system

Abstract

The system according to this embodiment aims to respond quickly to client inquiries and improve the work efficiency of the person in charge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a transmission unit, and a sharing unit. The reception unit receives questions from clients. The generation unit analyzes the questions received by the reception unit and generates a response based on data that can be shared externally without issue. The transmission unit sends the response generated by the generation unit to the client. The sharing unit shares the response generated by the generation unit with the person in charge.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of time is taken to respond to the client's questions, and the work efficiency of the person in charge is reduced.

[0005] The system according to the embodiment aims to quickly respond to the client's questions and improve the work efficiency of the person in charge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, and a sharing unit. The reception unit receives questions from clients. The generation unit analyzes the questions received by the reception unit and generates a response based on data that can be shared externally without issue. The transmission unit sends the response generated by the generation unit to the client. The sharing unit shares the response generated by the generation unit with the responsible person. [Effects of the Invention]

[0007] The system according to this embodiment can respond quickly to client inquiries and improve the work efficiency of the person in charge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The question-answering system according to an embodiment of the present invention is a system in which AI answers questions from clients. In this question-answering system, the client sends a question to a sales representative, the AI ​​receives the question, and generates a response based on data that can be shared externally without issue. This response is sent to the client and simultaneously shared with the sales representative. This improves the time efficiency of the sales representative, allows for better understanding of the client's needs, and eliminates waiting times for the client. For example, a client sends a question to a sales representative such as, "What is the price of the new product?" This information is input into the AI. Next, the AI ​​analyzes the input question and generates a response based on data that can be shared externally without issue. The AI ​​extracts appropriate information from a pre-configured database and generates a response to the client's question. For example, in response to the question, "What is the price of the new product?", the AI ​​generates a response such as, "The price of the new product is XX yen." The generated response is sent to the client and simultaneously shared with the sales representative. This allows the sales representative to understand both the client's question and the AI's response. For example, if a client asks, "What is the price of the new product?" and the AI ​​replies, "The price of the new product is ¥XX," this information is shared with the sales representative. This system improves the time efficiency of the sales representative. The representative no longer needs to directly respond to client inquiries, allowing them to focus on other tasks. It also allows the AI ​​to understand the client's needs. By analyzing the client's questions, the AI ​​can grasp the client's interests and needs. Furthermore, clients are spared waiting time. Because the AI ​​generates responses quickly, clients can get answers immediately. For example, if a client asks, "What is the price of the new product?" and the AI ​​replies, "The price of the new product is ¥XX," the client receives an answer immediately, eliminating waiting time. The sales representative can also understand the client's interests and needs by grasping this information, and use it to improve future sales activities. In this way, the question-answering system improves the time efficiency of the sales representative, helps to understand client needs, and eliminates client waiting times.

[0029] The question handling system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, and a sharing unit. The reception unit receives questions from clients. Client questions include, but are not limited to, questions in text format, voice format, or questions on specific topics. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions on specific topics. For example, the reception unit receives questions in text format entered by a client. The reception unit can also use speech recognition technology to convert voice questions into text format and accept them. The reception unit can also filter and accept questions on specific topics. The generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. The generation unit can, for example, use natural language processing technology to analyze questions. The generation unit can also use machine learning algorithms to analyze questions. Furthermore, the generation unit can also extract appropriate information from a pre-configured database and generate responses. For example, the generation unit analyzes the intent of a question using natural language processing technology and generates an appropriate response. The generation unit can also analyze the content of a question using machine learning algorithms and generate the optimal response. The generation unit can also generate a response by extracting reliable information from a pre-configured database. The transmission unit sends the response generated by the generation unit to the client. The transmission unit can, for example, send the response in real time. The transmission unit can also send the response with minimal delay. Furthermore, the transmission unit can select the optimal transmission method depending on the client's situation and send the response. For example, the transmission unit can send the response in real time. The transmission unit can also send the response using high-speed communication methods to minimize delay. If the client is on the move, the transmission unit can also send the response using mobile notifications. The sharing unit shares the response generated by the generation unit with the relevant personnel. The sharing unit can, for example, share the information via email. The sharing unit can also share the information via chat. Furthermore, the sharing unit can also share the information via a dashboard.For example, the shared unit sends the generated response to the person in charge via email. The shared unit can also share information with the person in charge using a chat application. The shared unit can also share information with the person in charge by displaying the generated response on a dashboard. As a result, the question handling system according to this embodiment can improve the time spent by the person in charge by having the AI ​​answer the client's questions on their behalf, pick up on the client's needs, and eliminate the client's waiting time.

[0030] The reception desk receives questions from clients. These questions may include, but are not limited to, text-based, audio-based, or topical questions. For example, the reception desk can accept text-based questions. Specifically, it can accept text-based questions entered by clients through web forms or chat interfaces. This allows clients to easily enter and submit questions. The reception desk can also accept audio-based questions. Audio-based questions are converted to text using speech recognition technology. For example, if a client enters a question using a smartphone or computer microphone, speech recognition technology analyzes the audio and converts it to text. Thus, audio-based questions are processed in the same way as text-based questions. Furthermore, the reception desk can filter and accept questions related to specific topics. For example, the reception desk can analyze the content of questions entered by clients and configure it to accept only questions related to specific topics. This allows the system to efficiently handle questions specific to particular fields. By combining these functions, the reception desk can accept diverse question formats and flexibly respond to client needs.

[0031] The generation unit analyzes questions received by the reception unit and generates responses based on data that can be shared externally without issue. For example, the generation unit analyzes questions using natural language processing technology. Specifically, it uses natural language processing technology to analyze the intent and content of questions and generate appropriate responses. For example, it extracts the context and keywords of a question and generates the optimal response based on them. The generation unit can also analyze questions using machine learning algorithms. Machine learning algorithms learn from past question and response data and generate the optimal response for new questions. Furthermore, the generation unit can also generate responses by extracting appropriate information from a pre-configured database. For example, the generation unit extracts highly reliable information from a pre-configured database and generates responses based on it. This allows the generation unit to generate accurate and reliable responses. By combining these technologies, the generation unit can generate quick and appropriate responses to client questions.

[0032] The transmitting unit sends the response generated by the generating unit to the client. The transmitting unit can, for example, send the response in real time. Specifically, it sends the response to the client immediately after the generating unit generates it. This allows the client to receive the response quickly. The transmitting unit can also send the response with minimal delay. For example, delay can be minimized by sending the response using a high-speed communication method. Furthermore, the transmitting unit can select the optimal transmission method depending on the client's situation and send the response. For example, if the client is on the move, the response can be sent using mobile notifications. This allows the client to receive the response no matter where they are. By combining these functions, the transmitting unit can send a fast and appropriate response to the client.

[0033] The sharing unit shares the responses generated by the generation unit with the person in charge. The sharing unit shares information, for example, via email. Specifically, it sends the generated responses to the person in charge via email. This allows the person in charge to check the generated responses and take action as needed. The sharing unit can also share information via chat. For example, by sharing information with the person in charge using a chat application, real-time information sharing becomes possible. Furthermore, the sharing unit can also share information via a dashboard. For example, the generated responses can be displayed on the dashboard so that the person in charge can check them at any time. This allows the person in charge to quickly check the generated responses and take the necessary action. By combining these functions, the sharing unit can provide quick and appropriate information sharing to the person in charge. As a result, the question answering system according to this embodiment can improve the time spent by the person in charge by having AI answer the client's questions on their behalf, pick up on the client's needs, and eliminate client waiting times.

