System

Abstract

An object of a system according to an embodiment is to efficiently support an application procedure and propose an optimal plan.SOLUTION: A system according to an embodiment includes a reception unit, an estimation unit, a proposal unit, an application support unit, and a script generation unit. The reception unit inputs detailed information. The trial calculation unit calculates a trial amount of money based on the information input by the reception unit. The proposal unit proposes an appropriate plan based on a result of the trial calculation by the trial calculation unit. The application support unit supports the application based on the plan proposed by the proposal unit. A script generation part generates a script on the basis of past service data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient support for the application process, and there is room for improvement, especially in customer service for new crew members and in unmanned stores.

[0005] The system according to the embodiment aims to efficiently support the application procedure and propose the most suitable plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an estimation unit, a proposal unit, an application support unit, and a script generation unit. The reception unit inputs detailed information. The estimation unit estimates the amount based on the information input by the reception unit. The proposal unit proposes an appropriate plan based on the results of the estimation by the estimation unit. The application support unit supports the application based on the plan proposed by the proposal unit. The script generation unit generates a script based on past customer service data. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently support the application procedure and propose the most suitable plan. [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) The AI ​​customer service supporter of an embodiment of the present invention is a system that supports the application process. This system allows customers to input detailed information such as family composition, mobile phone subscription period and carrier, device, landline, and electricity. Based on the input information, the AI ​​calculates the cost of switching, proposes the optimal plan, and supports the customer through the application process. Furthermore, the customer service chat accumulates information acquired through various previous patterns, uses a generation AI to create a crew daily report, and generates a script appropriate to the situation. This system can be used in stores without mass retailer crews or for customer service by new crew members. For example, the AI ​​customer service supporter inputs detailed information such as family composition, mobile phone subscription period and carrier, device, landline, and electricity. The input information is analyzed by the AI. Next, the AI ​​estimates the cost of switching based on the input information. For example, it can estimate the monthly fee and initial costs of switching from the current carrier to another. Furthermore, the customer service chat accumulates information acquired through various previous patterns, uses a generation AI to create a crew daily report, and generates a script appropriate to the situation. For example, based on past customer service data, it can analyze what conversations were effective in specific situations and generate a script based on the results. Finally, customers can enter their details on a monitor installed in the store, and the AI ​​will propose the optimal plan and support them through the application process. For example, when a customer enters information such as family composition and the device they use on the monitor, the AI ​​will propose the optimal plan and support them through the application process on the spot. This allows the AI ​​customer service supporter to support the application process quickly and accurately. This allows the AI ​​customer service supporter to support the application process quickly and accurately. For example, it can be used in stores that do not have staff at mass retailers or when serving new staff. Customers can also easily find the plan that is best for them, and the application process can be completed smoothly.

[0029] An AI customer supporter according to an embodiment includes a reception unit, an estimation unit, a proposal unit, an application support unit, and a script generation unit. The reception unit allows customers to input detailed information such as family composition, mobile phone subscription period, carrier, device, fixed line, and electricity. For example, the reception unit provides an interface for inputting information such as family composition and mobile phone subscription period. The reception unit can also analyze the input information using AI. The estimation unit estimates the cost of switching based on the input information. For example, the estimation unit estimates the monthly fee and initial cost of switching from the current carrier to another carrier. The estimation unit can also use AI to improve the accuracy of the estimate. The proposal unit proposes an optimal plan based on the calculation results. For example, the proposal unit proposes an optimal pricing plan or service plan to the customer based on the calculation results. The proposal unit can also improve the accuracy of the proposal using AI. The application support unit supports the application process based on the proposed plan. For example, the application support unit provides methods such as online application, telephone support, and in-person support. The application support unit can also use AI to improve the efficiency of the application process. The script generation unit generates scripts based on past customer service data. For example, the script generation unit analyzes past customer service data and generates a script of effective conversations for a specific situation. The script generation unit can also create a crew daily report using the generation AI. This allows the AI ​​customer service supporter according to the embodiment to quickly and accurately support the application process.

[0030] The reception unit can input detailed information such as family composition, mobile phone usage period or carrier, device used, fixed line, and electricity. Examples of detailed information include, but are not limited to, family composition, mobile phone usage period, carrier, device used, fixed line, and electricity. The reception unit provides an interface for inputting information such as family composition and mobile phone usage period. The reception unit can also analyze the input information using AI. For example, the reception unit can input the input information into AI and output the analysis results. By inputting detailed information, the user can make more accurate estimates and proposals.

