System

Abstract

A system according to an embodiment aims to suggest personalized smartphone plans based on customer needs and usage patterns.SOLUTION: A system includes a collection part, an analysis part, a proposal part, an interaction part, an analysis part, and an introduction part. The collection unit collects information on a customer. The analysis unit analyzes the information collected by the collection unit and determines the needs and usage patterns of the customer. The proposal unit proposes a personalized smartphone plan based on the determination result obtained by the analysis unit. The interaction unit performs interaction with the customer on the basis of the plan proposed by the proposal unit. The analysis unit analyzes the use state of the customer based on the information obtained by the interaction unit, and proposes an appropriate plan or upgrade. The introduction unit introduces a new function or service based on the result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technologies fall short in offering personalized smartphone plans based on customer needs and usage patterns, leaving room for improvement.

[0005] The system according to the embodiment aims to propose a personalized smartphone plan based on the customer's needs and usage patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, an interaction unit, an analysis unit, and an introduction unit. The collection unit collects customer information. The analysis unit analyzes the information collected by the collection unit and determines the customer's needs and usage patterns. The proposal unit proposes a personalized smartphone plan based on the determination results obtained by the analysis unit. The interaction unit interacts with the customer based on the plan proposed by the proposal unit. The analysis unit analyzes the customer's usage status based on the information obtained by the interaction unit and proposes appropriate plans and upgrades. The introduction unit introduces new features and services based on the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can offer personalized smartphone plans based on the customer's needs and usage patterns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to propose smartphone plans, thereby improving customer satisfaction and promoting business growth. This system promotes improved customer satisfaction and business growth by collecting and analyzing customer information and proposing personalized plans. For example, the system collects information such as the customer's call time, data usage, and regional coverage, and then proposes the optimal plan. Next, the system proposes strategies to enhance new customer acquisition and existing customer retention through customer interactions. Furthermore, the system provides support to help customers resolve issues and questions about their smartphone plans. For example, when a customer encounters a problem, the system provides real-time support and solutions via generative AI. The system also analyzes customer usage and proposes optimal plans and upgrades. Finally, the system introduces new features and services and provides customers with information about the latest technology. This system can promote improved customer satisfaction and business growth. For example, by providing personalized proposals and prompt support tailored to customer needs, the system strengthens customer relationships and achieves long-term business success.

[0029] A smartphone plan proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an interaction unit, an analysis unit, and an introduction unit. The collection unit collects customer information. The customer information includes, but is not limited to, call time, data usage, and regional coverage. For example, the collection unit may measure the customer's call time, monitor data usage, and collect regional coverage information. The analysis unit analyzes the collected information to determine the customer's needs and usage patterns. For example, the analysis unit may analyze the customer's usage patterns using data mining technology. The analysis unit may also determine the customer's needs using statistical analysis. The analysis unit may also predict the customer's usage patterns using a machine learning algorithm. The proposal unit proposes a personalized smartphone plan based on the determination result obtained by the analysis unit. For example, the proposal unit may propose an unlimited calling plan if the customer has a high call time. The proposal unit may also propose a high-capacity data plan if the customer has a high data usage. The proposal unit may also propose a plan with good coverage if regional coverage is important. The interaction unit interacts with the customer based on the plan proposed by the proposal unit. The interaction unit, for example, provides quick responses to customer questions. The interaction unit can also provide customized service proposals in response to customer complaints. The interaction unit can also interact with the customer through a chatbot. The analysis unit analyzes customer usage based on the information obtained by the interaction unit and proposes optimal plans and upgrades. The analysis unit, for example, understands the customer's calling, messaging, and data usage patterns. The analysis unit can also analyze usage patterns and perform trend analysis. The analysis unit can also predict the customer's future usage using a predictive model. The introduction unit introduces new features and services based on the results obtained by the analysis unit. The introduction unit can introduce, for example, new applications and additional services. The introduction unit can also provide upgrade options.Furthermore, the introduction unit can also provide information about the latest technology, which allows the smartphone plan proposal system according to the embodiment to improve customer satisfaction and promote business growth.

