System

Abstract

An object of a system according to an embodiment is to propose an optimal contact means or communication strategy in sales activities.SOLUTION: A system includes a collection unit, an analysis unit, and a feedback unit. The collection part collects attribute information and past business activity information of a customer. The analysis unit analyzes the information collected by the collection unit and proposes an appropriate contact means or communication strategy. The feedback unit collects feedback from the salesperson and provides the feedback to the analysis 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] With conventional technology, finding the optimal contact method and communication strategy for sales activities required a lot of time and effort.

[0005] The system according to the embodiment aims to propose optimal contact methods and communication strategies for sales activities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects customer attribute information and past sales activity information. The analysis unit analyzes the information collected by the collection unit and proposes appropriate contact methods and communication strategies. The feedback unit collects feedback from sales representatives and provides it to the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal contact methods and communication strategies for sales activities. [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 sales support system according to an embodiment of the present invention collects customer attribute information and past sales activity information, and a generation AI proposes optimal contact methods and communication strategies based on the collected information, collecting feedback and successively optimizing the system. The sales support system also includes a feedback function that collects real-time feedback from sales representatives and customer responses, and successively optimizes the generation AI's predictions and proposals based on the collected information. For example, the sales support system collects information such as a customer's age, occupation, and past purchase history. For example, the sales support system collects data such as which insurance products the customer purchased and how they responded to past sales activities. The sales support system then uses a generation AI to analyze the collected information and propose optimal contact methods and communication strategies. The input to the generation AI is the collected customer information itself, and the generation AI proposes optimal contact methods and communication strategies based on the collected information. For example, the generation AI may suggest, "Telephone is the best option for this customer." The sales support system then uses a feedback function to collect real-time feedback from sales representatives and customer responses. For example, when a sales representative inputs the content of their conversation with a customer and their reactions, the generation AI analyzes that information and improves the next contact method and communication strategy. This allows the sales support system to realize a new way of selling and significantly improve overall sales efficiency and quality. This allows the sales support system to significantly improve the efficiency and quality of sales activities. For example, sales representatives can use the optimal contact method and communication strategy suggested by the generation AI to efficiently contact customers and conduct effective sales activities. In addition, the feedback function allows for the collection of field voices and customer reactions in real time, which the generation AI then sequentially optimizes, improving the quality of sales activities. This will improve the efficiency of insurance sales activities and is expected to have a major impact on the huge market.

[0029] A sales support system according to an embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects customer attribute information and past sales activity information. The customer attribute information includes, but is not limited to, age, gender, occupation, and income. The past sales activity information includes, but is not limited to, purchase history, contact history, and response history. The collection unit, for example, acquires the customer's age and occupation from a database. The collection unit can also record customer responses to past sales activities. For example, the collection unit collects information on insurance products previously purchased by the customer. The analysis unit uses a generative AI to analyze the information collected by the collection unit and propose optimal contact methods and communication strategies. Examples of contact methods include, but are not limited to, telephone, email, and face-to-face contact. For example, the analysis unit proposes optimal contact methods based on the customer's age and occupation. The analysis unit can also improve the next contact method and communication strategy by taking into account responses to past sales activities. For example, the analysis unit prioritizes suggesting contact methods to which the customer has previously responded favorably. The feedback unit collects feedback from sales representatives and provides it to the analysis unit. Feedback includes, but is not limited to, for example, input of conversation content and reactions between sales representatives and customers. For example, the feedback unit inputs the conversation content between sales representatives and customers, and provides the information to the analysis unit. The feedback unit can also collect reactions from customers in real time. For example, the feedback unit records reactions shown by customers to sales representatives and provides the information to the analysis unit. Thus, the sales support system according to the embodiment can improve the efficiency and quality of sales activities through the collection and analysis of customer information and the collection of feedback.

[0030] The collection unit can collect information on the customer's age, occupation, and past purchase history. The collection unit, for example, acquires the customer's age from a database. For example, the collection unit can classify the customer's age into ranges such as teens, twenties, and thirties. The collection unit can also acquire the customer's occupation from the database. For example, the collection unit can classify the customer's occupation by specific occupation such as doctor, engineer, and teacher. The collection unit can also acquire the customer's past purchase history from the database. For example, the collection unit collects information on insurance products the customer has previously purchased. This allows for more accurate analysis by collecting detailed customer attribute information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's age and occupation information into the generation AI and have the generation AI classify the information.

[0031] The analysis unit can suggest an appropriate contact method based on the collected information. The analysis unit can suggest the optimal contact method based on, for example, the collected customer information's age and occupation. For example, if the customer is in their 20s, the analysis unit can suggest contact via email. Furthermore, if the customer is a doctor, the analysis unit can also suggest face-to-face contact. Furthermore, the analysis unit can suggest the next contact method taking into account responses to past sales activities. For example, if the customer responded favorably to phone contact in the past, the analysis unit can suggest phone contact again next time. This allows the effectiveness of sales activities to be improved by suggesting the optimal contact method based on customer attribute information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected customer information into a generation AI, causing the generation AI to suggest the optimal contact method.

[0032] The feedback unit allows a salesperson to input the content of a conversation with a customer or their reaction, and the generation AI can analyze that information and improve the next contact method or communication strategy. For example, the feedback unit collects information by having the salesperson input the content of the conversation with the customer. For example, the salesperson can input the content of the conversation with the customer in text format. The feedback unit can also collect information by inputting the customer's reaction. For example, the salesperson can input the customer's reaction in categories such as positive, negative, and neutral. The collected information is analyzed by the generation AI and used to improve the next contact method or communication strategy. For example, the generation AI analyzes the customer reaction input by the salesperson and suggests the next contact method. This allows the quality of sales activities to be improved by improving the next contact method or communication strategy based on feedback from the salesperson. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the customer reaction input by the salesperson into the generation AI, causing the generation AI to suggest the next contact method.