[0034] The generation unit can extract appropriate information from a pre-configured database and generate a response to a client's question. For example, the generation unit can extract highly reliable information from a pre-configured database. The generation unit can also extract the latest information to generate a response. The generation unit can also extract highly relevant information to generate a response. For example, the generation unit can extract highly reliable information from a pre-configured database and generate an accurate response to a client's question. The generation unit can also extract the latest information to generate a response. The generation unit can also extract highly relevant information to generate a response. This allows for the generation of accurate responses to client questions by extracting appropriate information from a pre-configured database. Appropriate information includes, but is not limited to, highly reliable information, the latest information, and highly relevant information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input a client's question into a generation AI, which can then extract appropriate information from the database to generate a response.

[0035] The sharing unit can share information about the client's questions and the AI's responses with the assigned personnel. For example, the sharing unit can send the client's questions to the assigned personnel via email. The sharing unit can also share the AI's responses with the assigned personnel via chat. The sharing unit can also share information about the client's questions and the AI's responses with the assigned personnel by displaying them on a dashboard. For example, the sharing unit can send the client's questions to the assigned personnel via email and share the AI's responses via chat. The sharing unit can also share information about the client's questions and the AI's responses with the assigned personnel by displaying them on a dashboard. This allows the assigned personnel to understand the client's interests and needs by sharing information about the client's questions and the AI's responses with them. Information sharing includes, but is not limited to, email, chat, and dashboards. Some or all of the above-described processes in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the client's questions and the AI's responses into a generating AI, and the generating AI can select how to share the information with the assigned personnel.

[0036] The generation unit can analyze the client's questions and understand the client's interests and needs. For example, the generation unit identifies the client's interests and needs by analyzing the client's questions. The generation unit can also understand the client's interests and needs by analyzing past question history. The generation unit can also understand the client's interests and needs by analyzing survey results. For example, the generation unit identifies the client's interests and needs by analyzing the client's questions. The generation unit can also understand the client's interests and needs by analyzing past question history. The generation unit can also understand the client's interests and needs by analyzing survey results. In this way, the client's interests and needs can be understood by analyzing the client's questions. Interests and needs include, but are not limited to, past question history and survey results. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the client's questions into a generation AI, which can understand the interests and needs.

[0037] The transmitting unit can quickly send the generated response to the client. The transmitting unit can, for example, send the response in real time. The transmitting unit can also send the response with minimal delay. The transmitting unit can also select the optimal transmission method depending on the client's situation and send the response. For example, the transmitting unit can send the response in real time. The transmitting unit can also send the response using high-speed communication methods to minimize delay. If the client is on the move, the transmitting unit can also send the response using mobile notifications. This allows the client to receive an answer without waiting by quickly sending the generated response to the client. Rapid transmission includes, but is not limited to, real-time transmission and minimizing delay. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input the generated response into a generating AI, and the generating AI can select a method for quickly sending it to the client.

[0038] The reception department can analyze the client's past question history and select the optimal reception method. For example, the reception department can prioritize receiving questions that the client has frequently asked in the past. The reception department can also extract specific patterns from the client's past question history and suggest the optimal reception method. The reception department can also analyze the client's past question history and automatically receive similar questions. For example, the reception department can prioritize receiving questions that the client has frequently asked in the past. The reception department can also extract specific patterns from the client's past question history and suggest the optimal reception method. The reception department can also analyze the client's past question history and automatically receive similar questions. This allows the reception department to select the optimal reception method by analyzing the client's past question history. Optimal reception methods include, but are not limited to, chatbots and telephone reception. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input the client's past question history into a generating AI, which can then select the optimal reception method.

[0039] The reception desk can filter questions based on the client's current projects and areas of interest when receiving them. For example, the reception desk prioritizes receiving questions related to the client's current projects. The reception desk can also filter relevant questions based on the client's areas of interest. The reception desk can also determine the priority of questions based on the client's current projects and areas of interest. For example, the reception desk prioritizes receiving questions related to the client's current projects. The reception desk can also filter relevant questions based on the client's areas of interest. The reception desk can also determine the priority of questions based on the client's current projects and areas of interest. This allows for prioritizing the receipt of highly relevant questions by filtering them based on the client's current projects and areas of interest. Filtering includes, but is not limited to, keyword matching and project relevance. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's current projects and areas of interest into a generating AI, which can then filter the questions.

[0040] The reception desk can prioritize receiving questions that are highly relevant, taking into account the client's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the client's geographical location information. The reception desk can also determine the priority of questions, taking into account the client's geographical location information. The reception desk can also prioritize receiving questions related to a specific region, taking into account the client's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the client's geographical location information. The reception desk can also determine the priority of questions, taking into account the client's geographical location information. The reception desk can also prioritize receiving questions related to a specific region, taking into account the client's geographical location information. This allows for prioritizing the reception of highly relevant questions by considering the client's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's geographical location information into a generating AI, which can then prioritize receiving highly relevant questions.

[0041] The reception desk can analyze the client's social media activity when receiving questions and accept relevant questions. For example, the reception desk can analyze the client's social media activity and accept relevant questions preferentially. The reception desk can also determine the priority of questions based on the client's social media activity. The reception desk can also analyze the client's social media activity and accept questions related to specific topics preferentially. For example, the reception desk can analyze the client's social media activity and accept relevant questions preferentially. The reception desk can also determine the priority of questions based on the client's social media activity. The reception desk can also analyze the client's social media activity and accept questions related to specific topics preferentially. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing the client's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's social media activity into a generating AI, which can then accept relevant questions.

[0042] The generation unit can adjust the level of detail in the response based on the importance of the question when generating the response. For example, the generation unit can generate a detailed response for a high-importance question. The generation unit can also generate a concise response for a low-importance question. The generation unit can also adjust the level of detail in the response according to the importance of the question. For example, the generation unit can generate a detailed response for a high-importance question. The generation unit can also generate a concise response for a low-importance question. The generation unit can also adjust the level of detail in the response according to the importance of the question. This allows for the generation of more appropriate responses by adjusting the level of detail in the response according to the importance of the question. Importance includes, but is not limited to, the content and impact of the question. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the question into the generation AI, and the generation AI can adjust the level of detail in the response.

[0043] The generation unit can apply different response algorithms depending on the question category when generating responses. For example, the generation unit can apply a specialized response algorithm to technical questions. The generation unit can also apply a concise response algorithm to general questions. The generation unit can also select the optimal response algorithm depending on the question category. For example, the generation unit can apply a specialized response algorithm to technical questions. The generation unit can also apply a concise response algorithm to general questions. The generation unit can also select the optimal response algorithm depending on the question category. This allows for the generation of more appropriate responses by applying the optimal response algorithm according to the question category. Categories include, for example, technical questions and business questions, but are not limited to these examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the question category into a generation AI, and the generation AI can apply the optimal response algorithm.

