system

Abstract

The system according to this embodiment aims to efficiently analyze the needs of client companies and generate and provide optimal proposals. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives requests from customer companies. The analysis unit analyzes the requests received by the reception unit. The generation unit generates proposals based on the requests analyzed by the analysis unit. The provision unit provides the proposals generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently analyze the demands of customer companies and generate optimal proposals.

[0005] The system according to the embodiment aims to efficiently analyze the demands of customer companies and generate and provide optimal proposals.

Means for Solving the Problems

[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 the demands of customer companies. The analysis unit analyzes the demands received by the reception unit. The generation unit generates proposals based on the demands analyzed by the analysis unit. The provision unit provides the proposals generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze the needs of client companies and generate and provide optimal proposals. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The sales support system according to an embodiment of the present invention is a system for improving sales challenges faced by large corporations with many group companies in B2B corporate sales. This sales support system adds a generation AI assistant to the corporate inquiry and quotation forms of each group company, allowing for collaborative consideration of the ambiguous requests of customer companies, rather than relying on conventional simple selection forms. This enables the system to capture the customer company's needs to the fullest extent. The AI-equipped forms will be used across the group, and background product and solution data will also be shared. This reduces the burden on customer companies of repeatedly contacting each company and enables powerful, unified proposals from the group. Next, by sharing sales resources within the group, synergies can be achieved regardless of which company the inquiry is directed to. This allows for mutual support even when a subsidiary alone cannot secure sales resources. Furthermore, the AI ​​analyzes the customer company's proposals, making the strengths and weaknesses of synergies visible and contributing to the strengthening of the group's overall capabilities. As a result, the sales support system can efficiently receive, analyze, generate, and provide proposals for customer companies.

[0029] The sales support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives requests from customer companies. For example, the reception unit receives requests submitted by customer companies through an inquiry form. The analysis unit analyzes the requests received by the reception unit. For example, the analysis unit analyzes the content of the requests using text analysis technology. The generation unit generates proposals based on the requests analyzed by the analysis unit. For example, the generation unit generates specific proposals that meet the customer company's requests using generation AI. The provision unit provides the proposals generated by the generation unit to the customer company. For example, the provision unit provides the generated proposals to the customer company via email or a web portal. This enables the sales support system to efficiently receive, analyze, generate, and provide requests from customer companies.

[0030] The reception department receives requests from client companies. For example, it receives requests submitted by client companies through inquiry forms. Specifically, the reception department can receive requests from client companies through multiple channels, such as inquiry forms on the website, chatbots, and email. The inquiry form includes category selection and free-text fields to make it easy for client companies to enter specific requests and questions. The chatbot uses natural language processing technology to respond to client company questions in real time and forwards detailed requests to the reception department as needed. With email, client companies can freely write their requests, which the reception department receives and processes. This allows the reception department to receive requests from client companies in a variety of ways, improving convenience for client companies. The reception department also has a function to automatically classify received requests and distribute them to the appropriate department or person in charge. For example, by distributing requests to departments such as technical support, sales, and customer service according to their content, a quick and appropriate response can be made. Furthermore, the reception department also has a function to manage the status of received requests and notify client companies of the progress. This allows client companies to check how their requests are being processed in real time, giving them peace of mind.

[0031] The analysis department analyzes requests received by the reception department. For example, the analysis department analyzes the content of requests using text analysis technology. Specifically, it uses natural language processing (NLP) technology to analyze the text data of requests and extract the subject and important keywords of the requests. For example, if a client company submits a request such as "We would like detailed information regarding the introduction of a new product," the analysis department will extract keywords such as "new product," "introduction," and "detailed information," and identify the subject of the request as "provision of information regarding the introduction of a new product." The analysis department can also use scoring algorithms to evaluate the urgency and importance of requests. For example, based on the content of the request and the history of past interactions, it can evaluate the urgency of requests in three stages: high, medium, and low, and prioritize processing requests with high importance. Furthermore, the analysis department builds a database to classify the content of requests and propose appropriate countermeasures. For example, by accumulating past requests and their countermeasures in the database, when a new request is received, it becomes possible to refer to similar requests and their countermeasures to respond quickly and appropriately. This allows the analysis unit to efficiently analyze requests received from the reception unit and accurately understand the content of those requests.

[0032] The generation unit generates proposals based on the requirements analyzed by the analysis unit. For example, the generation unit uses generation AI to generate specific proposals that meet the customer company's needs. Specifically, the generation AI generates the optimal proposal based on the content of the requirements provided by the analysis unit, while referring to past data and case studies. For example, if a customer company requests "detailed information on the introduction of a new product," the generation AI will refer to past new product introduction cases and related technical information to generate a detailed proposal that includes specific introduction procedures, benefits, and costs. The generation AI can use natural language generation (NLG) technology to describe the proposal content in an easy-to-understand yet professional manner. Furthermore, the generation unit has the function to customize the generated proposal to meet the customer company's needs. For example, it optimizes the proposal content by considering the customer company's industry, size, and past transaction history. This allows the generation unit to quickly generate high-quality, specific proposals in response to the customer company's requests. The generation unit also has the function to evaluate the quality of the generated proposals and make corrections or improvements as needed. For example, experts review the proposals generated by the generation AI to confirm the accuracy and appropriateness of the content, thereby guaranteeing the quality of the proposals. This allows the generation unit to provide reliable proposals to client companies.