[0031] The estimation unit can estimate the amount of money it would cost to switch carriers based on the input information. The amount of money it would cost to switch carriers includes, but is not limited to, monthly fees, initial costs, and discount application conditions, for example. The estimation unit estimates, for example, monthly fees and initial costs if the user switches from the current carrier to another carrier. The estimation unit can also improve the accuracy of the estimation using AI. For example, the estimation unit can input the input information into AI and output the estimated results. This allows the user to specifically understand the benefits of switching carriers.

[0032] The proposal unit can propose an appropriate plan based on the results of the trial calculation. Suitable plans include, but are not limited to, pricing plans, service plans, and terms of use. For example, the proposal unit proposes an optimal pricing plan or service plan to the customer based on the results of the trial calculation. The proposal unit can also use AI to improve the accuracy of the proposal. For example, the proposal unit can input the results of the trial calculation into AI and output the optimal plan. This makes it possible to propose an optimal plan to the user.

[0033] The application support unit can support the application procedure based on the proposed plan. The application procedure includes, but is not limited to, for example, online application, telephone support, and face-to-face support. For example, the application support unit can support online application based on the proposed plan. The application support unit can also provide telephone support based on the proposed plan. The application support unit can also provide face-to-face support based on the proposed plan. This allows the user to smoothly complete the application procedure based on the proposed plan.

[0034] The script generation unit can generate a script based on past customer service data. Script generation includes, but is not limited to, analyzing past customer service data, extracting effective conversations, and generating a script. For example, the script generation unit analyzes past customer service data and generates a script that contains effective conversations for a specific situation. The script generation unit can also create a crew daily report using the generation AI. For example, the script generation unit can input past customer service data into the AI ​​and output a script. This makes it possible to generate an effective script by utilizing past customer service data.

[0035] The script generation unit can create a crew daily report by utilizing the generation AI. Creation of the crew daily report includes, for example, collecting past customer service data, analyzing it, and creating a report, but is not limited to such examples. For example, the script generation unit collects past customer service data using the generation AI and creates a crew daily report based on the analysis results. The script generation unit can also automatically generate the contents of the report using the generation AI. For example, the script generation unit can input past customer service data into the generation AI and output a crew daily report. In this way, by utilizing the generation AI, the creation of the crew daily report is made more efficient.

[0036] The proposal unit can propose an appropriate plan to the customer through a monitor installed in the store. Examples of monitors installed in the store include, but are not limited to, touch panel monitors and digital signage. The proposal unit can, for example, propose an optimal plan to the customer through a monitor installed in the store. The proposal unit can also propose a plan to the customer based on the information displayed on the monitor. For example, the proposal unit can input the information displayed on the monitor into AI and output the optimal plan. This makes it possible to propose an optimal plan to the user through the monitor installed in the store.

[0037] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the user to input information more efficiently by selecting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0038] The reception unit can filter the detailed information based on the user's current living situation and areas of interest when the detailed information is input. The reception unit, for example, displays only relevant information based on the user's current living situation. The reception unit can also customize the information to be input based on the user's areas of interest. The reception unit can also filter and display optimal information based on the user's past behavior history. This allows the user to input more relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0039] When inputting detailed information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user desires voice input, the reception unit provides a voice recognition function. Furthermore, if the user desires text input, the reception unit can prioritize keyboard input. Furthermore, if the user desires image input, the reception unit can provide an image recognition function. This allows smooth input by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0040] When inputting detailed information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit can, for example, prioritize displaying relevant information based on the user's current location. The reception unit can also filter and display optimal information based on the user's geographical location information. The reception unit can also prioritize inputting information related to locations close to the user's current location. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0041] When inputting detailed information, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, inputs information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be input efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The reception unit can customize the input method by reflecting the user's past feedback when inputting detailed information. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without using AI.

[0043] The estimation unit can adjust the level of detail of the estimation based on the importance of the input information during the estimation. For example, when there is a lot of important information, the estimation unit displays detailed estimation results. Furthermore, when there is little important information, the estimation unit can also display concise estimation results. Furthermore, the estimation unit can adjust the level of detail of the estimation results according to the importance of the input information. In this way, by adjusting the level of detail of the estimation according to the importance of the input information, it is possible to provide appropriate estimation results. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI.