[0044] The generation unit can determine the priority of responses based on when the question was submitted when generating responses. For example, the generation unit determines the priority of responses based on when the question was submitted. The generation unit can also prioritize generating responses if the question was submitted a long time ago. The generation unit can also quickly generate responses if the question was submitted a long time ago. For example, the generation unit determines the priority of responses based on when the question was submitted. The generation unit can also prioritize generating responses if the question was submitted a long time ago. The generation unit can also quickly generate responses if the question was submitted a long time ago. This allows for faster response generation by determining the priority of responses based on when the question was submitted. The submission date includes, but is not limited to, the submission date and time, submission order, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the question submission date into a generation AI, and the generation AI can determine the priority of responses.

[0045] The generation unit can adjust the order of responses based on the relevance of the questions when generating responses. For example, the generation unit determines the order of responses based on the relevance of the questions. The generation unit can also prioritize generating responses for highly relevant questions. The generation unit can also postpone generating responses for less relevant questions. For example, the generation unit determines the order of responses based on the relevance of the questions. The generation unit can also prioritize generating responses for highly relevant questions. The generation unit can also postpone generating responses for less relevant questions. This allows for the generation of responses in a more appropriate order by adjusting the order of responses based on the relevance of the questions. Relevance includes, but is not limited to, similarity of question content and related topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the questions into a generation AI, which can then adjust the order of responses.

[0046] The sending unit can select the optimal sending method by referring to the client's past response history when sending a reply. For example, the sending unit selects the optimal sending method based on the client's past response history. The sending unit can also prioritize sending methods that the client has preferred in the past. The sending unit can also analyze the client's past response history and suggest the optimal sending method. For example, the sending unit selects the optimal sending method based on the client's past response history. The sending unit can also prioritize sending methods that the client has preferred in the past. The sending unit can also analyze the client's past response history and suggest the optimal sending method. This allows the optimal sending method to be selected by referring to the client's past response history. Response history includes, but is not limited to, responses to past replies and feedback. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's past response history into a generating AI, which can then select the optimal sending method.

[0047] The sending unit can customize the sending method based on the client's current status when sending a reply. For example, the sending unit can select the optimal sending method based on the client's current status. The sending unit can also prioritize mobile notifications if the client is on the go. The sending unit can also prioritize email delivery if the client is in the office. For example, the sending unit can select the optimal sending method based on the client's current status. The sending unit can also prioritize mobile notifications if the client is on the go. The sending unit can also prioritize email delivery if the client is in the office. This allows the reply to be sent by a more appropriate method by customizing the sending method based on the client's current status. Current status includes, but is not limited to, the user's activity status and usage environment. Some or all of the processing described above in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's current status into a generating AI, which can then customize the sending method.

[0048] The sending unit can select the optimal sending method when sending a reply, taking into account the client's geographical location information. For example, the sending unit selects the optimal sending method based on the client's geographical location information. The sending unit can also prioritize international calls or messaging apps if the client is overseas. The sending unit can also prioritize regular phone calls or emails if the client is domestic. For example, the sending unit selects the optimal sending method based on the client's geographical location information. The sending unit can also prioritize international calls or messaging apps if the client is overseas. The sending unit can also prioritize regular phone calls or emails if the client is domestic. This allows the optimal sending method to be selected by taking into account the client's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's geographical location information into a generating AI, which can then select the optimal sending method.

[0049] The sending unit can analyze the client's social media activity and suggest a sending method when sending a reply. For example, the sending unit can analyze the client's social media activity and suggest the optimal sending method. The sending unit can also prioritize the social media platforms that the client frequently uses. The sending unit can also customize the sending method based on the client's social media activity. For example, the sending unit can analyze the client's social media activity and suggest the optimal sending method. The sending unit can also prioritize the social media platforms that the client frequently uses. The sending unit can also customize the sending method based on the client's social media activity. This allows the sending unit to suggest the optimal sending method by analyzing the client's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the processing described above in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's social media activity into a generating AI, which can then suggest a sending method.

[0050] The sharing unit can select the optimal sharing method by referring to the past interaction history of the person in charge when sharing information. For example, the sharing unit selects the optimal information sharing method based on the person in charge's past interaction history. The sharing unit can also prioritize the information sharing method that the person in charge has preferred in the past. The sharing unit can also analyze the person in charge's past interaction history and propose the optimal information sharing method. For example, the sharing unit selects the optimal information sharing method based on the person in charge's past interaction history. The sharing unit can also prioritize the information sharing method that the person in charge has preferred in the past. The sharing unit can also analyze the person in charge's past interaction history and propose the optimal information sharing method. This allows the optimal sharing method to be selected by referring to the person in charge's past interaction history. Interaction history includes, for example, past interaction content and response results, but is not limited to such examples. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's past interaction history into a generating AI, and the generating AI can select the optimal information sharing method.

[0051] The sharing unit can customize the means of information sharing based on the current workload of the person in charge. For example, the sharing unit can select the optimal means of information sharing based on the person in charge's current workload. The sharing unit can also provide a concise method of information sharing when the person in charge is busy. The sharing unit can also provide a detailed method of information sharing when the person in charge has time. For example, the sharing unit can select the optimal means of information sharing based on the person in charge's current workload. The sharing unit can also provide a concise method of information sharing when the person in charge is busy. The sharing unit can also provide a detailed method of information sharing when the person in charge has time. This allows information to be shared in a more appropriate way by customizing the means of information sharing based on the person in charge's current workload. Current workload includes, but is not limited to, the person in charge's workload and work progress. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's current workload into a generating AI, which can then customize the means of information sharing.

[0052] The sharing unit can select the optimal sharing method when sharing information, taking into account the geographical location information of the person in charge. For example, the sharing unit selects the optimal information sharing method based on the geographical location information of the person in charge. The sharing unit can also prioritize international calls or messaging apps if the person in charge is overseas. The sharing unit can also prioritize regular phone calls or emails if the person in charge is domestic. For example, the sharing unit selects the optimal information sharing method based on the geographical location information of the person in charge. The sharing unit can also prioritize international calls or messaging apps if the person in charge is overseas. The sharing unit can also prioritize regular phone calls or emails if the person in charge is domestic. This allows the optimal sharing method to be selected by taking into account the geographical location information of the person in charge. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the geographical location information of the person in charge into a generating AI, and the generating AI can select the optimal information sharing method.

[0053] The sharing unit can analyze the social media activity of the person in charge and suggest sharing methods when sharing information. For example, the sharing unit can analyze the person in charge's social media activity and suggest the most suitable information sharing method. The sharing unit can also prioritize the social media platforms that the person in charge frequently uses. The sharing unit can also customize the information sharing methods based on the person in charge's social media activity. For example, the sharing unit can analyze the person in charge's social media activity and suggest the most suitable information sharing method. The sharing unit can also prioritize the social media platforms that the person in charge frequently uses. The sharing unit can also customize the information sharing methods based on the person in charge's social media activity. This allows the sharing unit to suggest the most suitable sharing method by analyzing the person in charge's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's social media activity into a generating AI, which can then suggest sharing methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The question-answering system not only uses AI to respond to client questions, but can also analyze the client's past question history to predict their interests and needs. For example, it can prioritize providing information on topics the client has frequently asked about in the past. It can also automatically send the latest information on products and services the client has shown interest in in the past. Furthermore, based on the client's question history, it can predict what the client is likely to ask next and provide pre-prepared answers. This allows for a more accurate understanding of client needs and enables quicker and more appropriate responses.