[0033] The provision department provides the proposals generated by the generation department to client companies. For example, the provision department provides the generated proposals to client companies via email or a web portal. Specifically, the provision department sends the generated proposals to client companies via email for review. It also allows client companies to view and download the proposals online through a web portal. The web portal organizes the proposals by category and provides a search function, enabling client companies to quickly find the information they need. Furthermore, the provision department has a function to collect feedback on the proposals and provide it to the generation department. For example, it provides a feedback form where client companies can post comments and questions about the proposals, and forwards this feedback to the generation department to help improve the proposals. The provision department also has a function to notify client companies of the receipt confirmation and progress of the proposals. This allows client companies to understand the receipt status and progress of the proposals in real time, enabling them to receive proposals with confidence. In addition, the provision department can diversify the methods of providing proposals, enabling flexible responses to meet the needs of client companies. For example, by providing opportunities to directly explain proposals through online meetings or by offering proposals in video format, client companies can receive proposals in a way that is easier to understand. This allows the service department to deliver proposals to client companies quickly and effectively, thereby improving customer satisfaction.

[0034] The generation unit can analyze the client company's requests using generational AI and generate specific proposals. For example, the generation unit can use generational AI to analyze the client company's requests and generate specific proposals. For example, the generation unit can use a natural language generation model to generate proposals that meet the client company's requests. For example, the generation unit can use an image generation model to generate visual proposals that meet the client company's requests. As a result, using generational AI improves the accuracy of converting the client company's requests into specific proposals.

[0035] The service department can provide the generated proposals to client companies. For example, the service department can send the generated proposals to client companies via email. The service department can also provide the generated proposals to client companies via a web portal. The service department can also print the generated proposals and mail them to client companies. This makes it possible to provide the generated proposals to client companies quickly.

[0036] The analysis department can delve deeper into the client company's needs and clarify vague requests. For example, the analysis department can delve deeper into the client company's needs by asking additional questions. For example, the analysis department can clarify vague requests by collecting detailed data. For example, the analysis department can clarify requests by providing concrete examples. By clarifying the client company's vague requests, it becomes possible to make more appropriate proposals.

[0037] The generation unit can generate optimal proposals by utilizing product and solution data within the group. For example, it can generate optimal proposals by referring to the group's product and solution database. It can also generate proposals by integrating data on products and services offered by each company within the group. Furthermore, it can generate proposals that combine multiple products and services according to the client company's needs. This allows for more optimal proposals by leveraging data within the group.

[0038] The service provider can visualize the strengths and weaknesses of synergies based on the generated proposals. For example, the service provider can highlight and display the strengths of synergies within the generated proposals. For example, the service provider can also point out the weaknesses of synergies within the generated proposals and propose improvement plans. For example, the service provider can visually display the strengths and weaknesses of synergies using graphs and charts. By visualizing the strengths and weaknesses of synergies, it becomes possible to strengthen the overall capabilities of the group.

[0039] The reception department can analyze past inquiry history and select the most appropriate reception method. For example, the reception department can propose the most suitable response method based on the types of inquiries that client companies have frequently made in the past. For example, the reception department can select the most suitable response method for a specific time period based on the client company's past inquiry history. For example, the reception department can analyze the client company's past inquiry history and select the most efficient response method. This enables efficient responses by selecting the most appropriate reception method based on past inquiry history.

[0040] The reception department can filter incoming inquiries based on the industry and size of the client company. For example, the reception department can prioritize retrieving relevant information based on the client company's industry. The reception department can also select an appropriate response method based on the client company's size. For example, the reception department can perform optimal filtering based on the client company's industry and size. This enables filtering tailored to the industry and size of the client company.

[0041] The reception department can prioritize obtaining highly relevant information by considering the geographical location of the client company during the reception process. For example, the reception department can prioritize obtaining relevant information based on the client company's location. For example, the reception department can also select the optimal response method based on the client company's geographical location. For example, the reception department can perform optimal filtering by considering the client company's geographical location. This makes it possible to obtain information based on the client company's geographical location.

[0042] The reception department can analyze the client company's social media activities and obtain relevant information upon receiving the request. For example, the reception department can analyze the client company's social media activities and prioritize obtaining relevant information. The reception department can also select the optimal response method based on the client company's social media activities. Furthermore, the reception department can perform optimal filtering, taking into account the client company's social media activities. This enables the acquisition of information based on the client company's social media activities.

[0043] The analysis unit can improve the accuracy of its analysis by referring to past inquiries from client companies. For example, the analysis unit can improve the accuracy of its analysis based on past inquiries from client companies. The analysis unit can also select the optimal analysis method by referring to past inquiries from client companies. For example, the analysis unit can analyze past inquiries from client companies to improve the accuracy of its analysis. This means that the accuracy of the analysis is improved by referring to past inquiries.

