system

Abstract

The system according to the embodiment aims to propose an appropriate plan and support applications even when a crew member is not present at the store or when a new crew member is serving customers. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit accepts customer input. The analysis unit analyzes the information accepted by the reception unit. The proposal unit proposes an appropriate plan based on the analysis results obtained by the analysis unit. The support unit supports the application based on the plan proposed by the proposal unit.

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, it was difficult to propose appropriate plans and support applications when a store crew member was not present or a new crew member was serving customers.

[0005] The system according to the embodiment aims to propose an appropriate plan and support applications even when a crew member is not present at the store or when a new crew member is serving customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit accepts input from customers. The analysis unit analyzes the information accepted by the reception unit. The proposal unit proposes an appropriate plan based on the analysis results obtained by the analysis unit. The support unit supports applications based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose appropriate plans and support applications even when there are no crew members at the store or when a new crew member is serving customers. [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) In an embodiment of the customer service support system of the present invention, AI analyzes customer service conversation patterns and creates a script tailored to the situation. This system is useful in stores without retail store staff or for new staff. Specifically, customers input their details into a monitor installed in the store. AI analyzes the input, proposes the optimal plan, and supports them through the application process. For example, AI analyzes customer service conversation patterns. It collects past customer service data and analyzes what conversations were effective in different situations. This allows it to create the optimal script tailored to the situation. Next, customers input details into a monitor installed in the store. For example, they input the type of product they want to purchase, their budget, and desired features. AI analyzes the input and proposes the optimal plan. Furthermore, based on the proposed plan, AI provides support up to the application process. For example, it guides customers through the necessary documents and explains the application procedure. This allows customers to complete the application smoothly. This system improves the efficiency of customer service in stores without retail store staff and for new staff, enabling consistent, high-quality service to be provided to customers. This allows the customer service support system to analyze customer conversation patterns and create scripts appropriate to the situation, improving the efficiency of customer service at stores that do not have mass retailer crew members or for new crew members, and providing consistent, high-quality service to customers.

[0029] A customer service support system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit accepts customer input. Examples of customer input include, but are not limited to, text input, voice input, and option input. The reception unit accepts details of the customer's desired product, budget, desired features, and the like, using text input. The reception unit can also accept customer requests using voice input. For example, voice recognition technology can be used to convert the customer's voice into text data, and the reception unit analyzes the text. The reception unit can also select desired items from options on a screen using option input. The analysis unit analyzes the information accepted by the reception unit. For example, data mining, statistical analysis, machine learning algorithms, and the like can be used for the analysis, but are not limited to these examples. The analysis unit extracts useful information from the customer's input data using, for example, data mining technology. The analysis unit can also analyze trends based on the customer's requests using statistical analysis technology. The analysis unit can also use machine learning algorithms to build a model for proposing an optimal plan based on the customer's input data. The proposal unit proposes an optimal plan based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, for example, price, functionality, and service content. The proposal unit proposes an optimal plan based on, for example, price. The proposal unit can also propose an optimal plan based on functionality. The proposal unit can also propose an optimal plan based on service content. The support unit supports the application based on the plan proposed by the proposal unit. Support includes, but is not limited to, for example, assistance in filling out an online form, telephone support, and chat support. The support unit provides, for example, assistance in filling out an online form. The support unit can also provide telephone support. The support unit can also provide chat support. This allows the customer service support system according to the embodiment to accept and analyze customer input, propose an optimal plan, and support the application.

[0030] Furthermore, the customer service support system includes a collection unit that collects past customer service data. The collection unit collects past customer service data. The past customer service data includes, for example, customer feedback, purchase history, inquiry history, etc., but is not limited to these examples. The collection unit collects, for example, customer feedback. The collection unit can also collect purchase history. Furthermore, the collection unit can also collect inquiry history. In this way, collecting past customer service data enables more accurate analysis.

[0031] The support unit can provide guidance on required documents. The support unit, for example, provides guidance on required documents. Required documents include, but are not limited to, for example, an identification card, a contract, and an application form. The support unit, for example, provides guidance on an identification card. The support unit can also provide guidance on a contract. Furthermore, the support unit can also provide guidance on an application form. By providing guidance on required documents, the application procedure can proceed smoothly.

[0032] The reception unit can receive details of the type of product desired to be purchased, the budget, and the desired functions. For example, the reception unit receives details of the type of product desired to be purchased, the budget, and the desired functions. The type of product desired to be purchased includes, but is not limited to, electronic devices, furniture, clothing, etc. For example, the reception unit receives types of electronic devices. The reception unit can also receive types of furniture. The reception unit can also receive types of clothing. The budget includes, but is not limited to, a price range, a maximum budget, a minimum budget, etc. For example, the reception unit can receive a price range. The reception unit can also receive a maximum budget. The reception unit can also receive a minimum budget. The desired functions include, but are not limited to, performance, design, additional functions, etc. For example, the reception unit can receive performance. The reception unit can also receive design. The reception unit can also receive additional functions. By receiving detailed requests from the customer, a more appropriate plan can be proposed.

[0033] The proposal unit can propose an appropriate plan. The proposal unit, for example, proposes an appropriate plan. The appropriate plan includes, for example, price, functions, service content, etc., but is not limited to these examples. The proposal unit, for example, proposes an optimal plan based on price. The proposal unit can also propose an optimal plan based on functions. Furthermore, the proposal unit can also propose an optimal plan based on service content. In this way, by proposing an optimal plan, customer satisfaction is improved.

[0034] The support department can explain the steps of the application procedure. The support department, for example, explains the steps of the application procedure. The steps of the application procedure include, but are not limited to, for example, a step-by-step guide and how to prepare the necessary documents. The support department, for example, provides a step-by-step guide. The support department can also explain how to prepare the necessary documents. By explaining the steps of the application procedure, the customer can smoothly complete the application.