[0044] The estimation unit can apply different estimation algorithms depending on the category of information when making the estimation. For example, the estimation unit applies the optimal estimation algorithm based on the period of mobile phone usage. The estimation unit can also apply the optimal estimation algorithm based on family composition. The estimation unit can also apply the optimal estimation algorithm based on the device used. In this way, by applying the optimal estimation algorithm depending on the category of information, highly accurate estimation results can be provided. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0045] The estimation unit can improve the accuracy of the estimation by referring to the user's past estimation results when performing the estimation. The estimation unit can improve the accuracy of the estimation, for example, based on the user's past estimation results. The estimation unit can also analyze the user's past estimation results and apply an optimal estimation algorithm. The estimation unit can also adjust the level of detail of the estimation by referring to the user's past estimation results. In this way, the accuracy of the estimation can be improved by referring to the user's past estimation results. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI.

[0046] The estimation unit can determine the priority of the estimation based on the time of submission of information during the estimation. For example, the estimation unit prioritizes the most recent information in the estimation. The estimation unit can also postpone information that has been submitted earlier. The estimation unit can also adjust the priority of the estimation based on the time of submission. In this way, by determining the priority of the estimation based on the time of submission of information, the most recent information can be prioritized in the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The estimation unit can adjust the order of estimation based on the relevance of information during estimation. For example, the estimation unit prioritizes estimation of highly relevant information. The estimation unit can also postpone estimation of less relevant information. The estimation unit can also adjust the order of estimation based on the relevance of information. In this way, by adjusting the order of estimation based on the relevance of information, highly relevant information can be prioritized for estimation. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0048] During the trial calculation, the estimation unit can adjust the use of technical terms in the trial calculation according to the user's level of expertise. For example, the estimation unit displays the trial calculation results in simple language for a user with little technical knowledge. The estimation unit can also display the trial calculation results using detailed technical terms for a user with much technical knowledge. The estimation unit can also adjust the way in which the trial calculation results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the trial calculation according to the user's level of expertise, it is possible to provide trial calculation results that are easy for the user to understand. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or without using AI.

[0049] The proposal unit can adjust the level of detail of the proposal based on the importance of the calculation results when making a proposal. For example, the proposal unit makes a detailed proposal based on important calculation results. The proposal unit can also make a concise proposal based on less important calculation results. The proposal unit can also adjust the level of detail of the proposal depending on the importance of the calculation results. This makes it possible to provide an appropriate proposal by adjusting the level of detail of the proposal depending on the importance of the calculation results. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI.

[0050] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. The proposal unit applies the optimal proposal algorithm based on, for example, a mobile plan. The proposal unit can also apply the optimal proposal algorithm based on a fixed-line plan. The proposal unit can also apply the optimal proposal algorithm based on an electricity plan. In this way, by applying the optimal proposal algorithm depending on the category of the plan, it is possible to provide a proposal with high accuracy. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI.

[0051] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and apply an optimal suggestion algorithm. The suggestion unit can also adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0052] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. For example, the proposal unit can prioritize the most recent plan. The proposal unit can also postpone plans that have been submitted earlier. The proposal unit can also adjust the priority of the proposal based on the time of submission. In this way, by determining the priority of the proposal based on the time of submission of the plan, the most recent plan can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The proposal unit can adjust the order of proposals based on the relevance of the plans when proposing them. For example, the proposal unit preferentially proposes highly relevant plans. The proposal unit can also postpone less relevant plans. The proposal unit can also adjust the order of proposals based on the relevance of the plans. In this way, by adjusting the order of proposals based on the relevance of the plans, highly relevant plans can be preferentially proposed. Some or all of the above-described processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can make a proposal in simple language for a user with little technical knowledge. The suggestion unit can also make a proposal using detailed technical terminology for a user with much technical knowledge. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[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 reception unit can analyze the user's past purchase history and propose the optimal plan. For example, the reception unit can propose related plans based on products and services the user has purchased in the past. The reception unit can also analyze usage trends over a specific period from the user's purchase history and propose the optimal plan. Furthermore, the reception unit can predict future needs based on the user's purchase history and customize the proposed plan. This makes it possible to make more personalized proposals by utilizing the user's past purchase history.

[0057] The estimation unit can acquire the user's current contract details in real time and reflect them in the estimation. For example, the estimation unit can acquire the user's current carrier contract information and estimate the cost of switching. The estimation unit can also acquire the user's current fixed-line contract information and estimate the optimal plan. Furthermore, the estimation unit can acquire the user's current electricity contract information and estimate the optimal electricity plan. This allows for more accurate estimation by reflecting the user's current contract details in real time.

[0058] The suggestion unit can suggest plans based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest a plan with a lot of mobile data. Also, if the user works from home, the suggestion unit can suggest a high-speed internet plan. Furthermore, if the user is eco-conscious, the suggestion unit can suggest a renewable energy plan. This makes it possible to suggest the optimal plan according to the user's lifestyle.