[0044] The analysis unit can apply different analysis algorithms depending on the industry and size of the client company during the analysis. For example, the analysis unit can apply the optimal analysis algorithm depending on the industry of the client company. The analysis unit can also select an appropriate analysis algorithm depending on the size of the client company. The analysis unit can also apply different analysis algorithms depending on the industry and size of the client company. This enables analysis tailored to the industry and size of the client company.

[0045] The analysis unit can determine the priority of analyses based on the submission timing of client companies during the analysis process. For example, the analysis unit can determine the optimal analysis priority based on the client company's submission timing. The analysis unit can also adjust the analysis priority, for example, by considering the client company's submission timing. Furthermore, the analysis unit can select the optimal analysis method based on the client company's submission timing. This enables the determination of priorities based on the client company's submission timing.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature from the client company during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature from the client company. The analysis unit can also select the optimal analysis method based on relevant literature from the client company. For example, the analysis unit can improve the accuracy of its analysis by analyzing relevant literature from the client company. Thus, the accuracy of the analysis is improved by referring to relevant literature.

[0047] The generation unit can adjust the level of detail in the proposal based on the importance of the client company's requests when generating the proposal. For example, the generation unit will provide a detailed proposal based on the importance of the client company's requests. The generation unit can also adjust the level of detail in the proposal, taking into consideration the importance of the client company's requests. For example, the generation unit can select the optimal proposal method based on the importance of the client company's requests. This makes it possible to adjust the level of detail in the proposal according to the importance of the client company's requests.

[0048] The generation unit can apply different proposal algorithms depending on the industry and size of the client company when generating proposals. For example, the generation unit can apply the optimal proposal algorithm depending on the industry of the client company. For example, the generation unit can also select an appropriate proposal algorithm depending on the size of the client company. For example, the generation unit can apply different proposal algorithms depending on the industry and size of the client company. This makes it possible to provide proposals tailored to the industry and size of the client company.

[0049] The generation unit can determine the priority of proposals based on the client company's submission timing when generating proposals. For example, the generation unit can determine the optimal proposal priority based on the client company's submission timing. The generation unit can also adjust the proposal priority, for example, taking into account the client company's submission timing. The generation unit can also select the optimal proposal method, for example, based on the client company's submission timing. This makes it possible to determine priorities based on the client company's submission timing.

[0050] The generation unit can adjust the order of proposals based on the relevance of the client companies during proposal generation. For example, the generation unit determines the optimal order of proposals based on the relevance of the client companies. The generation unit can also adjust the order of proposals considering the relevance of the client companies. Furthermore, the generation unit can select the optimal proposal method based on the relevance of the client companies. This enables the adjustment of the proposal order based on the relevance of the client companies.

[0051] The service provider can select the optimal service delivery method by referring to the client company's past proposal history when providing a proposal. For example, the service provider can select the optimal service delivery method based on the client company's past proposal history. The service provider can also select the optimal service delivery method by referring to the client company's past proposal history. For example, the service provider can also select the optimal service delivery method by analyzing the client company's past proposal history. This enables efficient service delivery by selecting the optimal service delivery method based on past proposal history.

[0052] The service provider can customize the delivery method based on the client company's industry and size when providing a proposal. For example, the service provider can select the optimal delivery method based on the client company's industry. The service provider can also select an appropriate delivery method based on the client company's size. Furthermore, the service provider can customize the delivery method based on the client company's industry and size. This allows for the customization of delivery methods to suit the client company's industry and size.

[0053] The service provider can select the optimal delivery method when proposing a solution, taking into account the geographical location information of the client company. For example, the service provider can select the optimal delivery method based on the client company's location. Alternatively, the service provider can select an appropriate delivery method based on the client company's geographical location information. The service provider can also select the optimal delivery method considering the client company's geographical location information. This makes it possible to select a delivery method based on the client company's geographical location information.

[0054] The service provider can analyze the client company's social media activities and propose delivery methods when providing proposals. For example, the service provider can analyze the client company's social media activities and propose the most suitable delivery method. For example, the service provider can select an appropriate delivery method based on the client company's social media activities. For example, the service provider can propose the most suitable delivery method considering the client company's social media activities. This makes it possible to propose delivery methods based on the client company's social media activities.

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

[0056] The reception department can analyze past inquiry history and select the most appropriate reception method. For example, it can propose the most suitable response method based on the types of inquiries a client company has frequently made in the past. It can also select the most suitable response method for a specific time of day based on the client company's past inquiry history. Furthermore, it can analyze the client company's past inquiry history and select the most efficient response method. This allows for efficient responses by selecting the most suitable reception method based on past inquiry history.

[0057] The reception desk can filter incoming requests based on the client company's industry and size. For example, it can prioritize the acquisition of relevant information based on the client company's industry. It can also select the appropriate response method based on the client company's size. Furthermore, it can perform optimal filtering based on the client company's industry and size. This enables filtering tailored to the client company's industry and size.

[0058] The reception department can prioritize retrieving highly relevant information by considering the geographical location of the client company during the reception process. For example, it can prioritize retrieving relevant information based on the client company's location. It can also select the most appropriate response method based on the client company's geographical location. Furthermore, it can perform optimal filtering considering the client company's geographical location. This makes it possible to retrieve information based on the client company's geographical location.