[0035] The reception unit can analyze the customer's past input history and suggest the optimal input method. The reception unit, for example, analyzes the customer's past input history and suggests the optimal input method. Past input history includes, for example, past input data, methods of analyzing input patterns, and the like, but is not limited to these examples. The reception unit, for example, automatically displays as candidates the types of products and budgets that the customer has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest the types of products and budgets to be used in a specific time period based on the customer's past input history. In this way, input efficiency is improved by suggesting the optimal input method based on the customer's past input history.

[0036] The reception unit can customize input items based on the customer's current purchasing intent and areas of interest at the time of input. The reception unit, for example, customizes input items based on the customer's current purchasing intent and areas of interest at the time of input. Purchasing intent includes, but is not limited to, high purchasing intent and low purchasing intent. For example, if the customer indicates high purchasing intent, the reception unit allows the customer to enter detailed product types, budget, and desired features. Furthermore, if the customer has a specific area of ​​interest, the reception unit can preferentially display input items related to that area. Furthermore, if the customer indicates low purchasing intent, the reception unit can provide simpler input items to reduce the effort required for input. This improves input accuracy by providing input items according to the customer's purchasing intent and areas of interest.

[0037] The reception unit can prioritize displaying highly relevant input items based on the customer's geographical location information when the customer enters information. For example, the reception unit prioritizes displaying highly relevant input items based on the customer's geographical location information when the customer enters information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the customer is in a specific area, the reception unit can prioritize displaying products and services that are popular in that area. Furthermore, if the customer is traveling, the reception unit can prioritize displaying the types of products and budgets available at the customer's travel destination. Furthermore, if the customer is at home, the reception unit can prioritize displaying the types of products and budgets available at home. This improves the accuracy of input by providing input items based on the customer's geographical location information.

[0038] The reception unit can analyze the customer's social media activity at the time of input and suggest related input items. For example, the reception unit can analyze the customer's social media activity at the time of input and suggest related input items. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement rates. For example, if the customer mentions a specific product on social media, the reception unit can prioritize displaying input items related to that product. Furthermore, if the customer has expressed specific interests or concerns on social media, the reception unit can suggest input items related to those interests or concerns. Furthermore, if the customer is participating in a specific event on social media, the reception unit can suggest input items related to that event. This improves the accuracy of input by providing input items based on the customer's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the information input during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information input during analysis. The importance of the information includes, for example, reliability, relevance, urgency, etc., but is not limited to these examples. For example, when important information is input, the analysis unit performs a detailed analysis and proposes an optimal plan. Furthermore, when general information is input, the analysis unit can perform a standard analysis and propose an appropriate plan. Furthermore, when simple information is input, the analysis unit can perform a concise analysis and quickly propose a plan. In this way, by performing an analysis according to the importance of the input information, the accuracy of the analysis is improved.

[0040] The analysis unit can apply different analysis methods depending on the category of information during analysis. For example, the analysis unit applies different analysis methods depending on the category of information during analysis. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies the optimal analysis method depending on the type of product desired to be purchased. Furthermore, the analysis unit can apply different analysis methods depending on the budget and propose the optimal plan. Furthermore, the analysis unit can apply different analysis methods depending on the desired functions and propose the optimal plan. In this way, by providing analysis methods depending on the category of information, the accuracy of the analysis is improved.

[0041] The analysis unit can determine the priority of analysis based on the submission time of input information during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of input information during analysis. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission timing. For example, when the latest information is input, the analysis unit performs analysis with priority and provides results quickly. Furthermore, when old information is input, the analysis unit can also perform analysis with standard priority. Furthermore, when important information is input, the analysis unit can also perform analysis with priority and provide detailed results. This improves the efficiency of analysis by performing analysis according to the submission time of the input information.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to related external data during analysis. Examples of external data include, but are not limited to, public databases, data provided by third parties, and API data. The analysis unit, for example, refers to external market data and proposes an optimal plan. The analysis unit can also refer to external customer reviews and reflect them in the analysis results. The analysis unit can also refer to external competitive information and reflect them in the analysis results. In this way, the accuracy of the analysis is improved by referring to related external data.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of the plan when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the plan when making the proposal. The importance of the plan includes, but is not limited to, customer needs, the impact of the plan, and cost. For example, the proposal unit can make a detailed proposal for an important plan and provide many options. Furthermore, the proposal unit can make a standard proposal for a general plan and provide appropriate options. Furthermore, the proposal unit can make a concise proposal for a simple plan and quickly provide options. This improves the accuracy of the proposal by making proposals according to the importance of the plan.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the plan when making a suggestion. Plan categories include, but are not limited to, service plans, product plans, and support plans. For example, the suggestion unit applies an optimal suggestion algorithm depending on the type of product desired to be purchased. The suggestion unit can also apply different suggestion algorithms depending on the budget to propose an optimal plan. The suggestion unit can also apply different suggestion algorithms depending on desired functions to propose an optimal plan. In this way, by providing a suggestion algorithm depending on the category of the plan, the accuracy of the suggestion is improved.

[0045] The proposal unit can determine the priority of proposals based on the time of submission of the plans at the time of proposal. The proposal unit, for example, determines the priority of proposals based on the time of submission of the plans at the time of proposal. The submission time includes, for example, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the proposal unit can prioritize proposals for the latest plans and quickly provide options. Furthermore, the proposal unit can also make proposals with standard priority for older plans. Furthermore, the proposal unit can prioritize proposals for important plans and provide detailed options. This improves the efficiency of proposals by making proposals according to the time of submission of the plans.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the plans when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of the plans when making a proposal. The relevance of the plans includes, but is not limited to, customer needs, interrelationships between plans, and the like. For example, the proposal unit proposes the most relevant plan first. The proposal unit can also propose plans of general relevance in a standard order. The proposal unit can also propose plans of low relevance last. This improves the accuracy of proposals by making proposals based on the relevance of the plans.