[0056] The question handling system can consider the client's current situation and environment when generating responses to their questions. For example, if the client is on the go, it can generate a concise and to-the-point response. If the client is in the office, it can generate a response that includes detailed explanations. Furthermore, if the client is in a meeting, the response can be sent via email for later review. This allows the system to provide appropriate responses tailored to the client's situation.

[0057] The question handling system can consider the client's geographical location when generating responses to their questions. For example, if the client is overseas, it can generate responses that take into account the local time zone and culture. Furthermore, if the client asks a question about a specific region, it can prioritize providing information relevant to that region. Additionally, if the client is on the move, responses can be sent via mobile notifications. This ensures that appropriate responses are provided based on the client's geographical location.

[0058] The question-answering system can analyze a client's social media activity and provide relevant information when generating responses to their questions. For example, if a client frequently posts about a particular topic on social media, the system can prioritize providing information related to that topic. It can also provide information that might be of interest to the client based on the accounts they follow and the groups they participate in. Furthermore, it can adjust the tone and expression of the response based on the client's social media activity. This allows the system to provide appropriate responses tailored to the client's social media activity.

[0059] The question-answering system can select the most appropriate response method by referencing the client's past response history when generating answers to client questions. For example, it can prioritize response methods that the client has preferred in the past. It can also analyze the client's past response history and suggest the most appropriate response method. Furthermore, it can adjust the tone and expression of the response based on the client's past response history. This allows the system to provide appropriate responses tailored to the client's past response history.

[0060] The question handling system can filter responses to client questions based on the client's current projects and areas of interest. For example, it can prioritize questions related to the client's current projects. It can also filter relevant questions based on the client's areas of interest. Furthermore, it can determine the priority of questions based on the client's current projects and areas of interest. This allows the system to prioritize highly relevant questions by filtering them based on the client's current projects and areas of interest.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives client inquiries. Client inquiries can be in text format, audio format, or on specific topics. The reception desk can accept text-based inquiries, and can also use speech recognition technology to convert audio inquiries into text format. It can also filter and accept inquiries on specific topics. Step 2: The generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. The generation unit analyzes the questions using natural language processing technology and machine learning algorithms, extracts appropriate information from a pre-configured database, and generates responses. Step 3: The transmitting unit sends the response generated by the generating unit to the client. The transmitting unit can send the response in real time, or it can send the response with minimal delay. It can also select the optimal transmission method depending on the client's situation and send the response. Step 4: The sharing unit shares the responses generated by the generation unit with the responsible person. The sharing unit can share information using email, chat, dashboards, etc.

[0063] (Example of form 2) The question-answering system according to an embodiment of the present invention is a system in which AI answers questions from clients. In this question-answering system, the client sends a question to a sales representative, the AI ​​receives the question, and generates a response based on data that can be shared externally without issue. This response is sent to the client and simultaneously shared with the sales representative. This improves the time efficiency of the sales representative, allows for better understanding of the client's needs, and eliminates waiting times for the client. For example, a client sends a question to a sales representative such as, "What is the price of the new product?" This information is input into the AI. Next, the AI ​​analyzes the input question and generates a response based on data that can be shared externally without issue. The AI ​​extracts appropriate information from a pre-configured database and generates a response to the client's question. For example, in response to the question, "What is the price of the new product?", the AI ​​generates a response such as, "The price of the new product is XX yen." The generated response is sent to the client and simultaneously shared with the sales representative. This allows the sales representative to understand both the client's question and the AI's response. For example, if a client asks, "What is the price of the new product?" and the AI ​​replies, "The price of the new product is ¥XX," this information is shared with the sales representative. This system improves the time efficiency of the sales representative. The representative no longer needs to directly respond to client inquiries, allowing them to focus on other tasks. It also allows the AI ​​to understand the client's needs. By analyzing the client's questions, the AI ​​can grasp the client's interests and needs. Furthermore, clients are spared waiting time. Because the AI ​​generates responses quickly, clients can get answers immediately. For example, if a client asks, "What is the price of the new product?" and the AI ​​replies, "The price of the new product is ¥XX," the client receives an answer immediately, eliminating waiting time. The sales representative can also understand the client's interests and needs by grasping this information, and use it to improve future sales activities. In this way, the question-answering system improves the time efficiency of the sales representative, helps to understand client needs, and eliminates client waiting times.

[0064] The question handling system according to this embodiment comprises a reception unit, a generation unit, a transmission unit, and a sharing unit. The reception unit receives questions from clients. Client questions include, but are not limited to, questions in text format, voice format, or questions on specific topics. The reception unit can, for example, receive questions in text format. The reception unit can also receive questions in voice format. Furthermore, the reception unit can also receive questions on specific topics. For example, the reception unit receives questions in text format entered by a client. The reception unit can also use speech recognition technology to convert voice questions into text format and accept them. The reception unit can also filter and accept questions on specific topics. The generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. The generation unit can, for example, use natural language processing technology to analyze questions. The generation unit can also use machine learning algorithms to analyze questions. Furthermore, the generation unit can also extract appropriate information from a pre-configured database and generate responses. For example, the generation unit analyzes the intent of a question using natural language processing technology and generates an appropriate response. The generation unit can also analyze the content of a question using machine learning algorithms and generate the optimal response. The generation unit can also generate a response by extracting reliable information from a pre-configured database. The transmission unit sends the response generated by the generation unit to the client. The transmission unit can, for example, send the response in real time. The transmission unit can also send the response with minimal delay. Furthermore, the transmission unit can select the optimal transmission method depending on the client's situation and send the response. For example, the transmission unit can send the response in real time. The transmission unit can also send the response using high-speed communication methods to minimize delay. If the client is on the move, the transmission unit can also send the response using mobile notifications. The sharing unit shares the response generated by the generation unit with the relevant personnel. The sharing unit can, for example, share the information via email. The sharing unit can also share the information via chat. Furthermore, the sharing unit can also share the information via a dashboard.For example, the shared unit sends the generated response to the person in charge via email. The shared unit can also share information with the person in charge using a chat application. The shared unit can also share information with the person in charge by displaying the generated response on a dashboard. As a result, the question handling system according to this embodiment can improve the time spent by the person in charge by having the AI ​​answer the client's questions on their behalf, pick up on the client's needs, and eliminate the client's waiting time.

[0065] The reception desk receives questions from clients. These questions may include, but are not limited to, text-based, audio-based, or topical questions. For example, the reception desk can accept text-based questions. Specifically, it can accept text-based questions entered by clients through web forms or chat interfaces. This allows clients to easily enter and submit questions. The reception desk can also accept audio-based questions. Audio-based questions are converted to text using speech recognition technology. For example, if a client enters a question using a smartphone or computer microphone, speech recognition technology analyzes the audio and converts it to text. Thus, audio-based questions are processed in the same way as text-based questions. Furthermore, the reception desk can filter and accept questions related to specific topics. For example, the reception desk can analyze the content of questions entered by clients and configure it to accept only questions related to specific topics. This allows the system to efficiently handle questions specific to particular fields. By combining these functions, the reception desk can accept diverse question formats and flexibly respond to client needs.