[0059] The reception department can analyze the client company's social media activities and obtain relevant information upon receiving a request. For example, it can analyze the client company's social media activities and prioritize obtaining relevant information. It can also select the optimal response method based on the client company's social media activities. Furthermore, it can perform optimal filtering considering the client company's social media activities. This makes it possible to obtain information based on the client company's social media activities.

[0060] The analysis unit can improve the accuracy of its analysis by referring to past inquiries from client companies. For example, it can improve the accuracy of its analysis based on past inquiries from client companies. It can also select the optimal analysis method by referring to past inquiries from client companies. Furthermore, it can improve the accuracy of its analysis by analyzing past inquiries from client companies. In this way, the accuracy of the analysis is improved by referring to past inquiries.

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

[0062] Step 1: The reception department receives requests from client companies. For example, it receives requests submitted by client companies through inquiry forms. Step 2: The analysis unit analyzes the requests received by the reception unit. For example, it analyzes the content of the requests using text analysis technology. Step 3: The generation unit generates proposals based on the requirements analyzed by the analysis unit. For example, it uses generation AI to generate specific proposals that meet the customer company's requirements. Step 4: The supply unit provides the proposals generated by the generation unit to the client company. For example, the generated proposals are provided to the client company via email or a web portal.

[0063] (Example of form 2) The sales support system according to an embodiment of the present invention is a system for improving sales challenges faced by large corporations with many group companies in B2B corporate sales. This sales support system adds a generation AI assistant to the corporate inquiry and quotation forms of each group company, allowing for collaborative consideration of the ambiguous requests of customer companies, rather than relying on conventional simple selection forms. This enables the system to capture the customer company's needs to the fullest extent. The AI-equipped forms will be used across the group, and background product and solution data will also be shared. This reduces the burden on customer companies of repeatedly contacting each company and enables powerful, unified proposals from the group. Next, by sharing sales resources within the group, synergies can be achieved regardless of which company the inquiry is directed to. This allows for mutual support even when a subsidiary alone cannot secure sales resources. Furthermore, the AI ​​analyzes the customer company's proposals, making the strengths and weaknesses of synergies visible and contributing to the strengthening of the group's overall capabilities. As a result, the sales support system can efficiently receive, analyze, generate, and provide proposals for customer companies.

[0064] The sales support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives requests from customer companies. For example, the reception unit receives requests submitted by customer companies through an inquiry form. The analysis unit analyzes the requests received by the reception unit. For example, the analysis unit analyzes the content of the requests using text analysis technology. The generation unit generates proposals based on the requests analyzed by the analysis unit. For example, the generation unit generates specific proposals that meet the customer company's requests using generation AI. The provision unit provides the proposals generated by the generation unit to the customer company. For example, the provision unit provides the generated proposals to the customer company via email or a web portal. This enables the sales support system to efficiently receive, analyze, generate, and provide requests from customer companies.

[0065] The reception department receives requests from client companies. For example, it receives requests submitted by client companies through inquiry forms. Specifically, the reception department can receive requests from client companies through multiple channels, such as inquiry forms on the website, chatbots, and email. The inquiry form includes category selection and free-text fields to make it easy for client companies to enter specific requests and questions. The chatbot uses natural language processing technology to respond to client company questions in real time and forwards detailed requests to the reception department as needed. With email, client companies can freely write their requests, which the reception department receives and processes. This allows the reception department to receive requests from client companies in a variety of ways, improving convenience for client companies. The reception department also has a function to automatically classify received requests and distribute them to the appropriate department or person in charge. For example, by distributing requests to departments such as technical support, sales, and customer service according to their content, a quick and appropriate response can be made. Furthermore, the reception department also has a function to manage the status of received requests and notify client companies of the progress. This allows client companies to check how their requests are being processed in real time, giving them peace of mind.

[0066] The analysis department analyzes requests received by the reception department. For example, the analysis department analyzes the content of requests using text analysis technology. Specifically, it uses natural language processing (NLP) technology to analyze the text data of requests and extract the subject and important keywords of the requests. For example, if a client company submits a request such as "We would like detailed information regarding the introduction of a new product," the analysis department will extract keywords such as "new product," "introduction," and "detailed information," and identify the subject of the request as "provision of information regarding the introduction of a new product." The analysis department can also use scoring algorithms to evaluate the urgency and importance of requests. For example, based on the content of the request and the history of past interactions, it can evaluate the urgency of requests in three stages: high, medium, and low, and prioritize processing requests with high importance. Furthermore, the analysis department builds a database to classify the content of requests and propose appropriate countermeasures. For example, by accumulating past requests and their countermeasures in the database, when a new request is received, it becomes possible to refer to similar requests and their countermeasures to respond quickly and appropriately. This allows the analysis unit to efficiently analyze requests received from the reception unit and accurately understand the content of those requests.