[0033] The analysis unit can improve the next contact method or communication strategy by taking into account responses to past sales activities. The analysis unit, for example, suggests the next contact method by taking into account customer responses to past sales activities. For example, if a customer has responded favorably to email contact in the past, the analysis unit can suggest email contact again next time. The analysis unit can also improve the next communication strategy based on the customer's response to past sales activities. For example, if a customer has responded negatively to face-to-face contact in the past, the analysis unit can suggest telephone contact next time. In this way, the next contact method or communication strategy can be optimized by taking into account responses to past sales activities. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input customer responses to past sales activities into a generation AI, causing the generation AI to suggest the next contact method.

[0034] The feedback unit can collect feedback from sales representatives in the field or responses from customers in real time. For example, the feedback unit can input customer responses collected by sales representatives in real time. For example, the feedback unit can allow sales representatives to input customer responses using a smartphone. The feedback unit can also collect customer responses in real time. For example, the feedback unit can allow customers to input their responses through an online form. The collected information is analyzed by the generation AI and used to improve the next contact method and communication strategy. For example, the generation AI analyzes customer responses collected in real time and suggests the next contact method. In this way, by collecting feedback in real time, the predictions and suggestions of the generation AI can be sequentially optimized. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input customer responses collected in real time into the generation AI, causing the generation AI to suggest the next contact method.

[0035] The collection unit can analyze the customer's past response history and select an appropriate collection method. The collection unit can, for example, retrieve and analyze the customer's past response history from a database. For example, the collection unit prioritizes selection of collection methods (such as telephone and email) to which the customer has responded favorably in the past. The collection unit can also select collection methods to which the customer has responded quickly in the past, enabling efficient information collection. Furthermore, the collection unit can avoid collection methods to which the customer has responded negatively in the past and select other methods. This enables efficient information collection by selecting the optimal collection method based on the past response history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the customer's past response history into a generation AI, causing the generation AI to select the optimal collection method.

[0036] When collecting customer information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the collection unit can obtain the customer's current living situation from a database and filter the information. For example, the collection unit can collect highly relevant information based on the customer's family structure and living situation. The collection unit can also obtain the customer's areas of interest from a database and filter the information. For example, the collection unit prioritizes collecting information related to hobbies and topics that the customer is interested in. Furthermore, the collection unit can filter and collect information that is likely to be of interest to the customer based on the customer's recent activity history. In this way, highly relevant information can be collected by filtering the information based on the customer's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the customer's living situation and areas of interest into a generation AI and have the generation AI filter the information.

[0037] When collecting customer information, the collection unit can select an appropriate collection method depending on the customer's input method. For example, if the customer prefers voice input, the collection unit can prioritize voice information collection. For example, if the customer provides information via voice using a microphone, the collection unit can collect information using voice recognition technology. Furthermore, if the customer prefers text input, the collection unit can also collect text-based information. For example, if the customer provides information via text using a keyboard, the collection unit can collect information using text analysis technology. Furthermore, if the customer provides information using an image, the collection unit can collect information using image analysis. For example, if the customer provides an image using a smartphone camera, the collection unit can collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection method depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data depending on the customer's input method into a generation AI, causing the generation AI to select the optimal collection method.

[0038] The collection unit can analyze the customer's purchase history and set priorities for the information to be collected. The collection unit can, for example, obtain and analyze the customer's purchase history from a database. For example, the collection unit prioritizes collecting information related to products the customer has previously purchased. The collection unit can also collect information related to products the customer is likely to purchase next from the customer's purchase history. Furthermore, the collection unit can also collect information about new products that the customer may be interested in based on the customer's purchase history. This enables efficient information collection by determining the priority of information based on the customer's purchase history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's purchase history into a generation AI and have the generation AI set the priority of information.

[0039] When collecting customer information, the collection unit can collect highly relevant information by taking into account the customer's geographical location information. The collection unit can, for example, obtain the customer's geographical location information from a database and collect the information. For example, the collection unit can prioritize collecting information related to the area where the customer is currently located. The collection unit can also collect information about nearby events and services based on the customer's geographical location information. Furthermore, the collection unit can also collect area-specific information by taking into account the customer's geographical location information. In this way, by taking into account the customer's geographical location information, highly relevant information can be collected efficiently. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location information to a generation AI and cause the generation AI to collect highly relevant information.

[0040] When collecting customer information, the collection unit can analyze the customer's social media activities and collect related information. The collection unit can, for example, retrieve and analyze the customer's social media activities from a database. For example, the collection unit can collect related information based on information the customer shared on social media. The collection unit can also analyze the customer's social media activity history and collect information that may be of interest to the customer. Furthermore, the collection unit can also collect related information by referring to the activities of the customer's friends on social media. In this way, by analyzing the customer's social media activities, highly relevant information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's social media activity data into a generation AI and cause the generation AI to collect related information.

[0041] When collecting customer information, the collection unit can set the collection method by reflecting the customer's past feedback. The collection unit can, for example, retrieve and analyze the customer's past feedback from a database. For example, the collection unit preferentially uses a collection method for which the customer has given favorable feedback in the past. The collection unit can also improve the collection method based on the customer's past feedback. Furthermore, the collection unit can customize the collection method by referring to the customer's past feedback. In this way, the collection method can be optimized by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI set the collection method.

[0042] The analysis unit can set the level of detail of the analysis based on the importance of the customer information during analysis. The analysis unit can, for example, obtain the importance of the customer information from a database and set the level of detail of the analysis. For example, the analysis unit can perform a detailed analysis on customer information with high importance. The analysis unit can also perform a simplified analysis on customer information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the customer information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the customer information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer information importance data into the generation AI and have the generation AI set the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the customer category. The analysis unit can, for example, obtain the customer category from a database and apply an appropriate analysis algorithm. For example, if the customer is an individual, the analysis unit can apply an analysis algorithm for individuals. Also, if the customer is a corporation, the analysis unit can apply an analysis algorithm for corporations. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the customer category (age, occupation, etc.). This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the customer category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer category data into a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the customer's past reaction results. The analysis unit can, for example, obtain the customer's past reaction results from a database and improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm based on the customer's past reaction results. The analysis unit can also improve the accuracy of the analysis by referring to the customer's past reaction results. Furthermore, the analysis unit can add the customer's past reaction results to a dataset and improve the analysis model. This improves the accuracy of the analysis by referring to the customer's past reaction results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the customer's past reaction result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can suggest an appropriate contact method by taking into account the customer's purchase history. The analysis unit can, for example, retrieve the customer's purchase history from a database and suggest an appropriate contact method. For example, the analysis unit can suggest the optimal contact method (telephone, email, etc.) based on the customer's purchase history. The analysis unit can also suggest a contact method related to a product that the customer is likely to purchase next based on the customer's purchase history. Furthermore, the analysis unit can select an effective contact method by taking into account the customer's purchase history. In this way, the optimal contact method can be suggested by taking into account the customer's purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the customer's purchase history data into the generation AI and have the generation AI suggest an appropriate contact method.

