System

Abstract

An object of a system according to an embodiment is to draw out potential needs of a user and propose an appropriate product based on the potential needs.SOLUTION: A system includes a drawer unit, a proposal unit, an introduction unit, and a takeover unit. The drawer draws the needs of the user. The proposal unit proposes a product based on the need retrieved by the retrieval unit. The introduction unit introduces a case related to the product proposed by the proposal unit. The handover unit performs handover to sales based on the case introduced by the introduction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively identify users' latent needs and propose appropriate products based on those needs, so there is room for improvement.

[0005] The system according to the embodiment aims to extract the latent needs of the user and propose appropriate products based on those needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a drawer unit, a proposal unit, an introduction unit, and a handover unit. The drawer unit draws out the needs of a user. The proposal unit proposes a product based on the needs drawn by the drawer unit. The introduction unit introduces case studies related to the product proposed by the proposal unit. The handover unit transfers the work to sales based on the case studies introduced by the introduction unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract the latent needs of the user and suggest appropriate products based on the latent needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention elicits user needs, proposes appropriate products, introduces case studies, and tosses the proposal to sales. This system contributes to the creation of new business opportunities by eliciting user needs, proposing products, introducing case studies, and tossing the proposal to sales. As a result, the system contributes to the creation of new business opportunities by eliciting user needs, proposing appropriate products, introducing case studies, and tossing the proposal to sales. For example, to elicit user needs, the system uses natural language processing technology to analyze the user's input and generate appropriate questions. Next, based on the elicited needs, the system references a product database and proposes optimal products. Furthermore, the system introduces how other companies are working on the SDGs, providing the user with a concrete image. Finally, if the user shows interest in the proposal, the system contacts a sales representative and advances specific business negotiations. As a result, the system embodies the user's vision of the SDGs and, by utilizing the products, advances efforts toward the Sustainable Development Goals.

[0029] The system according to the embodiment includes an elicitation unit, a suggestion unit, an introduction unit, and a handover unit. The elicitation unit elicits user needs. The elicitation unit analyzes user input using, for example, natural language processing technology and generates appropriate questions. For example, the elicitation unit can generate questions such as, "In what areas do you want to work on SDGs?" The elicitation unit can also analyze the user's past dialogue history and generate optimal questions. For example, the elicitation unit can generate questions based on topics in which the user has previously shown interest. The elicitation unit can also estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. For example, if the user is excited, the elicitation unit can avoid detailed questions and ask concise, specific questions. The suggestion unit suggests products based on the needs elicited by the elicitation unit. The suggestion unit, for example, references a product database and selects optimal products. For example, if the user is interested in improving energy efficiency, the suggestion unit can suggest energy-related products. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is excited, the proposal unit can make a concise and specific proposal. The introduction unit can introduce case studies related to the product proposed by the proposal unit. The introduction unit can introduce, for example, how other companies are working on the SDGs. For example, the introduction unit can introduce success stories of other companies to provide the user with a concrete image. The introduction unit can also estimate the user's emotions and adjust the display method of the case studies based on the estimated emotions. For example, if the user is excited, the introduction unit can display concise and specific case studies. The handover unit performs handover to sales based on the case studies introduced by the introduction unit. For example, if the user shows interest in the proposal, the handover unit contacts a sales representative and moves the proposal forward to specific negotiations. For example, if the user is excited, the handover unit can quickly toss up the proposal to sales. As a result, the system according to the embodiment can contribute to the creation of deals by eliciting user needs, proposing appropriate products, introducing case studies, and tossing up the proposal to sales.

[0030] The elicitation unit can analyze the user's input content using natural language processing technology and generate an appropriate question. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the elicitation unit can morphologically analyze the user's input content and generate an appropriate question. The elicitation unit can also analyze the user's input content using grammatical analysis and generate an appropriate question. The elicitation unit can also analyze the user's input content using semantic analysis and generate an appropriate question. For example, the elicitation unit generates an appropriate question by morphologically analyzing, grammatically analyzing, and semantically analyzing the user's input content. In this way, the user's input content can be analyzed and an appropriate question can be generated using natural language processing technology. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI or without AI. For example, the elicitation unit can input the user's input content to a generation AI and cause the generation AI to generate an appropriate question.

[0031] The suggestion unit can refer to a product database to select a product. The product database includes, but is not limited to, products, services, and solutions. For example, the suggestion unit can refer to the product database to select a product that best meets the user's needs. The suggestion unit can also periodically update the data in the product database to provide the latest product information. For example, the suggestion unit can update the data in the product database monthly to provide the latest product information. This allows the product that best meets the user's needs to be selected by referring to the product database. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data from the product database into a generation AI and cause the generation AI to select the optimal product.

[0032] The introduction unit can introduce other companies' efforts toward the SDGs. Examples of SDG efforts include, but are not limited to, environmental protection activities and social contribution activities. For example, the introduction unit can introduce other companies' environmental protection activities to provide a concrete image to the user. The introduction unit can also introduce other companies' social contribution activities to provide a concrete image to the user. For example, the introduction unit can introduce success stories of other companies to provide a concrete image to the user. By introducing the cases of other companies, a concrete image can be provided to the user. Some or all of the above-described processing in the introduction unit can be performed using, for example, AI, or without AI. For example, the introduction unit can input case data of other companies into the generation AI and have the generation AI select the most appropriate case.

[0033] If the user shows interest in the proposal, the handover unit can contact a sales representative to proceed with the business negotiations. Business negotiations include, but are not limited to, for example, product explanations, price negotiations, and contract signing. For example, if the user shows interest in the proposal, the handover unit can contact a sales representative to explain the product. Furthermore, if the user shows interest in the proposal, the handover unit can contact a sales representative to negotiate the price. Furthermore, if the user shows interest in the proposal, the handover unit can contact a sales representative to sign a contract. For example, if the user shows interest in the proposal, the handover unit can contact a sales representative to explain the product, negotiate the price, and sign a contract. In this way, if the user shows interest in the proposal, the sales representative can be quickly contacted and the business negotiations can proceed. Some or all of the above-described processing in the handover unit may be performed using, for example, AI, or may be performed without using AI. For example, the handover unit can input data indicating the user's interest into the generation AI and cause the generation AI to contact the sales representative.

[0034] The elicitation unit can analyze the user's past dialogue history and generate questions. The past dialogue history includes, for example, the content of the dialogue and the frequency of the dialogue, but is not limited to these examples. The elicitation unit can generate questions based on, for example, topics in which the user has shown interest in the past. The elicitation unit can also generate related questions by referring to the content of answers given by the user in the past. Furthermore, the elicitation unit can generate questions related to unresolved problems from the user's past dialogue history. For example, the elicitation unit generates questions based on topics in which the user has shown interest in the past. In this way, optimal questions for the user can be generated by analyzing the past dialogue history. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's past dialogue history into a generation AI and cause the generation AI to generate optimal questions.