[0067] The generation unit generates proposals based on the requirements analyzed by the analysis unit. For example, the generation unit uses generation AI to generate specific proposals that meet the customer company's needs. Specifically, the generation AI generates the optimal proposal based on the content of the requirements provided by the analysis unit, while referring to past data and case studies. For example, if a customer company requests "detailed information on the introduction of a new product," the generation AI will refer to past new product introduction cases and related technical information to generate a detailed proposal that includes specific introduction procedures, benefits, and costs. The generation AI can use natural language generation (NLG) technology to describe the proposal content in an easy-to-understand yet professional manner. Furthermore, the generation unit has the function to customize the generated proposal to meet the customer company's needs. For example, it optimizes the proposal content by considering the customer company's industry, size, and past transaction history. This allows the generation unit to quickly generate high-quality, specific proposals in response to the customer company's requests. The generation unit also has the function to evaluate the quality of the generated proposals and make corrections or improvements as needed. For example, experts review the proposals generated by the generation AI to confirm the accuracy and appropriateness of the content, thereby guaranteeing the quality of the proposals. This allows the generation unit to provide reliable proposals to client companies.

[0068] The provision department provides the proposals generated by the generation department to client companies. For example, the provision department provides the generated proposals to client companies via email or a web portal. Specifically, the provision department sends the generated proposals to client companies via email for review. It also allows client companies to view and download the proposals online through a web portal. The web portal organizes the proposals by category and provides a search function, enabling client companies to quickly find the information they need. Furthermore, the provision department has a function to collect feedback on the proposals and provide it to the generation department. For example, it provides a feedback form where client companies can post comments and questions about the proposals, and forwards this feedback to the generation department to help improve the proposals. The provision department also has a function to notify client companies of the receipt confirmation and progress of the proposals. This allows client companies to understand the receipt status and progress of the proposals in real time, enabling them to receive proposals with confidence. In addition, the provision department can diversify the methods of providing proposals, enabling flexible responses to meet the needs of client companies. For example, by providing opportunities to directly explain proposals through online meetings or by offering proposals in video format, client companies can receive proposals in a way that is easier to understand. This allows the service department to deliver proposals to client companies quickly and effectively, thereby improving customer satisfaction.

[0069] The generation unit can analyze the client company's requests using generational AI and generate specific proposals. For example, the generation unit can use generational AI to analyze the client company's requests and generate specific proposals. For example, the generation unit can use a natural language generation model to generate proposals that meet the client company's requests. For example, the generation unit can use an image generation model to generate visual proposals that meet the client company's requests. As a result, using generational AI improves the accuracy of converting the client company's requests into specific proposals.

[0070] The service department can provide the generated proposals to client companies. For example, the service department can send the generated proposals to client companies via email. The service department can also provide the generated proposals to client companies via a web portal. The service department can also print the generated proposals and mail them to client companies. This makes it possible to provide the generated proposals to client companies quickly.

[0071] The analysis department can delve deeper into the client company's needs and clarify vague requests. For example, the analysis department can delve deeper into the client company's needs by asking additional questions. For example, the analysis department can clarify vague requests by collecting detailed data. For example, the analysis department can clarify requests by providing concrete examples. By clarifying the client company's vague requests, it becomes possible to make more appropriate proposals.

[0072] The generation unit can generate optimal proposals by utilizing product and solution data within the group. For example, it can generate optimal proposals by referring to the group's product and solution database. It can also generate proposals by integrating data on products and services offered by each company within the group. Furthermore, it can generate proposals that combine multiple products and services according to the client company's needs. This allows for more optimal proposals by leveraging data within the group.

[0073] The service provider can visualize the strengths and weaknesses of synergies based on the generated proposals. For example, the service provider can highlight and display the strengths of synergies within the generated proposals. For example, the service provider can also point out the weaknesses of synergies within the generated proposals and propose improvement plans. For example, the service provider can visually display the strengths and weaknesses of synergies using graphs and charts. By visualizing the strengths and weaknesses of synergies, it becomes possible to strengthen the overall capabilities of the group.

[0074] The reception desk can estimate the emotions of the client company's representative and adjust its response based on the estimated emotions. For example, if the client company's representative is stressed, the reception desk can provide a simple and quick response. If the client company's representative is relaxed, the reception desk can also provide a more detailed explanation. If the client company's representative is in a hurry, the reception desk can prioritize voice input and provide a quick response. This enables responses that are tailored to the emotions of the client company's representative. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The reception department can analyze past inquiry history and select the most appropriate reception method. For example, the reception department can propose the most suitable response method based on the types of inquiries that client companies have frequently made in the past. For example, the reception department can select the most suitable response method for a specific time period based on the client company's past inquiry history. For example, the reception department can analyze the client company's past inquiry history and select the most efficient response method. This enables efficient responses by selecting the most appropriate reception method based on past inquiry history.

[0076] The reception department can filter incoming inquiries based on the industry and size of the client company. For example, the reception department can prioritize retrieving relevant information based on the client company's industry. The reception department can also select an appropriate response method based on the client company's size. For example, the reception department can perform optimal filtering based on the client company's industry and size. This enables filtering tailored to the industry and size of the client company.

[0077] The reception desk can estimate the emotions of the client company's representative and determine the priority of the reception based on the estimated emotions. For example, if the client company's representative is stressed, the reception desk will prioritize their attention. If the client company's representative is relaxed, the reception desk may also prioritize their attention at the normal level. If the client company's representative is in a hurry, the reception desk may also prioritize their attention at the highest level. This makes it possible to determine priorities according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception department can prioritize obtaining highly relevant information by considering the geographical location of the client company during the reception process. For example, the reception department can prioritize obtaining relevant information based on the client company's location. For example, the reception department can also select the optimal response method based on the client company's geographical location. For example, the reception department can perform optimal filtering by considering the client company's geographical location. This makes it possible to obtain information based on the client company's geographical location.