[0046] During analysis, the analysis unit can prioritize proposals based on the time of submission of the customer information. The analysis unit can, for example, obtain the time of submission of the customer information from a database and set the priority of proposals. For example, the analysis unit prioritizes proposals if the customer information was submitted recently. The analysis unit can also dynamically adjust the priority of proposals based on the time of submission of the customer information. Furthermore, the analysis unit can make proposals at the optimal timing, taking into account the time of submission of the customer information. This enables efficient proposals by determining the priority of proposals based on the time of submission of the customer information. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of submission of the customer information into the generation AI and cause the generation AI to set the priority of proposals.

[0047] During analysis, the analysis unit can set the order of proposals based on the relevance of the customer information. The analysis unit can, for example, obtain the relevance of the customer information from a database and set the order of proposals. For example, the analysis unit prioritizes proposals when the relevance of the customer information is high. The analysis unit can also dynamically adjust the order of proposals based on the relevance of the customer information. Furthermore, the analysis unit can make proposals in an optimal order taking into account the relevance of the customer information. This enables more effective proposals by adjusting the order of proposals based on the relevance of the customer information. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the customer information into the generation AI and have the generation AI set the order of proposals.

[0048] During analysis, the analysis unit can set the use of technical terminology in the proposal according to the customer's level of expertise. The analysis unit can, for example, obtain the customer's level of expertise from a database and set the use of technical terminology in the proposal. For example, if the customer has technical expertise, the analysis unit can make a proposal that uses a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can make a proposal in simple language. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the proposal according to the customer's level of expertise. This allows for a proposal that is easier to understand by adjusting the use of technical terminology according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the customer's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0049] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the sales representative's past feedback history. The feedback unit can, for example, retrieve and refer to the sales representative's past feedback history from a database. For example, the feedback unit preferentially uses a collection method for which the sales representative has previously provided favorable feedback. The feedback unit can also improve the collection method based on the sales representative's past feedback history. Furthermore, the feedback unit can also customize the collection method by referring to the sales representative's past feedback history. In this way, the optimal collection method can be selected by referring to the sales representative's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the sales representative's past feedback history data into a generation AI and have the generation AI select a collection method.

[0050] The feedback unit can set the collection means based on the sales representative's current situation when collecting feedback. The feedback unit can, for example, obtain the sales representative's current situation from a database and set the collection means. For example, the feedback unit can provide a brief feedback form when the sales representative is busy. The feedback unit can also request detailed feedback when the sales representative is relaxed. Furthermore, the feedback unit can dynamically adjust the collection means according to the sales representative's current situation. This enables efficient feedback collection by customizing the collection means according to the sales representative's current situation. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's current situation data into a generation AI and have the generation AI set the collection means.

[0051] The feedback unit can set the collection method by reflecting the sales representative's feedback when collecting feedback. The feedback unit can, for example, obtain and analyze the sales representative's feedback from a database. For example, the feedback unit can improve the collection method based on the sales representative's feedback. The feedback unit can also customize the collection means by referring to the sales representative's feedback. Furthermore, the feedback unit can dynamically adjust the collection method by reflecting the sales representative's feedback. In this way, the collection method can be optimized by reflecting the sales representative's feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's feedback data into a generation AI and have the generation AI set the collection method.

[0052] The feedback unit can analyze customer responses when collecting feedback and improve the next contact method or communication strategy. The feedback unit can, for example, obtain and analyze customer responses from a database. For example, the feedback unit can improve the next contact method based on the customer responses. The feedback unit can also analyze the customer responses and optimize the communication strategy. Furthermore, the feedback unit can dynamically adjust the next contact method or communication strategy based on the customer responses. In this way, the next contact method and communication strategy can be optimized by analyzing the customer responses. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input customer response data into a generation AI and have the generation AI improve the next contact method and communication strategy.

[0053] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the geographic location information of the salesperson. The feedback unit can, for example, obtain the geographic location information of the salesperson from a database and select the collection method. For example, the feedback unit can prioritize collecting feedback related to the area where the salesperson is currently located. The feedback unit can also collect feedback from nearby customers based on the geographic location information of the salesperson. Furthermore, the feedback unit can collect region-specific feedback by taking into account the geographic location information of the salesperson. In this way, the optimal collection method can be selected by taking into account the geographic location information of the salesperson. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the geographic location information data of the salesperson to a generation AI and cause the generation AI to select the collection method.

[0054] When collecting feedback, the feedback unit can analyze the sales representative's social media activities to collect relevant feedback. The feedback unit can, for example, retrieve and analyze the sales representative's social media activities from a database. For example, the feedback unit can collect relevant feedback based on information shared by the sales representative on social media. The feedback unit can also analyze the sales representative's social media activity history to collect feedback that is likely to be of interest. Furthermore, the feedback unit can collect relevant feedback by referring to the activities of the sales representative's friends on social media. In this way, highly relevant feedback can be collected by analyzing the sales representative's social media activities. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's social media activity data into a generation AI and cause the generation AI to collect relevant feedback.

[0055] When collecting feedback, the feedback unit can set the collection method by reflecting the sales representative's past feedback. The feedback unit can, for example, retrieve and analyze the sales representative's past feedback from a database. For example, the feedback unit can preferentially use a collection method for which the sales representative has given favorable feedback in the past. The feedback unit can also improve the collection method based on the sales representative's past feedback. Furthermore, the feedback unit can customize the collection method by referring to the sales representative's past feedback. In this way, the collection method can be optimized by reflecting the sales representative's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the sales representative's past feedback data into a generation AI and have the generation AI set the collection method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the customer's social media activity and collect information based on the customer's areas of interest. For example, it can collect topics that the customer frequently mentions on social media. It can also collect related information based on the activities of the customer's friends on social media. It can also analyze the content of the customer's social media posts and collect information that may be of interest to the customer. This allows for more accurate analysis by collecting information based on the customer's areas of interest.