[0035] When generating questions, the elicitation unit can provide questions customized according to the user's industry or job role. Examples of industries include, but are not limited to, manufacturing, service, and IT. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the elicitation unit can provide questions related to energy efficiency. Furthermore, if the user is in the education industry, the elicitation unit can provide questions related to educational programs. Furthermore, if the user is in the medical industry, the elicitation unit can provide questions related to medical technology. For example, if the user is in the energy industry, the elicitation unit can provide questions related to energy efficiency. This allows for more appropriate dialogue by providing questions customized according to the user's industry or job role. Some or all of the above-described processing in the elicitation unit may be performed using, or without, AI. For example, the elicitation unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to generate customized questions.

[0036] When generating a question, the elicitation unit can check the consistency of the user's answer and ask a re-question if there is a contradiction. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of a contradiction. For example, if the answer contradicts a previous answer given by the user, the elicitation unit can ask a question to confirm that point. Furthermore, if the user's answer is ambiguous, the elicitation unit can ask a question to seek specific details. Furthermore, if the user's answer is unclear, the elicitation unit can ask a question to confirm that point. For example, if the answer contradicts a previous answer given by the user, the elicitation unit can ask a question to confirm that point. In this way, the consistency of the user's answer can be checked and if there is a contradiction, a re-question can be asked, thereby eliciting more accurate information. Some or all of the above-described processing in the elicitation unit can be performed, for example, using AI or without AI. For example, the elicitation unit can input the user's answer data into the generation AI and cause the generation AI to check the consistency and generate a re-question.

[0037] When generating questions, the elicitation unit can provide relevant questions based on the user's geographical background. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the elicitation unit can provide questions related to the city's SDGs. Furthermore, if the user lives in a rural area, the elicitation unit can provide questions related to agriculture. Furthermore, if the user lives overseas, the elicitation unit can provide questions related to the country's SDGs. For example, if the user lives in an urban area, the elicitation unit can provide questions related to the city's SDGs. This allows for more appropriate dialogue by providing questions that take the user's geographical background into consideration. Some or all of the above-described processing in the elicitation unit may be performed using, or without, AI. For example, the elicitation unit can input the user's geographical background data into the generation AI and cause the generation AI to generate relevant questions.

[0038] When generating a question, the elicitation unit can analyze the user's social media activity and generate a related question. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The elicitation unit can generate a question based on, for example, topics in which the user has shown interest on social media. The elicitation unit can also analyze the content of the user's posts on social media to generate a related question. Furthermore, the elicitation unit can also generate a related question by referring to the activities of the user's friends on social media. For example, the elicitation unit generates a question based on topics in which the user has shown interest on social media. In this way, related questions can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's social media activity data into the generation AI and cause the generation AI to generate a related question.

[0039] When generating a question, the elicitation unit can customize the question content by reflecting the user's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, and the like. For example, the elicitation unit generates a related question based on feedback provided by the user in the past. The elicitation unit can also generate a question that reflects areas for improvement from the user's past feedback. Furthermore, the elicitation unit can analyze the user's past feedback and generate an optimal question. For example, the elicitation unit generates a related question based on feedback provided by the user in the past. In this way, more appropriate questions can be generated by reflecting the user's past feedback. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the question content.

[0040] When making a proposal, the suggestion unit can adjust the details of the proposal based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales contribution and customer satisfaction. For example, the suggestion unit provides detailed information for an important product. The suggestion unit can also provide concise information for a product with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the importance of the product. For example, the suggestion unit provides detailed information for an important product. This allows for adjusting the level of detail of the proposal depending on the importance of the product, thereby making it possible to make a more appropriate proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input product importance data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0041] When making a proposal, the suggestion unit can apply a different proposal algorithm depending on the category of the product. Product categories include, but are not limited to, product categories and service categories. For example, in the case of energy-related products, the suggestion unit can apply a proposal algorithm related to energy efficiency. Furthermore, in the case of education-related products, the suggestion unit can also apply a proposal algorithm related to educational programs. Furthermore, in the case of medical-related products, the suggestion unit can also apply a proposal algorithm related to medical technology. For example, in the case of energy-related products, the suggestion unit applies a proposal algorithm related to energy efficiency. This allows for more appropriate proposals by applying a proposal algorithm according to the category of the product. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input product category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0042] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, the success rate of the proposal, customer reactions, etc. For example, the suggestion unit makes relevant suggestions based on proposals previously accepted by the user. The suggestion unit can also make optimal suggestions by avoiding proposals previously rejected by the user. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit makes relevant suggestions based on proposals previously accepted by the user. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0043] When making a proposal, the proposal unit can determine the order of proposals based on the timing of product release. Examples of product release timing include, but are not limited to, seasonality and campaign periods. For example, the proposal unit prioritizes proposals for products that need to be released immediately. The proposal unit can also postpone proposals for products whose release dates are further away. Furthermore, the proposal unit can determine the priority of proposals based on the timing of product release. For example, the proposal unit prioritizes proposals for products that need to be released immediately. By determining the priority of proposals based on the timing of product release, proposals can be made at more appropriate times. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input product release timing data into the generation AI and have the generation AI determine the order of proposals.

[0044] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. Examples of the relevance of the products include, but are not limited to, similarity in function and commonality of use. For example, the suggestion unit prioritizes suggesting products that are most relevant to the user's needs. The suggestion unit can also postpone less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit prioritizes suggesting products that are most relevant to the user's needs. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of the products into a generation AI and cause the generation AI to adjust the order of suggestions.

[0045] When making a suggestion, the suggestion unit can adjust the use of terms in the suggestion according to the user's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the suggestion unit uses a lot of technical terms when the user has technical expertise. Furthermore, the suggestion unit can also use simple language when the user does not have technical expertise. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terms when the user has technical expertise. By adjusting the use of technical terms in the suggestion according to the user's level of expertise, a more understandable suggestion can be made. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terms in the suggestion.

[0046] When introducing a case, the introduction unit can select the latest case by referring to past case data. Past case data includes, for example, successful cases and unsuccessful cases, but is not limited to such examples. For example, the introduction unit selects the current case based on past successful cases. The introduction unit can also select the optimal case by avoiding past unsuccessful cases. Furthermore, the introduction unit can analyze past case data and select the current case. For example, the introduction unit selects the current case based on past successful cases. In this way, by referring to the past case data, the current case can be more appropriately selected. Some or all of the above-mentioned processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input past case data into the generation AI and cause the generation AI to select the latest case.

[0047] When introducing a case study, the introduction unit can provide a case study tailored to the user's industry or job role. Examples of industries include, but are not limited to, the manufacturing industry, the service industry, and the IT industry. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the introduction unit can provide a case study related to energy efficiency. Also, if the user is in the education industry, the introduction unit can provide a case study related to educational programs. Furthermore, if the user is in the medical industry, the introduction unit can provide a case study related to medical technology. For example, if the user is in the energy industry, the introduction unit can provide a case study related to energy efficiency. This allows for more appropriate case study introduction by providing a case study tailored to the user's industry or job role. Some or all of the above-described processing in the introduction unit may be performed using, or without, AI. For example, the introduction unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to provide customized case studies.

