System

Abstract

The system according to the embodiment is intended to respond to inquiries from inside and outside the company on behalf of employees on paid holidays.SOLUTION: A system according to an embodiment includes an inquiry handling unit, an analysis unit, and a notification unit. The query handling unit uses the generated AI to handle queries from inside and outside the company on behalf of the employee who has acquired a paid vacation. The analysis unit analyzes the inquiry content received by the inquiry handling unit and generates an appropriate answer. When there is an important inquiry, the notification unit notifies the employee on the paid leave of the inquiry.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, employees on paid leave are required to respond to inquiries from both inside and outside the company, which can reduce the quality of their vacation.

[0005] The system according to the embodiment aims to respond to inquiries from both inside and outside the company on behalf of employees on paid leave. [Means for solving the problem]

[0006] The system according to the embodiment includes an inquiry response unit, an analysis unit, and a notification unit. The inquiry response unit uses a generation AI to respond to inquiries from inside and outside the company on behalf of employees on paid leave. The analysis unit analyzes the content of the inquiry received by the inquiry response unit and generates an appropriate response. The notification unit notifies employees on paid leave when an important inquiry is received. [Effects of the Invention]

[0007] The system according to the embodiment can respond to inquiries from both inside and outside the company on behalf of employees on paid leave. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The inquiry response system according to the embodiment of the present invention is a system in which a generation AI responds to inquiries from both inside and outside the company when sales or back-office employees take paid leave. This is expected to improve the rate at which paid leave is taken, increase employee and customer satisfaction, and alleviate labor shortages.

[0029] An inquiry response system according to an embodiment includes an inquiry response unit, an analysis unit, and a notification unit. The inquiry response unit responds to inquiries from both inside and outside the company on behalf of employees on paid leave. For example, when a sales representative is on paid leave, the inquiry response unit uses a generation AI to respond by asking, "The sales representative is not currently available. What can we do for you?" and collect necessary information. In response to product-related inquiries from customers, the generation AI can provide information on product specifications and usage. In response to business-related inquiries from within the company, the generation AI can generate appropriate answers and respond quickly. The analysis unit analyzes the content of inquiries received by the inquiry response unit and generates an appropriate answer. For example, the analysis unit uses the generation AI to analyze the content of inquiries and generate an appropriate answer. The generation AI can analyze the content of inquiries using natural language processing technology and generate an appropriate answer. The analysis unit can also develop algorithms for the generation AI to analyze the content of inquiries and generate an appropriate answer. The notification unit notifies employees on paid leave when an important inquiry is received. For example, when the generation AI requires an urgent response, the notification unit sends a notification to the employee's contact information to encourage a prompt response. Furthermore, when the generation AI receives an important inquiry, the notification unit can send a notification to the employee's contact information to encourage a prompt response. As a result, the inquiry response system according to the embodiment is expected to improve paid leave utilization rates, improve employee and customer satisfaction, and alleviate labor shortages by having the generation AI respond to inquiries on behalf of employees on paid leave.

[0030] The inquiry response unit can refer to past response history and generate the optimal answer. For example, in the inquiry response unit, the generation AI searches a database for past inquiry history and generates the optimal answer based on past answers to similar inquiries. For example, if there was a previous inquiry about a similar product, the generation AI can refer to that answer to create a new answer. In addition, in the inquiry response unit, the generation AI analyzes past response history, creates templates for frequently asked questions, and quickly generates answers based on those templates. For example, standard answers to frequently asked questions can be prepared. In addition, the inquiry response unit generates more accurate answers by having the generation AI learn past response history and recognize inquiry patterns. For example, it can provide appropriate answers based on specific keywords or phrases. This allows more appropriate answers to be generated by referring to past response history.