[0047] The support department can analyze past support history and select the optimal support method when providing support. For example, the support department analyzes past support history and selects the optimal support method when providing support. Past support history includes, but is not limited to, the support content, support results, customer feedback, etc. For example, the support department selects the optimal support method based on the support content the customer has received in the past. The support department can also suggest an effective support method based on the customer's past support history. The support department can also analyze the customer's past support history and select the most efficient support method. In this way, by providing the optimal support method based on the past support history, the efficiency of support is improved.

[0048] The support department can customize support methods based on the customer's current situation when providing support. For example, the support department customizes support methods based on the customer's current situation when providing support. The customer's current situation includes, but is not limited to, current purchasing status, current usage status, etc. For example, the support department can provide quick support methods when the customer is in a hurry. Furthermore, the support department can provide detailed support methods when the customer is relaxed. Furthermore, if the customer has a specific problem, the support department can provide support methods specialized for that problem. This improves the quality of support by providing support methods that suit the customer's current situation.

[0049] The support department can select the optimal support method by taking into account the customer's geographical location information when providing support. For example, the support department selects the optimal support method by taking into account the customer's geographical location information when providing support. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the customer is in a specific area, the support department can provide support methods that can be used in that area. Furthermore, if the customer is traveling, the support department can provide support methods that can be used at the customer's travel destination. Furthermore, if the customer is at home, the support department can provide support methods that can be used at home. This improves the quality of support by providing support methods based on the customer's geographical location information.

[0050] The support department may analyze a customer's social media activity to suggest support methods when providing support. For example, the support department may analyze a customer's social media activity when providing support to suggest support methods. Social media activity includes, but is not limited to, the content of posts, follower responses, and engagement rates. For example, if a customer mentions a particular issue on social media, the support department may provide support methods related to that issue. Furthermore, if a customer shows particular interests or concerns on social media, the support department may also suggest support methods related to those interests or concerns. Furthermore, if a customer is participating in a particular event on social media, the support department may also suggest support methods related to that event. This improves the quality of support by providing support methods based on the customer's social media activity.

[0051] The collection unit can analyze past customer service data at the time of collection and select the optimal collection method. For example, the collection unit analyzes past customer service data at the time of collection and selects the optimal collection method. Past customer service data includes, for example, customer feedback, purchase history, inquiry history, etc., but is not limited to these examples. The collection unit selects the optimal data collection method based on, for example, the past customer service data. The collection unit can also suggest an effective data collection method from the past customer service data. The collection unit can also analyze the past customer service data and select the most efficient data collection method. This improves the efficiency of data collection by providing the optimal collection method based on the past customer service data.

[0052] The collection unit can collect data taking into account real-time customer information at the time of collection. The collection unit, for example, collects data taking into account real-time customer information at the time of collection. Real-time customer information includes, but is not limited to, real-time behavioral data and real-time purchase data. The collection unit, for example, selects an optimal data collection method based on the real-time customer information. The collection unit can also propose an effective data collection method from the real-time customer information. The collection unit can also analyze the real-time customer information and select the most efficient data collection method. This improves the accuracy of data collection by providing data collection based on real-time customer information.

[0053] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration at the time of collection. For example, the collection unit prioritizes collecting highly relevant data by taking geographical location information into consideration at the time of collection. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if a customer is in a specific area, the collection unit can prioritize collecting data that is highly relevant in that area. Furthermore, if a customer is traveling, the collection unit can prioritize collecting data that is highly relevant at the customer's travel destination. Furthermore, if a customer is at home, the collection unit can prioritize collecting data that is highly relevant at home. This improves the accuracy of data collection by providing data collection based on geographical location information.

[0054] The collection unit may analyze social media activity at the time of collection to collect related data. For example, the collection unit may analyze social media activity at the time of collection to collect related data. Social media activity includes, but is not limited to, post content, follower reactions, and engagement rates. For example, if a customer mentions a particular product on social media, the collection unit may collect data related to the product. Furthermore, if a customer expresses a particular interest or concern on social media, the collection unit may also collect data related to the interest or concern. Furthermore, if a customer participates in a particular event on social media, the collection unit may collect data related to the event. This improves the accuracy of data collection by providing data collection based on social media activity.

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

[0056] The analysis unit can analyze the user's purchasing history and predict the next product that the user is likely to purchase based on their past purchasing patterns. For example, if the user has frequently purchased electronic devices from a specific brand in the past, new products from that brand can be preferentially suggested. Also, if the user tends to purchase specific products each season, products suited to that season can be suggested. Furthermore, if the user prefers products in a specific price range, products in that price range can be suggested. This improves the accuracy of suggestions by making suggestions based on the user's purchasing history.

[0057] The support department can analyze the user's past support history and select the optimal support method. For example, if the user has previously preferred chat support, the support department can provide chat support as a priority. If the user has previously used telephone support, the support department can suggest telephone support. Furthermore, if the user has previously preferred filling out online forms, the support department can provide assistance with filling out online forms. This improves the efficiency of support by providing support based on the user's past support history.

[0058] The reception unit can suggest highly relevant products and services based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest products and services that are popular in that area. If the user is traveling, the reception unit can suggest products and services that can be used at the user's travel destination. Furthermore, if the user is at home, the reception unit can suggest products and services that can be used at home. This improves the accuracy of suggestions by making suggestions based on the user's geographical location information.

[0059] The analysis unit can analyze a user's social media activity and suggest related products and services. For example, if a user mentions a specific product on social media, products and services related to that product can be suggested. Also, if a user shows specific interests on social media, products and services related to those interests can be suggested. Furthermore, if a user participates in a specific event on social media, products and services related to that event can be suggested. This improves the accuracy of suggestions by making suggestions based on the user's social media activity.