[0059] The application support unit can customize the support content based on the user's language setting. For example, if the user speaks English, the application support unit can provide support in English. If the user speaks Spanish, the application support unit can also provide support in Spanish. Furthermore, if the user speaks multiple languages, the application support unit can also provide support in multiple languages. This makes it possible to provide optimal support according to the user's language setting.

[0060] The script generation unit can improve the script based on user feedback. For example, the script generation unit can analyze the feedback provided by the user and improve the content of the script. The script generation unit can also generate a new script based on the user feedback. Furthermore, the script generation unit can reflect the user feedback in real time and improve the accuracy of the script. This makes it possible to generate a more effective script by utilizing the user feedback.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception department allows the customer to enter detailed information such as family composition, mobile phone usage period, carrier, device, fixed line, and electricity. For example, the reception department provides an interface for entering information such as family composition and mobile phone usage period. The reception department can also analyze the entered information using AI. Step 2: The calculation unit estimates the amount of money it would cost to switch carriers based on the information entered. For example, the calculation unit estimates the monthly fees and initial costs of switching from your current carrier to another. The calculation unit can also use AI to improve the accuracy of its calculations. Step 3: The proposal department proposes the optimal plan based on the estimate results. For example, the proposal department proposes the optimal pricing plan and service plan for the customer based on the estimate results. The proposal department can also use AI to improve the accuracy of proposals. Step 4: The application support department supports the application process based on the proposed plan. For example, the application support department provides methods such as online application, telephone support, and face-to-face support. The application support department can also use AI to improve the efficiency of the application process. Step 5: The script generation unit generates a script based on past customer service data. For example, the script generation unit analyzes past customer service data and generates a script that will be effective in a specific situation. The script generation unit can also use the generation AI to create a crew daily report.

[0063] (Example 2) The AI ​​customer service supporter of an embodiment of the present invention is a system that supports the application process. This system allows customers to input detailed information such as family composition, mobile phone subscription period and carrier, device, landline, and electricity. Based on the input information, the AI ​​calculates the cost of switching, proposes the optimal plan, and supports the customer through the application process. Furthermore, the customer service chat accumulates information acquired through various previous patterns, uses a generation AI to create a crew daily report, and generates a script appropriate to the situation. This system can be used in stores without mass retailer crews or for customer service by new crew members. For example, the AI ​​customer service supporter inputs detailed information such as family composition, mobile phone subscription period and carrier, device, landline, and electricity. The input information is analyzed by the AI. Next, the AI ​​estimates the cost of switching based on the input information. For example, it can estimate the monthly fee and initial costs of switching from the current carrier to another. Furthermore, the customer service chat accumulates information acquired through various previous patterns, uses a generation AI to create a crew daily report, and generates a script appropriate to the situation. For example, based on past customer service data, it can analyze what conversations were effective in specific situations and generate a script based on the results. Finally, customers can enter their details on a monitor installed in the store, and the AI ​​will propose the optimal plan and support them through the application process. For example, when a customer enters information such as family composition and the device they use on the monitor, the AI ​​will propose the optimal plan and support them through the application process on the spot. This allows the AI ​​customer service supporter to support the application process quickly and accurately. This allows the AI ​​customer service supporter to support the application process quickly and accurately. For example, it can be used in stores that do not have staff at mass retailers or when serving new staff. Customers can also easily find the plan that is best for them, and the application process can be completed smoothly.

[0064] An AI customer supporter according to an embodiment includes a reception unit, an estimation unit, a proposal unit, an application support unit, and a script generation unit. The reception unit allows customers to input detailed information such as family composition, mobile phone subscription period, carrier, device, fixed line, and electricity. For example, the reception unit provides an interface for inputting information such as family composition and mobile phone subscription period. The reception unit can also analyze the input information using AI. The estimation unit estimates the cost of switching based on the input information. For example, the estimation unit estimates the monthly fee and initial cost of switching from the current carrier to another carrier. The estimation unit can also use AI to improve the accuracy of the estimate. The proposal unit proposes an optimal plan based on the calculation results. For example, the proposal unit proposes an optimal pricing plan or service plan to the customer based on the calculation results. The proposal unit can also improve the accuracy of the proposal using AI. The application support unit supports the application process based on the proposed plan. For example, the application support unit provides methods such as online application, telephone support, and in-person support. The application support unit can also use AI to improve the efficiency of the application process. The script generation unit generates scripts based on past customer service data. For example, the script generation unit analyzes past customer service data and generates a script of effective conversations for a specific situation. The script generation unit can also create a crew daily report using the generation AI. This allows the AI ​​customer service supporter according to the embodiment to quickly and accurately support the application process.

