answer generation unit can also analyze the content of the inquiry and determine the priority of answers based on the content. In this way, efficient answer generation is possible by determining the priority of answers based on the time of submission of the inquiry. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input inquiry submission time data into the generation AI and have the generation AI determine the priority of answers.

[0088] The answer generation unit can adjust the order of answers based on the relevance of the inquiry when generating an answer. The answer generation unit adjusts the order of answers based on the relevance of the inquiry when generating an answer. For example, the answer generation unit generates an answer with the highest priority for a highly relevant inquiry. The answer generation unit can also generate an answer with a moderate priority for a moderately relevant inquiry. Furthermore, the answer generation unit can postpone generating an answer for a lowly relevant inquiry. For example, the answer generation unit analyzes the relevance of the inquiry and adjusts the order of answers based on the relevance. The answer generation unit can also analyze the content of the inquiry and adjust the order of answers based on the content. In this way, adjusting the order of answers based on the relevance of the inquiry enables efficient answer generation. Some or all of the above-mentioned processing in the answer generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the answer generation unit can input query relevance data to the generation AI and have the generation AI adjust the order of the answers.

[0089] The providing unit can estimate the user's emotions and adjust the answer provision method based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the answer provision method based on the estimated user emotions. For example, if the user is stressed, the providing unit uses a simple, highly visible provision method. Furthermore, if the user is relaxed, the providing unit can use a provision method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can use a quick and concise provision method. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient answer provision by adjusting the answer provision method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, causing the generation AI to adjust the way in which the answer is provided.

[0090] The providing unit can select the optimal delivery method by referring to the user's past inquiry history when providing the information. The providing unit can select the optimal delivery method by referring to the user's past inquiry history when providing the information. For example, the providing unit preferentially suggests delivery methods (email, chat, etc.) that the user has used in the past. The providing unit can also predict and suggest a delivery method to be used during a specific time period based on the user's past inquiry history. The providing unit can also analyze the content of the user's past inquiries and select the optimal delivery method. For example, if the user has frequently requested replies via email in the past, the providing unit preferentially suggests delivery via email. Also, if the user has frequently requested replies via chat during a specific time period, the providing unit can suggest delivery via chat during that time period. In this way, the optimal delivery method can be selected by referring to the user's past inquiry history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal delivery method.

[0091] The providing unit can customize the means of delivery based on the user's current work status at the time of delivery. The providing unit customizes the means of delivery based on the user's current work status at the time of delivery. For example, if the user is in a meeting, the providing unit sends a notification after the meeting ends. Furthermore, if the user is out and about, the providing unit can use a delivery method optimized for mobile devices. Furthermore, if the user is doing desk work, the providing unit can prioritize desktop notifications. For example, the providing unit analyzes the user's work status and selects the optimal delivery means based on the work status. Furthermore, the providing unit can update the user's work status in real time and customize the delivery means based on the latest work status. This enables efficient answer provision by customizing the delivery means based on the user's work status. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit inputs the user's work status data into a generating AI and allows the generating AI to customize the delivery means.

[0092] The providing unit can estimate the user's emotions and prioritize answers to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and prioritize answers to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can postpone providing less important answers and prioritize providing more important answers. The providing unit can also prioritize providing detailed answers when the user is relaxed. Furthermore, the providing unit can prioritize providing the most important answers when the user is in a hurry. For example, the providing unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This enables efficient answer provision by prioritizing answers according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, causing the generation AI to determine the priority of answers.

[0093] The providing unit can select the optimal delivery method by taking into account the user's geographical location information when providing information. The providing unit selects the optimal delivery method by taking into account the user's geographical location information when providing information. For example, if the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, if the user is on a business trip, the providing unit can prioritize providing information related to the business trip destination. Furthermore, if the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit can obtain the user's geographical location information from GPS data or an IP address and provide related information based on that information. Furthermore, the providing unit can update the user's geographical location information in real time and provide information based on the latest location information. In this way, the optimal delivery method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal delivery method.

[0094] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of providing the information. The providing unit can analyze the user's social media activity and suggest a means of provision at the time of providing the information. For example, the providing unit can provide related information based on information shared by the user on social media. The providing unit can also provide related information based on information about accounts the user follows on social media. The providing unit can also provide related information based on information about groups the user participates in on social media. For example, the providing unit can analyze posts shared by the user on social media and provide information related to the content. The providing unit can also analyze posts from accounts the user follows and provide related information. In this way, the optimal means of provision can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI suggest the optimal means of provision.