[0066] The generation unit analyzes questions received by the reception unit and generates responses based on data that can be shared externally without issue. For example, the generation unit analyzes questions using natural language processing technology. Specifically, it uses natural language processing technology to analyze the intent and content of questions and generate appropriate responses. For example, it extracts the context and keywords of a question and generates the optimal response based on them. The generation unit can also analyze questions using machine learning algorithms. Machine learning algorithms learn from past question and response data and generate the optimal response for new questions. Furthermore, the generation unit can also generate responses by extracting appropriate information from a pre-configured database. For example, the generation unit extracts highly reliable information from a pre-configured database and generates responses based on it. This allows the generation unit to generate accurate and reliable responses. By combining these technologies, the generation unit can generate quick and appropriate responses to client questions.

[0067] The transmitting unit sends the response generated by the generating unit to the client. The transmitting unit can, for example, send the response in real time. Specifically, it sends the response to the client immediately after the generating unit generates it. This allows the client to receive the response quickly. The transmitting unit can also send the response with minimal delay. For example, delay can be minimized by sending the response using a high-speed communication method. Furthermore, the transmitting unit can select the optimal transmission method depending on the client's situation and send the response. For example, if the client is on the move, the response can be sent using mobile notifications. This allows the client to receive the response no matter where they are. By combining these functions, the transmitting unit can send a fast and appropriate response to the client.

[0068] The sharing unit shares the responses generated by the generation unit with the person in charge. The sharing unit shares information, for example, via email. Specifically, it sends the generated responses to the person in charge via email. This allows the person in charge to check the generated responses and take action as needed. The sharing unit can also share information via chat. For example, by sharing information with the person in charge using a chat application, real-time information sharing becomes possible. Furthermore, the sharing unit can also share information via a dashboard. For example, the generated responses can be displayed on the dashboard so that the person in charge can check them at any time. This allows the person in charge to quickly check the generated responses and take the necessary action. By combining these functions, the sharing unit can provide quick and appropriate information sharing to the person in charge. As a result, the question answering system according to this embodiment can improve the time spent by the person in charge by having AI answer the client's questions on their behalf, pick up on the client's needs, and eliminate client waiting times.

[0069] The generation unit can extract appropriate information from a pre-configured database and generate a response to a client's question. For example, the generation unit can extract highly reliable information from a pre-configured database. The generation unit can also extract the latest information to generate a response. The generation unit can also extract highly relevant information to generate a response. For example, the generation unit can extract highly reliable information from a pre-configured database and generate an accurate response to a client's question. The generation unit can also extract the latest information to generate a response. The generation unit can also extract highly relevant information to generate a response. This allows for the generation of accurate responses to client questions by extracting appropriate information from a pre-configured database. Appropriate information includes, but is not limited to, highly reliable information, the latest information, and highly relevant information. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input a client's question into a generation AI, which can then extract appropriate information from the database to generate a response.

[0070] The sharing unit can share information about the client's questions and the AI's responses with the assigned personnel. For example, the sharing unit can send the client's questions to the assigned personnel via email. The sharing unit can also share the AI's responses with the assigned personnel via chat. The sharing unit can also share information about the client's questions and the AI's responses with the assigned personnel by displaying them on a dashboard. For example, the sharing unit can send the client's questions to the assigned personnel via email and share the AI's responses via chat. The sharing unit can also share information about the client's questions and the AI's responses with the assigned personnel by displaying them on a dashboard. This allows the assigned personnel to understand the client's interests and needs by sharing information about the client's questions and the AI's responses with them. Information sharing includes, but is not limited to, email, chat, and dashboards. Some or all of the above-described processes in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the client's questions and the AI's responses into a generating AI, and the generating AI can select how to share the information with the assigned personnel.

[0071] The generation unit can analyze the client's questions and understand the client's interests and needs. For example, the generation unit identifies the client's interests and needs by analyzing the client's questions. The generation unit can also understand the client's interests and needs by analyzing past question history. The generation unit can also understand the client's interests and needs by analyzing survey results. For example, the generation unit identifies the client's interests and needs by analyzing the client's questions. The generation unit can also understand the client's interests and needs by analyzing past question history. The generation unit can also understand the client's interests and needs by analyzing survey results. In this way, the client's interests and needs can be understood by analyzing the client's questions. Interests and needs include, but are not limited to, past question history and survey results. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the client's questions into a generation AI, which can understand the interests and needs.

[0072] The transmitting unit can quickly send the generated response to the client. The transmitting unit can, for example, send the response in real time. The transmitting unit can also send the response with minimal delay. The transmitting unit can also select the optimal transmission method depending on the client's situation and send the response. For example, the transmitting unit can send the response in real time. The transmitting unit can also send the response using high-speed communication methods to minimize delay. If the client is on the move, the transmitting unit can also send the response using mobile notifications. This allows the client to receive an answer without waiting by quickly sending the generated response to the client. Rapid transmission includes, but is not limited to, real-time transmission and minimizing delay. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input the generated response into a generating AI, and the generating AI can select a method for quickly sending it to the client.

[0073] The reception desk can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, if the user is stressed, the reception desk will quickly receive the question and respond immediately. If the user is relaxed, the reception desk can also receive the question at a normal time. If the user is in a hurry, the reception desk can also prioritize receiving the question and process it quickly. For example, if the user is stressed, the reception desk will quickly receive the question and respond immediately. If the user is relaxed, the reception desk can also receive the question at a normal time. If the user is in a hurry, the reception desk can also prioritize receiving the question and process it quickly. This allows for question reception at a more appropriate time by adjusting the timing of question reception according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI, which can then adjust the timing of question reception.

[0074] The reception department can analyze the client's past question history and select the optimal reception method. For example, the reception department can prioritize receiving questions that the client has frequently asked in the past. The reception department can also extract specific patterns from the client's past question history and suggest the optimal reception method. The reception department can also analyze the client's past question history and automatically receive similar questions. For example, the reception department can prioritize receiving questions that the client has frequently asked in the past. The reception department can also extract specific patterns from the client's past question history and suggest the optimal reception method. The reception department can also analyze the client's past question history and automatically receive similar questions. This allows the reception department to select the optimal reception method by analyzing the client's past question history. Optimal reception methods include, but are not limited to, chatbots and telephone reception. Some or all of the above processing in the reception department may be performed using, for example, AI, or not using AI. For example, the reception department can input the client's past question history into a generating AI, which can then select the optimal reception method.

[0075] The reception desk can filter questions based on the client's current projects and areas of interest when receiving them. For example, the reception desk prioritizes receiving questions related to the client's current projects. The reception desk can also filter relevant questions based on the client's areas of interest. The reception desk can also determine the priority of questions based on the client's current projects and areas of interest. For example, the reception desk prioritizes receiving questions related to the client's current projects. The reception desk can also filter relevant questions based on the client's areas of interest. The reception desk can also determine the priority of questions based on the client's current projects and areas of interest. This allows for prioritizing the receipt of highly relevant questions by filtering them based on the client's current projects and areas of interest. Filtering includes, but is not limited to, keyword matching and project relevance. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's current projects and areas of interest into a generating AI, which can then filter the questions.