[0065] The reception unit can input detailed information such as family composition, mobile phone usage period or carrier, device used, fixed line, and electricity. Examples of detailed information include, but are not limited to, family composition, mobile phone usage period, carrier, device used, fixed line, and electricity. The reception unit provides an interface for inputting information such as family composition and mobile phone usage period. The reception unit can also analyze the input information using AI. For example, the reception unit can input the input information into AI and output the analysis results. By inputting detailed information, the user can make more accurate estimates and proposals.

[0066] The estimation unit can estimate the amount of money it would cost to switch carriers based on the input information. The amount of money it would cost to switch carriers includes, but is not limited to, monthly fees, initial costs, and discount application conditions, for example. The estimation unit estimates, for example, monthly fees and initial costs if the user switches from the current carrier to another carrier. The estimation unit can also improve the accuracy of the estimation using AI. For example, the estimation unit can input the input information into AI and output the estimated results. This allows the user to specifically understand the benefits of switching carriers.

[0067] The proposal unit can propose an appropriate plan based on the results of the trial calculation. Suitable plans include, but are not limited to, pricing plans, service plans, and terms of use. For example, the proposal unit proposes an optimal pricing plan or service plan to the customer based on the results of the trial calculation. The proposal unit can also use AI to improve the accuracy of the proposal. For example, the proposal unit can input the results of the trial calculation into AI and output the optimal plan. This makes it possible to propose an optimal plan to the user.

[0068] The application support unit can support the application procedure based on the proposed plan. The application procedure includes, but is not limited to, for example, online application, telephone support, and face-to-face support. For example, the application support unit can support online application based on the proposed plan. The application support unit can also provide telephone support based on the proposed plan. The application support unit can also provide face-to-face support based on the proposed plan. This allows the user to smoothly complete the application procedure based on the proposed plan.

[0069] The script generation unit can generate a script based on past customer service data. Script generation includes, but is not limited to, analyzing past customer service data, extracting effective conversations, and generating a script. For example, the script generation unit analyzes past customer service data and generates a script that contains effective conversations for a specific situation. The script generation unit can also create a crew daily report using the generation AI. For example, the script generation unit can input past customer service data into the AI ​​and output a script. This makes it possible to generate an effective script by utilizing past customer service data.

[0070] The script generation unit can create a crew daily report by utilizing the generation AI. Creation of the crew daily report includes, for example, collecting past customer service data, analyzing it, and creating a report, but is not limited to such examples. For example, the script generation unit collects past customer service data using the generation AI and creates a crew daily report based on the analysis results. The script generation unit can also automatically generate the contents of the report using the generation AI. For example, the script generation unit can input past customer service data into the generation AI and output a crew daily report. In this way, by utilizing the generation AI, the creation of the crew daily report is made more efficient.

[0071] The proposal unit can propose an appropriate plan to the customer through a monitor installed in the store. Examples of monitors installed in the store include, but are not limited to, touch panel monitors and digital signage. The proposal unit can, for example, propose an optimal plan to the customer through a monitor installed in the store. The proposal unit can also propose a plan to the customer based on the information displayed on the monitor. For example, the proposal unit can input the information displayed on the monitor into AI and output the optimal plan. This makes it possible to propose an optimal plan to the user through the monitor installed in the store.

[0072] The reception unit can estimate the user's emotions and adjust the timing of inputting detailed information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can simplify the input procedure and request only the minimum amount of information. Furthermore, if the user is relaxed, the reception unit can provide detailed input options, allowing the user to freely input information. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input information. This allows smoother input by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI.

[0073] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the user to input information more efficiently by selecting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The reception unit can filter the detailed information based on the user's current living situation and areas of interest when the detailed information is input. The reception unit, for example, displays only relevant information based on the user's current living situation. The reception unit can also customize the information to be input based on the user's areas of interest. The reception unit can also filter and display optimal information based on the user's past behavior history. This allows the user to input more relevant information by filtering information based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0075] When inputting detailed information, the reception unit can select the optimal input means depending on the user's input method. For example, if the user desires voice input, the reception unit provides a voice recognition function. Furthermore, if the user desires text input, the reception unit can prioritize keyboard input. Furthermore, if the user desires image input, the reception unit can provide an image recognition function. This allows smooth input by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can cause the user to input important information preferentially. Furthermore, when the user is relaxed, the reception unit can cause the user to input detailed information sequentially. Furthermore, when the user is in a hurry, the reception unit can cause the user to input only the most important information. In this way, by determining the priority of information according to the user's emotions, important information can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without AI.

