System

Abstract

To provide a system that quickly and efficiently responds to inquiries from sales representatives.SOLUTION: A system according to an embodiment comprises: a reception unit; an analysis unit; a generation unit; and a provision unit. The reception unit receives inquiries from sales representatives. The analysis unit analyzes the inquiries received by the reception unit. The generation unit generates responses on the basis of the inquiries analyzed by the analysis unit. The provision unit provides the responses generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to quickly and efficiently respond to a large number of inquiries from sales representatives, and there is room for improvement in business efficiency and cost reduction.

[0005] The system according to the embodiment aims to quickly and efficiently respond to inquiries from sales representatives. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives inquiries from sales representatives. The analysis unit analyzes the inquiries received by the reception unit. The generation unit generates an answer based on the inquiries analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can quickly and efficiently respond to inquiries from sales representatives. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for efficiently resolving inquiries from sales representatives by utilizing in-house knowledge. In this system, a sales representative inputs an inquiry about a product, a generation AI analyzes the inquiry, generates an optimal answer from an in-house knowledge database, and provides it to the sales representative. For example, a sales representative inputs a specific question such as "What is the price of this product?" or "What is the stock status of this product?" This information is input to the generation AI. Next, the generation AI analyzes the input inquiry. The generation AI understands the content of the inquiry and searches for an optimal answer from the in-house knowledge database. For example, in response to an inquiry such as "What is the price of this product?", the generation AI searches the database for price information of the corresponding product and generates an answer. The generated answer is provided to the sales representative. For example, a specific answer such as "The price of this product is 1000 yen" is provided. This allows the sales representative to quickly obtain an answer to the inquiry. This mechanism allows the inquiry to be resolved without human intervention. The sales representative can quickly obtain an answer by utilizing the generation AI without having to inquire with a marketing representative. This realizes business efficiency and cost reduction. For example, by automatically processing 150,000 inquiries per month, it is expected that the need for human resources will be reduced. This allows the system to efficiently resolve inquiries from sales representatives, improving work efficiency and reducing costs.

[0029] The inquiry solving system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a sales representative. The inquiry from the sales representative includes, for example, a text format, a voice format, an image format, and the like, but is not limited to such examples. The reception unit receives, for example, an inquiry in a text format. The reception unit can also receive an inquiry in a voice format. Furthermore, the reception unit can also receive an inquiry in an image format. For example, the reception unit receives text input by a sales representative. An inquiry in a voice format is converted into text using a voice recognition technology. An inquiry in an image format is analyzed using an image recognition technology. The analysis unit uses a generation AI to analyze the inquiry received by the reception unit. The analysis is performed, for example, using keyword extraction or natural language processing, but is not limited to such examples. For example, the analysis unit analyzes the inquiry content using a keyword extraction technology. The analysis unit can also analyze the inquiry content using a natural language processing technology. Furthermore, the analysis unit can analyze the inquiry content using grammatical analysis or semantic analysis. The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed, for example, by searching for an optimal answer from an in-house knowledge database, but is not limited to such an example. For example, the generation unit searches for relevant information from an in-house knowledge database to generate an answer. The generation unit can also generate an answer by referring to past inquiry history and similar inquiries. Furthermore, the generation unit has a function of being supervised by a marketing person. The provision unit provides the answer generated by the generation unit. The provision is performed, for example, by means of email or chat, but is not limited to such an example. For example, the provision unit provides the generated answer by email. The provision unit can also provide the generated answer by chat. Furthermore, the provision unit can display the generated answer on a dashboard. As a result, the inquiry resolution system according to the embodiment can efficiently resolve inquiries from sales representatives, and achieve business efficiency and cost reduction.

[0030] The reception unit receives inquiries from sales representatives. Inquiries from sales representatives include, but are not limited to, for example, text format, voice format, and image format. The reception unit receives, for example, inquiries in text format. The reception unit can also receive inquiries in voice format. Furthermore, the reception unit can also receive inquiries in image format. For example, the reception unit receives text input by a sales representative. Voice format inquiries are converted into text using voice recognition technology. Image format inquiries are analyzed using image recognition technology. The reception unit has a sophisticated interface in order to efficiently process inquiries in these various formats. For example, text format inquiries are received through a dedicated input form or chatbot and are stored in a database in real time. Voice format inquiries are converted into text with high accuracy using a voice recognition engine, and are further converted into clear text data using noise removal and voice filtering technology. Image format inquiries are extracted from text and important information in the image using OCR (optical character recognition) technology and image analysis algorithms. This allows the reception unit to respond to various inquiry formats from sales representatives and quickly and accurately collect data. Furthermore, the reception unit has a function for automatically determining the priority and urgency of inquiries, allowing it to quickly process important inquiries. For example, inquiries containing specific keywords or phrases are determined to have high priority and are immediately sent to the analysis unit. This allows the reception unit to quickly respond to the needs of sales representatives and achieve efficient inquiry processing.

[0031] The analysis unit uses the generation AI to analyze the inquiry received by the reception unit. The analysis is performed, for example, using keyword extraction and natural language processing, but is not limited to such examples. For example, the analysis unit analyzes the inquiry content using keyword extraction technology. The analysis unit can also analyze the inquiry content using natural language processing technology. Furthermore, the analysis unit can analyze the inquiry content using grammatical analysis and semantic analysis. The analysis unit performs a deep analysis of the inquiry content using the generation AI. Specifically, the generation AI grammatically analyzes the inquiry content and understands the structure and meaning of the sentence. For example, the generation AI identifies the subject, predicate, and object in the sentence and analyzes the relationship between them. In addition, the generation AI refers to past inquiry data and related information in order to understand the context and accurately grasp the intention of the inquiry. Furthermore, the generation AI understands technical terms and industry-specific expressions included in the inquiry content and performs appropriate analysis. For example, it recognizes specific product names and service names used by sales representatives and extracts information related to them. The analysis unit classifies the inquiry content based on these analysis results and provides basic data for generating appropriate answers. Furthermore, the analysis unit can also perform sentiment analysis of the inquiry content to grasp the emotional state of the sales representative. For example, it can analyze positive and negative expressions contained in the inquiry content to evaluate the satisfaction or dissatisfaction of the sales representative. This allows the analysis unit to analyze the inquiry content from multiple angles and provide high-quality data for the generation unit to generate the optimal answer.