[0048] When introducing a case study, the introduction unit may check the consistency of the user's answer and reintroduce the answer if there is a discrepancy. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of a discrepancy. For example, if the introduction unit finds a discrepancy with the user's previous answer, it may introduce an example to confirm that point. Furthermore, if the user's answer is ambiguous, the introduction unit may also introduce an example to request specific details. Furthermore, if the user's answer is unclear, the introduction unit may introduce an example to confirm that point. For example, if the introduction unit finds a discrepancy with the user's previous answer, it may introduce an example to confirm that point. In this way, by checking the consistency of the user's answer and reintroducing the answer if there is a discrepancy, more accurate information can be provided. Some or all of the above-described processing in the introduction unit may be performed, for example, using AI, or may be performed without AI. For example, the introduction unit may input the user's answer data into a generation AI and cause the generation AI to check the consistency and generate a reintroduction.

[0049] When introducing a case study, the introduction unit can provide relevant cases based on the user's geographical background. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the introduction unit can provide cases related to urban SDGs. Furthermore, if the user lives in a rural area, the introduction unit can provide cases related to agriculture. Furthermore, if the user lives overseas, the introduction unit can provide cases related to the SDGs of that country. For example, if the user lives in an urban area, the introduction unit can provide cases related to urban SDGs. This allows for more appropriate case study introductions by providing cases that take the user's geographical background into consideration. Some or all of the above-described processing in the introduction unit may be performed using, or without, AI. For example, the introduction unit can input the user's geographical background data into the generation AI and cause the generation AI to provide relevant cases.

[0050] When introducing a case study, the introduction unit can analyze the user's social media activity and introduce related cases. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. For example, the introduction unit can introduce cases based on topics in which the user has shown interest on social media. The introduction unit can also analyze the content of the user's social media posts and introduce related cases. Furthermore, the introduction unit can introduce related cases by referring to the activities of the user's friends on social media. For example, the introduction unit introduces cases based on topics in which the user has shown interest on social media. In this way, related cases can be introduced by analyzing the user's social media activity. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the user's social media activity data into the generation AI and cause the generation AI to introduce related cases.

[0051] When introducing a case study, the introduction unit can individually adjust the case study content by reflecting the user's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, etc. For example, the introduction unit can introduce related case studies based on feedback previously provided by the user. The introduction unit can also introduce case studies that reflect improvements based on the user's past feedback. Furthermore, the introduction unit can analyze the user's past feedback and introduce optimal case studies. For example, the introduction unit can introduce related case studies based on feedback previously provided by the user. In this way, more appropriate case studies can be introduced by reflecting the user's past feedback. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the case study content.

[0052] The handover unit can analyze the user's past interaction history at the time of toss-up and select a salesperson. The past interaction history includes, for example, the content of the interaction and the frequency of the interaction, but is not limited to these examples. For example, the handover unit selects a salesperson to whom the user has previously shown a favorable response. The handover unit can also select the optimal salesperson from the user's past interaction history. Furthermore, the handover unit can select a salesperson that best meets the user's needs. For example, the handover unit selects a salesperson to whom the user has previously shown a favorable response. In this way, the optimal salesperson can be selected by analyzing the user's past interaction history. Some or all of the above-described processing in the handover unit may be performed, for example, using AI or without using AI. For example, the handover unit can input the user's past interaction history data into the generation AI and cause the generation AI to select a salesperson.

[0053] The handover unit can provide sales materials tailored to the user's industry or job role at the time of toss-up. Examples of industries include, but are not limited to, manufacturing, service, and IT. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the handover unit can provide sales materials related to energy efficiency. Also, if the user is in the education industry, the handover unit can provide sales materials related to educational programs. Furthermore, if the user is in the medical industry, the handover unit can provide sales materials related to medical technology. For example, if the user is in the energy industry, the handover unit can provide sales materials related to energy efficiency. This allows for more effective sales activities by providing sales materials tailored to the user's industry and job role. Some or all of the above-described processing in the handover unit may be performed using, or without, AI. For example, the handover unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to provide customized sales materials.

[0054] The handover unit can check the consistency of the user's answer at the time of toss-up and reconfirm any inconsistencies. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of inconsistencies. For example, if the answer contradicts the user's previous answer, the handover unit can ask a second question to confirm that point. Furthermore, if the user's answer is unclear, the handover unit can ask a second question to seek specific details. Furthermore, if the user's answer is unclear, the handover unit can ask a second question to confirm that point. For example, if the answer contradicts the user's previous answer, the handover unit can ask a second question to confirm that point. In this way, by checking the consistency of the user's answer and reconfirming any inconsistencies, more accurate information can be provided. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's answer data into a generation AI and cause the generation AI to check the consistency and generate a second question.

[0055] The handover unit can select a relevant salesperson based on the user's geographical background during a toss-up. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the handover unit can select an urban salesperson. Furthermore, if the user lives in a rural area, the handover unit can select a salesperson knowledgeable in agriculture. Furthermore, if the user lives overseas, the handover unit can select a salesperson from that country. For example, if the user lives in an urban area, the handover unit selects an urban salesperson. This allows for more effective sales activities by selecting a salesperson that takes the user's geographical background into consideration. Some or all of the above-described processing in the handover unit can be performed using, or without, AI. For example, the handover unit can input the user's geographical background data into the generation AI and cause the generation AI to select a relevant salesperson.

[0056] The handover unit can analyze the user's social media activity at the time of toss-up and provide relevant sales materials. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The handover unit can provide sales materials, for example, based on topics in which the user has shown interest on social media. The handover unit can also analyze the content of the user's social media posts and provide relevant sales materials. Furthermore, the handover unit can provide relevant sales materials by referring to the activities of the user's friends on social media. For example, the handover unit provides sales materials based on topics in which the user has shown interest on social media. In this way, relevant sales materials can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant sales materials.

[0057] The handover unit can individually adjust the toss-up content by reflecting the user's past feedback during the toss-up. Examples of past feedback include, but are not limited to, evaluation comments and survey results. For example, the handover unit can provide relevant sales materials based on feedback previously provided by the user. The handover unit can also provide sales materials that reflect improvements based on the user's past feedback. Furthermore, the handover unit can analyze the user's past feedback and provide optimal sales materials. For example, the handover unit can provide relevant sales materials based on feedback previously provided by the user. This allows more appropriate sales materials to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the toss-up content.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The system can also analyze the user's past purchase history and generate related questions to elicit the user's needs. For example, the elicitation unit generates related questions based on products and services the user has purchased in the past. The elicitation unit can also generate questions related to unresolved problems from the user's purchase history. Furthermore, the elicitation unit can analyze the user's purchase history and generate optimal questions. In this way, the system can generate optimal questions for the user by analyzing the past purchase history.