[0077] When inputting detailed information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. The reception unit can, for example, prioritize displaying relevant information based on the user's current location. The reception unit can also filter and display optimal information based on the user's geographical location information. The reception unit can also prioritize inputting information related to locations close to the user's current location. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without using AI.

[0078] When inputting detailed information, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, inputs information about places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be input efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting detailed information. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also improve the input procedure by reflecting the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without using AI.

[0080] The estimation unit can estimate the user's emotions and adjust the way the estimation is presented based on the estimated user's emotions. For example, if the user is relaxed, the estimation unit can display detailed estimation results. If the user is in a hurry, the estimation unit can also display simple estimation results. If the user is stressed, the estimation unit can also display visually easy-to-understand estimation results. This allows the estimation unit to adjust the way the estimation is presented according to the user's emotions, thereby providing easier-to-understand estimation results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the estimation unit can be performed, for example, using AI, or can be performed without using AI.

[0081] The estimation unit can adjust the level of detail of the estimation based on the importance of the input information during the estimation. For example, when there is a lot of important information, the estimation unit displays detailed estimation results. Furthermore, when there is little important information, the estimation unit can also display concise estimation results. Furthermore, the estimation unit can adjust the level of detail of the estimation results according to the importance of the input information. In this way, by adjusting the level of detail of the estimation according to the importance of the input information, it is possible to provide appropriate estimation results. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI.

[0082] The estimation unit can apply different estimation algorithms depending on the category of information when making the estimation. For example, the estimation unit applies the optimal estimation algorithm based on the period of mobile phone usage. The estimation unit can also apply the optimal estimation algorithm based on family composition. The estimation unit can also apply the optimal estimation algorithm based on the device used. In this way, by applying the optimal estimation algorithm depending on the category of information, highly accurate estimation results can be provided. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0083] The estimation unit can improve the accuracy of the estimation by referring to the user's past estimation results when performing the estimation. The estimation unit can improve the accuracy of the estimation, for example, based on the user's past estimation results. The estimation unit can also analyze the user's past estimation results and apply an optimal estimation algorithm. The estimation unit can also adjust the level of detail of the estimation by referring to the user's past estimation results. In this way, the accuracy of the estimation can be improved by referring to the user's past estimation results. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI.

[0084] The estimation unit can estimate the user's emotions and adjust the length of the estimation based on the estimated user emotions. For example, if the user is in a hurry, the estimation unit can display a short estimation result. Furthermore, if the user is relaxed, the estimation unit can display a detailed estimation result. Furthermore, if the user is stressed, the estimation unit can display a visually easy-to-understand estimation result. By adjusting the length of the estimation according to the user's emotions, it is possible to provide the optimal estimation result for the user. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the estimation unit can be performed, for example, using AI, or can be performed without using AI.

[0085] The estimation unit can determine the priority of the estimation based on the time of submission of information during the estimation. For example, the estimation unit prioritizes the most recent information in the estimation. The estimation unit can also postpone information that has been submitted earlier. The estimation unit can also adjust the priority of the estimation based on the time of submission. In this way, by determining the priority of the estimation based on the time of submission of information, the most recent information can be prioritized in the estimation. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The estimation unit can adjust the order of estimation based on the relevance of information during estimation. For example, the estimation unit prioritizes estimation of highly relevant information. The estimation unit can also postpone estimation of less relevant information. The estimation unit can also adjust the order of estimation based on the relevance of information. In this way, by adjusting the order of estimation based on the relevance of information, highly relevant information can be prioritized for estimation. Some or all of the above-described processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI.

[0087] During the trial calculation, the estimation unit can adjust the use of technical terms in the trial calculation according to the user's level of expertise. For example, the estimation unit displays the trial calculation results in simple language for a user with little technical knowledge. The estimation unit can also display the trial calculation results using detailed technical terms for a user with much technical knowledge. The estimation unit can also adjust the way in which the trial calculation results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the trial calculation according to the user's level of expertise, it is possible to provide trial calculation results that are easy for the user to understand. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or without using AI.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide concise suggestions. If the user is stressed, the suggestion unit can also provide visually easy-to-understand suggestions. This allows the suggestion unit to provide optimal suggestions for the user by adjusting the way the suggestions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI.

[0089] The proposal unit can adjust the level of detail of the proposal based on the importance of the calculation results when making a proposal. For example, the proposal unit makes a detailed proposal based on important calculation results. The proposal unit can also make a concise proposal based on less important calculation results. The proposal unit can also adjust the level of detail of the proposal depending on the importance of the calculation results. This makes it possible to provide an appropriate proposal by adjusting the level of detail of the proposal depending on the importance of the calculation results. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI.