[0032] The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed, for example, by searching for an optimal answer from an internal knowledge database, but is not limited to such an example. For example, the generation unit searches for relevant information from an internal knowledge database to generate an answer. The generation unit can also generate an answer by referring to past inquiry history and similar inquiries. Furthermore, the generation unit has a function to be supervised by a marketing staff. The generation unit uses the generation AI to generate an optimal answer based on data provided by the analysis unit. Specifically, the generation AI accesses an internal knowledge database and quickly searches for related information. For example, when an inquiry is made about a specific product, the generation AI searches technical documents and FAQs about the product to generate an optimal answer. Furthermore, the generation AI refers to past inquiry history and generates a new answer based on answers to similar inquiries. This allows the generation unit to generate answers quickly and accurately. Furthermore, the generation unit has a function to be supervised by a marketing staff and checks the quality of the generated answers. For example, the marketing staff reviews the answers generated by the generation AI and makes corrections and supplements as necessary. This enables the generation unit to provide high-quality answers. Furthermore, the generation unit has a function for evaluating the effectiveness of the generated answers and continuously improving them. For example, by collecting feedback from sales representatives and improving the generation AI's algorithm, it is possible to generate more accurate answers. This enables the generation unit to always provide high-quality answers based on the latest information, improving the satisfaction of sales representatives.

[0033] The providing unit provides the answer generated by the generating unit. The answer is provided by, for example, means such as email or chat, but is not limited to such examples. For example, the providing unit provides the generated answer by email. The providing unit can also provide the generated answer by chat. Furthermore, the providing unit can display the generated answer on a dashboard. The providing unit has various means for quickly and effectively providing the answer generated by the generating unit to the sales representative. For example, the providing unit provides the answer by email by automatically sending the generated answer to an email address specified by the sales representative. The email can include not only the answer content but also related links and attachments. The providing unit provides the answer in real time to a chat platform used by the sales representative. By utilizing a chatbot, the sales representative can receive the answer immediately. Furthermore, the providing unit has a function of displaying the generated answer on a dashboard, so that the sales representative can access it at any time. The dashboard also displays past inquiry history and answers, allowing the sales representative to quickly search for the information they need. The providing unit can provide the answer to the sales representative in an optimal manner by combining these means. Furthermore, the providing unit has a function of evaluating the effectiveness of the provided answer and continuously improving it. For example, feedback from sales representatives is collected and the method of providing the information and the content of the responses are improved. This allows the department to respond quickly and accurately to the needs of sales representatives and improve the efficiency of solving inquiries.

[0034] The generation unit can analyze the inquiry content using the generation AI and search for an appropriate answer from an internal knowledge database. The generation unit, for example, analyzes the inquiry content using the generation AI. The generation AI analyzes the inquiry content using, for example, a text generation AI (for example, GPT or Gemini). The generation unit also searches for an appropriate answer from an internal knowledge database. For example, the generation unit searches for relevant information from a database based on the inquiry content and generates an answer. In this way, by using the generation AI, an optimal answer can be generated quickly. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the inquiry content to the generation AI and cause the generation AI to generate an appropriate answer.

[0035] The generation unit can refer to past inquiry history and similar inquiries. The generation unit, for example, refers to past inquiry history. The past inquiry history includes, for example, text format, voice format, image format, and the like, but is not limited to such examples. For example, the generation unit refers to past inquiry history in text format. The generation unit can also refer to past inquiry history in voice format. Furthermore, the generation unit can also refer to past inquiry history in image format. The generation unit, for example, searches for similar inquiries based on the past inquiry history. As a result, a more appropriate answer can be generated by referring to the past inquiry history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past inquiry history to the generation AI and cause the generation AI to generate an appropriate answer.

[0036] The providing unit can provide the generated answer by means of email or chat. For example, the providing unit provides the generated answer by email. For example, the email is sent to the email address of the sales representative. The providing unit can also provide the generated answer by chat. For example, a specific chat app is used for chat. In this way, the generated answer is provided by an appropriate means, so that the sales representative can quickly obtain the answer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to AI and cause the AI ​​to provide the answer by email or chat.

[0037] The analysis unit can use keyword extraction or natural language processing. The analysis unit analyzes the inquiry content using, for example, a keyword extraction technique. Keyword extraction includes, for example, TF-IDF (Term Frequency-Inverse Document Frequency) and co-occurrence network analysis. The analysis unit can also analyze the inquiry content using a natural language processing technique. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. As a result, the inquiry content can be accurately analyzed using keyword extraction and natural language processing. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the inquiry content to a generation AI and cause the generation AI to execute keyword extraction and natural language processing.

[0038] The generation unit may include a function for supervision by a marketing representative. The generation unit may include, for example, a system for a marketing representative to review the generated answers. The review system may include, for example, a function for evaluating the quality of the generated answers. The generation unit may also build a feedback loop so that the marketing representative can provide feedback on the generated answers. This allows the marketing representative to supervise and ensure the quality of the generated answers. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit may provide the generated answers to the marketing representative and have the AI ​​perform the review and feedback.

[0039] The reception unit can analyze the sales representative's past inquiry history and select the optimal reception method. For example, the reception unit can automatically display the inquiry contents that the sales representative frequently made in the past as candidates. The reception unit can also preferentially suggest the input method (voice, text, etc.) that the sales representative used in the past. Furthermore, the reception unit can predict and suggest the inquiry contents to be used in a specific time period from the sales representative's past inquiry history. In this way, the optimal reception method can be provided by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past inquiry history to a generation AI and cause the generation AI to select the optimal reception method.

[0040] The reception unit can perform filtering based on the current business situation and areas of interest of the sales representative when receiving an inquiry. For example, the reception unit preferentially receives inquiries related to a project that the sales representative is currently working on. The reception unit can also filter related inquiries based on the areas of interest of the sales representative. Furthermore, inquiries with a high degree of urgency can be preferentially received according to the business situation of the sales representative. This makes it possible to more appropriately receive inquiries by filtering based on the business situation and areas of interest of the sales representative. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input data on the business situation and areas of interest of the sales representative to the generation AI and have the generation AI perform filtering.

[0041] The reception unit can preferentially receive inquiries with high relevance based on the geographical location information of the sales representative when receiving an inquiry. For example, when the sales representative is in a specific area, the reception unit preferentially receives inquiries related to that area. In addition, when the sales representative is on the move, the reception unit can also preferentially receive related inquiries based on the current location. Furthermore, when the sales representative frequently visits a specific area, the reception unit can also preferentially receive inquiries related to that area. In this way, by taking the geographical location information into consideration, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the sales representative to the generation AI and cause the generation AI to select inquiries with high relevance.