[0076] The reception desk can estimate the user's emotions and determine the priority of the questions to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will set the question priority higher. If the user is relaxed, the reception desk can also set the question priority to normal. If the user is in a hurry, the reception desk can also set the question priority to the highest priority. For example, if the user is stressed, the reception desk will set the question priority higher. If the user is relaxed, the reception desk can also set the question priority to normal. If the user is in a hurry, the reception desk can also set the question priority to the highest priority. This allows questions to be processed in a more appropriate order by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI, which can then determine the priority of questions.

[0077] The reception desk can prioritize receiving questions that are highly relevant, taking into account the client's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the client's geographical location information. The reception desk can also determine the priority of questions, taking into account the client's geographical location information. The reception desk can also prioritize receiving questions related to a specific region, taking into account the client's geographical location information. For example, the reception desk can prioritize receiving relevant questions based on the client's geographical location information. The reception desk can also determine the priority of questions, taking into account the client's geographical location information. The reception desk can also prioritize receiving questions related to a specific region, taking into account the client's geographical location information. This allows for prioritizing the reception of highly relevant questions by considering the client's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's geographical location information into a generating AI, which can then prioritize receiving highly relevant questions.

[0078] The reception desk can analyze the client's social media activity when receiving questions and accept relevant questions. For example, the reception desk can analyze the client's social media activity and accept relevant questions preferentially. The reception desk can also determine the priority of questions based on the client's social media activity. The reception desk can also analyze the client's social media activity and accept questions related to specific topics preferentially. For example, the reception desk can analyze the client's social media activity and accept relevant questions preferentially. The reception desk can also determine the priority of questions based on the client's social media activity. The reception desk can also analyze the client's social media activity and accept questions related to specific topics preferentially. This allows the reception desk to prioritize the acceptance of relevant questions by analyzing the client's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the client's social media activity into a generating AI, which can then accept relevant questions.

[0079] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and clear response. If the user is relaxed, the generation unit can also generate a response that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a quick and concise response. For example, if the user is stressed, the generation unit can generate a simple and clear response. If the user is relaxed, the generation unit can also generate a response that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a quick and concise response. This allows for the generation of more appropriate responses by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust how the response is expressed.

[0080] The generation unit can adjust the level of detail in the response based on the importance of the question when generating the response. For example, the generation unit can generate a detailed response for a high-importance question. The generation unit can also generate a concise response for a low-importance question. The generation unit can also adjust the level of detail in the response according to the importance of the question. For example, the generation unit can generate a detailed response for a high-importance question. The generation unit can also generate a concise response for a low-importance question. The generation unit can also adjust the level of detail in the response according to the importance of the question. This allows for the generation of more appropriate responses by adjusting the level of detail in the response according to the importance of the question. Importance includes, but is not limited to, the content and impact of the question. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the question into the generation AI, and the generation AI can adjust the level of detail in the response.

[0081] The generation unit can apply different response algorithms depending on the question category when generating responses. For example, the generation unit can apply a specialized response algorithm to technical questions. The generation unit can also apply a concise response algorithm to general questions. The generation unit can also select the optimal response algorithm depending on the question category. For example, the generation unit can apply a specialized response algorithm to technical questions. The generation unit can also apply a concise response algorithm to general questions. The generation unit can also select the optimal response algorithm depending on the question category. This allows for the generation of more appropriate responses by applying the optimal response algorithm according to the question category. Categories include, for example, technical questions and business questions, but are not limited to these examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the question category into a generation AI, and the generation AI can apply the optimal response algorithm.

[0082] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a short, to-the-point response. If the user is relaxed, the generation unit can also generate a longer response with more detailed explanations. If the user is in a hurry, the generation unit can also generate a quick and concise response. For example, if the user is stressed, the generation unit will generate a short, to-the-point response. If the user is relaxed, the generation unit can also generate a longer response with more detailed explanations. If the user is in a hurry, the generation unit can also generate a quick and concise response. This allows for the generation of more appropriate responses by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the response.

[0083] The generation unit can determine the priority of responses based on when the question was submitted when generating responses. For example, the generation unit determines the priority of responses based on when the question was submitted. The generation unit can also prioritize generating responses if the question was submitted a long time ago. The generation unit can also quickly generate responses if the question was submitted a long time ago. For example, the generation unit determines the priority of responses based on when the question was submitted. The generation unit can also prioritize generating responses if the question was submitted a long time ago. The generation unit can also quickly generate responses if the question was submitted a long time ago. This allows for faster response generation by determining the priority of responses based on when the question was submitted. The submission date includes, but is not limited to, the submission date and time, submission order, etc. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the question submission date into a generation AI, and the generation AI can determine the priority of responses.

[0084] The generation unit can adjust the order of responses based on the relevance of the questions when generating responses. For example, the generation unit determines the order of responses based on the relevance of the questions. The generation unit can also prioritize generating responses for highly relevant questions. The generation unit can also postpone generating responses for less relevant questions. For example, the generation unit determines the order of responses based on the relevance of the questions. The generation unit can also prioritize generating responses for highly relevant questions. The generation unit can also postpone generating responses for less relevant questions. This allows for the generation of responses in a more appropriate order by adjusting the order of responses based on the relevance of the questions. Relevance includes, but is not limited to, similarity of question content and related topics. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the questions into a generation AI, which can then adjust the order of responses.

[0085] The sending unit can estimate the user's emotions and adjust the timing of sending a response based on the estimated emotions. For example, if the user is stressed, the sending unit will send a response quickly. If the user is relaxed, the sending unit can also send a response at a normal time. If the user is in a hurry, the sending unit can also send a response with top priority. For example, if the user is stressed, the sending unit will send a response quickly. If the user is relaxed, the sending unit can also send a response at a normal time. If the user is in a hurry, the sending unit can also send a response with top priority. This allows for sending responses at a more appropriate time by adjusting the timing of response sending according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can input user emotion data into a generative AI, which can then adjust the timing of response sending.

[0086] The sending unit can select the optimal sending method by referring to the client's past response history when sending a reply. For example, the sending unit selects the optimal sending method based on the client's past response history. The sending unit can also prioritize sending methods that the client has preferred in the past. The sending unit can also analyze the client's past response history and suggest the optimal sending method. For example, the sending unit selects the optimal sending method based on the client's past response history. The sending unit can also prioritize sending methods that the client has preferred in the past. The sending unit can also analyze the client's past response history and suggest the optimal sending method. This allows the optimal sending method to be selected by referring to the client's past response history. Response history includes, but is not limited to, responses to past replies and feedback. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's past response history into a generating AI, which can then select the optimal sending method.

[0087] The sending unit can customize the sending method based on the client's current status when sending a reply. For example, the sending unit can select the optimal sending method based on the client's current status. The sending unit can also prioritize mobile notifications if the client is on the go. The sending unit can also prioritize email delivery if the client is in the office. For example, the sending unit can select the optimal sending method based on the client's current status. The sending unit can also prioritize mobile notifications if the client is on the go. The sending unit can also prioritize email delivery if the client is in the office. This allows the reply to be sent by a more appropriate method by customizing the sending method based on the client's current status. Current status includes, but is not limited to, the user's activity status and usage environment. Some or all of the processing described above in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's current status into a generating AI, which can then customize the sending method.