[0095] The agent guidance unit can estimate the user's emotions and adjust the agent guidance method based on the estimated user emotions. The agent guidance unit can estimate the user's emotions and adjust the agent guidance method based on the estimated user emotions. For example, if the user is stressed, the agent guidance unit can use a simple and easy-to-understand guidance method. Furthermore, if the user is relaxed, the agent guidance unit can use a guidance method that includes detailed information. Furthermore, if the user is in a hurry, the agent guidance unit can use a quick and concise guidance method. For example, the agent guidance unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The agent guidance unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the agent guidance unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient agent guidance by adjusting the agent guidance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the representative guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the representative guidance unit may input user emotion data into the generation AI, causing the generation AI to adjust the representative guidance method.

[0096] When providing guidance to a representative, the representative guidance unit can select the optimal guidance method by referring to the user's past inquiry history. When providing guidance to a representative, the representative guidance unit selects the optimal guidance method by referring to the user's past inquiry history. For example, the representative guidance unit prioritizes suggesting guidance methods (email, chat, etc.) that the user has used in the past. The representative guidance unit can also predict and suggest a guidance method to use during a specific time period based on the user's past inquiry history. The representative guidance unit can also analyze the content of the user's past inquiries and select the optimal guidance method. For example, if the user has frequently requested guidance via email in the past, the representative guidance unit prioritizes suggesting guidance via email. Also, if the user has frequently requested guidance via chat during a specific time period, the representative guidance unit can suggest guidance via chat during that time period. In this way, the optimal guidance method can be selected by referring to the user's past inquiry history. Some or all of the above-described processing in the representative guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the representative guidance unit can input the user's past inquiry history data into a generation AI and have the generation AI select the optimal guidance method.

[0097] The staff guidance unit can customize the guidance method based on the user's current work status when providing staff guidance. The staff guidance unit customizes the guidance method based on the user's current work status when providing staff guidance. For example, if the user is in a meeting, the staff guidance unit sends a notification after the meeting ends. Furthermore, if the user is out, the staff guidance unit can use a guidance method optimized for mobile devices. Furthermore, if the user is doing desk work, the staff guidance unit can prioritize desktop notifications. For example, the staff guidance unit analyzes the user's work status and selects the optimal guidance method based on the work status. Furthermore, the staff guidance unit can update the user's work status in real time and customize the guidance method based on the latest work status. This enables efficient staff guidance by customizing the guidance method based on the user's work status. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the staff guidance unit inputs the user's work status data into a generation AI and allows the generation AI to customize the guidance method.

[0098] The agent guidance unit can estimate the user's emotions and determine the priority of the agent to guide based on the estimated user emotions. The agent guidance unit can estimate the user's emotions and determine the priority of the agent to guide based on the estimated user emotions. For example, when the user is feeling stressed, the agent guidance unit can postpone less important agents and prioritize more important agents. When the user is relaxed, the agent guidance unit can prioritize agents who can provide detailed information. When the user is in a hurry, the agent guidance unit can prioritize the most important agents. For example, the agent guidance unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The agent guidance unit can also record the user's voice and estimate the emotion using voice analysis technology. The agent guidance unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables efficient agent guidance by determining the priority of the agent to guide based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the agent guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the agent guidance unit may input user emotion data into the generation AI and have the generation AI determine the priority of the agent to guide the user.

[0099] The staff guidance unit can select the optimal guidance method by taking into account the user's geographical location information when providing staff guidance. The staff guidance unit selects the optimal guidance method by taking into account the user's geographical location information when providing staff guidance. For example, if the user is in a specific area, the staff guidance unit can prioritize guidance to staff related to that area. Furthermore, if the user is on a business trip, the staff guidance unit can prioritize guidance to staff related to the business trip destination. Furthermore, if the user is at home, the staff guidance unit can prioritize guidance to staff related to the user's home. For example, the staff guidance unit can obtain the user's geographical location information from GPS data or IP address and guide related staff based on that information. Furthermore, the staff guidance unit can update the user's geographical location information in real time and guide staff based on the latest location information. In this way, the optimal guidance method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the staff guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the staff guidance unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal guidance method.