[0042] The reception unit can analyze the social media activity of the sales representative when receiving an inquiry and receive related inquiries. For example, the reception unit preferentially receives inquiries related to a product mentioned by the sales representative on social media. The reception unit can also receive inquiries related to products of high interest from the social media activity of the sales representative. Furthermore, the reception unit can also preferentially receive inquiries related to companies and brands that the sales representative follows on social media. In this way, by analyzing social media activity, related inquiries can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the sales representative to the generation AI and cause the generation AI to select related inquiries.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, the analysis unit performs a detailed analysis for an inquiry with a high level of importance. Also, the analysis unit can perform a concise analysis for an inquiry with a low level of importance. Furthermore, the depth and scope of the analysis can be adjusted according to the importance. As a result, an appropriate analysis result can be provided by adjusting the level of detail of the analysis based on the importance of the inquiry. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply an algorithm for analyzing price information to an inquiry about price. Also, the analysis unit can apply an algorithm for analyzing inventory information to an inquiry about inventory status. Furthermore, the analysis unit can apply an algorithm for analyzing product information to an inquiry about product specifications. In this way, an accurate analysis result can be provided by applying an appropriate analysis algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the analysis priority based on the time of submission of the inquiry during analysis. For example, the analysis unit prioritizes analysis of the most recently submitted inquiry. Also, inquiries submitted earlier can be postponed. Furthermore, the analysis priority can be dynamically adjusted according to the submission time. In this way, by determining the analysis priority based on the time of submission of the inquiry, appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the queries during analysis. For example, the analysis unit prioritizes analysis of queries with high relevance. Also, queries with low relevance can be postponed. Furthermore, the order of analysis can be dynamically adjusted according to the relevance. As a result, appropriate analysis results can be provided by adjusting the order of analysis based on the relevance of the queries. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the answer to be generated based on the importance of the inquiry at the time of generation. For example, the generation unit generates a detailed answer for an inquiry of high importance. Also, for an inquiry of low importance, the generation unit can generate a concise answer. Furthermore, the depth and scope of the answer can be adjusted according to the importance. As a result, an appropriate answer can be provided by adjusting the level of detail of the answer based on the importance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0048] The generation unit can apply different generation algorithms depending on the category of the inquiry at the time of generation. For example, the generation unit applies an algorithm for generating price information to an inquiry regarding price. Also, the generation unit can apply an algorithm for generating inventory information to an inquiry regarding stock status. Furthermore, the generation unit can apply an algorithm for generating product information to an inquiry regarding product specifications. In this way, an accurate answer can be provided by applying an appropriate generation algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0049] The generation unit can determine the priority of the answers to be generated based on the submission time of the inquiry at the time of generation. For example, the generation unit can generate answers preferentially for inquiries submitted recently. Also, inquiries submitted earlier can be postponed. Furthermore, the priority of the answers can be dynamically adjusted according to the submission time. In this way, it is possible to provide an appropriate answer by determining the priority of the answers based on the submission time of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the priority of the answers.

[0050] The generation unit can adjust the order of answers to be generated based on the relevance of the inquiry at the time of generation. For example, the generation unit generates answers preferentially for inquiries with high relevance. Also, inquiries with low relevance can be postponed. Furthermore, the order of answers can be dynamically adjusted according to the relevance. In this way, an appropriate answer can be provided by adjusting the order of answers based on the relevance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the inquiry to the generation AI and cause the generation AI to adjust the order of the answers.

[0051] The providing unit can select the optimal display method by referring to the past operation history of the sales representative at the time of providing. For example, the providing unit preferentially provides a display method that the sales representative has used in the past. The providing unit can also suggest the optimal display method from the past operation history of the sales representative. Furthermore, a customized display method can be provided based on the display method that the sales representative has used in the past. In this way, the optimal display method can be provided by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the past operation history data of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0052] The providing unit can select the optimal display method by considering the device information of the sales representative at the time of providing. For example, if the sales representative uses a smartphone, the providing unit can provide a display method that matches the screen size. In addition, if the sales representative uses a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the sales representative uses a desktop, the providing unit can provide a display method that includes detailed information. In this way, the optimal display method can be provided by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the device information of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the sales representative's past inquiry history and select the optimal reception method. For example, the contents of inquiries frequently made by the sales representative in the past are automatically displayed as candidates. Also, the reception unit can preferentially suggest input methods (voice, text, etc.) that the sales representative has used in the past. Furthermore, the reception unit can predict and suggest inquiry contents to be used in a specific time period from the sales representative's past inquiry history. In this way, the reception unit can provide the optimal reception method by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past inquiry history into a generation AI and cause the generation AI to select the optimal reception method.

[0055] The generation unit can adjust the level of detail of the answer to be generated based on the importance of the inquiry at the time of generation. For example, a detailed answer can be generated for an inquiry of high importance. Also, a concise answer can be generated for an inquiry of low importance. Furthermore, the depth and scope of the answer can be adjusted according to the importance. As a result, an appropriate answer can be provided by adjusting the level of detail of the answer based on the importance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0056] The reception unit can preferentially receive inquiries with high relevance based on the geographical location information of the sales representative. For example, when the sales representative is in a specific area, inquiries related to that area are preferentially received. Also, when the sales representative is on the move, related inquiries can be preferentially received based on the current location. Furthermore, when the sales representative frequently visits a specific area, inquiries related to that area can be preferentially received. In this way, by taking the geographical location information into consideration, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the sales representative to the generation AI and cause the generation AI to select inquiries with high relevance.