[0060] The suggestion unit can also make customization suggestions for the product based on the user's needs. For example, if the user is looking for a specific function, the suggestion unit can suggest a product with enhanced functionality. The suggestion unit can also customize the product package according to the user's needs. Furthermore, the suggestion unit can also make suggestions for improving the product based on user feedback. This makes it possible to make more appropriate suggestions by making customization suggestions according to the user's needs.

[0061] The introduction unit can also provide interactive case studies to attract the user's interest. For example, the introduction unit can provide detailed information about cases that the user has shown interest in. The introduction unit can also provide an interactive platform where the user can ask questions. Furthermore, the introduction unit can adjust the content of the case studies in real time according to the user's responses. This makes it possible to provide an interactive case study that attracts the user's interest and provides a more concrete image.

[0062] The handover unit can also select the most suitable sales representative based on the user's needs. For example, if the user is interested in a particular field, the handover unit can select a sales representative who is knowledgeable in that field. The handover unit can also select a sales representative taking into consideration the sales representative's skill set according to the user's needs. Furthermore, the handover unit can select the most suitable sales representative based on the user's past interaction history. This allows for more effective sales activities by selecting the most suitable sales representative according to the user's needs.

[0063] When making a suggestion, the suggestion unit can analyze the user's purchase history and suggest related products. For example, the suggestion unit can suggest related products based on products or services the user has purchased in the past. The suggestion unit can also suggest products related to unresolved problems based on the user's purchase history. Furthermore, the suggestion unit can analyze the user's purchase history and suggest optimal products. In this way, the most optimal products can be suggested to the user by analyzing the user's past purchase history.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The elicitation unit elicits the user's needs. The elicitation unit uses natural language processing technology to analyze the user's input and generate appropriate questions. For example, it generates questions such as, "In what areas would you like to work on SDGs?" It can also analyze the user's past dialogue history to generate optimal questions. It can also estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. Step 2: The suggestion unit suggests products based on the needs extracted by the elicitation unit. The suggestion unit references the product database and selects the most suitable product. For example, if the user is interested in improving energy efficiency, the suggestion unit suggests energy-related products. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. Step 3: The introduction section introduces case studies related to the products proposed by the proposal section. The introduction section introduces how other companies are working on the SDGs and provides success stories. It can also estimate the user's emotions and adjust the way the case studies are displayed based on the estimated emotions. Step 4: The handover department transfers the case to sales based on the case introduced by the introduction department. If the user shows interest in the proposal, the handover department contacts the sales representative and moves the case forward to specific negotiations. For example, if the user is excited, the case can be transferred to sales immediately.

[0066] (Example 2) A system according to an embodiment of the present invention elicits user needs, proposes appropriate products, introduces case studies, and tosses the proposal to sales. This system contributes to the creation of new business opportunities by eliciting user needs, proposing products, introducing case studies, and tossing the proposal to sales. As a result, the system contributes to the creation of new business opportunities by eliciting user needs, proposing appropriate products, introducing case studies, and tossing the proposal to sales. For example, to elicit user needs, the system uses natural language processing technology to analyze the user's input and generate appropriate questions. Next, based on the elicited needs, the system references a product database and proposes optimal products. Furthermore, the system introduces how other companies are working on the SDGs, providing the user with a concrete image. Finally, if the user shows interest in the proposal, the system contacts a sales representative and advances specific business negotiations. As a result, the system embodies the user's vision of the SDGs and, by utilizing the products, advances efforts toward the Sustainable Development Goals.

[0067] The system according to the embodiment includes an elicitation unit, a suggestion unit, an introduction unit, and a handover unit. The elicitation unit elicits user needs. The elicitation unit analyzes user input using, for example, natural language processing technology and generates appropriate questions. For example, the elicitation unit can generate questions such as, "In what areas do you want to work on SDGs?" The elicitation unit can also analyze the user's past dialogue history and generate optimal questions. For example, the elicitation unit can generate questions based on topics in which the user has previously shown interest. The elicitation unit can also estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. For example, if the user is excited, the elicitation unit can avoid detailed questions and ask concise, specific questions. The suggestion unit suggests products based on the needs elicited by the elicitation unit. The suggestion unit, for example, references a product database and selects optimal products. For example, if the user is interested in improving energy efficiency, the suggestion unit can suggest energy-related products. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is excited, the proposal unit can make a concise and specific proposal. The introduction unit can introduce case studies related to the product proposed by the proposal unit. The introduction unit can introduce, for example, how other companies are working on the SDGs. For example, the introduction unit can introduce success stories of other companies to provide the user with a concrete image. The introduction unit can also estimate the user's emotions and adjust the display method of the case studies based on the estimated emotions. For example, if the user is excited, the introduction unit can display concise and specific case studies. The handover unit performs handover to sales based on the case studies introduced by the introduction unit. For example, if the user shows interest in the proposal, the handover unit contacts a sales representative and moves the proposal forward to specific negotiations. For example, if the user is excited, the handover unit can quickly toss up the proposal to sales. As a result, the system according to the embodiment can contribute to the creation of deals by eliciting user needs, proposing appropriate products, introducing case studies, and tossing up the proposal to sales.

[0068] The elicitation unit can analyze the user's input content using natural language processing technology and generate an appropriate question. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the elicitation unit can morphologically analyze the user's input content and generate an appropriate question. The elicitation unit can also analyze the user's input content using grammatical analysis and generate an appropriate question. The elicitation unit can also analyze the user's input content using semantic analysis and generate an appropriate question. For example, the elicitation unit generates an appropriate question by morphologically analyzing, grammatically analyzing, and semantically analyzing the user's input content. In this way, the user's input content can be analyzed and an appropriate question can be generated using natural language processing technology. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI or without AI. For example, the elicitation unit can input the user's input content to a generation AI and cause the generation AI to generate an appropriate question.

[0069] The suggestion unit can refer to a product database to select a product. The product database includes, but is not limited to, products, services, and solutions. For example, the suggestion unit can refer to the product database to select a product that best meets the user's needs. The suggestion unit can also periodically update the data in the product database to provide the latest product information. For example, the suggestion unit can update the data in the product database monthly to provide the latest product information. This allows the product that best meets the user's needs to be selected by referring to the product database. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data from the product database into a generation AI and cause the generation AI to select the optimal product.

[0070] The introduction unit can introduce other companies' efforts toward the SDGs. Examples of SDG efforts include, but are not limited to, environmental protection activities and social contribution activities. For example, the introduction unit can introduce other companies' environmental protection activities to provide a concrete image to the user. The introduction unit can also introduce other companies' social contribution activities to provide a concrete image to the user. For example, the introduction unit can introduce success stories of other companies to provide a concrete image to the user. By introducing the cases of other companies, a concrete image can be provided to the user. Some or all of the above-described processing in the introduction unit can be performed using, for example, AI, or without AI. For example, the introduction unit can input case data of other companies into the generation AI and have the generation AI select the most appropriate case.