[0090] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. The proposal unit applies the optimal proposal algorithm based on, for example, a mobile plan. The proposal unit can also apply the optimal proposal algorithm based on a fixed-line plan. The proposal unit can also apply the optimal proposal algorithm based on an electricity plan. In this way, by applying the optimal proposal algorithm depending on the category of the plan, it is possible to provide a proposal with high accuracy. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI.

[0091] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit improves the accuracy of the suggestion, for example, based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and apply an optimal suggestion algorithm. The suggestion unit can also adjust the level of detail of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is stressed, the suggestion unit can also provide visually easy-to-understand suggestions. By adjusting the length of the suggestions according to the user's emotions, the suggestion unit can provide optimal suggestions for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI.

[0093] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. For example, the proposal unit can prioritize the most recent plan. The proposal unit can also postpone plans that have been submitted earlier. The proposal unit can also adjust the priority of the proposal based on the time of submission. In this way, by determining the priority of the proposal based on the time of submission of the plan, the most recent plan can be prioritized. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The proposal unit can adjust the order of proposals based on the relevance of the plans when proposing them. For example, the proposal unit preferentially proposes highly relevant plans. The proposal unit can also postpone less relevant plans. The proposal unit can also adjust the order of proposals based on the relevance of the plans. In this way, by adjusting the order of proposals based on the relevance of the plans, highly relevant plans can be preferentially proposed. Some or all of the above-described processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI.

[0095] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit can make a proposal in simple language for a user with little technical knowledge. The suggestion unit can also make a proposal using detailed technical terminology for a user with much technical knowledge. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or without using AI.

[0096] The application support unit can estimate the user's emotions and adjust the application support method based on the estimated user emotions. For example, if the user is nervous, the application support unit can provide simple, highly visible application support. Furthermore, if the user is relaxed, the application support unit can provide detailed application support. Furthermore, if the user is in a hurry, the application support unit can provide quick application support. This allows the application support method to be adjusted according to the user's emotions, thereby providing optimal support for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the application support unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, estimation unit, proposal unit, application support unit, and script generation 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 allows customers to input detailed information such as family composition and the period of mobile phone usage using the reception device 38 of the smart device 14. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the amount of money it will cost to switch based on the input information. 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 calculation results. The application support unit is realized by the control unit 46A of the smart device 14 and supports the application procedure based on the proposed plan. The script generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a script based on past customer service data. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, estimation unit, proposal unit, application support unit, and script generation 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 allows the customer to input detailed information such as family composition and the period of mobile phone use using the microphone 238 of the smart glasses 214. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the amount of money required to switch based on the input information. 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 calculation results. The application support unit is realized by the control unit 46A of the smart glasses 214 and supports the application procedure based on the proposed plan. The script generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a script based on past customer service data. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, estimation unit, proposal unit, application support unit, and script generation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit allows the customer to input detailed information such as family composition and the period of mobile phone use using the microphone 238 of the headset terminal 314. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the amount of money it will cost to switch based on the input information. 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 calculation results. The application support unit is realized by the control unit 46A of the headset terminal 314 and supports the application procedure based on the proposed plan. The script generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a script based on past customer service data. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, estimation unit, proposal unit, application support unit, and script generation 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 allows the customer to input detailed information such as family composition and the period of mobile phone use using the microphone 238 of the robot 414. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the amount of money it will cost to switch based on the input information. 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 calculation results. The application support unit is realized by the control unit 46A of the robot 414 and supports the application procedure based on the proposed plan. The script generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a script based on past customer service data.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the user's past purchase history and propose the optimal plan. For example, the reception unit can propose related plans based on products and services the user has purchased in the past. The reception unit can also analyze usage trends over a specific period from the user's purchase history and propose the optimal plan. Furthermore, the reception unit can predict future needs based on the user's purchase history and customize the proposed plan. This makes it possible to make more personalized proposals by utilizing the user's past purchase history.

[0099] The estimation unit can acquire the user's current contract details in real time and reflect them in the estimation. For example, the estimation unit can acquire the user's current carrier contract information and estimate the cost of switching. The estimation unit can also acquire the user's current fixed-line contract information and estimate the optimal plan. Furthermore, the estimation unit can acquire the user's current electricity contract information and estimate the optimal electricity plan. This allows for more accurate estimation by reflecting the user's current contract details in real time.

[0100] The suggestion unit can suggest plans based on the user's lifestyle. For example, if the user likes outdoor activities, the suggestion unit can suggest a plan with a lot of mobile data. Also, if the user works from home, the suggestion unit can suggest a high-speed internet plan. Furthermore, if the user is eco-conscious, the suggestion unit can suggest a renewable energy plan. This makes it possible to suggest the optimal plan according to the user's lifestyle.