[0057] During the analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, an algorithm for analyzing price information can be applied to an inquiry regarding price. Also, an algorithm for analyzing inventory information can be applied to an inquiry regarding inventory status. Furthermore, an algorithm for analyzing product information can be applied to an inquiry regarding product specifications. In this way, an accurate analysis result can be provided by applying an appropriate analysis algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0058] The providing unit can select the optimal display method by considering the device information of the sales representative at the time of providing. For example, if the sales representative uses a smartphone, a display method that matches the screen size can be provided. Also, if the sales representative uses a tablet, a display method optimized for a large screen can be provided. Furthermore, if the sales representative uses a desktop, a display method including detailed information can be provided. In this way, the optimal display method can be provided by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0059] The analysis unit can determine the analysis priority based on the time of submission of the inquiry during analysis. For example, the most recently submitted inquiry is given priority for analysis. Also, inquiries submitted earlier can be postponed. Furthermore, the analysis priority can be dynamically adjusted according to the submission time. In this way, by determining the analysis priority based on the time of submission of the inquiry, appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the analysis priority.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit receives an inquiry from a sales representative. The inquiry from the sales representative may be in a text format, a voice format, an image format, or the like. For example, the reception unit receives text entered by the sales representative. An inquiry in voice format is converted into text using a voice recognition technology, and an inquiry in image format is analyzed using an image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit. The analysis is performed using keyword extraction, natural language processing, grammatical analysis, and semantic analysis. Step 3: The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed by searching the in-house knowledge database for the best answer. Answers can also be generated by referring to past inquiry history or similar inquiries. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit provides the answer by email, chat, display on the dashboard, or other means.

[0062] (Example 2) The system according to the embodiment of the present invention is a system for efficiently resolving inquiries from sales representatives by utilizing in-house knowledge. In this system, a sales representative inputs an inquiry about a product, a generation AI analyzes the inquiry, generates an optimal answer from an in-house knowledge database, and provides it to the sales representative. For example, a sales representative inputs a specific question such as "What is the price of this product?" or "What is the stock status of this product?" This information is input to the generation AI. Next, the generation AI analyzes the input inquiry. The generation AI understands the content of the inquiry and searches for an optimal answer from the in-house knowledge database. For example, in response to an inquiry such as "What is the price of this product?", the generation AI searches the database for price information of the corresponding product and generates an answer. The generated answer is provided to the sales representative. For example, a specific answer such as "The price of this product is 1000 yen" is provided. This allows the sales representative to quickly obtain an answer to the inquiry. This mechanism allows the inquiry to be resolved without human intervention. The sales representative can quickly obtain an answer by utilizing the generation AI without having to inquire with a marketing representative. This realizes business efficiency and cost reduction. For example, by automatically processing 150,000 inquiries per month, it is expected that the need for human resources will be reduced. This allows the system to efficiently resolve inquiries from sales representatives, improving work efficiency and reducing costs.

[0063] The inquiry solving system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an inquiry from a sales representative. The inquiry from the sales representative includes, for example, a text format, a voice format, an image format, and the like, but is not limited to such examples. The reception unit receives, for example, an inquiry in a text format. The reception unit can also receive an inquiry in a voice format. Furthermore, the reception unit can also receive an inquiry in an image format. For example, the reception unit receives text input by a sales representative. An inquiry in a voice format is converted into text using a voice recognition technology. An inquiry in an image format is analyzed using an image recognition technology. The analysis unit uses a generation AI to analyze the inquiry received by the reception unit. The analysis is performed, for example, using keyword extraction or natural language processing, but is not limited to such examples. For example, the analysis unit analyzes the inquiry content using a keyword extraction technology. The analysis unit can also analyze the inquiry content using a natural language processing technology. Furthermore, the analysis unit can analyze the inquiry content using grammatical analysis or semantic analysis. The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed, for example, by searching for an optimal answer from an in-house knowledge database, but is not limited to such an example. For example, the generation unit searches for relevant information from an in-house knowledge database to generate an answer. The generation unit can also generate an answer by referring to past inquiry history and similar inquiries. Furthermore, the generation unit has a function of being supervised by a marketing person. The provision unit provides the answer generated by the generation unit. The provision is performed, for example, by means of email or chat, but is not limited to such an example. For example, the provision unit provides the generated answer by email. The provision unit can also provide the generated answer by chat. Furthermore, the provision unit can display the generated answer on a dashboard. As a result, the inquiry resolution system according to the embodiment can efficiently resolve inquiries from sales representatives, and achieve business efficiency and cost reduction.

[0064] The reception unit receives inquiries from sales representatives. Inquiries from sales representatives include, but are not limited to, for example, text format, voice format, and image format. The reception unit receives, for example, inquiries in text format. The reception unit can also receive inquiries in voice format. Furthermore, the reception unit can also receive inquiries in image format. For example, the reception unit receives text input by a sales representative. Voice format inquiries are converted into text using voice recognition technology. Image format inquiries are analyzed using image recognition technology. The reception unit has a sophisticated interface in order to efficiently process inquiries in these various formats. For example, text format inquiries are received through a dedicated input form or chatbot and are stored in a database in real time. Voice format inquiries are converted into text with high accuracy using a voice recognition engine, and are further converted into clear text data using noise removal and voice filtering technology. Image format inquiries are extracted from text and important information in the image using OCR (optical character recognition) technology and image analysis algorithms. This allows the reception unit to respond to various inquiry formats from sales representatives and quickly and accurately collect data. Furthermore, the reception unit has a function for automatically determining the priority and urgency of inquiries, allowing it to quickly process important inquiries. For example, inquiries containing specific keywords or phrases are determined to have high priority and are immediately sent to the analysis unit. This allows the reception unit to quickly respond to the needs of sales representatives and achieve efficient inquiry processing.

[0065] The analysis unit uses the generation AI to analyze the inquiry received by the reception unit. The analysis is performed, for example, using keyword extraction and natural language processing, but is not limited to such examples. For example, the analysis unit analyzes the inquiry content using keyword extraction technology. The analysis unit can also analyze the inquiry content using natural language processing technology. Furthermore, the analysis unit can analyze the inquiry content using grammatical analysis and semantic analysis. The analysis unit performs a deep analysis of the inquiry content using the generation AI. Specifically, the generation AI grammatically analyzes the inquiry content and understands the structure and meaning of the sentence. For example, the generation AI identifies the subject, predicate, and object in the sentence and analyzes the relationship between them. In addition, the generation AI refers to past inquiry data and related information in order to understand the context and accurately grasp the intention of the inquiry. Furthermore, the generation AI understands technical terms and industry-specific expressions included in the inquiry content and performs appropriate analysis. For example, it recognizes specific product names and service names used by sales representatives and extracts information related to them. The analysis unit classifies the inquiry content based on these analysis results and provides basic data for generating appropriate answers. Furthermore, the analysis unit can also perform sentiment analysis of the inquiry content to grasp the emotional state of the sales representative. For example, it can analyze positive and negative expressions contained in the inquiry content to evaluate the satisfaction or dissatisfaction of the sales representative. This allows the analysis unit to analyze the inquiry content from multiple angles and provide high-quality data for the generation unit to generate the optimal answer.