[0071] If the user shows interest in the proposal, the handover unit can contact a sales representative to proceed with the business negotiations. Business negotiations include, but are not limited to, for example, product explanations, price negotiations, and contract signing. For example, if the user shows interest in the proposal, the handover unit can contact a sales representative to explain the product. Furthermore, if the user shows interest in the proposal, the handover unit can contact a sales representative to negotiate the price. Furthermore, if the user shows interest in the proposal, the handover unit can contact a sales representative to sign a contract. For example, if the user shows interest in the proposal, the handover unit can contact a sales representative to explain the product, negotiate the price, and sign a contract. In this way, if the user shows interest in the proposal, the sales representative can be quickly contacted and the business negotiations can proceed. Some or all of the above-described processing in the handover unit may be performed using, for example, AI, or may be performed without using AI. For example, the handover unit can input data indicating the user's interest into the generation AI and cause the generation AI to contact the sales representative.

[0072] The elicitation unit can estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, when the user is excited, the elicitation unit avoids detailed questions and asks simple, specific questions. Furthermore, when the user is relaxed, the elicitation unit can ask detailed questions to promote in-depth dialogue. Furthermore, when the user is stressed, the elicitation unit can reduce the frequency of questions and conduct dialogue at the user's pace. For example, when the user is excited, the elicitation unit asks simple, specific questions. This allows for more appropriate dialogue by adjusting the content and timing of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the elicitation unit may be performed using, for example, an AI, or without an AI. For example, the elicitation unit can input the user's emotional data into the generation AI and have the generation AI adjust the content and timing of the questions.

[0073] The elicitation unit can analyze the user's past dialogue history and generate questions. The past dialogue history includes, for example, the content of the dialogue and the frequency of the dialogue, but is not limited to these examples. The elicitation unit can generate questions based on, for example, topics in which the user has shown interest in the past. The elicitation unit can also generate related questions by referring to the content of answers given by the user in the past. Furthermore, the elicitation unit can generate questions related to unresolved problems from the user's past dialogue history. For example, the elicitation unit generates questions based on topics in which the user has shown interest in the past. In this way, optimal questions for the user can be generated by analyzing the past dialogue history. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's past dialogue history into a generation AI and cause the generation AI to generate optimal questions.

[0074] When generating questions, the elicitation unit can provide questions customized according to the user's industry or job role. Examples of industries include, but are not limited to, manufacturing, service, and IT. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the elicitation unit can provide questions related to energy efficiency. Furthermore, if the user is in the education industry, the elicitation unit can provide questions related to educational programs. Furthermore, if the user is in the medical industry, the elicitation unit can provide questions related to medical technology. For example, if the user is in the energy industry, the elicitation unit can provide questions related to energy efficiency. This allows for more appropriate dialogue by providing questions customized according to the user's industry or job role. Some or all of the above-described processing in the elicitation unit may be performed using, or without, AI. For example, the elicitation unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to generate customized questions.

[0075] When generating a question, the elicitation unit can check the consistency of the user's answer and ask a re-question if there is a contradiction. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of a contradiction. For example, if the answer contradicts a previous answer given by the user, the elicitation unit can ask a question to confirm that point. Furthermore, if the user's answer is ambiguous, the elicitation unit can ask a question to seek specific details. Furthermore, if the user's answer is unclear, the elicitation unit can ask a question to confirm that point. For example, if the answer contradicts a previous answer given by the user, the elicitation unit can ask a question to confirm that point. In this way, the consistency of the user's answer can be checked and if there is a contradiction, a re-question can be asked, thereby eliciting more accurate information. Some or all of the above-described processing in the elicitation unit can be performed, for example, using AI or without AI. For example, the elicitation unit can input the user's answer data into the generation AI and cause the generation AI to check the consistency and generate a re-question.

[0076] The elicitation unit can estimate the user's emotions and determine the order of questions based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is excited, the elicitation unit can prioritize important questions. Furthermore, when the user is relaxed, the elicitation unit can postpone detailed questions. Furthermore, when the user is stressed, the elicitation unit can start with simple questions. For example, when the user is excited, the elicitation unit prioritizes important questions. This allows for more effective dialogue by determining the order of questions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the elicitation unit can be performed using, for example, an AI. For example, the elicitation unit can input the user's emotion data into the generation AI and have the generation AI determine the order of questions.

[0077] When generating questions, the elicitation unit can provide relevant questions based on the user's geographical background. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the elicitation unit can provide questions related to the city's SDGs. Furthermore, if the user lives in a rural area, the elicitation unit can provide questions related to agriculture. Furthermore, if the user lives overseas, the elicitation unit can provide questions related to the country's SDGs. For example, if the user lives in an urban area, the elicitation unit can provide questions related to the city's SDGs. This allows for more appropriate dialogue by providing questions that take the user's geographical background into consideration. Some or all of the above-described processing in the elicitation unit may be performed using, or without, AI. For example, the elicitation unit can input the user's geographical background data into the generation AI and cause the generation AI to generate relevant questions.

[0078] When generating a question, the elicitation unit can analyze the user's social media activity and generate a related question. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The elicitation unit can generate a question based on, for example, topics in which the user has shown interest on social media. The elicitation unit can also analyze the content of the user's posts on social media to generate a related question. Furthermore, the elicitation unit can also generate a related question by referring to the activities of the user's friends on social media. For example, the elicitation unit generates a question based on topics in which the user has shown interest on social media. In this way, related questions can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's social media activity data into the generation AI and cause the generation AI to generate a related question.

[0079] When generating a question, the elicitation unit can customize the question content by reflecting the user's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, and the like. For example, the elicitation unit generates a related question based on feedback provided by the user in the past. The elicitation unit can also generate a question that reflects areas for improvement from the user's past feedback. Furthermore, the elicitation unit can analyze the user's past feedback and generate an optimal question. For example, the elicitation unit generates a related question based on feedback provided by the user in the past. In this way, more appropriate questions can be generated by reflecting the user's past feedback. Some or all of the above-described processing in the elicitation unit may be performed using, for example, AI, or may be performed without using AI. For example, the elicitation unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the question content.

[0080] The suggestion unit can estimate the user's emotion and adjust the format of suggestions based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is excited, the suggestion unit can provide concise and specific suggestions. Furthermore, when the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, when the user is stressed, the suggestion unit can reduce the frequency of suggestions and provide suggestions tailored to the user's pace. For example, when the user is excited, the suggestion unit provides concise and specific suggestions. This allows for more effective suggestions by adjusting the way suggestions are expressed according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format of the suggestions.