[0079] The reception department can analyze the client company's social media activities and obtain relevant information upon receiving the request. For example, the reception department can analyze the client company's social media activities and prioritize obtaining relevant information. The reception department can also select the optimal response method based on the client company's social media activities. Furthermore, the reception department can perform optimal filtering, taking into account the client company's social media activities. This enables the acquisition of information based on the client company's social media activities.

[0080] The analysis unit can estimate the emotions of the client company's representative and adjust the analysis method based on the estimated emotions. For example, if the client company's representative is stressed, the analysis unit can perform a simple and rapid analysis. For example, if the client company's representative is relaxed, the analysis unit can also perform a detailed analysis. For example, if the client company's representative is in a hurry, the analysis unit can perform an analysis in the shortest possible time. This makes it possible to adjust the analysis method according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can improve the accuracy of its analysis by referring to past inquiries from client companies. For example, the analysis unit can improve the accuracy of its analysis based on past inquiries from client companies. The analysis unit can also select the optimal analysis method by referring to past inquiries from client companies. For example, the analysis unit can analyze past inquiries from client companies to improve the accuracy of its analysis. This means that the accuracy of the analysis is improved by referring to past inquiries.

[0082] The analysis unit can apply different analysis algorithms depending on the industry and size of the client company during the analysis. For example, the analysis unit can apply the optimal analysis algorithm depending on the industry of the client company. The analysis unit can also select an appropriate analysis algorithm depending on the size of the client company. The analysis unit can also apply different analysis algorithms depending on the industry and size of the client company. This enables analysis tailored to the industry and size of the client company.

[0083] The analysis unit can estimate the emotions of the client company's representative and determine the priority of analysis based on the estimated emotions. For example, if the client company's representative is feeling stressed, the analysis unit will prioritize the analysis. For example, if the client company's representative is relaxed, the analysis unit can also perform the analysis with the normal priority. For example, if the client company's representative is in a hurry, the analysis unit can also prioritize the analysis. This makes it possible to determine priorities according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analyses based on the submission timing of client companies during the analysis process. For example, the analysis unit can determine the optimal analysis priority based on the client company's submission timing. The analysis unit can also adjust the analysis priority, for example, by considering the client company's submission timing. Furthermore, the analysis unit can select the optimal analysis method based on the client company's submission timing. This enables the determination of priorities based on the client company's submission timing.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature from the client company during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature from the client company. The analysis unit can also select the optimal analysis method based on relevant literature from the client company. For example, the analysis unit can improve the accuracy of its analysis by analyzing relevant literature from the client company. Thus, the accuracy of the analysis is improved by referring to relevant literature.

[0086] The generation unit can estimate the emotions of the client company's representative and adjust the way the proposal is presented based on the estimated emotions. For example, if the client company's representative is stressed, the generation unit will make a simple and quick proposal. For example, if the client company's representative is relaxed, the generation unit can also make a detailed proposal. For example, if the client company's representative is in a hurry, the generation unit can also make a proposal in the shortest possible time. This makes it possible to adjust the way the proposal is presented according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The generation unit can adjust the level of detail in the proposal based on the importance of the client company's requests when generating the proposal. For example, the generation unit will provide a detailed proposal based on the importance of the client company's requests. The generation unit can also adjust the level of detail in the proposal, taking into consideration the importance of the client company's requests. For example, the generation unit can select the optimal proposal method based on the importance of the client company's requests. This makes it possible to adjust the level of detail in the proposal according to the importance of the client company's requests.

[0088] The generation unit can apply different proposal algorithms depending on the industry and size of the client company when generating proposals. For example, the generation unit can apply the optimal proposal algorithm depending on the industry of the client company. For example, the generation unit can also select an appropriate proposal algorithm depending on the size of the client company. For example, the generation unit can apply different proposal algorithms depending on the industry and size of the client company. This makes it possible to provide proposals tailored to the industry and size of the client company.

[0089] The generation unit can estimate the emotions of the client company's representative and adjust the length of the proposal based on the estimated emotions. For example, if the client company's representative is stressed, the generation unit will make a short, to-the-point proposal. For example, if the client company's representative is relaxed, the generation unit can also make a detailed proposal. For example, if the client company's representative is in a hurry, the generation unit can make a proposal in the shortest possible time. This makes it possible to adjust the length of the proposal according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The generation unit can determine the priority of proposals based on the client company's submission timing when generating proposals. For example, the generation unit can determine the optimal proposal priority based on the client company's submission timing. The generation unit can also adjust the proposal priority, for example, taking into account the client company's submission timing. The generation unit can also select the optimal proposal method, for example, based on the client company's submission timing. This makes it possible to determine priorities based on the client company's submission timing.