[0088] The sending unit can estimate the user's emotions and determine the priority of sending replies based on the estimated emotions. For example, if the user is stressed, the sending unit will set a higher priority for sending replies. If the user is relaxed, the sending unit can also set the priority of sending replies to a normal level. If the user is in a hurry, the sending unit can also set the priority of sending replies to the highest level. For example, if the user is stressed, the sending unit will set a higher priority for sending replies. If the user is relaxed, the sending unit can also set the priority of sending replies to a normal level. If the user is in a hurry, the sending unit can also set the priority of sending replies to the highest level. This allows replies to be sent in a more appropriate order by determining the priority of sending replies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input user emotion data into a generating AI, which can then determine the priority order for sending responses.

[0089] The sending unit can select the optimal sending method when sending a reply, taking into account the client's geographical location information. For example, the sending unit selects the optimal sending method based on the client's geographical location information. The sending unit can also prioritize international calls or messaging apps if the client is overseas. The sending unit can also prioritize regular phone calls or emails if the client is domestic. For example, the sending unit selects the optimal sending method based on the client's geographical location information. The sending unit can also prioritize international calls or messaging apps if the client is overseas. The sending unit can also prioritize regular phone calls or emails if the client is domestic. This allows the optimal sending method to be selected by taking into account the client's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's geographical location information into a generating AI, which can then select the optimal sending method.

[0090] The sending unit can analyze the client's social media activity and suggest a sending method when sending a reply. For example, the sending unit can analyze the client's social media activity and suggest the optimal sending method. The sending unit can also prioritize the social media platforms that the client frequently uses. The sending unit can also customize the sending method based on the client's social media activity. For example, the sending unit can analyze the client's social media activity and suggest the optimal sending method. The sending unit can also prioritize the social media platforms that the client frequently uses. The sending unit can also customize the sending method based on the client's social media activity. This allows the sending unit to suggest the optimal sending method by analyzing the client's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the processing described above in the sending unit may be performed using, for example, AI, or not using AI. For example, the sending unit can input the client's social media activity into a generating AI, which can then suggest a sending method.

[0091] The sharing section can estimate the user's emotions and adjust the method of information sharing based on the estimated emotions. For example, if the user is stressed, the sharing section can provide a simple and clear method of information sharing. If the user is relaxed, the sharing section can also provide a method of information sharing that includes detailed information. If the user is in a hurry, the sharing section can also provide a quick and concise method of information sharing. For example, if the user is stressed, the sharing section can provide a simple and clear method of information sharing. If the user is relaxed, the sharing section can also provide a method of information sharing that includes detailed information. If the user is in a hurry, the sharing section can also provide a quick and concise method of information sharing. This allows information to be shared in a more appropriate way by adjusting the method of information sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI, for example, or without AI. For example, the sharing unit can input user emotion data into a generating AI, which can then adjust the method of information sharing.

[0092] The sharing unit can select the optimal sharing method by referring to the past interaction history of the person in charge when sharing information. For example, the sharing unit selects the optimal information sharing method based on the person in charge's past interaction history. The sharing unit can also prioritize the information sharing method that the person in charge has preferred in the past. The sharing unit can also analyze the person in charge's past interaction history and propose the optimal information sharing method. For example, the sharing unit selects the optimal information sharing method based on the person in charge's past interaction history. The sharing unit can also prioritize the information sharing method that the person in charge has preferred in the past. The sharing unit can also analyze the person in charge's past interaction history and propose the optimal information sharing method. This allows the optimal sharing method to be selected by referring to the person in charge's past interaction history. Interaction history includes, for example, past interaction content and response results, but is not limited to such examples. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's past interaction history into a generating AI, and the generating AI can select the optimal information sharing method.

[0093] The sharing unit can customize the means of information sharing based on the current workload of the person in charge. For example, the sharing unit can select the optimal means of information sharing based on the person in charge's current workload. The sharing unit can also provide a concise method of information sharing when the person in charge is busy. The sharing unit can also provide a detailed method of information sharing when the person in charge has time. For example, the sharing unit can select the optimal means of information sharing based on the person in charge's current workload. The sharing unit can also provide a concise method of information sharing when the person in charge is busy. The sharing unit can also provide a detailed method of information sharing when the person in charge has time. This allows information to be shared in a more appropriate way by customizing the means of information sharing based on the person in charge's current workload. Current workload includes, but is not limited to, the person in charge's workload and work progress. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's current workload into a generating AI, which can then customize the means of information sharing.

[0094] The sharing unit can estimate the user's emotions and determine the priority of information sharing based on the estimated emotions. For example, if the user is stressed, the sharing unit will set a higher priority for information sharing. If the user is relaxed, the sharing unit can also set the priority of information sharing to normal. If the user is in a hurry, the sharing unit can also set the priority of information sharing to the highest priority. For example, if the user is stressed, the sharing unit will set a higher priority for information sharing. If the user is relaxed, the sharing unit can also set the priority of information sharing to normal. If the user is in a hurry, the sharing unit can also set the priority of information sharing to the highest priority. This allows information to be shared in a more appropriate order by determining the priority of information sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing function can input user emotion data into a generating AI, which can then determine the priority of information sharing.

[0095] The sharing unit can select the optimal sharing method when sharing information, taking into account the geographical location information of the person in charge. For example, the sharing unit selects the optimal information sharing method based on the geographical location information of the person in charge. The sharing unit can also prioritize international calls or messaging apps if the person in charge is overseas. The sharing unit can also prioritize regular phone calls or emails if the person in charge is domestic. For example, the sharing unit selects the optimal information sharing method based on the geographical location information of the person in charge. The sharing unit can also prioritize international calls or messaging apps if the person in charge is overseas. The sharing unit can also prioritize regular phone calls or emails if the person in charge is domestic. This allows the optimal sharing method to be selected by taking into account the geographical location information of the person in charge. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the geographical location information of the person in charge into a generating AI, and the generating AI can select the optimal information sharing method.

[0096] The sharing unit can analyze the social media activity of the person in charge and suggest sharing methods when sharing information. For example, the sharing unit can analyze the person in charge's social media activity and suggest the most suitable information sharing method. The sharing unit can also prioritize the social media platforms that the person in charge frequently uses. The sharing unit can also customize the information sharing methods based on the person in charge's social media activity. For example, the sharing unit can analyze the person in charge's social media activity and suggest the most suitable information sharing method. The sharing unit can also prioritize the social media platforms that the person in charge frequently uses. The sharing unit can also customize the information sharing methods based on the person in charge's social media activity. This allows the sharing unit to suggest the most suitable sharing method by analyzing the person in charge's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the sharing unit may be performed using, for example, AI, or not using AI. For example, the sharing unit can input the person in charge's social media activity into a generating AI, which can then suggest sharing methods.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The question-answering system not only uses AI to respond to client questions, but can also analyze the client's past question history to predict their interests and needs. For example, it can prioritize providing information on topics the client has frequently asked about in the past. It can also automatically send the latest information on products and services the client has shown interest in in the past. Furthermore, based on the client's question history, it can predict what the client is likely to ask next and provide pre-prepared answers. This allows for a more accurate understanding of client needs and enables quicker and more appropriate responses.

[0099] The question handling system can consider the client's current situation and environment when generating responses to their questions. For example, if the client is on the go, it can generate a concise and to-the-point response. If the client is in the office, it can generate a response that includes detailed explanations. Furthermore, if the client is in a meeting, the response can be sent via email for later review. This allows the system to provide appropriate responses tailored to the client's situation.