[0030] The collection unit can collect information on customer call duration, data usage, and regional coverage. The collection unit, for example, measures customer call duration. For example, the collection unit records the start and end times of calls and calculates the call duration. The collection unit can also monitor data usage. For example, the collection unit records the amount of data sent and received and calculates the data usage. The collection unit can also collect regional coverage information. For example, the collection unit obtains customer location information and collects regional coverage information. This allows for more detailed customer usage information to be collected, enabling more accurate plan proposals. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input customer call duration, data usage, and regional coverage information into a generation AI, which can then analyze and collect this information.

[0031] The analysis unit can analyze the collected information and determine customer needs and usage patterns. The analysis unit can analyze the collected information using, for example, data mining technology. For example, the analysis unit can analyze information on customer call duration, data usage, and regional coverage to determine customer usage patterns. The analysis unit can also determine customer needs using statistical analysis. For example, the analysis unit can analyze statistical data on customer call duration and data usage to determine customer needs. The analysis unit can also predict customer usage patterns using machine learning algorithms. For example, the analysis unit can predict customer future usage patterns based on past data. This makes it possible to accurately determine customer needs and usage patterns and propose optimal plans. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI, which can analyze the information to determine customer needs and usage patterns.

[0032] The proposal unit can propose a personalized smartphone plan based on the determination result. For example, the proposal unit can propose an unlimited calling plan if the customer has a large amount of call time. For example, the proposal unit can propose an unlimited calling plan based on data on the customer's call time. The proposal unit can also propose a large-capacity data plan if the customer has a large amount of data usage. For example, the proposal unit can propose a large-capacity data plan based on data on the customer's data usage. Furthermore, the proposal unit can also propose a plan with good coverage if regional coverage is important. For example, the proposal unit can propose a plan with good coverage based on coverage information by the customer's region. This improves customer satisfaction by proposing the optimal plan to the customer. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input the determination result into a generation AI, which can then propose a personalized smartphone plan based on this information.

[0033] The interaction unit can provide quick responses to customer questions and complaints and offer customized services. The interaction unit, for example, provides quick responses to customer questions. For example, when a customer inquires about a pricing plan, the interaction unit immediately suggests the most suitable plan. The interaction unit can also offer customized services in response to customer complaints. For example, when a customer experiences a data communication problem, the interaction unit immediately identifies the cause and proposes a solution. Furthermore, the interaction unit can interact with customers through a chatbot. For example, the interaction unit automatically answers customer questions using a chatbot. This improves customer satisfaction by quickly responding to customer questions and complaints. Some or all of the above-described processing in the interaction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interaction unit inputs customer questions and complaints into a generation AI, which can then provide quick responses and offer customized services based on this information.

[0034] The analysis unit can understand a customer's calling, messaging, and data usage patterns and recommend appropriate plans and upgrades. The analysis unit, for example, understands a customer's calling patterns. For example, the analysis unit analyzes the frequency and duration of a customer's calls to understand the calling patterns. The analysis unit can also understand a customer's messaging patterns. For example, the analysis unit analyzes the amount of messages sent and received by a customer and the time of day to understand the messaging patterns. The analysis unit can also understand a customer's data usage patterns. For example, the analysis unit analyzes a customer's data usage amount and the time of day to understand the data usage patterns. This allows for analyzing the customer's usage status and making recommendations for optimal plans and upgrades. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs a customer's calling, messaging, and data usage patterns into a generation AI, which can then recommend optimal plans and upgrades based on this information.

[0035] The introduction unit can introduce new features and services and provide customers with information about the latest technology. The introduction unit, for example, introduces new applications and additional services. For example, the introduction unit introduces new smartphone features and applications. The introduction unit can also provide upgrade options. For example, the introduction unit introduces the latest upgrade options to customers. The introduction unit can also provide information about the latest technology. For example, the introduction unit provides news and information about the latest technology to customers. This attracts customer interest and improves customer satisfaction by introducing new features and services. Some or all of the above-mentioned processing in the introduction unit may be performed using, or without, the generation AI. For example, the introduction unit can input information about new features and services into the generation AI, which can then introduce the features and services to customers based on this information.