[0031] The inquiry response unit can automatically determine the urgency of an inquiry and respond accordingly. For example, the generation AI analyzes the content of the inquiry and uses an algorithm to score the urgency, and responds immediately to inquiries with a high level of urgency. For example, if the urgency is high, a notification is sent immediately to the person in charge. The generation AI also analyzes the keywords and phrases in the inquiry to determine the urgency. For example, if the inquiry contains keywords such as "urgent" or "emergency," it will be handled as a priority. The generation AI also predicts the urgency based on the content of the inquiry and past data, and responds according to the urgency. For example, if a similar inquiry in the past required an emergency response, a similar response will be taken. This automatically determines the urgency of the inquiry, enabling a quick response.

[0032] The inquiry response unit can respond by voice using voice recognition technology. For example, the inquiry response unit uses a generation AI to convert the inquirer's voice into text using voice recognition technology and generates a response based on that text. For example, it responds by voice to inquiries made over the phone. Furthermore, the inquiry response unit uses a generation AI to analyze the inquirer's voice in real time using voice recognition technology and instantly generates a voice response. For example, it functions as a voice assistant. Furthermore, the inquiry response unit uses a generation AI to analyze the inquirer's voice using voice recognition technology and generates an appropriate voice response. For example, it uses different voice templates depending on the content of the inquiry. This makes it possible to respond by voice using voice recognition technology.

[0033] The inquiry response unit can work in cooperation with a chatbot to respond through multiple channels. In the inquiry response unit, for example, the generation AI works in cooperation with a chatbot to analyze the content of an inquiry, generate an appropriate response, and respond through multiple channels (email, chat, telephone). For example, the chatbot provides a response to an inquiry made by email. In addition, the generation AI works in cooperation with a chatbot to analyze the content of an inquiry in real time and respond through multiple channels. For example, the chatbot provides an initial response, and the generation AI provides a detailed response as needed. In addition, the inquiry response unit works in cooperation with a chatbot to analyze the content of an inquiry, generate an appropriate response, and respond through multiple channels. For example, the chatbot provides a response to an inquiry made by telephone. In this way, by working in cooperation with a chatbot, it is possible to respond through multiple channels.

[0034] The analysis unit uses natural language processing technology to understand the context and generate a more appropriate answer. In the analysis unit, for example, the generation AI uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In addition, the analysis unit uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In addition, the analysis unit uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In this way, by understanding the context, a more appropriate answer can be generated.

[0035] The analysis unit can refer to related databases and provide the latest information. For example, the generation AI analyzes the inquiry content and refers to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. The analysis unit can also have the generation AI analyze the inquiry content and refer to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. The analysis unit can also have the generation AI analyze the inquiry content and refer to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. In this way, the latest information can be provided by referring to related databases.

[0036] The analysis unit can work in cooperation with other AI systems to integrate data from multiple sources and generate answers. For example, the generation AI works in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. The analysis unit can work in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. The analysis unit can work in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. This makes it possible to generate more accurate answers by integrating data from multiple sources.

[0037] When an important inquiry is detected, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, the generation AI detects an important inquiry, and refers to the employee's schedule and sends a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. Also, when the generation AI detects an important inquiry, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. Also, when the generation AI detects an important inquiry, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. In this way, by referring to the employee's schedule, notifications can be sent at the optimal timing.

[0038] When an important inquiry is detected, the notification unit can automatically generate notification content and suggest a specific response method. In the notification unit, for example, the generation AI detects an important inquiry, automatically generates notification content, and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In addition, the notification unit, when the generation AI detects an important inquiry, automatically generates notification content and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In addition, the notification unit, when the generation AI detects an important inquiry, automatically generates notification content and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In this way, a specific response method can be suggested by automatically generating notification content.

[0039] When an important inquiry is detected, the notification unit can notify another person in charge on behalf of the employee and request a response. For example, the generation AI detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. Also, the notification unit detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. Also, the notification unit detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. This makes it possible to respond quickly even when an employee is absent by notifying another person in charge.

[0040] When an important inquiry is detected, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the generation AI in the notification unit detects an important inquiry and sends notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. Also, when the generation AI detects an important inquiry, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. Also, when the generation AI detects an important inquiry, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. In this way, by sending notifications via multiple channels, notifications can be received reliably.