[0066] The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed, for example, by searching for an optimal answer from an internal knowledge database, but is not limited to such an example. For example, the generation unit searches for relevant information from an internal knowledge database to generate an answer. The generation unit can also generate an answer by referring to past inquiry history and similar inquiries. Furthermore, the generation unit has a function to be supervised by a marketing staff. The generation unit uses the generation AI to generate an optimal answer based on data provided by the analysis unit. Specifically, the generation AI accesses an internal knowledge database and quickly searches for related information. For example, when an inquiry is made about a specific product, the generation AI searches technical documents and FAQs about the product to generate an optimal answer. Furthermore, the generation AI refers to past inquiry history and generates a new answer based on answers to similar inquiries. This allows the generation unit to generate answers quickly and accurately. Furthermore, the generation unit has a function to be supervised by a marketing staff and checks the quality of the generated answers. For example, the marketing staff reviews the answers generated by the generation AI and makes corrections and supplements as necessary. This enables the generation unit to provide high-quality answers. Furthermore, the generation unit has a function for evaluating the effectiveness of the generated answers and continuously improving them. For example, by collecting feedback from sales representatives and improving the generation AI's algorithm, it is possible to generate more accurate answers. This enables the generation unit to always provide high-quality answers based on the latest information, improving the satisfaction of sales representatives.

[0067] The providing unit provides the answer generated by the generating unit. The answer is provided by, for example, means such as email or chat, but is not limited to such examples. For example, the providing unit provides the generated answer by email. The providing unit can also provide the generated answer by chat. Furthermore, the providing unit can display the generated answer on a dashboard. The providing unit has various means for quickly and effectively providing the answer generated by the generating unit to the sales representative. For example, the providing unit provides the answer by email by automatically sending the generated answer to an email address specified by the sales representative. The email can include not only the answer content but also related links and attachments. The providing unit provides the answer in real time to a chat platform used by the sales representative. By utilizing a chatbot, the sales representative can receive the answer immediately. Furthermore, the providing unit has a function of displaying the generated answer on a dashboard, so that the sales representative can access it at any time. The dashboard also displays past inquiry history and answers, allowing the sales representative to quickly search for the information they need. The providing unit can provide the answer to the sales representative in an optimal manner by combining these means. Furthermore, the providing unit has a function of evaluating the effectiveness of the provided answer and continuously improving it. For example, feedback from sales representatives is collected and the method of providing the information and the content of the responses are improved. This allows the department to respond quickly and accurately to the needs of sales representatives and improve the efficiency of solving inquiries.

[0068] The generation unit can analyze the inquiry content using the generation AI and search for an appropriate answer from an internal knowledge database. The generation unit, for example, analyzes the inquiry content using the generation AI. The generation AI analyzes the inquiry content using, for example, a text generation AI (for example, GPT or Gemini). The generation unit also searches for an appropriate answer from an internal knowledge database. For example, the generation unit searches for relevant information from a database based on the inquiry content and generates an answer. In this way, by using the generation AI, an optimal answer can be generated quickly. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the inquiry content to the generation AI and cause the generation AI to generate an appropriate answer.

[0069] The generation unit can refer to past inquiry history and similar inquiries. The generation unit, for example, refers to past inquiry history. The past inquiry history includes, for example, text format, voice format, image format, and the like, but is not limited to such examples. For example, the generation unit refers to past inquiry history in text format. The generation unit can also refer to past inquiry history in voice format. Furthermore, the generation unit can also refer to past inquiry history in image format. The generation unit, for example, searches for similar inquiries based on the past inquiry history. As a result, a more appropriate answer can be generated by referring to the past inquiry history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past inquiry history to the generation AI and cause the generation AI to generate an appropriate answer.

[0070] The providing unit can provide the generated answer by means of email or chat. For example, the providing unit provides the generated answer by email. For example, the email is sent to the email address of the sales representative. The providing unit can also provide the generated answer by chat. For example, a specific chat app is used for chat. In this way, the generated answer is provided by an appropriate means, so that the sales representative can quickly obtain the answer. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to AI and cause the AI ​​to provide the answer by email or chat.

[0071] The analysis unit can use keyword extraction or natural language processing. The analysis unit analyzes the inquiry content using, for example, a keyword extraction technique. Keyword extraction includes, for example, TF-IDF (Term Frequency-Inverse Document Frequency) and co-occurrence network analysis. The analysis unit can also analyze the inquiry content using a natural language processing technique. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. As a result, the inquiry content can be accurately analyzed using keyword extraction and natural language processing. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the inquiry content to a generation AI and cause the generation AI to execute keyword extraction and natural language processing.

[0072] The generation unit may include a function for supervision by a marketing representative. The generation unit may include, for example, a system for a marketing representative to review the generated answers. The review system may include, for example, a function for evaluating the quality of the generated answers. The generation unit may also build a feedback loop so that the marketing representative can provide feedback on the generated answers. This allows the marketing representative to supervise and ensure the quality of the generated answers. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit may provide the generated answers to the marketing representative and have the AI ​​perform the review and feedback.

[0073] The reception unit can estimate the emotion of the sales representative and adjust the reception method of the inquiry based on the estimated emotion. For example, when the sales representative is stressed, the reception unit can provide a simple interface and minimize the input procedure. Also, when the sales representative is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the sales representative is in a hurry, voice input can be prioritized so that the inquiry can be input quickly. This allows for more appropriate inquiry reception by adjusting the reception method according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The reception unit can analyze the sales representative's past inquiry history and select the optimal reception method. For example, the reception unit can automatically display the inquiry contents that the sales representative frequently made in the past as candidates. The reception unit can also preferentially suggest the input method (voice, text, etc.) that the sales representative used in the past. Furthermore, the reception unit can predict and suggest the inquiry contents to be used in a specific time period from the sales representative's past inquiry history. In this way, the optimal reception method can be provided by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past inquiry history to a generation AI and cause the generation AI to select the optimal reception method.