[0100] The question-answering system can estimate the client's emotions when generating responses to their questions, and adjust the tone and expression of the response based on those emotions. For example, if the client is stressed, it can generate a simple and clear response. If the client is relaxed, it can generate a response that includes detailed explanations. Furthermore, if the client is in a hurry, it can generate a quick and concise response. This allows the system to provide appropriate responses tailored to the client's emotions.

[0101] The question handling system can consider the client's geographical location when generating responses to their questions. For example, if the client is overseas, it can generate responses that take into account the local time zone and culture. Furthermore, if the client asks a question about a specific region, it can prioritize providing information relevant to that region. Additionally, if the client is on the move, responses can be sent via mobile notifications. This ensures that appropriate responses are provided based on the client's geographical location.

[0102] The question-answering system can analyze a client's social media activity and provide relevant information when generating responses to their questions. For example, if a client frequently posts about a particular topic on social media, the system can prioritize providing information related to that topic. It can also provide information that might be of interest to the client based on the accounts they follow and the groups they participate in. Furthermore, it can adjust the tone and expression of the response based on the client's social media activity. This allows the system to provide appropriate responses tailored to the client's social media activity.

[0103] The question-answering system can estimate the client's emotions when generating responses to their questions and adjust the level of detail in the response based on that estimation. For example, if the client is stressed, it can generate a concise and to-the-point response. If the client is relaxed, it can generate a response that includes detailed explanations. Furthermore, if the client is in a hurry, it can generate a quick and concise response. This allows the system to provide appropriate responses tailored to the client's emotions.

[0104] The question-answering system can select the most appropriate response method by referencing the client's past response history when generating answers to client questions. For example, it can prioritize response methods that the client has preferred in the past. It can also analyze the client's past response history and suggest the most appropriate response method. Furthermore, it can adjust the tone and expression of the response based on the client's past response history. This allows the system to provide appropriate responses tailored to the client's past response history.

[0105] The question handling system can filter responses to client questions based on the client's current projects and areas of interest. For example, it can prioritize questions related to the client's current projects. It can also filter relevant questions based on the client's areas of interest. Furthermore, it can determine the priority of questions based on the client's current projects and areas of interest. This allows the system to prioritize highly relevant questions by filtering them based on the client's current projects and areas of interest.

[0106] The question-answering system can estimate the client's emotions when generating responses to their questions and adjust the timing of response delivery based on those emotions. For example, if the client is stressed, the response can be sent quickly. If the client is relaxed, the response can be sent at a normal time. Furthermore, if the client is in a hurry, the response can be sent as a top priority. This ensures that responses are delivered at the appropriate time according to the client's emotions.

[0107] The question-answering system can estimate the client's emotions when generating responses to their questions and prioritize responses based on those emotions. For example, if the client is stressed, the response priority can be set higher. If the client is relaxed, the response priority can be set to normal. Furthermore, if the client is in a hurry, the response priority can be set to the highest priority. This allows the system to generate responses in an appropriate order according to the client's emotions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives client inquiries. Client inquiries can be in text format, audio format, or on specific topics. The reception desk can accept text-based inquiries, and can also use speech recognition technology to convert audio inquiries into text format. It can also filter and accept inquiries on specific topics. Step 2: The generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. The generation unit analyzes the questions using natural language processing technology and machine learning algorithms, extracts appropriate information from a pre-configured database, and generates responses. Step 3: The transmitting unit sends the response generated by the generating unit to the client. The transmitting unit can send the response in real time, or it can send the response with minimal delay. It can also select the optimal transmission method depending on the client's situation and send the response. Step 4: The sharing unit shares the responses generated by the generation unit with the responsible person. The sharing unit can share information using email, chat, dashboards, etc.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the receiving unit, generation unit, transmission unit, and sharing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the receiving unit is implemented by the receiving device 38 of the smart device 14 and receives a question from the client. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question and generates a response. The transmission unit is implemented by the communication I / F 44 of the smart device 14 and sends the generated response to the client. The sharing unit is implemented by the communication I / F 26 of the data processing unit 12 and shares the generated response with the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, and sharing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the client's question. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the question and generates a response. The transmission unit is implemented, for example, by the communication I / F 44 of the smart glasses 214 and transmits the generated response to the client. The sharing unit is implemented, for example, by the communication I / F 26 of the data processing unit 12 and shares the generated response with the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, and sharing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the client's question. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the question and generates a response. The transmission unit is implemented by the communication I / F 44 of the headset terminal 314 and transmits the generated response to the client. The sharing unit is implemented by the communication I / F 26 of the data processing unit 12 and shares the generated response with the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, generation unit, transmission unit, and sharing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives questions from the client. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the questions and generates responses. The transmission unit is implemented, for example, by the communication I / F 44 of the robot 414 and transmits the generated responses to the client. The sharing unit is implemented, for example, by the communication I / F 26 of the data processing unit 12 and shares the generated responses with the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A reception desk that handles client inquiries, A generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. A transmission unit that sends the response generated by the generation unit to the client, The system includes a sharing unit that shares the response generated by the generation unit with the person in charge. A system characterized by the following features. (Note 2) The generating unit is Extract appropriate information from a pre-configured database and generate responses to client questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned shared portion is, Share the client's questions and the AI's responses with the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Analyze the client's questions to understand their interests and needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmitting unit Send the generated response to the client quickly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the client's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving inquiries, we filter them based on the client's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving questions, we prioritize questions that are highly relevant, taking into account the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, we analyze the client's social media activity and gather relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a response, adjust the level of detail in the response based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a response, apply a different response algorithm depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating responses, the system prioritizes responses based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating responses, the order of responses is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmitting unit It estimates the user's emotions and adjusts the timing of sending responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit When sending a reply, the system will refer to the client's past response history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmitting unit When sending a reply, customize the sending method based on the client's current status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmitting unit The system estimates the user's emotions and determines the priority of sending responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmitting unit When sending a reply, the system selects the optimal sending method considering the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmitting unit When sending a reply, we analyze the client's social media activity and suggest a suitable method of sending the reply. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, It estimates user emotions and adjusts the way information is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, When sharing information, refer to the past interaction history of the person in charge to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing information, customize the sharing method based on the current workload of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, When sharing information, the optimal sharing method will be selected, taking into account the geographical location of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing information, we analyze the social media activity of the person in charge and propose sharing methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles client inquiries, A generation unit analyzes the questions received by the reception unit and generates responses based on data that can be shared externally without issue. A transmission unit that sends the response generated by the generation unit to the client, The system includes a sharing unit that shares the response generated by the generation unit with the person in charge. A system characterized by the following features.

2. The generating unit is Extract appropriate information from a pre-configured database and generate responses to client questions. The system according to feature 1.

3. The aforementioned shared portion is, Share the client's questions and the AI's responses with the person in charge. The system according to feature 1.

4. The generating unit is Analyze the client's questions to understand their interests and needs. The system according to feature 1.

5. The aforementioned transmitting unit Send the generated response to the client quickly. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the client's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

8. The aforementioned reception unit is When receiving inquiries, we filter them based on the client's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving questions, we prioritize questions that are highly relevant, taking into account the client's geographical location. The system according to feature 1.

Images