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

[0042] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0043] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0044] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0045] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0046] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0047] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0048] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0049] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0050] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0051] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The inquiry response department responds to inquiries from both inside and outside the company on behalf of employees who are on paid leave. For example, if a sales representative is on paid leave, the generation AI responds by asking, "The sales representative is not here right now. What can we do for you?" and collects the necessary information. In addition, in response to product inquiries from customers, the generation AI can provide information on product specifications and usage. Furthermore, the generation AI can generate appropriate answers and respond quickly to business-related inquiries from within the company. Step 2: The analysis unit analyzes the inquiry received by the inquiry response unit and generates an appropriate response. For example, the generation AI analyzes the inquiry using natural language processing technology and generates an appropriate response. The analysis unit can also develop an algorithm that enables the generation AI to analyze the inquiry and generate an appropriate response. Step 3: The notification unit notifies employees on paid leave when an important inquiry is made. For example, if an urgent response is required, the generation AI can send a notification to the employee's contact information to encourage a prompt response. Also, if an important inquiry is made, the generation AI can send a notification to the employee's contact information to encourage a prompt response.

[0054] (Example 2) The inquiry response system according to the embodiment of the present invention is a system in which a generation AI responds to inquiries from both inside and outside the company when sales or back-office employees take paid leave. This is expected to improve the rate at which paid leave is taken, increase employee and customer satisfaction, and alleviate labor shortages.

[0055] An inquiry response system according to an embodiment includes an inquiry response unit, an analysis unit, and a notification unit. The inquiry response unit responds to inquiries from both inside and outside the company on behalf of employees on paid leave. For example, when a sales representative is on paid leave, the inquiry response unit uses a generation AI to respond by asking, "The sales representative is not currently available. What can we do for you?" and collect necessary information. In response to product-related inquiries from customers, the generation AI can provide information on product specifications and usage. In response to business-related inquiries from within the company, the generation AI can generate appropriate answers and respond quickly. The analysis unit analyzes the content of inquiries received by the inquiry response unit and generates an appropriate answer. For example, the analysis unit uses the generation AI to analyze the content of inquiries and generate an appropriate answer. The generation AI can analyze the content of inquiries using natural language processing technology and generate an appropriate answer. The analysis unit can also develop algorithms for the generation AI to analyze the content of inquiries and generate an appropriate answer. The notification unit notifies employees on paid leave when an important inquiry is received. For example, when the generation AI requires an urgent response, the notification unit sends a notification to the employee's contact information to encourage a prompt response. Furthermore, when the generation AI receives an important inquiry, the notification unit can send a notification to the employee's contact information to encourage a prompt response. As a result, the inquiry response system according to the embodiment is expected to improve paid leave utilization rates, improve employee and customer satisfaction, and alleviate labor shortages by having the generation AI respond to inquiries on behalf of employees on paid leave.

[0056] The inquiry response unit can refer to past response history and generate the optimal answer. For example, in the inquiry response unit, the generation AI searches a database for past inquiry history and generates the optimal answer based on past answers to similar inquiries. For example, if there was a previous inquiry about a similar product, the generation AI can refer to that answer to create a new answer. In addition, in the inquiry response unit, the generation AI analyzes past response history, creates templates for frequently asked questions, and quickly generates answers based on those templates. For example, standard answers to frequently asked questions can be prepared. In addition, the inquiry response unit generates more accurate answers by having the generation AI learn past response history and recognize inquiry patterns. For example, it can provide appropriate answers based on specific keywords or phrases. This allows more appropriate answers to be generated by referring to past response history.