[0060] The collection unit can analyze past customer service data at the time of collection and select the optimal collection method. For example, the optimal data collection method is selected based on past customer service data. It can also suggest an effective data collection method from past customer service data. It can also analyze past customer service data and select the most efficient data collection method. This improves the efficiency of data collection by providing the optimal collection method based on past customer service data.

[0061] The collection unit can collect data taking into account real-time customer information at the time of collection. For example, the optimal data collection method can be selected based on real-time customer information. The collection unit can also propose an effective data collection method based on real-time customer information. Furthermore, the collection unit can analyze real-time customer information and select the most efficient data collection method. This improves the accuracy of data collection by providing data collection based on real-time customer information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit accepts customer input. Customer input can include text input, voice input, and option input. For example, text input is used to accept details such as the customer's desired product, budget, and desired features. Voice recognition technology is also used to convert the customer's voice into text data, which the reception unit analyzes. Furthermore, option input allows customers to select desired items from options on the screen. Step 2: The analysis unit analyzes the information received by the reception unit. Data mining, statistical analysis, machine learning algorithms, etc. are used for the analysis. For example, data mining technology is used to extract useful information from the customer's input data, and statistical analysis technology is used to analyze trends based on the customer's requests. Furthermore, machine learning algorithms are used to build a model to propose the optimal plan based on the customer's input data. Step 3: The proposal unit proposes an optimal plan based on the analysis results obtained by the analysis unit. The proposal includes price, functions, service content, etc. For example, it is possible to propose an optimal plan based on price, an optimal plan based on functions, or even an optimal plan based on service content. Step 4: The support department supports the application based on the plan proposed by the proposal department. The support may include assistance in filling out an online form, telephone support, chat support, etc. For example, the support department may provide assistance in filling out an online form, and may also provide telephone support or chat support.

[0064] (Example 2) In an embodiment of the customer service support system of the present invention, AI analyzes customer service conversation patterns and creates a script tailored to the situation. This system is useful in stores without retail store staff or for new staff. Specifically, customers input their details into a monitor installed in the store. AI analyzes the input, proposes the optimal plan, and supports them through the application process. For example, AI analyzes customer service conversation patterns. It collects past customer service data and analyzes what conversations were effective in different situations. This allows it to create the optimal script tailored to the situation. Next, customers input details into a monitor installed in the store. For example, they input the type of product they want to purchase, their budget, and desired features. AI analyzes the input and proposes the optimal plan. Furthermore, based on the proposed plan, AI provides support up to the application process. For example, it guides customers through the necessary documents and explains the application procedure. This allows customers to complete the application smoothly. This system improves the efficiency of customer service in stores without retail store staff and for new staff, enabling consistent, high-quality service to be provided to customers. This allows the customer service support system to analyze customer conversation patterns and create scripts appropriate to the situation, improving the efficiency of customer service at stores that do not have mass retailer crew members or for new crew members, and providing consistent, high-quality service to customers.

[0065] A customer service support system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a support unit. The reception unit accepts customer input. Examples of customer input include, but are not limited to, text input, voice input, and option input. The reception unit accepts details of the customer's desired product, budget, desired features, and the like, using text input. The reception unit can also accept customer requests using voice input. For example, voice recognition technology can be used to convert the customer's voice into text data, and the reception unit analyzes the text. The reception unit can also select desired items from options on a screen using option input. The analysis unit analyzes the information accepted by the reception unit. For example, data mining, statistical analysis, machine learning algorithms, and the like can be used for the analysis, but are not limited to these examples. The analysis unit extracts useful information from the customer's input data using, for example, data mining technology. The analysis unit can also analyze trends based on the customer's requests using statistical analysis technology. The analysis unit can also use machine learning algorithms to build a model for proposing an optimal plan based on the customer's input data. The proposal unit proposes an optimal plan based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, for example, price, functionality, and service content. The proposal unit proposes an optimal plan based on, for example, price. The proposal unit can also propose an optimal plan based on functionality. The proposal unit can also propose an optimal plan based on service content. The support unit supports the application based on the plan proposed by the proposal unit. Support includes, but is not limited to, for example, assistance in filling out an online form, telephone support, and chat support. The support unit provides, for example, assistance in filling out an online form. The support unit can also provide telephone support. The support unit can also provide chat support. This allows the customer service support system according to the embodiment to accept and analyze customer input, propose an optimal plan, and support the application.

[0066] Furthermore, the customer service support system includes a collection unit that collects past customer service data. The collection unit collects past customer service data. The past customer service data includes, for example, customer feedback, purchase history, inquiry history, etc., but is not limited to these examples. The collection unit collects, for example, customer feedback. The collection unit can also collect purchase history. Furthermore, the collection unit can also collect inquiry history. In this way, collecting past customer service data enables more accurate analysis.

[0067] The support unit can provide guidance on required documents. The support unit, for example, provides guidance on required documents. Required documents include, but are not limited to, for example, an identification card, a contract, and an application form. The support unit, for example, provides guidance on an identification card. The support unit can also provide guidance on a contract. Furthermore, the support unit can also provide guidance on an application form. By providing guidance on required documents, the application procedure can proceed smoothly.

[0068] The reception unit can receive details of the type of product desired to be purchased, the budget, and the desired functions. For example, the reception unit receives details of the type of product desired to be purchased, the budget, and the desired functions. The type of product desired to be purchased includes, but is not limited to, electronic devices, furniture, clothing, etc. For example, the reception unit receives types of electronic devices. The reception unit can also receive types of furniture. The reception unit can also receive types of clothing. The budget includes, but is not limited to, a price range, a maximum budget, a minimum budget, etc. For example, the reception unit can receive a price range. The reception unit can also receive a maximum budget. The reception unit can also receive a minimum budget. The desired functions include, but are not limited to, performance, design, additional functions, etc. For example, the reception unit can receive performance. The reception unit can also receive design. The reception unit can also receive additional functions. By receiving detailed requests from the customer, a more appropriate plan can be proposed.