[0081] When making a proposal, the suggestion unit can adjust the details of the proposal based on the importance of the product. Examples of the importance of the product include, but are not limited to, sales contribution and customer satisfaction. For example, the suggestion unit provides detailed information for an important product. The suggestion unit can also provide concise information for a product with low importance. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the importance of the product. For example, the suggestion unit provides detailed information for an important product. This allows for adjusting the level of detail of the proposal depending on the importance of the product, thereby making it possible to make a more appropriate proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input product importance data into a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] When making a proposal, the suggestion unit can apply a different proposal algorithm depending on the category of the product. Product categories include, but are not limited to, product categories and service categories. For example, in the case of energy-related products, the suggestion unit can apply a proposal algorithm related to energy efficiency. Furthermore, in the case of education-related products, the suggestion unit can also apply a proposal algorithm related to educational programs. Furthermore, in the case of medical-related products, the suggestion unit can also apply a proposal algorithm related to medical technology. For example, in the case of energy-related products, the suggestion unit applies a proposal algorithm related to energy efficiency. This allows for more appropriate proposals by applying a proposal algorithm according to the category of the product. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input product category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0083] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Past proposal results include, but are not limited to, the success rate of the proposal, customer reactions, etc. For example, the suggestion unit makes relevant suggestions based on proposals previously accepted by the user. The suggestion unit can also make optimal suggestions by avoiding proposals previously rejected by the user. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit makes relevant suggestions based on proposals previously accepted by the user. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0084] The suggestion unit can estimate the user's emotion and adjust the content of the suggestion based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is excited, the suggestion unit can provide a short and specific suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the user is stressed, the suggestion unit can provide a concise suggestion. For example, if the user is excited, the suggestion unit can provide a short and specific suggestion. This allows for adjusting the length of the suggestion according to the user's emotion, thereby making the suggestion more effective. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the suggestion.

[0085] When making a proposal, the proposal unit can determine the order of proposals based on the timing of product release. Examples of product release timing include, but are not limited to, seasonality and campaign periods. For example, the proposal unit prioritizes proposals for products that need to be released immediately. The proposal unit can also postpone proposals for products whose release dates are further away. Furthermore, the proposal unit can determine the priority of proposals based on the timing of product release. For example, the proposal unit prioritizes proposals for products that need to be released immediately. By determining the priority of proposals based on the timing of product release, proposals can be made at more appropriate times. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input product release timing data into the generation AI and have the generation AI determine the order of proposals.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. Examples of the relevance of the products include, but are not limited to, similarity in function and commonality of use. For example, the suggestion unit prioritizes suggesting products that are most relevant to the user's needs. The suggestion unit can also postpone less relevant products. Furthermore, the suggestion unit can adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit prioritizes suggesting products that are most relevant to the user's needs. This allows for more appropriate suggestions by adjusting the order of suggestions based on the relevance of the products. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of the products into a generation AI and cause the generation AI to adjust the order of suggestions.

[0087] When making a suggestion, the suggestion unit can adjust the use of terms in the suggestion according to the user's level of expertise. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, the suggestion unit uses a lot of technical terms when the user has technical expertise. Furthermore, the suggestion unit can also use simple language when the user does not have technical expertise. Furthermore, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, the suggestion unit uses a lot of technical terms when the user has technical expertise. By adjusting the use of technical terms in the suggestion according to the user's level of expertise, a more understandable suggestion can be made. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terms in the suggestion.

[0088] The introduction unit can estimate the user's emotions and adjust the display format of the case studies based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. For example, when the user is excited, the introduction unit displays concise and specific case studies. Furthermore, when the user is relaxed, the introduction unit can display detailed case studies. Furthermore, when the user is stressed, the introduction unit can reduce the frequency of case study display and display the case studies at a pace that suits the user. For example, when the user is excited, the introduction unit displays concise and specific case studies. This allows for more effective case study introduction by adjusting the case study display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the introduction unit may be performed using, for example, an AI. For example, the introduction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the case study display format.

[0089] When introducing a case, the introduction unit can select the latest case by referring to past case data. Past case data includes, for example, successful cases and unsuccessful cases, but is not limited to such examples. For example, the introduction unit selects the current case based on past successful cases. The introduction unit can also select the optimal case by avoiding past unsuccessful cases. Furthermore, the introduction unit can analyze past case data and select the current case. For example, the introduction unit selects the current case based on past successful cases. In this way, by referring to the past case data, the current case can be more appropriately selected. Some or all of the above-mentioned processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input past case data into the generation AI and cause the generation AI to select the latest case.

[0090] When introducing a case study, the introduction unit can provide a case study tailored to the user's industry or job role. Examples of industries include, but are not limited to, the manufacturing industry, the service industry, and the IT industry. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the introduction unit can provide a case study related to energy efficiency. Also, if the user is in the education industry, the introduction unit can provide a case study related to educational programs. Furthermore, if the user is in the medical industry, the introduction unit can provide a case study related to medical technology. For example, if the user is in the energy industry, the introduction unit can provide a case study related to energy efficiency. This allows for more appropriate case study introduction by providing a case study tailored to the user's industry or job role. Some or all of the above-described processing in the introduction unit may be performed using, or without, AI. For example, the introduction unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to provide customized case studies.

[0091] When introducing a case study, the introduction unit may check the consistency of the user's answer and reintroduce the answer if there is a discrepancy. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of a discrepancy. For example, if the introduction unit finds a discrepancy with the user's previous answer, it may introduce an example to confirm that point. Furthermore, if the user's answer is ambiguous, the introduction unit may also introduce an example to request specific details. Furthermore, if the user's answer is unclear, the introduction unit may introduce an example to confirm that point. For example, if the introduction unit finds a discrepancy with the user's previous answer, it may introduce an example to confirm that point. In this way, by checking the consistency of the user's answer and reintroducing the answer if there is a discrepancy, more accurate information can be provided. Some or all of the above-described processing in the introduction unit may be performed, for example, using AI, or may be performed without AI. For example, the introduction unit may input the user's answer data into a generation AI and cause the generation AI to check the consistency and generate a reintroduction.

[0092] The introduction unit can estimate the user's emotions and determine the order of the cases based on the estimated user emotions. Examples of emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is excited, the introduction unit can prioritize introducing important cases. Furthermore, if the user is relaxed, the introduction unit can postpone introducing detailed cases. Furthermore, if the user is stressed, the introduction unit can start with simple cases. For example, if the user is excited, the introduction unit can prioritize introducing important cases. This allows for more effective case introduction by determining the priority of cases according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the introduction unit can be performed using, for example, an AI. For example, the introduction unit can input the user's emotion data into the generation AI and have the generation AI determine the order of the cases.