[0058] The analysis unit can analyze a customer's purchase history and predict the product that the customer is likely to purchase next. For example, it can suggest products related to products that the customer has previously purchased. It can also prioritize suggestions for products that the customer is likely to purchase next based on the customer's purchase history. It can also suggest new products that the customer may be interested in based on the customer's purchase history. This makes it possible to make optimal suggestions based on the customer's purchase history, thereby increasing the effectiveness of sales activities.

[0059] The analysis unit can adjust the content of the proposal according to the customer's level of expertise. For example, if the customer has specialized knowledge, the analysis unit can make a proposal that includes detailed technical information. If the customer does not have specialized knowledge, the analysis unit can make a proposal in simple language. Furthermore, the analysis unit can dynamically adjust the content of the proposal according to the customer's level of expertise. This allows the proposal to be made easier to understand by adjusting the content of the proposal according to the customer's level of expertise.

[0060] The collection unit can collect area-specific information by taking into account the geographical location information of the customer. For example, it can collect information about events and services related to the area where the customer is currently located. It can also collect information about nearby stores and services based on the geographical location information of the customer. It can also collect area-specific news and topics by taking into account the geographical location information of the customer. This allows for efficient collection of highly relevant information by taking into account the geographical location information of the customer.

[0061] The feedback unit can select the optimal collection method by referring to the sales representative's past feedback history. For example, it can preferentially use collection methods for which the sales representative has given favorable feedback in the past. It can also improve the collection method based on the sales representative's past feedback history. Furthermore, it can also customize the collection method by referring to the sales representative's past feedback history. In this way, it is possible to select the optimal collection method by referring to the sales representative's past feedback history.

[0062] The analysis unit can improve the content of the next proposal based on the customer's past response results. For example, it can prioritize the use of proposals that the customer has responded favorably to in the past. It can also dynamically adjust the content of the proposal based on the customer's past response results. Furthermore, it can also create new proposals by referring to the customer's past response results. In this way, the content of the next proposal can be optimized by referring to the customer's past response results.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects customer attribute information and past sales activity information. Customer attribute information includes age, gender, occupation, income, etc., and past sales activity information includes past purchase history, contact history, response history, etc. The collection unit can obtain the customer's age and occupation from the database and record the customer's response to past sales activities. Step 2: The analysis department uses generation AI to analyze the information collected by the collection department and proposes the optimal contact method and communication strategy. Contact methods include telephone, email, and face-to-face meetings, and proposes the optimal contact method based on the customer's age and occupation. It also takes into account responses to past sales activities to improve the next contact method and communication strategy. Step 3: The feedback department collects feedback from sales representatives and provides it to the analysis department. Feedback includes sales representatives inputting the content of their conversations with customers and their reactions, and it is also possible to collect reactions from customers in real time. This allows the analysis department to improve the next sales activity based on the feedback.

[0065] (Example 2) A sales support system according to an embodiment of the present invention collects customer attribute information and past sales activity information, and a generation AI proposes optimal contact methods and communication strategies based on the collected information, collecting feedback and successively optimizing the system. The sales support system also includes a feedback function that collects real-time feedback from sales representatives and customer responses, and successively optimizes the generation AI's predictions and proposals based on the collected information. For example, the sales support system collects information such as a customer's age, occupation, and past purchase history. For example, the sales support system collects data such as which insurance products the customer purchased and how they responded to past sales activities. The sales support system then uses a generation AI to analyze the collected information and propose optimal contact methods and communication strategies. The input to the generation AI is the collected customer information itself, and the generation AI proposes optimal contact methods and communication strategies based on the collected information. For example, the generation AI may suggest, "Telephone is the best option for this customer." The sales support system then uses a feedback function to collect real-time feedback from sales representatives and customer responses. For example, when a sales representative inputs the content of their conversation with a customer and their reactions, the generation AI analyzes that information and improves the next contact method and communication strategy. This allows the sales support system to realize a new way of selling and significantly improve overall sales efficiency and quality. This allows the sales support system to significantly improve the efficiency and quality of sales activities. For example, sales representatives can use the optimal contact method and communication strategy suggested by the generation AI to efficiently contact customers and conduct effective sales activities. In addition, the feedback function allows for the collection of field voices and customer reactions in real time, which the generation AI then sequentially optimizes, improving the quality of sales activities. This will improve the efficiency of insurance sales activities and is expected to have a major impact on the huge market.

[0066] A sales support system according to an embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects customer attribute information and past sales activity information. The customer attribute information includes, but is not limited to, age, gender, occupation, and income. The past sales activity information includes, but is not limited to, purchase history, contact history, and response history. The collection unit, for example, acquires the customer's age and occupation from a database. The collection unit can also record customer responses to past sales activities. For example, the collection unit collects information on insurance products previously purchased by the customer. The analysis unit uses a generative AI to analyze the information collected by the collection unit and propose optimal contact methods and communication strategies. Examples of contact methods include, but are not limited to, telephone, email, and face-to-face contact. For example, the analysis unit proposes optimal contact methods based on the customer's age and occupation. The analysis unit can also improve the next contact method and communication strategy by taking into account responses to past sales activities. For example, the analysis unit prioritizes suggesting contact methods to which the customer has previously responded favorably. The feedback unit collects feedback from sales representatives and provides it to the analysis unit. Feedback includes, but is not limited to, for example, input of conversation content and reactions between sales representatives and customers. For example, the feedback unit inputs the conversation content between sales representatives and customers, and provides the information to the analysis unit. The feedback unit can also collect reactions from customers in real time. For example, the feedback unit records reactions shown by customers to sales representatives and provides the information to the analysis unit. Thus, the sales support system according to the embodiment can improve the efficiency and quality of sales activities through the collection and analysis of customer information and the collection of feedback.