[0069] The proposal unit can propose an appropriate plan. The proposal unit, for example, proposes an appropriate plan. The appropriate plan includes, for example, price, functions, service content, etc., but is not limited to these examples. The proposal unit, for example, proposes an optimal plan based on price. The proposal unit can also propose an optimal plan based on functions. Furthermore, the proposal unit can also propose an optimal plan based on service content. In this way, by proposing an optimal plan, customer satisfaction is improved.

[0070] The support department can explain the steps of the application procedure. The support department, for example, explains the steps of the application procedure. The steps of the application procedure include, but are not limited to, for example, a step-by-step guide and how to prepare the necessary documents. The support department, for example, provides a step-by-step guide. The support department can also explain how to prepare the necessary documents. By explaining the steps of the application procedure, the customer can smoothly complete the application.

[0071] The reception unit can estimate a customer's emotions and adjust the display method of the input interface based on the estimated customer emotions. For example, the reception unit can estimate a customer's emotions and adjust the display method of the input interface based on the estimated customer emotions. Customer emotions include, but are not limited to, stress, relaxation, and hurry. For example, if the customer is stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the customer is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the customer is in a hurry, the reception unit can prioritize voice input, allowing the customer to quickly enter the product type, budget, and desired features. This improves ease of input by providing an interface that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The reception unit can analyze the customer's past input history and suggest the optimal input method. The reception unit, for example, analyzes the customer's past input history and suggests the optimal input method. Past input history includes, for example, past input data, methods of analyzing input patterns, and the like, but is not limited to these examples. The reception unit, for example, automatically displays as candidates the types of products and budgets that the customer has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the customer has used in the past. The reception unit can also predict and suggest the types of products and budgets to be used in a specific time period based on the customer's past input history. In this way, input efficiency is improved by suggesting the optimal input method based on the customer's past input history.

[0073] The reception unit can customize input items based on the customer's current purchasing intent and areas of interest at the time of input. The reception unit, for example, customizes input items based on the customer's current purchasing intent and areas of interest at the time of input. Purchasing intent includes, but is not limited to, high purchasing intent and low purchasing intent. For example, if the customer indicates high purchasing intent, the reception unit allows the customer to enter detailed product types, budget, and desired features. Furthermore, if the customer has a specific area of ​​interest, the reception unit can preferentially display input items related to that area. Furthermore, if the customer indicates low purchasing intent, the reception unit can provide simpler input items to reduce the effort required for input. This improves input accuracy by providing input items according to the customer's purchasing intent and areas of interest.

[0074] The reception unit can estimate the customer's emotions and prioritize input based on the estimated customer emotions. The reception unit, for example, estimates the customer's emotions and prioritizes input based on the estimated customer emotions. Customer emotions include, but are not limited to, tension, relaxation, and urgency. For example, if the customer is nervous, the reception unit can prioritize displaying important input fields to enable the customer to complete input quickly. Furthermore, if the customer is relaxed, the reception unit can provide detailed input fields and suggest a customizable input method. Furthermore, if the customer is in a hurry, the reception unit can display the most important input fields first to enable the customer to complete input quickly. This improves input efficiency by prioritizing input based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can prioritize displaying highly relevant input items based on the customer's geographical location information when the customer enters information. For example, the reception unit prioritizes displaying highly relevant input items based on the customer's geographical location information when the customer enters information. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the customer is in a specific area, the reception unit can prioritize displaying products and services that are popular in that area. Furthermore, if the customer is traveling, the reception unit can prioritize displaying the types of products and budgets available at the customer's travel destination. Furthermore, if the customer is at home, the reception unit can prioritize displaying the types of products and budgets available at home. This improves the accuracy of input by providing input items based on the customer's geographical location information.

[0076] The reception unit can analyze the customer's social media activity at the time of input and suggest related input items. For example, the reception unit can analyze the customer's social media activity at the time of input and suggest related input items. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement rates. For example, if the customer mentions a specific product on social media, the reception unit can prioritize displaying input items related to that product. Furthermore, if the customer has expressed specific interests or concerns on social media, the reception unit can suggest input items related to those interests or concerns. Furthermore, if the customer is participating in a specific event on social media, the reception unit can suggest input items related to that event. This improves the accuracy of input by providing input items based on the customer's social media activity.

[0077] The analysis unit can estimate the customer's emotions and adjust the analysis algorithm based on the estimated customer emotions. The analysis unit, for example, estimates the customer's emotions and adjusts the analysis algorithm based on the estimated customer emotions. Customer emotions include, but are not limited to, relaxation, hurry, excitement, etc. For example, if the customer is relaxed, the analysis unit can perform a detailed analysis and propose an optimal plan. If the customer is in a hurry, the analysis unit can perform a quick analysis and propose a concise plan. If the customer is excited, the analysis unit can provide analysis results with visually stimulating effects. This improves the accuracy of the analysis by providing an analysis algorithm that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the information input during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information input during analysis. The importance of the information includes, for example, reliability, relevance, urgency, etc., but is not limited to these examples. For example, when important information is input, the analysis unit performs a detailed analysis and proposes an optimal plan. Furthermore, when general information is input, the analysis unit can perform a standard analysis and propose an appropriate plan. Furthermore, when simple information is input, the analysis unit can perform a concise analysis and quickly propose a plan. In this way, by performing an analysis according to the importance of the input information, the accuracy of the analysis is improved.