[0057] The inquiry response unit can automatically determine the urgency of an inquiry and respond accordingly. For example, the generation AI analyzes the content of the inquiry and uses an algorithm to score the urgency, and responds immediately to inquiries with a high level of urgency. For example, if the urgency is high, a notification is sent immediately to the person in charge. The generation AI also analyzes the keywords and phrases in the inquiry to determine the urgency. For example, if the inquiry contains keywords such as "urgent" or "emergency," it will be handled as a priority. The generation AI also predicts the urgency based on the content of the inquiry and past data, and responds according to the urgency. For example, if a similar inquiry in the past required an emergency response, a similar response will be taken. This automatically determines the urgency of the inquiry, enabling a quick response.

[0058] The inquiry response unit can use the emotion estimation function to estimate the emotion of the inquirer and respond accordingly. For example, the generation AI in the inquiry response unit analyzes the inquirer's writing and estimates their emotion using an emotion estimation algorithm. For example, if the inquirer expresses strong feelings of anger or frustration, the unit responds politely. The generation AI in the inquiry response unit also analyzes the inquirer's voice data and estimates their emotion from the tone and intonation of the voice. For example, if the voice tone is high, the unit determines that the situation is urgent and responds quickly. The generation AI in the inquiry response unit also monitors the inquirer's emotion in real time and responds according to the emotion. For example, if the inquirer is calm, the unit responds normally, and if the inquirer is emotionally charged, the unit responds in a special way. This makes it possible to estimate the inquirer's emotion and provide a more appropriate response.

[0059] The inquiry response unit can respond by voice using voice recognition technology. For example, the inquiry response unit uses a generation AI to convert the inquirer's voice into text using voice recognition technology and generates a response based on that text. For example, it responds by voice to inquiries made over the phone. Furthermore, the inquiry response unit uses a generation AI to analyze the inquirer's voice in real time using voice recognition technology and instantly generates a voice response. For example, it functions as a voice assistant. Furthermore, the inquiry response unit uses a generation AI to analyze the inquirer's voice using voice recognition technology and generates an appropriate voice response. For example, it uses different voice templates depending on the content of the inquiry. This makes it possible to respond by voice using voice recognition technology.

[0060] The inquiry response unit can work in cooperation with a chatbot to respond through multiple channels. In the inquiry response unit, for example, the generation AI works in cooperation with a chatbot to analyze the content of an inquiry, generate an appropriate response, and respond through multiple channels (email, chat, telephone). For example, the chatbot provides a response to an inquiry made by email. In addition, the generation AI works in cooperation with a chatbot to analyze the content of an inquiry in real time and respond through multiple channels. For example, the chatbot provides an initial response, and the generation AI provides a detailed response as needed. In addition, the inquiry response unit works in cooperation with a chatbot to analyze the content of an inquiry, generate an appropriate response, and respond through multiple channels. For example, the chatbot provides a response to an inquiry made by telephone. In this way, by working in cooperation with a chatbot, it is possible to respond through multiple channels.

[0061] The inquiry response unit can use the emotion estimation function to generate a customized response according to the emotion of the inquirer. In the inquiry response unit, for example, the generation AI uses the emotion estimation function to analyze the emotion of the inquirer and generate a customized response according to the emotion. For example, if the emotion is strong, a response including a polite apology is provided. In addition, in the inquiry response unit, the generation AI uses the emotion estimation function to analyze the emotion of the inquirer in real time and generate a customized response according to the emotion. For example, if the emotion is calm, a normal response is provided. In addition, in the inquiry response unit, the generation AI uses the emotion estimation function to analyze the emotion of the inquirer and generate a customized response according to the emotion. For example, if the emotion is high, a special response is provided. In this way, by generating a customized response according to the emotion of the inquirer, a more appropriate response is possible.

[0062] The analysis unit uses natural language processing technology to understand the context and generate a more appropriate answer. In the analysis unit, for example, the generation AI uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In addition, the analysis unit uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In addition, the analysis unit uses natural language processing technology to analyze the context of the inquiry content and generate an appropriate answer. For example, by understanding the context, a more specific answer is provided. In this way, by understanding the context, a more appropriate answer can be generated.