[0091] The generation unit can adjust the order of proposals based on the relevance of the client companies during proposal generation. For example, the generation unit determines the optimal order of proposals based on the relevance of the client companies. The generation unit can also adjust the order of proposals considering the relevance of the client companies. Furthermore, the generation unit can select the optimal proposal method based on the relevance of the client companies. This enables the adjustment of the proposal order based on the relevance of the client companies.

[0092] The delivery unit can estimate the emotions of the client company's representative and adjust the way the proposal is delivered based on the estimated emotions. For example, if the client company's representative is stressed, the delivery unit may select a simple and quick delivery method. For example, if the client company's representative is relaxed, the delivery unit may select a delivery method that includes detailed explanations. For example, if the client company's representative is in a hurry, the delivery unit may select a delivery method that takes the shortest possible time. This makes it possible to adjust the delivery method according to the emotions of the client company's representative. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can select the optimal service delivery method by referring to the client company's past proposal history when providing a proposal. For example, the service provider can select the optimal service delivery method based on the client company's past proposal history. The service provider can also select the optimal service delivery method by referring to the client company's past proposal history. For example, the service provider can also select the optimal service delivery method by analyzing the client company's past proposal history. This enables efficient service delivery by selecting the optimal service delivery method based on past proposal history.

[0094] The service provider can customize the delivery method based on the client company's industry and size when providing a proposal. For example, the service provider can select the optimal delivery method based on the client company's industry. The service provider can also select an appropriate delivery method based on the client company's size. Furthermore, the service provider can customize the delivery method based on the client company's industry and size. This allows for the customization of delivery methods to suit the client company's industry and size.

[0095] The service provider can estimate the emotions of the client company's representative and determine the order in which proposals are delivered based on the estimated emotions. For example, if the client company's representative is feeling stressed, the service provider will prioritize delivering proposals. For example, if the client company's representative is relaxed, the service provider can deliver proposals in the normal order. For example, if the client company's representative is in a hurry, the service provider can deliver proposals with the highest priority. This makes it possible to determine the order of delivery according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The service provider can select the optimal delivery method when proposing a solution, taking into account the geographical location information of the client company. For example, the service provider can select the optimal delivery method based on the client company's location. Alternatively, the service provider can select an appropriate delivery method based on the client company's geographical location information. The service provider can also select the optimal delivery method considering the client company's geographical location information. This makes it possible to select a delivery method based on the client company's geographical location information.

[0097] The service provider can analyze the client company's social media activities and propose delivery methods when providing proposals. For example, the service provider can analyze the client company's social media activities and propose the most suitable delivery method. For example, the service provider can select an appropriate delivery method based on the client company's social media activities. For example, the service provider can propose the most suitable delivery method considering the client company's social media activities. This makes it possible to propose delivery methods based on the client company's social media activities.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the emotions of the client company's representative and adjust its response based on the estimated emotions. For example, if the client company's representative is stressed, a simple and quick response can be provided. If the client company's representative is relaxed, a response including a detailed explanation can be provided. Furthermore, if the client company's representative is in a hurry, voice input can be prioritized for a quick response. This enables responses that are tailored to the emotions of the client company's representative. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The analysis unit can estimate the emotions of the client company's representative and adjust the analysis method based on the estimated emotions. For example, if the client company's representative is stressed, a simple and rapid analysis can be performed. If the client company's representative is relaxed, a detailed analysis can be performed. Furthermore, if the client company's representative is in a hurry, an analysis can be performed in the shortest possible time. This makes it possible to adjust the analysis method according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The generation unit can estimate the emotions of the client company's representative and adjust the way the proposal is presented based on the estimated emotions. For example, if the client company's representative is stressed, a simple and quick proposal can be made. Conversely, if the client company's representative is relaxed, a detailed proposal can be made. Furthermore, if the client company's representative is in a hurry, a proposal can be made in the shortest possible time. This makes it possible to adjust the way the proposal is presented according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The delivery unit can estimate the emotions of the client company's representative and adjust the delivery method of the proposal based on the estimated emotions. For example, if the client company's representative is stressed, a simple and quick delivery method can be selected. If the client company's representative is relaxed, a delivery method including detailed explanations can be selected. Furthermore, if the client company's representative is in a hurry, a method that can be delivered in the shortest possible time can be selected. This makes it possible to adjust the delivery method according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The service provider can estimate the emotions of the client company's representative and determine the order in which proposals are delivered based on the estimated emotions. For example, if the client company's representative is feeling stressed, proposals can be delivered preferentially. Conversely, if the client company's representative is relaxed, proposals can be delivered in the normal order. Furthermore, if the client company's representative is in a hurry, proposals can be delivered with the highest priority. This makes it possible to determine the order of delivery according to the emotions of the client company's representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The reception department can analyze past inquiry history and select the most appropriate reception method. For example, it can propose the most suitable response method based on the types of inquiries a client company has frequently made in the past. It can also select the most suitable response method for a specific time of day based on the client company's past inquiry history. Furthermore, it can analyze the client company's past inquiry history and select the most efficient response method. This allows for efficient responses by selecting the most suitable reception method based on past inquiry history.

[0105] The reception desk can filter incoming requests based on the client company's industry and size. For example, it can prioritize the acquisition of relevant information based on the client company's industry. It can also select the appropriate response method based on the client company's size. Furthermore, it can perform optimal filtering based on the client company's industry and size. This enables filtering tailored to the client company's industry and size.