[0093] When introducing a case study, the introduction unit can provide relevant cases based on the user's geographical background. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the introduction unit can provide cases related to urban SDGs. Furthermore, if the user lives in a rural area, the introduction unit can provide cases related to agriculture. Furthermore, if the user lives overseas, the introduction unit can provide cases related to the SDGs of that country. For example, if the user lives in an urban area, the introduction unit can provide cases related to urban SDGs. This allows for more appropriate case study introductions by providing cases that take the user's geographical background into consideration. Some or all of the above-described processing in the introduction unit may be performed using, or without, AI. For example, the introduction unit can input the user's geographical background data into the generation AI and cause the generation AI to provide relevant cases.

[0094] When introducing a case study, the introduction unit can analyze the user's social media activity and introduce related cases. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. For example, the introduction unit can introduce cases based on topics in which the user has shown interest on social media. The introduction unit can also analyze the content of the user's social media posts and introduce related cases. Furthermore, the introduction unit can introduce related cases by referring to the activities of the user's friends on social media. For example, the introduction unit introduces cases based on topics in which the user has shown interest on social media. In this way, related cases can be introduced by analyzing the user's social media activity. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the user's social media activity data into the generation AI and cause the generation AI to introduce related cases.

[0095] When introducing a case study, the introduction unit can individually adjust the case study content by reflecting the user's past feedback. Past feedback includes, but is not limited to, evaluation comments, survey results, etc. For example, the introduction unit can introduce related case studies based on feedback previously provided by the user. The introduction unit can also introduce case studies that reflect improvements based on the user's past feedback. Furthermore, the introduction unit can analyze the user's past feedback and introduce optimal case studies. For example, the introduction unit can introduce related case studies based on feedback previously provided by the user. In this way, more appropriate case studies can be introduced by reflecting the user's past feedback. Some or all of the above-described processing in the introduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the introduction unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the case study content.

[0096] The handover unit can estimate the user's emotion and adjust the timing of handover to sales based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is excited, the handover unit quickly tosses the ball to sales. Also, if the user is relaxed, the handover unit can toss the ball to sales at an appropriate timing. Furthermore, if the user is stressed, the handover unit can delay the timing of the toss. For example, if the user is excited, the handover unit quickly tosses the ball to sales. This allows for more effective sales activities by adjusting the timing of the toss to sales according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the handover unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the handover unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of the toss-up to the salesperson.

[0097] The handover unit can analyze the user's past interaction history at the time of toss-up and select a salesperson. The past interaction history includes, for example, the content of the interaction and the frequency of the interaction, but is not limited to these examples. For example, the handover unit selects a salesperson to whom the user has previously shown a favorable response. The handover unit can also select the optimal salesperson from the user's past interaction history. Furthermore, the handover unit can select a salesperson that best meets the user's needs. For example, the handover unit selects a salesperson to whom the user has previously shown a favorable response. In this way, the optimal salesperson can be selected by analyzing the user's past interaction history. Some or all of the above-described processing in the handover unit may be performed, for example, using AI or without using AI. For example, the handover unit can input the user's past interaction history data into the generation AI and cause the generation AI to select a salesperson.

[0098] The handover unit can provide sales materials tailored to the user's industry or job role at the time of toss-up. Examples of industries include, but are not limited to, manufacturing, service, and IT. Examples of job roles include, but are not limited to, sales, technical, and management roles. For example, if the user is in the energy industry, the handover unit can provide sales materials related to energy efficiency. Also, if the user is in the education industry, the handover unit can provide sales materials related to educational programs. Furthermore, if the user is in the medical industry, the handover unit can provide sales materials related to medical technology. For example, if the user is in the energy industry, the handover unit can provide sales materials related to energy efficiency. This allows for more effective sales activities by providing sales materials tailored to the user's industry and job role. Some or all of the above-described processing in the handover unit may be performed using, or without, AI. For example, the handover unit can input data related to the user's industry and job role into the generation AI and cause the generation AI to provide customized sales materials.

[0099] The handover unit can check the consistency of the user's answer at the time of toss-up and reconfirm any inconsistencies. Consistency includes, but is not limited to, the consistency of the answer and the presence or absence of inconsistencies. For example, if the answer contradicts the user's previous answer, the handover unit can ask a second question to confirm that point. Furthermore, if the user's answer is unclear, the handover unit can ask a second question to seek specific details. Furthermore, if the user's answer is unclear, the handover unit can ask a second question to confirm that point. For example, if the answer contradicts the user's previous answer, the handover unit can ask a second question to confirm that point. In this way, by checking the consistency of the user's answer and reconfirming any inconsistencies, more accurate information can be provided. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's answer data into a generation AI and cause the generation AI to check the consistency and generate a second question.

[0100] The handover unit can estimate the user's emotions and determine the order of handover based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. For example, if the user is excited, the handover unit prioritizes tossing. Furthermore, if the user is relaxed, the handover unit can also toss at an appropriate timing. Furthermore, if the user is stressed, the handover unit can lower the priority of tossing. For example, if the user is excited, the handover unit prioritizes tossing. This allows for more effective sales activities by determining the priority of tossing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the handover unit may be performed using, for example, an AI, or without an AI. For example, the handover unit can input the user's emotional data into the generation AI and have the generation AI determine the order of handover.

[0101] The handover unit can select a relevant salesperson based on the user's geographical background during a toss-up. Examples of geographical background include, but are not limited to, the user's place of residence and regional characteristics. For example, if the user lives in an urban area, the handover unit can select an urban salesperson. Furthermore, if the user lives in a rural area, the handover unit can select a salesperson knowledgeable in agriculture. Furthermore, if the user lives overseas, the handover unit can select a salesperson from that country. For example, if the user lives in an urban area, the handover unit selects an urban salesperson. This allows for more effective sales activities by selecting a salesperson that takes the user's geographical background into consideration. Some or all of the above-described processing in the handover unit can be performed using, or without, AI. For example, the handover unit can input the user's geographical background data into the generation AI and cause the generation AI to select a relevant salesperson.

[0102] The handover unit can analyze the user's social media activity at the time of toss-up and provide relevant sales materials. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The handover unit can provide sales materials, for example, based on topics in which the user has shown interest on social media. The handover unit can also analyze the content of the user's social media posts and provide relevant sales materials. Furthermore, the handover unit can provide relevant sales materials by referring to the activities of the user's friends on social media. For example, the handover unit provides sales materials based on topics in which the user has shown interest on social media. In this way, relevant sales materials can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's social media activity data into the generation AI and cause the generation AI to provide relevant sales materials.