[0079] The analysis unit can apply different analysis methods depending on the category of information during analysis. For example, the analysis unit applies different analysis methods depending on the category of information during analysis. Information categories include, but are not limited to, text data, numerical data, and image data. For example, the analysis unit applies the optimal analysis method depending on the type of product desired to be purchased. Furthermore, the analysis unit can apply different analysis methods depending on the budget and propose the optimal plan. Furthermore, the analysis unit can apply different analysis methods depending on the desired functions and propose the optimal plan. In this way, by providing analysis methods depending on the category of information, the accuracy of the analysis is improved.

[0080] The analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. For example, the analysis unit can estimate the customer's emotions and adjust the display method of the analysis results based on the estimated customer emotions. Customer emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the customer is tension, the analysis unit can provide a simple, highly visible display method. Furthermore, if the customer is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the customer is hurrying, the analysis unit can provide a display method that focuses on the main points. This improves understanding of the analysis results by providing a display method that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The analysis unit can determine the priority of analysis based on the submission time of input information during analysis. The analysis unit, for example, determines the priority of analysis based on the submission time of input information during analysis. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, and the submission timing. For example, when the latest information is input, the analysis unit performs analysis with priority and provides results quickly. Furthermore, when old information is input, the analysis unit can also perform analysis with standard priority. Furthermore, when important information is input, the analysis unit can also perform analysis with priority and provide detailed results. This improves the efficiency of analysis by performing analysis according to the submission time of the input information.

[0082] The analysis unit can improve the accuracy of the analysis by referring to related external data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to related external data during analysis. Examples of external data include, but are not limited to, public databases, data provided by third parties, and API data. The analysis unit, for example, refers to external market data and proposes an optimal plan. The analysis unit can also refer to external customer reviews and reflect them in the analysis results. The analysis unit can also refer to external competitive information and reflect them in the analysis results. In this way, the accuracy of the analysis is improved by referring to related external data.

[0083] The suggestion unit can estimate the customer's emotions and adjust the way the suggestions are expressed based on the estimated customer emotions. For example, the suggestion unit can estimate the customer's emotions and adjust the way the suggestions are expressed based on the estimated customer emotions. Customer emotions include, but are not limited to, relaxation, hurry, excitement, etc. For example, if the customer is relaxed, the suggestion unit can provide detailed suggestions and many options. If the customer is in a hurry, the suggestion unit can provide concise suggestions and quickly provide options. If the customer is excited, the suggestion unit can provide suggestions with visually stimulating effects. This improves the likelihood of the suggestions being accepted by providing a way to express the suggestions based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The proposal unit can adjust the level of detail of the proposal based on the importance of the plan when making the proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the plan when making the proposal. The importance of the plan includes, but is not limited to, customer needs, the impact of the plan, and cost. For example, the proposal unit can make a detailed proposal for an important plan and provide many options. Furthermore, the proposal unit can make a standard proposal for a general plan and provide appropriate options. Furthermore, the proposal unit can make a concise proposal for a simple plan and quickly provide options. This improves the accuracy of the proposal by making proposals according to the importance of the plan.

[0085] The suggestion unit can apply different suggestion algorithms depending on the category of the plan when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the plan when making a suggestion. Plan categories include, but are not limited to, service plans, product plans, and support plans. For example, the suggestion unit applies an optimal suggestion algorithm depending on the type of product desired to be purchased. The suggestion unit can also apply different suggestion algorithms depending on the budget to propose an optimal plan. The suggestion unit can also apply different suggestion algorithms depending on desired functions to propose an optimal plan. In this way, by providing a suggestion algorithm depending on the category of the plan, the accuracy of the suggestion is improved.

[0086] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. The suggestion unit, for example, estimates the customer's emotions and adjusts the length of the suggestion based on the estimated customer emotions. Customer emotions include, but are not limited to, hurry, relaxation, excitement, etc. For example, if the customer is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the customer is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the customer is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This improves the acceptability of the suggestion by providing a suggestion length that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The proposal unit can determine the priority of proposals based on the time of submission of the plans at the time of proposal. The proposal unit, for example, determines the priority of proposals based on the time of submission of the plans at the time of proposal. The submission time includes, for example, but is not limited to, the submission date and time, the submission frequency, and the submission timing. For example, the proposal unit can prioritize proposals for the latest plans and quickly provide options. Furthermore, the proposal unit can also make proposals with standard priority for older plans. Furthermore, the proposal unit can prioritize proposals for important plans and provide detailed options. This improves the efficiency of proposals by making proposals according to the time of submission of the plans.

[0088] The proposal unit can adjust the order of proposals based on the relevance of the plans when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of the plans when making a proposal. The relevance of the plans includes, but is not limited to, customer needs, interrelationships between plans, and the like. For example, the proposal unit proposes the most relevant plan first. The proposal unit can also propose plans of general relevance in a standard order. The proposal unit can also propose plans of low relevance last. This improves the accuracy of proposals by making proposals based on the relevance of the plans.

[0089] The support unit can estimate the customer's emotions and adjust the support method based on the estimated customer emotions. For example, the support unit can estimate the customer's emotions and adjust the support method based on the estimated customer emotions. Customer emotions include, but are not limited to, tension, relaxation, and urgency. For example, if the customer is tension, the support unit can provide support in a calm voice. If the customer is relaxed, the support unit can provide support in a cheerful voice. If the customer is hurried, the support unit can provide quick and concise support. This improves the quality of support by providing a support method that corresponds to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The support department can analyze past support history and select the optimal support method when providing support. For example, the support department analyzes past support history and selects the optimal support method when providing support. Past support history includes, but is not limited to, the support content, support results, customer feedback, etc. For example, the support department selects the optimal support method based on the support content the customer has received in the past. The support department can also suggest an effective support method based on the customer's past support history. The support department can also analyze the customer's past support history and select the most efficient support method. In this way, by providing the optimal support method based on the past support history, the efficiency of support is improved.