[0067] The collection unit can collect information on the customer's age, occupation, and past purchase history. The collection unit, for example, acquires the customer's age from a database. For example, the collection unit can classify the customer's age into ranges such as teens, twenties, and thirties. The collection unit can also acquire the customer's occupation from the database. For example, the collection unit can classify the customer's occupation by specific occupation such as doctor, engineer, and teacher. The collection unit can also acquire the customer's past purchase history from the database. For example, the collection unit collects information on insurance products the customer has previously purchased. This allows for more accurate analysis by collecting detailed customer attribute information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the customer's age and occupation information into the generation AI and have the generation AI classify the information.

[0068] The analysis unit can suggest an appropriate contact method based on the collected information. The analysis unit can suggest the optimal contact method based on, for example, the collected customer information's age and occupation. For example, if the customer is in their 20s, the analysis unit can suggest contact via email. Furthermore, if the customer is a doctor, the analysis unit can also suggest face-to-face contact. Furthermore, the analysis unit can suggest the next contact method taking into account responses to past sales activities. For example, if the customer responded favorably to phone contact in the past, the analysis unit can suggest phone contact again next time. This allows the effectiveness of sales activities to be improved by suggesting the optimal contact method based on customer attribute information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the collected customer information into a generation AI, causing the generation AI to suggest the optimal contact method.

[0069] The feedback unit allows a salesperson to input the content of a conversation with a customer or their reaction, and the generation AI can analyze that information and improve the next contact method or communication strategy. For example, the feedback unit collects information by having the salesperson input the content of the conversation with the customer. For example, the salesperson can input the content of the conversation with the customer in text format. The feedback unit can also collect information by inputting the customer's reaction. For example, the salesperson can input the customer's reaction in categories such as positive, negative, and neutral. The collected information is analyzed by the generation AI and used to improve the next contact method or communication strategy. For example, the generation AI analyzes the customer reaction input by the salesperson and suggests the next contact method. This allows the quality of sales activities to be improved by improving the next contact method or communication strategy based on feedback from the salesperson. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input the customer reaction input by the salesperson into the generation AI, causing the generation AI to suggest the next contact method.

[0070] The analysis unit can improve the next contact method or communication strategy by taking into account responses to past sales activities. The analysis unit, for example, suggests the next contact method by taking into account customer responses to past sales activities. For example, if a customer has responded favorably to email contact in the past, the analysis unit can suggest email contact again next time. The analysis unit can also improve the next communication strategy based on the customer's response to past sales activities. For example, if a customer has responded negatively to face-to-face contact in the past, the analysis unit can suggest telephone contact next time. In this way, the next contact method or communication strategy can be optimized by taking into account responses to past sales activities. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input customer responses to past sales activities into a generation AI, causing the generation AI to suggest the next contact method.

[0071] The feedback unit can collect feedback from sales representatives in the field or responses from customers in real time. For example, the feedback unit can input customer responses collected by sales representatives in real time. For example, the feedback unit can allow sales representatives to input customer responses using a smartphone. The feedback unit can also collect customer responses in real time. For example, the feedback unit can allow customers to input their responses through an online form. The collected information is analyzed by the generation AI and used to improve the next contact method and communication strategy. For example, the generation AI analyzes customer responses collected in real time and suggests the next contact method. In this way, by collecting feedback in real time, the predictions and suggestions of the generation AI can be sequentially optimized. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input customer responses collected in real time into the generation AI, causing the generation AI to suggest the next contact method.

[0072] The collection unit can estimate the user's emotions and set the timing for collecting customer information based on the estimated user emotions. The collection unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit sets the timing for collecting customer information based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect information in a relaxed state. Furthermore, if the user is relaxed, the collection unit can collect information immediately and efficiently collect data. This enables efficient information collection by adjusting the collection timing according to the user's emotions. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI set the collection timing.

[0073] The collection unit can analyze the customer's past response history and select an appropriate collection method. The collection unit can, for example, retrieve and analyze the customer's past response history from a database. For example, the collection unit prioritizes selection of collection methods (such as telephone and email) to which the customer has responded favorably in the past. The collection unit can also select collection methods to which the customer has responded quickly in the past, enabling efficient information collection. Furthermore, the collection unit can avoid collection methods to which the customer has responded negatively in the past and select other methods. This enables efficient information collection by selecting the optimal collection method based on the past response history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the customer's past response history into a generation AI, causing the generation AI to select the optimal collection method.

[0074] When collecting customer information, the collection unit can filter the information based on the customer's current living situation and areas of interest. For example, the collection unit can obtain the customer's current living situation from a database and filter the information. For example, the collection unit can collect highly relevant information based on the customer's family structure and living situation. The collection unit can also obtain the customer's areas of interest from a database and filter the information. For example, the collection unit prioritizes collecting information related to hobbies and topics that the customer is interested in. Furthermore, the collection unit can filter and collect information that is likely to be of interest to the customer based on the customer's recent activity history. In this way, highly relevant information can be collected by filtering the information based on the customer's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the customer's living situation and areas of interest into a generation AI and have the generation AI filter the information.

[0075] When collecting customer information, the collection unit can select an appropriate collection method depending on the customer's input method. For example, if the customer prefers voice input, the collection unit can prioritize voice information collection. For example, if the customer provides information via voice using a microphone, the collection unit can collect information using voice recognition technology. Furthermore, if the customer prefers text input, the collection unit can also collect text-based information. For example, if the customer provides information via text using a keyboard, the collection unit can collect information using text analysis technology. Furthermore, if the customer provides information using an image, the collection unit can collect information using image analysis. For example, if the customer provides an image using a smartphone camera, the collection unit can collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection method depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data depending on the customer's input method into a generation AI, causing the generation AI to select the optimal collection method.

[0076] The collection unit can analyze the customer's purchase history and set priorities for the information to be collected. The collection unit can, for example, obtain and analyze the customer's purchase history from a database. For example, the collection unit prioritizes collecting information related to products the customer has previously purchased. The collection unit can also collect information related to products the customer is likely to purchase next from the customer's purchase history. Furthermore, the collection unit can also collect information about new products that the customer may be interested in based on the customer's purchase history. This enables efficient information collection by determining the priority of information based on the customer's purchase history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's purchase history into a generation AI and have the generation AI set the priority of information.