[0103] The handover unit can individually adjust the toss-up content by reflecting the user's past feedback during the toss-up. Examples of past feedback include, but are not limited to, evaluation comments and survey results. For example, the handover unit can provide relevant sales materials based on feedback previously provided by the user. The handover unit can also provide sales materials that reflect improvements based on the user's past feedback. Furthermore, the handover unit can analyze the user's past feedback and provide optimal sales materials. For example, the handover unit can provide relevant sales materials based on feedback previously provided by the user. This allows more appropriate sales materials to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the handover unit can be performed, for example, using AI or without AI. For example, the handover unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the toss-up content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned drawer, suggestion unit, introduction unit, and handover unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the drawer unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the introduction unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the handover unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned drawer, suggestion unit, introduction unit, and handover unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the drawer is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the introduction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the handover unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned drawer, suggestion unit, introduction unit, and handover unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the drawer is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the introduction unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the handover unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned retraction unit, suggestion unit, introduction unit, and handover unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the retraction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the introduction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the handover unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The system can also analyze the user's past purchase history and generate related questions to elicit the user's needs. For example, the elicitation unit generates related questions based on products and services the user has purchased in the past. The elicitation unit can also generate questions related to unresolved problems from the user's purchase history. Furthermore, the elicitation unit can analyze the user's purchase history and generate optimal questions. In this way, the system can generate optimal questions for the user by analyzing the past purchase history.

[0106] The suggestion unit can also make customization suggestions for the product based on the user's needs. For example, if the user is looking for a specific function, the suggestion unit can suggest a product with enhanced functionality. The suggestion unit can also customize the product package according to the user's needs. Furthermore, the suggestion unit can also make suggestions for improving the product based on user feedback. This makes it possible to make more appropriate suggestions by making customization suggestions according to the user's needs.

[0107] The introduction unit can also provide interactive case studies to attract the user's interest. For example, the introduction unit can provide detailed information about cases that the user has shown interest in. The introduction unit can also provide an interactive platform where the user can ask questions. Furthermore, the introduction unit can adjust the content of the case studies in real time according to the user's responses. This makes it possible to provide an interactive case study that attracts the user's interest and provides a more concrete image.

[0108] The handover unit can also select the most suitable sales representative based on the user's needs. For example, if the user is interested in a particular field, the handover unit can select a sales representative who is knowledgeable in that field. The handover unit can also select a sales representative taking into consideration the sales representative's skill set according to the user's needs. Furthermore, the handover unit can select the most suitable sales representative based on the user's past interaction history. This allows for more effective sales activities by selecting the most suitable sales representative according to the user's needs.

[0109] The elicitor can also estimate the user's emotions and adjust the difficulty of questions based on the estimated user's emotions. For example, the elicitor can ask detailed and difficult questions when the user is relaxed. Alternatively, the elicitor can ask simple and specific questions when the user is stressed. Furthermore, the elicitor can adjust the frequency of questions according to the user's emotions. This allows for more appropriate dialogue by adjusting the difficulty of questions according to the user's emotions.

[0110] The suggestion unit can also estimate the user's emotions and adjust the timing of the suggestion based on the estimated user's emotions. For example, the suggestion unit can make a suggestion immediately if the user is excited. Also, the suggestion unit can make a suggestion at an appropriate time if the user is relaxed. Furthermore, the suggestion unit can delay the timing of the suggestion if the user is feeling stressed. In this way, by adjusting the timing of the suggestion according to the user's emotions, more effective suggestions can be made.

[0111] The introduction unit can also estimate the user's emotions and adjust the level of detail of the cases based on the estimated user's emotions. For example, if the user is excited, the introduction unit can display concise and specific cases. Also, if the user is relaxed, the introduction unit can display detailed cases. Furthermore, if the user is feeling stressed, the introduction unit can reduce the frequency of case display and display cases at a pace that suits the user. In this way, by adjusting the level of detail of the cases according to the user's emotions, more effective case introduction can be performed.

[0112] The handover unit can also estimate the user's emotions and adjust the content of the handover to sales based on the estimated user's emotions. For example, if the user is excited, the handover unit can provide concise and specific handover content. Also, if the user is relaxed, the handover unit can provide detailed handover content. Furthermore, if the user is feeling stressed, the handover unit can simplify the handover content and perform the handover at a pace that suits the user. In this way, by adjusting the handover content according to the user's emotions, more effective sales activities can be performed.

[0113] The elicitor can also estimate the user's emotions and determine the order of questions based on the estimated user's emotions. For example, if the user is excited, the elicitor can prioritize important questions. If the user is relaxed, the elicitor can postpone detailed questions. Furthermore, if the user is feeling stressed, the elicitor can start with simple questions. In this way, by determining the order of questions according to the user's emotions, a more effective dialogue can be achieved.

[0114] When making a suggestion, the suggestion unit can analyze the user's purchase history and suggest related products. For example, the suggestion unit can suggest related products based on products or services the user has purchased in the past. The suggestion unit can also suggest products related to unresolved problems based on the user's purchase history. Furthermore, the suggestion unit can analyze the user's purchase history and suggest optimal products. In this way, the most optimal products can be suggested to the user by analyzing the user's past purchase history.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The elicitation unit elicits the user's needs. The elicitation unit uses natural language processing technology to analyze the user's input and generate appropriate questions. For example, it generates questions such as, "In what areas would you like to work on SDGs?" It can also analyze the user's past dialogue history to generate optimal questions. It can also estimate the user's emotions and adjust the content and timing of questions based on the estimated emotions. Step 2: The suggestion unit suggests products based on the needs extracted by the elicitation unit. The suggestion unit references the product database and selects the most suitable product. For example, if the user is interested in improving energy efficiency, the suggestion unit suggests energy-related products. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. Step 3: The introduction section introduces case studies related to the products proposed by the proposal section. The introduction section introduces how other companies are working on the SDGs and provides success stories. It can also estimate the user's emotions and adjust the way the case studies are displayed based on the estimated emotions. Step 4: The handover department transfers the case to sales based on the case introduced by the introduction department. If the user shows interest in the proposal, the handover department contacts the sales representative and moves the case forward to specific negotiations. For example, if the user is excited, the case can be transferred to sales immediately.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A drawer that draws out user needs; a proposal unit that proposes commercial products based on the needs extracted by the extraction unit; an introduction unit that introduces examples related to the merchandise proposed by the proposal unit; a handover unit that transfers the case to sales based on the case introduced by the introduction unit; Equipped with A system characterized by:

2. The drawer portion is Analyzes user input using natural language processing technology and generates appropriate questions 2. The system of claim 1.

3. The proposal unit Refer to SoftBank's product database and select products 2. The system of claim 1.

4. The introduction unit Introducing other companies' SDG initiatives 2. The system of claim 1.

5. The transition portion is If the user is interested in the proposal, they will contact the sales representative and move forward with the deal.

2. The system of claim 1.

6. The drawer portion is Estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions.

2. The system of claim 1.

7. The drawer portion is Analyze the user's past conversation history and generate questions 2. The system of claim 1.

8. The drawer portion is Provides questions tailored to the user's industry or job role at question generation time 2. The system of claim 1.

9. The drawer portion is When generating a question, check the consistency of the user's answers and ask the question again if there are any inconsistencies.

2. The system of claim 1.

10. The drawer portion is Estimate the user's emotions and determine the order of questions based on the estimated user emotions 2. The system of claim 1.

Images