[0075] The reception unit can perform filtering based on the current business situation and areas of interest of the sales representative when receiving an inquiry. For example, the reception unit preferentially receives inquiries related to a project that the sales representative is currently working on. The reception unit can also filter related inquiries based on the areas of interest of the sales representative. Furthermore, inquiries with a high degree of urgency can be preferentially received according to the business situation of the sales representative. This makes it possible to more appropriately receive inquiries by filtering based on the business situation and areas of interest of the sales representative. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input data on the business situation and areas of interest of the sales representative to the generation AI and have the generation AI perform filtering.

[0076] The reception unit can estimate the emotion of the sales representative and determine the priority of the inquiries to be received based on the estimated emotion. For example, when the sales representative is stressed, the reception unit prioritizes inquiries with high urgency. Also, when the sales representative is relaxed, the reception unit can process inquiries with normal priority. Furthermore, when the sales representative is in a hurry, the reception unit can prioritize inquiries that require a quick response. This enables more appropriate inquiry processing by determining the priority according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The reception unit can preferentially receive inquiries with high relevance based on the geographical location information of the sales representative when receiving an inquiry. For example, when the sales representative is in a specific area, the reception unit preferentially receives inquiries related to that area. In addition, when the sales representative is on the move, the reception unit can also preferentially receive related inquiries based on the current location. Furthermore, when the sales representative frequently visits a specific area, the reception unit can also preferentially receive inquiries related to that area. In this way, by taking the geographical location information into consideration, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the sales representative to the generation AI and cause the generation AI to select inquiries with high relevance.

[0078] The reception unit can analyze the social media activity of the sales representative when receiving an inquiry and receive related inquiries. For example, the reception unit preferentially receives inquiries related to a product mentioned by the sales representative on social media. The reception unit can also receive inquiries related to products of high interest from the social media activity of the sales representative. Furthermore, the reception unit can also preferentially receive inquiries related to companies and brands that the sales representative follows on social media. In this way, by analyzing social media activity, related inquiries can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the social media activity data of the sales representative to the generation AI and cause the generation AI to select related inquiries.

[0079] The analysis unit can estimate the emotion of the sales representative and adjust the expression method of the analysis based on the estimated emotion. For example, when the sales representative is relaxed, the analysis unit provides a detailed analysis result. Also, when the sales representative is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. Furthermore, when the sales representative is stressed, the analysis unit can provide a visually easy-to-understand analysis result. In this way, by adjusting the expression method of the analysis according to the emotion of the sales representative, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute the emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the inquiry during analysis. For example, the analysis unit performs a detailed analysis for an inquiry with a high level of importance. Also, the analysis unit can perform a concise analysis for an inquiry with a low level of importance. Furthermore, the depth and scope of the analysis can be adjusted according to the importance. As a result, an appropriate analysis result can be provided by adjusting the level of detail of the analysis based on the importance of the inquiry. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the inquiry during analysis. For example, the analysis unit can apply an algorithm for analyzing price information to an inquiry about price. Also, the analysis unit can apply an algorithm for analyzing inventory information to an inquiry about inventory status. Furthermore, the analysis unit can apply an algorithm for analyzing product information to an inquiry about product specifications. In this way, an accurate analysis result can be provided by applying an appropriate analysis algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the emotion of the sales representative and adjust the length of the analysis based on the estimated emotion. For example, when the sales representative is in a hurry, the analysis unit provides a short and to the point analysis result. Also, when the sales representative is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the sales representative is stressed, the analysis unit can provide a visually easy-to-understand analysis result. In this way, by adjusting the length of the analysis according to the emotion of the sales representative, an appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The analysis unit can determine the analysis priority based on the time of submission of the inquiry during analysis. For example, the analysis unit prioritizes analysis of the most recently submitted inquiry. Also, inquiries submitted earlier can be postponed. Furthermore, the analysis priority can be dynamically adjusted according to the submission time. In this way, by determining the analysis priority based on the time of submission of the inquiry, appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the queries during analysis. For example, the analysis unit prioritizes analysis of queries with high relevance. Also, queries with low relevance can be postponed. Furthermore, the order of analysis can be dynamically adjusted according to the relevance. As a result, appropriate analysis results can be provided by adjusting the order of analysis based on the relevance of the queries. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input query relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0085] The generation unit can estimate the emotion of the sales representative and adjust the expression method of the answer to be generated based on the estimated emotion. For example, when the sales representative is relaxed, the generation unit provides a detailed answer. Also, when the sales representative is in a hurry, the generation unit can provide a concise answer that focuses on the main points. Furthermore, when the sales representative is stressed, the generation unit can provide a visually easy-to-understand answer. In this way, by adjusting the expression method of the answer according to the emotion of the sales representative, an appropriate answer can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The generation unit can adjust the level of detail of the answer to be generated based on the importance of the inquiry at the time of generation. For example, the generation unit generates a detailed answer for an inquiry of high importance. Also, for an inquiry of low importance, the generation unit can generate a concise answer. Furthermore, the depth and scope of the answer can be adjusted according to the importance. As a result, an appropriate answer can be provided by adjusting the level of detail of the answer based on the importance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0087] The generation unit can apply different generation algorithms depending on the category of the inquiry at the time of generation. For example, the generation unit applies an algorithm for generating price information to an inquiry regarding price. Also, the generation unit can apply an algorithm for generating inventory information to an inquiry regarding stock status. Furthermore, the generation unit can apply an algorithm for generating product information to an inquiry regarding product specifications. In this way, an accurate answer can be provided by applying an appropriate generation algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0088] The generation unit can estimate the emotion of the sales representative and adjust the length of the answer to be generated based on the estimated emotion. For example, when the sales representative is in a hurry, the generation unit generates a short answer that is to the point. Also, when the sales representative is relaxed, the generation unit can generate a longer answer that includes a detailed explanation. Furthermore, when the sales representative is stressed, the generation unit can generate an answer that is visually easy to understand. In this way, an appropriate answer can be provided by adjusting the length of the answer according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The generation unit can determine the priority of the answers to be generated based on the submission time of the inquiry at the time of generation. For example, the generation unit can generate answers preferentially for inquiries submitted recently. Also, inquiries submitted earlier can be postponed. Furthermore, the priority of the answers can be dynamically adjusted according to the submission time. In this way, it is possible to provide an appropriate answer by determining the priority of the answers based on the submission time of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the priority of the answers.