[0091] The support department can customize support methods based on the customer's current situation when providing support. For example, the support department customizes support methods based on the customer's current situation when providing support. The customer's current situation includes, but is not limited to, current purchasing status, current usage status, etc. For example, the support department can provide quick support methods when the customer is in a hurry. Furthermore, the support department can provide detailed support methods when the customer is relaxed. Furthermore, if the customer has a specific problem, the support department can provide support methods specialized for that problem. This improves the quality of support by providing support methods that suit the customer's current situation.

[0092] The support unit can estimate the customer's emotions and determine support priorities based on the estimated customer emotions. For example, the support unit can estimate the customer's emotions and determine support priorities based on the estimated customer emotions. Customer emotions include, but are not limited to, tension, relaxation, and urgency. For example, the support unit can provide support with priority if the customer is tension. Furthermore, the support unit can provide support with standard priority if the customer is relaxed. Furthermore, the support unit can provide support quickly if the customer is urgency. This improves support efficiency by providing support priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The support department can select the optimal support method by taking into account the customer's geographical location information when providing support. For example, the support department selects the optimal support method by taking into account the customer's geographical location information when providing support. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if the customer is in a specific area, the support department can provide support methods that can be used in that area. Furthermore, if the customer is traveling, the support department can provide support methods that can be used at the customer's travel destination. Furthermore, if the customer is at home, the support department can provide support methods that can be used at home. This improves the quality of support by providing support methods based on the customer's geographical location information.

[0094] The support department may analyze a customer's social media activity to suggest support methods when providing support. For example, the support department may analyze a customer's social media activity when providing support to suggest support methods. Social media activity includes, but is not limited to, the content of posts, follower responses, and engagement rates. For example, if a customer mentions a particular issue on social media, the support department may provide support methods related to that issue. Furthermore, if a customer shows particular interests or concerns on social media, the support department may also suggest support methods related to those interests or concerns. Furthermore, if a customer is participating in a particular event on social media, the support department may also suggest support methods related to that event. This improves the quality of support by providing support methods based on the customer's social media activity.

[0095] The collection unit can estimate the customer's emotions and adjust the timing of data collection based on the estimated customer emotions. For example, the collection unit can estimate the customer's emotions and adjust the timing of data collection based on the estimated customer emotions. Customer emotions include, but are not limited to, relaxation, hurry, excitement, etc. For example, the collection unit can collect detailed data when the customer is relaxed. Furthermore, the collection unit can collect data quickly when the customer is hurrying. Furthermore, the collection unit can collect data with visually stimulating effects when the customer is excited. This improves the efficiency of data collection by providing the timing of data collection according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The collection unit can analyze past customer service data at the time of collection and select the optimal collection method. For example, the collection unit analyzes past customer service data at the time of collection and selects the optimal collection method. Past customer service data includes, for example, customer feedback, purchase history, inquiry history, etc., but is not limited to these examples. The collection unit selects the optimal data collection method based on, for example, the past customer service data. The collection unit can also suggest an effective data collection method from the past customer service data. The collection unit can also analyze the past customer service data and select the most efficient data collection method. This improves the efficiency of data collection by providing the optimal collection method based on the past customer service data.

[0097] The collection unit can collect data taking into account real-time customer information at the time of collection. The collection unit, for example, collects data taking into account real-time customer information at the time of collection. Real-time customer information includes, but is not limited to, real-time behavioral data and real-time purchase data. The collection unit, for example, selects an optimal data collection method based on the real-time customer information. The collection unit can also propose an effective data collection method from the real-time customer information. The collection unit can also analyze the real-time customer information and select the most efficient data collection method. This improves the accuracy of data collection by providing data collection based on real-time customer information.

[0098] The collection unit can estimate the customer's emotions and determine the priority of data to be collected based on the estimated customer emotions. The collection unit, for example, estimates the customer's emotions and determines the priority of data to be collected based on the estimated customer emotions. Customer emotions include, but are not limited to, tension, relaxation, and hurry. For example, if the customer is tension, the collection unit can prioritize collecting important data. Furthermore, if the customer is relaxed, the collection unit can also collect detailed data. Furthermore, if the customer is in a hurry, the collection unit can quickly collect important data. This improves the efficiency of data collection by providing data prioritization according to the customer's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration at the time of collection. For example, the collection unit prioritizes collecting highly relevant data by taking geographical location information into consideration at the time of collection. Geographical location information includes, but is not limited to, GPS data, IP address, location information services, etc. For example, if a customer is in a specific area, the collection unit can prioritize collecting data that is highly relevant in that area. Furthermore, if a customer is traveling, the collection unit can prioritize collecting data that is highly relevant at the customer's travel destination. Furthermore, if a customer is at home, the collection unit can prioritize collecting data that is highly relevant at home. This improves the accuracy of data collection by providing data collection based on geographical location information.

[0100] The collection unit may analyze social media activity at the time of collection to collect related data. For example, the collection unit may analyze social media activity at the time of collection to collect related data. Social media activity includes, but is not limited to, post content, follower reactions, and engagement rates. For example, if a customer mentions a particular product on social media, the collection unit may collect data related to the product. Furthermore, if a customer expresses a particular interest or concern on social media, the collection unit may also collect data related to the interest or concern. Furthermore, if a customer participates in a particular event on social media, the collection unit may collect data related to the event. This improves the accuracy of data collection by providing data collection based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, support unit, and collection 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 reception unit is realized by the control unit 46A of the smart device 14 and accepts text input and voice input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes information using data mining or machine learning algorithms. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan based on the analysis results. The support unit is realized by the control unit 46A of the smart device 14 and provides assistance in filling out online forms and chat support. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects past customer service data. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, support unit, and collection 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 reception unit is realized by the control unit 46A of the smart glasses 214 and accepts text input and voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using data mining or machine learning algorithms. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides support for filling out online forms and chat support. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past customer service data. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, support unit, and collection unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and accepts text input and voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using data mining or machine learning algorithms. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan based on the analysis results. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides assistance in filling out online forms and chat support. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past customer service data. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, proposal unit, support unit, and collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts text input and voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using data mining or machine learning algorithms. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan based on the analysis results. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides assistance in filling out online forms and chat support. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects past customer service data.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can analyze the user's purchasing history and predict the next product that the user is likely to purchase based on their past purchasing patterns. For example, if the user has frequently purchased electronic devices from a specific brand in the past, new products from that brand can be preferentially suggested. Also, if the user tends to purchase specific products each season, products suited to that season can be suggested. Furthermore, if the user prefers products in a specific price range, products in that price range can be suggested. This improves the accuracy of suggestions by making suggestions based on the user's purchasing history.