[0063] The analysis unit can refer to related databases and provide the latest information. For example, the generation AI analyzes the inquiry content and refers to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. The analysis unit can also have the generation AI analyze the inquiry content and refer to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. The analysis unit can also have the generation AI analyze the inquiry content and refer to related databases to provide the latest information. For example, the latest information on product specifications and usage methods is provided. In this way, the latest information can be provided by referring to related databases.

[0064] The analysis unit can use the emotion estimation function to analyze the emotional nuances of the inquiry content and generate a response that corresponds to the emotion. For example, in the analysis unit, the generation AI uses the emotion estimation function to analyze the emotional nuances of the inquiry content and generate a response that corresponds to the emotion. For example, if the emotion is strong, a response that includes a polite apology is provided. In addition, the analysis unit can use the emotion estimation function to analyze the emotional nuances of the inquiry content and generate a response that corresponds to the emotion. For example, if the emotion is calm, a normal response is provided. In addition, the analysis unit can use the emotion estimation function to analyze the emotional nuances of the inquiry content and generate a response that corresponds to the emotion. For example, if the emotion is heightened, a special response is provided. In this way, by analyzing the emotional nuances, a more appropriate response can be generated.

[0065] The analysis unit can work in cooperation with other AI systems to integrate data from multiple sources and generate answers. For example, the generation AI works in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. The analysis unit can work in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. The analysis unit can work in cooperation with other AI systems to analyze the content of an inquiry, integrate data from multiple sources, and generate an answer. For example, it provides information on product specifications and usage methods. This makes it possible to generate more accurate answers by integrating data from multiple sources.

[0066] The analysis unit can use the emotion estimation function to analyze the emotional aspects of the inquiry content and generate an answer that takes emotions into consideration. For example, in the analysis unit, the generation AI uses the emotion estimation function to analyze the emotional aspects of the inquiry content and generate an answer that takes emotions into consideration. For example, if the emotion is strong, an answer that includes a polite apology is provided. In addition, the analysis unit can use the emotion estimation function to analyze the emotional aspects of the inquiry content and generate an answer that takes emotions into consideration. For example, if the emotion is calm, an ordinary answer is provided. In addition, the analysis unit can use the emotion estimation function to analyze the emotional aspects of the inquiry content and generate an answer that takes emotions into consideration. For example, if the emotion is high, a special response is provided. In this way, by analyzing the emotional aspects, an answer that takes emotions into consideration can be generated.

[0067] When an important inquiry is detected, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, the generation AI detects an important inquiry, and refers to the employee's schedule and sends a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. Also, when the generation AI detects an important inquiry, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. Also, when the generation AI detects an important inquiry, the notification unit can refer to the employee's schedule and send a notification at the optimal timing. For example, sending a notification while avoiding meetings or breaks. In this way, by referring to the employee's schedule, notifications can be sent at the optimal timing.

[0068] When an important inquiry is detected, the notification unit can automatically generate notification content and suggest a specific response method. In the notification unit, for example, the generation AI detects an important inquiry, automatically generates notification content, and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In addition, the notification unit, when the generation AI detects an important inquiry, automatically generates notification content and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In addition, the notification unit, when the generation AI detects an important inquiry, automatically generates notification content and sends it to employees. For example, a specific response method is suggested along with a summary of the inquiry. In this way, a specific response method can be suggested by automatically generating notification content.

[0069] The notification unit can use the emotion estimation function to comprehensively determine the urgency and emotional importance of the inquiry and determine the priority of the notification. For example, the generation AI uses the emotion estimation function to comprehensively determine the urgency and emotional importance of the inquiry and determine the priority of the notification. For example, if the urgency is high and the emotional importance is also high, a notification is sent immediately. The notification unit can also use the emotion estimation function to comprehensively determine the urgency and emotional importance of the inquiry and determine the priority of the notification. For example, if the urgency is high and the emotional importance is also high, a notification is sent immediately. The notification unit can also use the emotion estimation function to comprehensively determine the urgency and emotional importance of the inquiry and determine the priority of the notification. For example, if the urgency is high and the emotional importance is also high, a notification is sent immediately. In this way, the priority of the notification can be determined by comprehensively determining the urgency and emotional importance.