[0077] The collection unit can estimate the user's emotions and prioritize the customer information to be collected based on the estimated user emotions. The collection unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. Based on the estimated emotions, the collection unit prioritizes the customer information to be collected. For example, if the user is excited, the collection unit can prioritize collecting information of high importance. Furthermore, if the user is relaxed, the collection unit can collect detailed information. This enables efficient information collection by prioritizing information according to the user's emotions. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI prioritize the information.

[0078] When collecting customer information, the collection unit can collect highly relevant information by taking into account the customer's geographical location information. The collection unit can, for example, obtain the customer's geographical location information from a database and collect the information. For example, the collection unit can prioritize collecting information related to the area where the customer is currently located. The collection unit can also collect information about nearby events and services based on the customer's geographical location information. Furthermore, the collection unit can also collect area-specific information by taking into account the customer's geographical location information. In this way, by taking into account the customer's geographical location information, highly relevant information can be collected efficiently. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's geographical location information to a generation AI and cause the generation AI to collect highly relevant information.

[0079] When collecting customer information, the collection unit can analyze the customer's social media activities and collect related information. The collection unit can, for example, retrieve and analyze the customer's social media activities from a database. For example, the collection unit can collect related information based on information the customer shared on social media. The collection unit can also analyze the customer's social media activity history and collect information that may be of interest to the customer. Furthermore, the collection unit can also collect related information by referring to the activities of the customer's friends on social media. In this way, by analyzing the customer's social media activities, highly relevant information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's social media activity data into a generation AI and cause the generation AI to collect related information.

[0080] When collecting customer information, the collection unit can set the collection method by reflecting the customer's past feedback. The collection unit can, for example, retrieve and analyze the customer's past feedback from a database. For example, the collection unit preferentially uses a collection method for which the customer has given favorable feedback in the past. The collection unit can also improve the collection method based on the customer's past feedback. Furthermore, the collection unit can customize the collection method by referring to the customer's past feedback. In this way, the collection method can be optimized by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customer's past feedback data into the generation AI and have the generation AI set the collection method.

[0081] The analysis unit can estimate the user's emotions and set the suggestion expression method based on the estimated user's emotions. The analysis unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Based on the estimated emotions, the analysis unit sets the suggestion expression method. For example, the analysis unit can provide detailed suggestions when the user is relaxed. For example, the analysis unit can provide concise and to-the-point suggestions when the user is in a hurry. This enables more effective suggestions by adjusting the suggestion expression method according to the user's emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI set the suggestion expression method.

[0082] The analysis unit can set the level of detail of the analysis based on the importance of the customer information during analysis. The analysis unit can, for example, obtain the importance of the customer information from a database and set the level of detail of the analysis. For example, the analysis unit can perform a detailed analysis on customer information with high importance. The analysis unit can also perform a simplified analysis on customer information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the customer information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the customer information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer information importance data into the generation AI and have the generation AI set the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the customer category. The analysis unit can, for example, obtain the customer category from a database and apply an appropriate analysis algorithm. For example, if the customer is an individual, the analysis unit can apply an analysis algorithm for individuals. Also, if the customer is a corporation, the analysis unit can apply an analysis algorithm for corporations. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the customer category (age, occupation, etc.). This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the customer category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer category data into a generation AI and have the generation AI apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis based on the customer's past reaction results. The analysis unit can, for example, obtain the customer's past reaction results from a database and improve the accuracy of the analysis. For example, the analysis unit can adjust the analysis algorithm based on the customer's past reaction results. The analysis unit can also improve the accuracy of the analysis by referring to the customer's past reaction results. Furthermore, the analysis unit can add the customer's past reaction results to a dataset and improve the analysis model. This improves the accuracy of the analysis by referring to the customer's past reaction results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the customer's past reaction result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0085] During analysis, the analysis unit can suggest an appropriate contact method by taking into account the customer's purchase history. The analysis unit can, for example, retrieve the customer's purchase history from a database and suggest an appropriate contact method. For example, the analysis unit can suggest the optimal contact method (telephone, email, etc.) based on the customer's purchase history. The analysis unit can also suggest a contact method related to a product that the customer is likely to purchase next based on the customer's purchase history. Furthermore, the analysis unit can select an effective contact method by taking into account the customer's purchase history. In this way, the optimal contact method can be suggested by taking into account the customer's purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the customer's purchase history data into the generation AI and have the generation AI suggest an appropriate contact method.

[0086] The analysis unit can estimate the user's emotions and set the length of the suggestions based on the estimated user emotions. The analysis unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit sets the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide short, concise suggestions. On the other hand, if the user is relaxed, the analysis unit can provide longer suggestions with detailed explanations. This allows for more effective suggestions by adjusting the length of the suggestions according to the user's emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI set the length of the suggestions.

[0087] During analysis, the analysis unit can prioritize proposals based on the time of submission of the customer information. The analysis unit can, for example, obtain the time of submission of the customer information from a database and set the priority of proposals. For example, the analysis unit prioritizes proposals if the customer information was submitted recently. The analysis unit can also dynamically adjust the priority of proposals based on the time of submission of the customer information. Furthermore, the analysis unit can make proposals at the optimal timing, taking into account the time of submission of the customer information. This enables efficient proposals by determining the priority of proposals based on the time of submission of the customer information. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of submission of the customer information into the generation AI and cause the generation AI to set the priority of proposals.

[0088] During analysis, the analysis unit can set the order of proposals based on the relevance of the customer information. The analysis unit can, for example, obtain the relevance of the customer information from a database and set the order of proposals. For example, the analysis unit prioritizes proposals when the relevance of the customer information is high. The analysis unit can also dynamically adjust the order of proposals based on the relevance of the customer information. Furthermore, the analysis unit can make proposals in an optimal order taking into account the relevance of the customer information. This enables more effective proposals by adjusting the order of proposals based on the relevance of the customer information. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the customer information into the generation AI and have the generation AI set the order of proposals.