[0106] The reception department can prioritize retrieving highly relevant information by considering the geographical location of the client company during the reception process. For example, it can prioritize retrieving relevant information based on the client company's location. It can also select the most appropriate response method based on the client company's geographical location. Furthermore, it can perform optimal filtering considering the client company's geographical location. This makes it possible to retrieve information based on the client company's geographical location.

[0107] The reception department can analyze the client company's social media activities and obtain relevant information upon receiving a request. For example, it can analyze the client company's social media activities and prioritize obtaining relevant information. It can also select the optimal response method based on the client company's social media activities. Furthermore, it can perform optimal filtering considering the client company's social media activities. This makes it possible to obtain information based on the client company's social media activities.

[0108] The analysis unit can improve the accuracy of its analysis by referring to past inquiries from client companies. For example, it can improve the accuracy of its analysis based on past inquiries from client companies. It can also select the optimal analysis method by referring to past inquiries from client companies. Furthermore, it can improve the accuracy of its analysis by analyzing past inquiries from client companies. In this way, the accuracy of the analysis is improved by referring to past inquiries.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception department receives requests from client companies. For example, it receives requests submitted by client companies through inquiry forms. Step 2: The analysis unit analyzes the requests received by the reception unit. For example, it analyzes the content of the requests using text analysis technology. Step 3: The generation unit generates proposals based on the requirements analyzed by the analysis unit. For example, it uses generation AI to generate specific proposals that meet the customer company's requirements. Step 4: The supply unit provides the proposals generated by the generation unit to the client company. For example, the generated proposals are provided to the client company via email or a web portal.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives requests submitted by customer companies through an inquiry form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the requests using text analysis technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific proposals that meet the customer company's requests using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and provides the generated proposals to customer companies via email or a web portal. The correspondence between each unit and the devices or control units is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives requests submitted by customer companies through an inquiry form. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the requests using text analysis technology. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates specific proposals that meet the customer company's requests using generation AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and provides the generated proposals to customer companies via email or a web portal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives requests submitted by customer companies through an inquiry form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the requests using text analysis technology. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates specific proposals that meet the customer company's requests using generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and provides the generated proposals to customer companies via email or a web portal. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives requests submitted by customer companies through an inquiry form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the requests using text analysis technology. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates specific proposals that meet the customer company's requests using generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing unit 12 and provides the generated proposals to customer companies via email or a web portal. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reception department that handles requests from client companies, An analysis unit analyzes the requests received by the aforementioned reception unit, A generation unit that generates proposals based on the requirements analyzed by the analysis unit, The system comprises a providing unit that provides proposals generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates specific proposals by analyzing the needs of client companies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide the generated proposals to our client companies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We delve deeper into the client company's needs and make vague requests concrete. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We leverage product and solution data within the group to generate optimal proposals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Based on the generated proposals, visualize the strengths and weaknesses of the synergies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the emotions of the client company's representative and adjust the receptionist's response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze past inquiry history and select the most suitable method of receiving inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During the application process, filtering is performed based on the industry and size of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the emotions of the client company's representative and determines the priority of reception based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During the registration process, the system prioritizes obtaining highly relevant information by considering the geographical location of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During registration, we analyze the client company's social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of the client company's representatives and adjust the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to past inquiries from client companies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analytical algorithms are applied depending on the industry and size of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, We estimate the emotions of the client company's representatives and determine the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the submission timing of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature from the client company to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the emotions of the client company's representative and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a proposal, adjust the level of detail based on the importance of the client company's requests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating proposals, different proposal algorithms are applied depending on the industry and size of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is We estimate the emotions of the client company's representative and adjust the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating proposals, we prioritize them based on the submission timing of the client companies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating proposals, the order of proposals is adjusted based on the relevance of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the emotions of the client company's representative and adjust the way we deliver proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a proposal, we will select the most suitable delivery method by referring to the client company's past proposal history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing proposals, we customize the delivery methods based on the industry and size of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the emotions of the client company's representative and determines the order in which proposals are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a proposal, we will select the most suitable delivery method by considering the geographical location information of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a proposal, we analyze the client company's social media activities and suggest methods for sharing information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception department that handles requests from client companies, An analysis unit analyzes the requests received by the aforementioned reception unit, A generation unit that generates proposals based on the requirements analyzed by the analysis unit, The system comprises a providing unit that provides proposals generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates specific proposals by analyzing the needs of client companies. The system according to feature 1.

3. The aforementioned supply unit is, We provide the generated proposals to our client companies. The system according to feature 1.

4. The aforementioned analysis unit, We delve deeper into the client company's needs and make vague requests concrete. The system according to feature 1.

5. The generating unit is We leverage product and solution data within the group to generate optimal proposals. The system according to feature 1.

6. The aforementioned supply unit is, Based on the generated proposals, visualize the strengths and weaknesses of the synergies. The system according to feature 1.

7. The aforementioned reception unit is We estimate the emotions of the client company's representative and adjust the receptionist's response based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze past inquiry history and select the most suitable method of receiving inquiries. The system according to feature 1.

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