[0070] When an important inquiry is detected, the notification unit can notify another person in charge on behalf of the employee and request a response. For example, the generation AI detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. Also, the notification unit detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. Also, the notification unit detects an important inquiry and notifies another person in charge on behalf of the employee, requesting a response. For example, if the person in charge is absent, a notification is sent to a substitute person in charge. This makes it possible to respond quickly even when an employee is absent by notifying another person in charge.

[0071] When an important inquiry is detected, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the generation AI in the notification unit detects an important inquiry and sends notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. Also, when the generation AI detects an important inquiry, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. Also, when the generation AI detects an important inquiry, the notification unit can send notifications via multiple channels (email, SMS, app notification, etc.). For example, the notification is sent by both email and SMS. In this way, by sending notifications via multiple channels, notifications can be received reliably.

[0072] The notification unit can use the emotion estimation function to determine the emotional importance of the inquiry and generate notification content that takes the emotion into consideration. In the notification unit, for example, the generation AI uses the emotion estimation function to determine the emotional importance of the inquiry and generate notification content that takes the emotion into consideration. For example, if the emotion is high, a polite notification is provided. In addition, the notification unit can use the emotion estimation function to determine the emotional importance of the inquiry and generate notification content that takes the emotion into consideration. For example, if the emotion is high, a polite notification is provided. In addition, the notification unit can use the emotion estimation function to determine the emotional importance of the inquiry and generate notification content that takes the emotion into consideration. For example, if the emotion is high, a polite notification is provided. In this way, by generating notification content that takes the emotion into consideration, a more appropriate response is possible.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0075] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0076] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0077] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0078] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0079] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0080] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0081] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0082] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0083] The inquiry response unit can automatically search for and provide relevant materials and documents based on the content of the inquiry. For example, it can automatically search for product manuals and FAQs and provide them to the inquirer. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. The inquiry response unit can also quickly provide the information the inquirer needs by having the generation AI automatically search for and provide relevant materials. For example, it can provide a product troubleshooting guide. This allows the information the inquirer needs to be quickly provided by having the generation AI automatically search for and provide relevant materials.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The inquiry response department responds to inquiries from both inside and outside the company on behalf of employees who are on paid leave. For example, if a sales representative is on paid leave, the generation AI responds by asking, "The sales representative is not here right now. What can we do for you?" and collects the necessary information. In addition, in response to product inquiries from customers, the generation AI can provide information on product specifications and usage. Furthermore, the generation AI can generate appropriate answers and respond quickly to business-related inquiries from within the company. Step 2: The analysis unit analyzes the inquiry received by the inquiry response unit and generates an appropriate response. For example, the generation AI analyzes the inquiry using natural language processing technology and generates an appropriate response. The analysis unit can also develop an algorithm that enables the generation AI to analyze the inquiry and generate an appropriate response. Step 3: The notification unit notifies employees on paid leave when an important inquiry is made. For example, if an urgent response is required, the generation AI can send a notification to the employee's contact information to encourage a prompt response. Also, if an important inquiry is made, the generation AI can send a notification to the employee's contact information to encourage a prompt response.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

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

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

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 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. Using generative AI, The Inquiry Department handles inquiries from both inside and outside the company on behalf of employees on paid leave. an analysis unit that analyzes the content of the inquiry received by the inquiry response unit and generates an appropriate response; A notification section that notifies employees on paid leave when there is an important inquiry. A system characterized by:

2. The inquiry response unit: Refer to past response history to generate the best answer 2. The system of claim 1.

3. The inquiry response unit: Automatically determine the urgency of inquiries and respond accordingly 2. The system of claim 1.

4. The inquiry response unit: Estimate the emotions of the person making the inquiry and respond accordingly 2. The system of claim 1.

5. The inquiry response unit: Uses voice recognition technology to respond by voice 2. The system of claim 1.

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