[0090] The generation unit can adjust the order of answers to be generated based on the relevance of the inquiry at the time of generation. For example, the generation unit generates answers preferentially for inquiries with high relevance. Also, inquiries with low relevance can be postponed. Furthermore, the order of answers can be dynamically adjusted according to the relevance. In this way, an appropriate answer can be provided by adjusting the order of answers based on the relevance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the relevance data of the inquiry to the generation AI and cause the generation AI to adjust the order of the answers.

[0091] The providing unit can estimate the emotion of the sales representative and adjust the display method of the answer to be provided based on the estimated emotion. For example, when the sales representative is nervous, the providing unit can provide a simple and highly visible display method. Also, when the sales representative is relaxed, the providing unit can provide a display method including detailed information. Furthermore, when the sales representative is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, by adjusting the display method according to the emotion of the sales representative, an appropriate answer can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The providing unit can select the optimal display method by referring to the past operation history of the sales representative at the time of providing. For example, the providing unit preferentially provides a display method that the sales representative has used in the past. The providing unit can also suggest the optimal display method from the past operation history of the sales representative. Furthermore, a customized display method can be provided based on the display method that the sales representative has used in the past. In this way, the optimal display method can be provided by referring to the past operation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the past operation history data of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0093] The providing unit can estimate the emotion of the sales representative and adjust the operation procedure of the answer to be provided based on the estimated emotion. For example, when the sales representative is nervous, the providing unit can provide a simple and intuitive operation procedure. Also, when the sales representative is relaxed, the providing unit can provide a detailed operation procedure. Furthermore, when the sales representative is in a hurry, the providing unit can provide a procedure that can be operated quickly. In this way, an appropriate answer can be provided by adjusting the operation procedure according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute the emotion estimation.

[0094] The providing unit can select the optimal display method by considering the device information of the sales representative at the time of providing. For example, if the sales representative uses a smartphone, the providing unit can provide a display method that matches the screen size. In addition, if the sales representative uses a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the sales representative uses a desktop, the providing unit can provide a display method that includes detailed information. In this way, the optimal display method can be provided by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the device information of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The reception unit can analyze the sales representative's past inquiry history and select the optimal reception method. For example, the contents of inquiries frequently made by the sales representative in the past are automatically displayed as candidates. Also, the reception unit can preferentially suggest input methods (voice, text, etc.) that the sales representative has used in the past. Furthermore, the reception unit can predict and suggest inquiry contents to be used in a specific time period from the sales representative's past inquiry history. In this way, the reception unit can provide the optimal reception method by analyzing the past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past inquiry history into a generation AI and cause the generation AI to select the optimal reception method.

[0097] The analysis unit can estimate the emotion of the sales representative and adjust the expression method of the analysis based on the estimated emotion. For example, if the sales representative is relaxed, a detailed analysis result can be provided. Also, if the sales representative is in a hurry, a concise analysis result with the main points can be provided. Furthermore, if the sales representative is stressed, a visually easy-to-understand analysis result can be provided. This allows for more appropriate analysis results to be provided by adjusting the expression method of the analysis according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0098] The generation unit can adjust the level of detail of the answer to be generated based on the importance of the inquiry at the time of generation. For example, a detailed answer can be generated for an inquiry of high importance. Also, a concise answer can be generated for an inquiry of low importance. Furthermore, the depth and scope of the answer can be adjusted according to the importance. As a result, an appropriate answer can be provided by adjusting the level of detail of the answer based on the importance of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance data of the inquiry to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0099] The providing unit can estimate the emotion of the sales representative and adjust the display method of the answer to be provided based on the estimated emotion. For example, if the sales representative is nervous, a simple and highly visible display method can be provided. Also, if the sales representative is relaxed, a display method including detailed information can be provided. Furthermore, if the sales representative is in a hurry, a display method that focuses on the main points can be provided. In this way, an appropriate answer can be provided by adjusting the display method according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0100] The reception unit can preferentially receive inquiries with high relevance based on the geographical location information of the sales representative. For example, when the sales representative is in a specific area, inquiries related to that area are preferentially received. Also, when the sales representative is on the move, related inquiries can be preferentially received based on the current location. Furthermore, when the sales representative frequently visits a specific area, inquiries related to that area can be preferentially received. In this way, by taking the geographical location information into consideration, it is possible to preferentially receive inquiries with high relevance. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the sales representative to the generation AI and cause the generation AI to select inquiries with high relevance.

[0101] During the analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry. For example, an algorithm for analyzing price information can be applied to an inquiry regarding price. Also, an algorithm for analyzing inventory information can be applied to an inquiry regarding inventory status. Furthermore, an algorithm for analyzing product information can be applied to an inquiry regarding product specifications. In this way, an accurate analysis result can be provided by applying an appropriate analysis algorithm depending on the category of the inquiry. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input inquiry category data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0102] The generation unit can estimate the emotion of the sales representative and adjust the expression method of the answer to be generated based on the estimated emotion. For example, if the sales representative is relaxed, a detailed answer can be provided. Also, if the sales representative is in a hurry, a concise answer that focuses on the main points can be provided. Furthermore, if the sales representative is stressed, a visually easy-to-understand answer can be provided. In this way, an appropriate answer can be provided by adjusting the expression method of the answer according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0103] The providing unit can select the optimal display method by considering the device information of the sales representative at the time of providing. For example, if the sales representative uses a smartphone, a display method that matches the screen size can be provided. Also, if the sales representative uses a tablet, a display method optimized for a large screen can be provided. Furthermore, if the sales representative uses a desktop, a display method including detailed information can be provided. In this way, the optimal display method can be provided by considering the device information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the sales representative to the generating AI and cause the generating AI to select the optimal display method.

[0104] The reception unit can estimate the emotion of the sales representative and determine the priority of the inquiries to be received based on the estimated emotion. For example, if the sales representative is stressed, inquiries with high urgency are given priority. Also, if the sales representative is relaxed, inquiries can be processed with normal priority. Furthermore, if the sales representative is in a hurry, inquiries that require a quick response can be processed with priority. This enables more appropriate inquiry processing by determining the priority according to the emotion of the sales representative. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input emotion data of the sales representative to the generation AI and cause the generation AI to execute emotion estimation.

[0105] The analysis unit can determine the analysis priority based on the time of submission of the inquiry during analysis. For example, the most recently submitted inquiry is given priority for analysis. Also, inquiries submitted earlier can be postponed. Furthermore, the analysis priority can be dynamically adjusted according to the submission time. In this way, by determining the analysis priority based on the time of submission of the inquiry, appropriate analysis results can be provided. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the submission time data of the inquiry to the generation AI and cause the generation AI to determine the analysis priority.