[0089] During analysis, the analysis unit can set the use of technical terminology in the proposal according to the customer's level of expertise. The analysis unit can, for example, obtain the customer's level of expertise from a database and set the use of technical terminology in the proposal. For example, if the customer has technical expertise, the analysis unit can make a proposal that uses a lot of technical terminology. Alternatively, if the customer does not have technical expertise, the analysis unit can make a proposal in simple language. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the proposal according to the customer's level of expertise. This allows for a proposal that is easier to understand by adjusting the use of technical terminology according to the customer's level of expertise. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the customer's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the proposal.

[0090] The feedback unit can estimate the user's emotion and set a feedback collection method based on the estimated user's emotion. The feedback unit can use, for example, facial expression recognition technology to estimate the user's emotion. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The feedback unit can also estimate the user's emotion using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice to estimate the emotion. Based on the estimated emotion, the feedback unit sets a feedback collection method. For example, the feedback unit can request detailed feedback if the user is relaxed. Alternatively, the feedback unit can request concise feedback if the user is in a hurry. This enables more effective feedback collection by adjusting the feedback collection method according to the user's emotion. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input the user's emotion data into a generation AI and have the generation AI set the feedback collection method.

[0091] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the sales representative's past feedback history. The feedback unit can, for example, retrieve and refer to the sales representative's past feedback history from a database. For example, the feedback unit preferentially uses a collection method for which the sales representative has previously provided favorable feedback. The feedback unit can also improve the collection method based on the sales representative's past feedback history. Furthermore, the feedback unit can also customize the collection method by referring to the sales representative's past feedback history. In this way, the optimal collection method can be selected by referring to the sales representative's past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the sales representative's past feedback history data into a generation AI and have the generation AI select a collection method.

[0092] The feedback unit can set the collection means based on the sales representative's current situation when collecting feedback. The feedback unit can, for example, obtain the sales representative's current situation from a database and set the collection means. For example, the feedback unit can provide a brief feedback form when the sales representative is busy. The feedback unit can also request detailed feedback when the sales representative is relaxed. Furthermore, the feedback unit can dynamically adjust the collection means according to the sales representative's current situation. This enables efficient feedback collection by customizing the collection means according to the sales representative's current situation. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's current situation data into a generation AI and have the generation AI set the collection means.

[0093] The feedback unit can set the collection method by reflecting the sales representative's feedback when collecting feedback. The feedback unit can, for example, obtain and analyze the sales representative's feedback from a database. For example, the feedback unit can improve the collection method based on the sales representative's feedback. The feedback unit can also customize the collection means by referring to the sales representative's feedback. Furthermore, the feedback unit can dynamically adjust the collection method by reflecting the sales representative's feedback. In this way, the collection method can be optimized by reflecting the sales representative's feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's feedback data into a generation AI and have the generation AI set the collection method.

[0094] The feedback unit can analyze customer responses when collecting feedback and improve the next contact method or communication strategy. The feedback unit can, for example, obtain and analyze customer responses from a database. For example, the feedback unit can improve the next contact method based on the customer responses. The feedback unit can also analyze the customer responses and optimize the communication strategy. Furthermore, the feedback unit can dynamically adjust the next contact method or communication strategy based on the customer responses. In this way, the next contact method and communication strategy can be optimized by analyzing the customer responses. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input customer response data into a generation AI and have the generation AI improve the next contact method and communication strategy.

[0095] The feedback unit can estimate the user's emotions and set a priority of feedback based on the estimated user's emotions. The feedback unit can use, for example, facial expression recognition technology to estimate the user's emotions. For example, the feedback unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The feedback unit can also estimate the user's emotions using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice to estimate the emotions. The feedback unit sets a priority of feedback based on the estimated emotions. For example, the feedback unit can prioritize collection of feedback based on the user's ...

[0096] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the geographic location information of the salesperson. The feedback unit can, for example, obtain the geographic location information of the salesperson from a database and select the collection method. For example, the feedback unit can prioritize collecting feedback related to the area where the salesperson is currently located. The feedback unit can also collect feedback from nearby customers based on the geographic location information of the salesperson. Furthermore, the feedback unit can collect region-specific feedback by taking into account the geographic location information of the salesperson. In this way, the optimal collection method can be selected by taking into account the geographic location information of the salesperson. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the geographic location information data of the salesperson to a generation AI and cause the generation AI to select the collection method.

[0097] When collecting feedback, the feedback unit can analyze the sales representative's social media activities to collect relevant feedback. The feedback unit can, for example, retrieve and analyze the sales representative's social media activities from a database. For example, the feedback unit can collect relevant feedback based on information shared by the sales representative on social media. The feedback unit can also analyze the sales representative's social media activity history to collect feedback that is likely to be of interest. Furthermore, the feedback unit can collect relevant feedback by referring to the activities of the sales representative's friends on social media. In this way, highly relevant feedback can be collected by analyzing the sales representative's social media activities. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the sales representative's social media activity data into a generation AI and cause the generation AI to collect relevant feedback.

[0098] When collecting feedback, the feedback unit can set the collection method by reflecting the sales representative's past feedback. The feedback unit can, for example, retrieve and analyze the sales representative's past feedback from a database. For example, the feedback unit can preferentially use a collection method for which the sales representative has given favorable feedback in the past. The feedback unit can also improve the collection method based on the sales representative's past feedback. Furthermore, the feedback unit can customize the collection method by referring to the sales representative's past feedback. In this way, the collection method can be optimized by reflecting the sales representative's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the sales representative's past feedback data into a generation AI and have the generation AI set the collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect customer attribute information and past sales activity information using the camera 42 and microphone 38B of the smart device 14. The collection unit can also acquire information such as the customer's age and occupation from the database 24 using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI proposes optimal contact means and communication strategies based on the collected information. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and can collect feedback from sales representatives and provide it to the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect customer attribute information and past sales activity information using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also acquire information such as the customer's age and occupation from the database 24 using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI proposes optimal contact means and communication strategies based on the collected information. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and can collect feedback from sales representatives and provide it to the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect customer attribute information and past sales activity information using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also acquire information such as the customer's age and occupation from the database 24 using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI proposes optimal contact means and communication strategies based on the collected information. The feedback unit is realized, for example, by the control unit 46A of the headset terminal 314, and can collect feedback from sales representatives and provide it to the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect customer attribute information and past sales activity information using the camera 42 and microphone 238 of the robot 414. The collection unit can also acquire information such as the customer's age and occupation from the database 24 using the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI proposes optimal contact means and communication strategies based on the collected information. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and can collect feedback from sales representatives and provide it to the analysis unit.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The analysis unit can estimate the customer's emotions and adjust the timing of proposals based on the estimated emotions. For example, if the customer is feeling stressed, the analysis unit can delay the proposal. Alternatively, if the customer is relaxed, the analysis unit can make an immediate proposal. Furthermore, if the customer is excited, the analysis unit can make a detailed proposal. This allows for more effective sales activities by adjusting the timing of proposals according to the customer's emotions.