[0100] When providing assistance to a user, the agent guidance unit can analyze the user's social media activity and suggest a means of guidance. When providing assistance to a user, the agent guidance unit analyzes the user's social media activity and suggest a means of guidance. For example, the agent guidance unit guides the user to relevant agents based on information shared by the user on social media. The agent guidance unit can also guide the user to relevant agents based on information about accounts the user follows on social media. The agent guidance unit can also guide the user to relevant agents based on information about groups the user participates in on social media. For example, the agent guidance unit analyzes posts shared by the user on social media and suggests agents related to the posts. The agent guidance unit can also analyze posts from accounts the user follows and suggest relevant agents. In this way, the user's social media activity can be analyzed to suggest the optimal means of guidance. Some or all of the above-described processing in the agent guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the agent guidance unit inputs the user's social media activity data into a generation AI, which can then suggest the optimal means of guidance. === Hard Collateral 1-1 === Each of the multiple elements, including the information input unit, inquiry reception unit, response generation unit, provision unit, and staff guidance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information input unit is realized by the control unit 46A of the smart device 14, and allows a user to input personnel-related information in digital form. The inquiry reception unit is realized by the control unit 46A of the smart device 14, and receives questions in natural language from the user. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14, and provides the generated response to the user. The staff guidance unit is realized by the specific processing unit 290 of the data processing device 12, and guides an appropriate staff member in response to an inquiry beyond the scope of the system. === Hard Collateral 1-2 === Each of the multiple elements, including the information input unit, inquiry reception unit, response generation unit, provision unit, and staff guidance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information input unit is realized by the control unit 46A of the smart glasses 214, and allows a user to input personnel-related information in digital form. The inquiry reception unit is realized by the control unit 46A of the smart glasses 214, and receives questions in natural language from the user. The response generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an appropriate response using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides the generated response to the user. The staff guidance unit is realized by the specific processing unit 290 of the data processing device 12, and guides an appropriate staff member in response to an inquiry beyond the scope of the system. === Hard Collateral 1-3 === Each of the multiple elements including the information input unit, inquiry reception unit, answer generation unit, provision unit, and staff guidance unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information input unit is realized by the control unit 46A of the headset terminal 314, and allows a user to input personnel-related information in digital form. The inquiry reception unit is realized by the control unit 46A of the headset terminal 314, and receives questions in natural language from the user. The answer generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an appropriate answer using a generation AI. The provision unit is realized by the control unit 46A of the headset terminal 314, and provides the generated answer to the user. The staff guidance unit is realized by the specific processing unit 290 of the data processing device 12, and guides an appropriate staff member in response to an inquiry beyond the scope of the system. === Hard Collateral 1-4 === Each of the multiple elements including the information input unit, inquiry reception unit, answer generation unit, provision unit, and staff guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information input unit is realized by the control unit 46A of the robot 414, and allows a user to input personnel-related information in digital form. The inquiry reception unit is realized by the control unit 46A of the robot 414, and receives questions in natural language from the user. The answer generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an appropriate answer using a generation AI. The provision unit is realized by the control unit 46A of the robot 414, and provides the generated answer to the user. The staff guidance unit is realized by the specific processing unit 290 of the data processing device 12, and guides an appropriate staff member in response to an inquiry beyond the scope of the system.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The inquiry reception unit can analyze the user's past inquiry history and select the optimal reception method. For example, it can prioritize and suggest inquiry methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the reception method to be used during a specific time period based on the user's past inquiry history. It can also analyze patterns of the content of inquiries made by the user in the past and select the optimal reception method. In this way, it is possible to provide the optimal reception method to the user by analyzing the past inquiry history.

[0103] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand expression can be used. If the user is relaxed, an expression that includes detailed information can be used. Furthermore, if the user is in a hurry, a concise expression that gets straight to the point can be used. This allows for efficient answer generation by adjusting the way the answer is expressed depending on the user's emotions.

[0104] The providing unit can select the optimal providing method by taking into consideration the geographical location information of the user. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is on a business trip, information related to the business trip destination can be provided preferentially. Furthermore, if the user is at home, information related to the home can be provided preferentially. In this way, the optimal providing method can be selected by taking into consideration the geographical location information of the user.

[0105] The agent guidance unit can estimate the user's emotions and adjust the method of providing agent guidance based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand guidance method can be used. If the user is relaxed, a guidance method including detailed information can be used. Furthermore, if the user is in a hurry, a quick and concise guidance method can be used. In this way, by adjusting the method of providing agent guidance according to the user's emotions, efficient agent guidance can be achieved.