[0106] The process flow of the second embodiment will be briefly described below.

[0107] Step 1: The reception unit receives an inquiry from a sales representative. The inquiry from the sales representative may be in a text format, a voice format, an image format, or the like. For example, the reception unit receives text entered by the sales representative. An inquiry in voice format is converted into text using a voice recognition technology, and an inquiry in image format is analyzed using an image recognition technology. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit. The analysis is performed using keyword extraction, natural language processing, grammatical analysis, and semantic analysis. Step 3: The generation unit uses the generation AI to generate an answer based on the inquiry analyzed by the analysis unit. The generation is performed by searching the in-house knowledge database for the best answer. Answers can also be generated by referring to past inquiry history or similar inquiries. Step 4: The providing unit provides the answer generated by the generating unit. The providing unit provides the answer by email, chat, display on the dashboard, or other means.

[0108] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0110] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0111] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives inquiries in text, voice, and image formats from sales representatives. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes inquiries using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal answer from an in-house knowledge database. For example, the provision unit is realized by the output device 40 of the smart device 14, and provides the generated answer by email or chat. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0119] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0122] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0124] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0127] For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and receives inquiries in text, voice, and image formats from sales representatives. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes inquiries using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal answer from an in-house knowledge database. For example, the provision unit is realized by the speaker 240 of the smart glasses 214, and provides the generated answer by email or chat. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0143] For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and receives inquiries in text, voice, and image formats from sales representatives. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes inquiries using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal answer from an in-house knowledge database. For example, the provision unit is realized by the display 343 of the headset terminal 314, and provides the generated answer by email or chat. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] For example, the reception unit is realized by the microphone 238 of the robot 414, and receives inquiries in text, voice, and image formats from sales representatives. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes inquiries using a generation AI. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates an optimal answer from an in-house knowledge database. For example, the provision unit is realized by the speaker 240 of the robot 414, and provides the generated answer by email or chat. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0162] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0166] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0169] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0170] In addition, 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 upon request from the data processing device 12.

[0171] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0172] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0173] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0174] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0175] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0177] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0179] (Appendix 1) A reception desk that accepts inquiries from sales staff; an analysis unit that analyzes the inquiry received by the reception unit; a generation unit that generates an answer based on the inquiry analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; A system characterized by: (Appendix 2) The generation unit is Generative AI analyzes the inquiry and searches the internal knowledge database for the appropriate answer 2. The system of claim 1, (Appendix 3) The generation unit is View past and similar inquiries 2. The system of claim 1, (Appendix 4) The providing unit is Provide generated answers via email or chat 2. The system of claim 1, (Appendix 5) The analysis unit is Use keyword extraction or natural language processing 2. The system of claim 1, (Appendix 6) The generation unit is Provide a function overseen by marketing 2. The system of claim 1, (Appendix 7) The reception unit is Infer a sales rep's emotions and adjust how they handle inquiries based on those emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze sales representatives' past inquiry history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is Filter incoming inquiries based on the sales rep's current work situation and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is Estimate sales rep's sentiment and prioritize incoming tickets based on the estimated sentiment 2. The system of claim 1, (Appendix 11) The reception unit is Prioritize relevant inquiries based on sales rep's geographic location when accepting inquiries 2. The system of claim 1, (Appendix 12) The reception unit is When accepting inquiries, analyze the social media usage of sales representatives and accept related inquiries. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer a sales rep's sentiment and adjust the presentation of the analysis based on that sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the query 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, apply different parsing algorithms depending on the category of the query 2. The system of claim 1, (Appendix 16) The analysis unit is Infer sales rep sentiment and adjust the length of the analysis based on the inferred sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the query was submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During parsing, adjust the order of parsing based on the relevance of the query 2. The system of claim 1, (Appendix 19) The generation unit is Infer a sales rep's sentiment and tailor the way you generate answers based on that sentiment 2. The system of claim 1, (Appendix 20) The generation unit is At generation time, adjust the level of detail of the generated answer based on the importance of the inquiry 2. The system of claim 1, (Appendix 21) The generation unit is During generation, different generation algorithms are applied depending on the category of the query. 2. The system of claim 1, (Appendix 22) The generation unit is Infer a sales rep's sentiment and adjust the length of the answer they generate based on that sentiment 2. The system of claim 1, (Appendix 23) The generation unit is At generation time, prioritize the responses you generate based on when the query was submitted 2. The system of claim 1, (Appendix 24) The generation unit is At generation time, adjust the order of generated answers based on the relevance of the query 2. The system of claim 1, (Appendix 25) The providing unit is Estimate a sales rep's sentiment and adjust how the answers they provide are presented based on that sentiment 2. The system of claim 1, (Appendix 26) The providing unit is When providing the information, select the optimal display method by referring to the sales representative's past operation history. 2. The system of claim 1, (Appendix 27) The providing unit is Infer a sales rep's sentiment and tailor the instructions for the answers they provide based on that sentiment 2. The system of claim 1, (Appendix 28) The providing unit is When providing the information, select the optimal display method taking into account the device information of the sales representative 2. The system of claim 1, [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts inquiries from sales staff; an analysis unit that analyzes the inquiry received by the reception unit; a generation unit that generates an answer based on the inquiry analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; A system characterized by:

2. The generation unit is Analyze the inquiry using generative AI and search for the appropriate answer from the company's knowledge database 2. The system of claim 1 .

3. The generation unit is View past and similar inquiries 2. The system of claim 1 .

4. The providing unit is Provide generated answers via email or chat 2. The system of claim 1 .

5. The analysis unit is Use keyword extraction or natural language processing 2. The system of claim 1 .

6. The generation unit is Provide a function overseen by marketing 2. The system of claim 1 .

7. The reception unit is Infer a sales rep's emotions and adjust how they handle inquiries based on those emotions 2. The system of claim 1 .

8. The reception unit is Analyze sales representatives' past inquiry history and select the appropriate reception method 2. The system of claim 1 .

9. The reception unit is Filter incoming inquiries based on the sales rep's current work situation and areas of interest 2. The system of claim 1 .

10. The reception unit is Estimate sales rep's sentiment and prioritize incoming tickets based on the estimated sentiment 2. The system of claim 1 .

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