[0101] The collection unit can analyze the customer's social media activity and collect information based on the customer's areas of interest. For example, it can collect topics that the customer frequently mentions on social media. It can also collect related information based on the activities of the customer's friends on social media. It can also analyze the content of the customer's social media posts and collect information that may be of interest to the customer. This allows for more accurate analysis by collecting information based on the customer's areas of interest.

[0102] The analysis unit can analyze a customer's purchase history and predict the product that the customer is likely to purchase next. For example, it can suggest products related to products that the customer has previously purchased. It can also prioritize suggestions for products that the customer is likely to purchase next based on the customer's purchase history. It can also suggest new products that the customer may be interested in based on the customer's purchase history. This makes it possible to make optimal suggestions based on the customer's purchase history, thereby increasing the effectiveness of sales activities.

[0103] The feedback unit can estimate the customer's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the customer is relaxed, detailed feedback can be requested. If the customer is in a hurry, brief feedback can be requested. Furthermore, if the customer is excited, feedback of high importance can be collected preferentially. In this way, by adjusting the feedback collection method according to the customer's emotions, more effective feedback collection is possible.

[0104] The analysis unit can adjust the content of the proposal according to the customer's level of expertise. For example, if the customer has specialized knowledge, the analysis unit can make a proposal that includes detailed technical information. If the customer does not have specialized knowledge, the analysis unit can make a proposal in simple language. Furthermore, the analysis unit can dynamically adjust the content of the proposal according to the customer's level of expertise. This allows the proposal to be made easier to understand by adjusting the content of the proposal according to the customer's level of expertise.

[0105] The collection unit can collect area-specific information by taking into account the geographical location information of the customer. For example, it can collect information about events and services related to the area where the customer is currently located. It can also collect information about nearby stores and services based on the geographical location information of the customer. It can also collect area-specific news and topics by taking into account the geographical location information of the customer. This allows for efficient collection of highly relevant information by taking into account the geographical location information of the customer.

[0106] The analysis unit can estimate the customer's emotions and adjust the way in which proposals are expressed based on the estimated emotions. For example, if the customer is relaxed, a detailed proposal can be made. If the customer is in a hurry, a concise proposal can be made that gets to the point. Furthermore, if the customer is excited, a proposal can be made that uses expressions that appeal to the customer's emotions. This makes it possible to make more effective proposals by adjusting the way proposals are expressed depending on the customer's emotions.

[0107] The feedback unit can select the optimal collection method by referring to the sales representative's past feedback history. For example, it can preferentially use collection methods for which the sales representative has given favorable feedback in the past. It can also improve the collection method based on the sales representative's past feedback history. Furthermore, it can also customize the collection method by referring to the sales representative's past feedback history. In this way, it is possible to select the optimal collection method by referring to the sales representative's past feedback history.

[0108] The collection unit can estimate the customer's emotions and set the priority of information to be collected based on the estimated emotions. For example, if the customer is excited, it is possible to prioritize collection of information of high importance. Also, if the customer is relaxed, it is possible to collect detailed information. Furthermore, if the customer is feeling stressed, it is possible to delay the timing of collection and collect information in a relaxed state. In this way, by setting the priority of information according to the customer's emotions, it is possible to collect information efficiently.

[0109] The analysis unit can improve the content of the next proposal based on the customer's past response results. For example, it can prioritize the use of proposals that the customer has responded favorably to in the past. It can also dynamically adjust the content of the proposal based on the customer's past response results. Furthermore, it can also create new proposals by referring to the customer's past response results. In this way, the content of the next proposal can be optimized by referring to the customer's past response results.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The collection unit collects customer attribute information and past sales activity information. Customer attribute information includes age, gender, occupation, income, etc., and past sales activity information includes past purchase history, contact history, response history, etc. The collection unit can obtain the customer's age and occupation from the database and record the customer's response to past sales activities. Step 2: The analysis department uses generation AI to analyze the information collected by the collection department and proposes the optimal contact method and communication strategy. Contact methods include telephone, email, and face-to-face meetings, and proposes the optimal contact method based on the customer's age and occupation. It also takes into account responses to past sales activities to improve the next contact method and communication strategy. Step 3: The feedback department collects feedback from sales representatives and provides it to the analysis department. Feedback includes sales representatives inputting the content of their conversations with customers and their reactions, and it is also possible to collect reactions from customers in real time. This allows the analysis department to improve the next sales activity based on the feedback.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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, in order to avoid confusion and to 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 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 collection department that collects customer attribute information and past sales activity information; an analysis unit that analyzes the information collected by the collection unit and proposes appropriate contact means and communication strategies; a feedback unit that collects feedback from sales representatives and provides it to the analysis unit. A system characterized by:

2. The collecting unit Collect information about the customer's age, occupation, and past purchase history 2. The system of claim 1.

3. The analysis unit Suggest appropriate contact methods based on collected information 2. The system of claim 1.

4. The feedback unit Salespeople input the content of their conversations with customers or their reactions, and the generative AI analyzes that information to improve the next contact method or communication strategy.

2. The system of claim 1.

5. The analysis unit Consider responses to past sales efforts to improve your next contact or communication strategy 2. The system of claim 1.

6. The feedback unit Collect real-time feedback from salespeople or customer responses 2. The system of claim 1.

7. The collecting unit Estimate user emotions and set the timing for collecting customer information based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze customer response history and select the appropriate collection method 2. The system of claim 1.

Images