[0103] The suggestion unit can estimate the user's emotions and customize the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest products or services that will help them relax. If the user is excited, it can also suggest products or services that are highly entertaining. Furthermore, if the user is calm, it can provide detailed information to allow the user to carefully make a selection. In this way, suggestions that are tailored to the user's emotions can be made more likely to be accepted.

[0104] The support department can analyze the user's past support history and select the optimal support method. For example, if the user has previously preferred chat support, the support department can provide chat support as a priority. If the user has previously used telephone support, the support department can suggest telephone support. Furthermore, if the user has previously preferred filling out online forms, the support department can provide assistance with filling out online forms. This improves the efficiency of support by providing support based on the user's past support history.

[0105] The reception unit can suggest highly relevant products and services based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can suggest products and services that are popular in that area. If the user is traveling, the reception unit can suggest products and services that can be used at the user's travel destination. Furthermore, if the user is at home, the reception unit can suggest products and services that can be used at home. This improves the accuracy of suggestions by making suggestions based on the user's geographical location information.

[0106] The analysis unit can analyze a user's social media activity and suggest related products and services. For example, if a user mentions a specific product on social media, products and services related to that product can be suggested. Also, if a user shows specific interests on social media, products and services related to those interests can be suggested. Furthermore, if a user participates in a specific event on social media, products and services related to that event can be suggested. This improves the accuracy of suggestions by making suggestions based on the user's social media activity.

[0107] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be made and many options can be provided. If the user is in a hurry, concise suggestions can be made and options can be provided quickly. Furthermore, if the user is excited, suggestions can be made with visually stimulating effects. In this way, by providing a way of expressing suggestions according to the user's emotions, the likelihood of the suggestions being accepted can be improved.

[0108] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. If the user is relaxed, the support unit can provide support in a cheerful voice. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. This improves the quality of support by providing a support method that suits the user's emotions.

[0109] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is relaxed, detailed data collection can be performed. If the user is in a hurry, quick data collection can be performed. Furthermore, if the user is excited, data collection can be performed with visually stimulating effects. This improves the efficiency of data collection by providing the timing of data collection according to the user's emotions.

[0110] The collection unit can analyze past customer service data at the time of collection and select the optimal collection method. For example, the optimal data collection method is selected based on past customer service data. It can also suggest an effective data collection method from past customer service data. It can also analyze past customer service data and select the most efficient data collection method. This improves the efficiency of data collection by providing the optimal collection method based on past customer service data.

[0111] The collection unit can collect data taking into account real-time customer information at the time of collection. For example, the optimal data collection method can be selected based on real-time customer information. The collection unit can also propose an effective data collection method based on real-time customer information. Furthermore, the collection unit can analyze real-time customer information and select the most efficient data collection method. This improves the accuracy of data collection by providing data collection based on real-time customer information.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit accepts customer input. Customer input can include text input, voice input, and option input. For example, text input is used to accept details such as the customer's desired product, budget, and desired features. Voice recognition technology is also used to convert the customer's voice into text data, which the reception unit analyzes. Furthermore, option input allows customers to select desired items from options on the screen. Step 2: The analysis unit analyzes the information received by the reception unit. Data mining, statistical analysis, machine learning algorithms, etc. are used for the analysis. For example, data mining technology is used to extract useful information from the customer's input data, and statistical analysis technology is used to analyze trends based on the customer's requests. Furthermore, machine learning algorithms are used to build a model to propose the optimal plan based on the customer's input data. Step 3: The proposal unit proposes an optimal plan based on the analysis results obtained by the analysis unit. The proposal includes price, functions, service content, etc. For example, it is possible to propose an optimal plan based on price, an optimal plan based on functions, or even an optimal plan based on service content. Step 4: The support department supports the application based on the plan proposed by the proposal department. The support may include assistance in filling out an online form, telephone support, chat support, etc. For example, the support department may provide assistance in filling out an online form, and may also provide telephone support or chat support.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from customers; an analysis unit that analyzes the information received by the reception unit; a proposal unit that proposes an appropriate plan based on the analysis result obtained by the analysis unit; a support unit that supports an application based on the plan proposed by the proposal unit. A system characterized by:

2. Equipped with a collection unit that collects past customer service data 2. The system of claim 1.

3. The support portion is Provide guidance on the necessary documents 2. The system of claim 1.

4. The reception unit Accept details about the type of product you want to purchase, your budget, and desired features 2. The system of claim 1.

5. The proposal unit Propose the right plan 2. The system of claim 1.

6. The support portion is Explain the application procedure 2. The system of claim 1.

7. The reception unit Estimate the customer's emotions and adjust the display method of the input interface based on the estimated customer emotions.

2. The system of claim 1.

8. The reception unit Analyze your past input history and suggest the best input method 2. The system of claim 1.

9. The reception unit Customize your input based on your current purchasing intent and interests as you enter it 2. The system of claim 1.

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