[0106] The information input unit can analyze the user's social media activity and input relevant information. For example, the information input unit can input relevant information based on information shared by the user on social media. The information input unit can also input relevant information based on information about accounts the user follows on social media. Furthermore, the information input unit can also input relevant information based on information about groups the user participates in on social media. In this way, highly relevant information can be input by analyzing the user's social media activity.

[0107] The inquiry reception unit can estimate the user's emotions and determine the priority of inquiries to be received based on the estimated user's emotions. For example, if the user is feeling stressed, inquiries of low importance can be postponed and inquiries of high importance can be received with priority. Also, if the user is relaxed, detailed inquiries can be received with priority. Furthermore, if the user is in a hurry, the most important inquiries can be received with priority. In this way, by determining the priority of inquiries according to the user's emotions, efficient inquiry reception is possible.

[0108] The answer generation unit can apply different answer algorithms depending on the category of the inquiry. For example, an algorithm specialized for payroll calculations can be applied to an inquiry about salary. Also, an algorithm specialized for vacation systems can be applied to an inquiry about vacation systems. Furthermore, an algorithm specialized for promotions can be applied to an inquiry about promotions. This allows for efficient answer generation by applying different answer algorithms depending on the category of the inquiry.

[0109] The providing unit can estimate the user's emotions and determine the priority of answers to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, answers with low importance can be postponed and answers with high importance can be provided preferentially. Also, if the user is relaxed, detailed answers can be provided preferentially. Furthermore, if the user is in a hurry, the most important answers can be provided preferentially. In this way, by determining the priority of answers according to the user's emotions, efficient answer provision becomes possible.

[0110] When guiding a person to a person, the person guidance unit can select the optimal guidance method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, it can prioritize guidance to a person related to that area. Also, if the user is on a business trip, it can prioritize guidance to a person related to the business trip destination. Furthermore, if the user is at home, it can prioritize guidance to a person related to the user's home. In this way, it is possible to select the optimal guidance method by taking into consideration the user's geographical location information.

[0111] The information input unit can analyze the past information input history and select the optimal input method. For example, it can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze the patterns of information that the user has input in the past and select the optimal input method. In this way, it is possible to provide the optimal input method to the user by analyzing the past information input history.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The information input section inputs personnel-related information. Personnel-related information includes details such as salary, benefits, vacation policy, promotion, etc. The information input section supports information input in digital form, making it easy for users to input information. Step 2: The inquiry reception unit receives inquiries from users. Users can input questions to the system in natural language. The inquiry reception unit analyzes the user's questions and provides information to generate appropriate answers. Step 3: The answer generation unit uses the generation AI to analyze the received inquiry and generate an appropriate answer. For example, it references the user's paid vacation data, calculates the number of days remaining, and generates an answer. Step 4: The providing unit provides the generated answer to the user. The providing unit provides the answer via email or chat, and provides the answer in the optimal format depending on the user's device. Step 5: If the inquiry exceeds the scope of the system, the person in charge guidance section will guide the person to whom the inquiry should be directed. For example, the guidance may say, "For detailed payroll calculation information, please contact Tanaka-san, who is in charge of payroll."

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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, in order to avoid confusion and to 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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. an information input section for inputting personnel-related information; an inquiry receiving unit that receives an inquiry from a user based on the information input by the information input unit; a response generation unit that analyzes the content of the inquiry received by the inquiry reception unit and generates a response; a providing unit that provides the answer generated by the answer generating unit to a user; and a person in charge guidance unit that guides the person in charge when the inquiry content satisfies specific conditions that are beyond the scope of the system. A system characterized by:

2. The information input unit Enter details about salary, benefits, leave, promotions, etc.

2. The system of claim 1.

3. The inquiry reception unit Accept natural language questions from the user 2. The system of claim 1.

4. The answer generation unit Refer to the user's paid vacation data, calculate the remaining paid vacation days, and provide the answer 2. The system of claim 1.

5. The providing unit Providing the generated answer to the user 2. The system of claim 1.

6. The person in charge guidance unit Directing you to a representative for specific, detailed inquiries 2. The system of claim 1.

7. The information input unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions.

2. The system of claim 1.

8. The information input unit Analyze past information input history and select the optimal input method 2. The system of claim 1.

9. The information input unit Filter information as it is entered based on the user's current work situation and areas of interest 2. The system of claim 1.

10. The information input unit Estimate the user's emotions and prioritize the information to be input based on the estimated user emotions.

2. The system of claim 1.

Images