[0101] The application support unit can customize the support content based on the user's language setting. For example, if the user speaks English, the application support unit can provide support in English. If the user speaks Spanish, the application support unit can also provide support in Spanish. Furthermore, if the user speaks multiple languages, the application support unit can also provide support in multiple languages. This makes it possible to provide optimal support according to the user's language setting.

[0102] The script generation unit can improve the script based on user feedback. For example, the script generation unit can analyze the feedback provided by the user and improve the content of the script. The script generation unit can also generate a new script based on the user feedback. Furthermore, the script generation unit can reflect the user feedback in real time and improve the accuracy of the script. This makes it possible to generate a more effective script by utilizing the user feedback.

[0103] The reception unit can estimate the user's emotions and customize the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input information quickly. In this way, customizing the input interface according to the user's emotions enables smoother input.

[0104] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated user's emotions. For example, if the user is relaxed, detailed calculation results can be displayed. If the user is in a hurry, simple calculation results can be displayed. Furthermore, if the user is stressed, calculation results that are visually easy to understand can be displayed. In this way, by adjusting the display method of the calculation results according to the user's emotions, calculation results that are easier to understand can be provided.

[0105] The suggestion unit can estimate the user's emotions and customize the suggestion content based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be made. If the user is in a hurry, concise suggestions can be made. Furthermore, if the user is feeling stressed, visually easy-to-understand suggestions can be made. In this way, by customizing the suggestion content according to the user's emotions, it is possible to provide the most suitable suggestions for the user.

[0106] The application support unit can estimate the user's emotions and adjust the speed at which the application procedure proceeds based on the estimated user emotions. For example, if the user is nervous, the procedure can proceed at a slower pace. If the user is relaxed, the procedure can proceed at a normal pace. Furthermore, if the user is in a hurry, the procedure can proceed quickly. In this way, by adjusting the speed at which the application procedure proceeds according to the user's emotions, the optimal procedure can be provided for the user.

[0107] The script generation unit can estimate the user's emotions and adjust the tone of the script based on the estimated user's emotions. For example, if the user is relaxed, the script can be generated in a friendly tone. If the user is stressed, the script can be generated in a calm tone. Furthermore, if the user is in a hurry, the script can be generated in a concise and clear tone. This allows for more effective communication by adjusting the tone of the script according to the user's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception department allows the customer to enter detailed information such as family composition, mobile phone usage period, carrier, device, fixed line, and electricity. For example, the reception department provides an interface for entering information such as family composition and mobile phone usage period. The reception department can also analyze the entered information using AI. Step 2: The calculation unit estimates the amount of money it would cost to switch carriers based on the information entered. For example, the calculation unit estimates the monthly fees and initial costs of switching from your current carrier to another. The calculation unit can also use AI to improve the accuracy of its calculations. Step 3: The proposal department proposes the optimal plan based on the estimate results. For example, the proposal department proposes the optimal pricing plan and service plan for the customer based on the estimate results. The proposal department can also use AI to improve the accuracy of proposals. Step 4: The application support department supports the application process based on the proposed plan. For example, the application support department provides methods such as online application, telephone support, and face-to-face support. The application support department can also use AI to improve the efficiency of the application process. Step 5: The script generation unit generates a script based on past customer service data. For example, the script generation unit analyzes past customer service data and generates a script that will be effective in a specific situation. The script generation unit can also use the generation AI to create a crew daily report.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 section for inputting detailed information; an estimation unit that estimates the amount based on the information input by the reception unit; a proposal unit that proposes an appropriate plan based on the results of the estimation by the estimation unit; an application support unit that supports an application based on the plan proposed by the proposal unit; A script generation unit that generates a script based on past customer service data. A system characterized by:

2. The reception unit Enter details of family composition, mobile phone usage period or carrier, device used, fixed line, and electricity 2. The system of claim 1.

3. The estimation unit Calculate the cost of switching based on the information you entered 2. The system of claim 1.

4. The proposal unit Propose an appropriate plan based on the results of the calculation 2. The system of claim 1.

5. The application support unit Support the application process based on the proposed plan 2. The system of claim 1.

6. The script generation unit Generate scripts based on past customer service data 2. The system of claim 1.

7. The script generation unit Creating crew daily reports using generative AI 2. The system of claim 1.

8. The proposal unit Propose appropriate plans to customers through monitors installed in stores 2. The system of claim 1.

Images