[0036] The collection unit can analyze the customer's past call history and data usage history and select the optimal information collection method. For example, if the customer has used a lot of data in the past, the collection unit collects information based on the amount of data usage. For example, the collection unit analyzes the customer's past data usage history and selects the optimal information collection method. Furthermore, if the customer's call duration is long, the collection unit can also collect information based on the call history. For example, the collection unit analyzes the customer's past call history and selects the optimal information collection method. Furthermore, if the customer uses a lot of data during a specific time period, the collection unit can also collect information during that time period. For example, the collection unit collects information during a specific time period based on the customer's past data usage history. This enables more effective information collection by analyzing the past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the customer's past call history and data usage history into the generation AI, which can select the optimal information collection method based on this information.

[0037] When collecting information, the collection unit can filter the information based on the customer's current lifestyle and areas of interest. For example, if the customer is traveling, the collection unit prioritizes collecting travel-related information. For example, the collection unit acquires the customer's location information and collects travel-related information. Furthermore, if the customer has a new hobby, the collection unit can also collect information related to the hobby. For example, the collection unit analyzes the customer's social media activity and collects information related to the new hobby. Furthermore, if the customer is at work, the collection unit can also collect work-related information. For example, the collection unit acquires the customer's calendar information and collects work-related information. This allows more relevant information to be collected by filtering the information based on the customer's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input information about the customer's current lifestyle and areas of interest into the generation AI, which can then perform filtering based on this information.

[0038] When collecting information, the collection unit can select the optimal collection means depending on the customer's input method. For example, if the customer uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit acquires the customer's voice data and collects information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit acquires the customer's text data and collects information using text analysis technology. Furthermore, if the customer uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit acquires the customer's image data and collects information using image analysis technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input data depending on the customer's input method into the generation AI, which can select the optimal collection means based on this information.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit acquires the customer's location information and collects information related to that area. Furthermore, if the customer is traveling, the collection unit can prioritize collecting information related to the customer's travel destination. For example, the collection unit acquires the customer's location information and collects information related to the travel destination. Furthermore, if the customer is at home, the collection unit can prioritize collecting information around the customer's home. For example, the collection unit acquires the customer's location information and collects information around the customer's home. In this way, more relevant information can be collected by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the customer's geographical location information into the generation AI, which can then prioritize collecting highly relevant information based on this information.

[0040] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can collect information related to topics in which the customer expressed interest on social media. For example, the collection unit can analyze the customer's social media activities and collect information related to the topics in which the customer expressed interest. The collection unit can also collect information related to accounts the customer follows on social media. For example, the collection unit can analyze the accounts the customer follows and collect related information. The collection unit can also collect information related to content the customer shared on social media. For example, the collection unit can analyze the content the customer shared and collect related information. This allows for analyzing the customer's social media activities to collect more relevant information. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input data on the customer's social media activities into the generation AI, which can then collect related information based on this information.

[0041] When collecting information, the collection unit can customize the collection method by reflecting the customer's past feedback. The collection unit, for example, adjusts the information collection method based on feedback provided by the customer in the past. For example, the collection unit analyzes the customer's past feedback and customizes the information collection method. The collection unit can also prioritize collecting information that the customer has previously preferred. For example, the collection unit prioritizes collecting information that the customer has preferred based on the customer's past feedback. Furthermore, the collection unit can avoid collecting information about which the customer has previously expressed dissatisfaction. For example, the collection unit avoids collecting information about which the customer has previously expressed dissatisfaction based on the customer's past feedback. This enables more effective information collection by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the customer's past feedback into the generation AI, which can customize the collection method based on this information.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit performs a detailed analysis on important information based on information on the customer's call time, data usage, and regional coverage. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on general information based on information on the customer's call time, data usage, and regional coverage. The analysis unit can also perform a detailed analysis on information in which the customer is particularly interested. For example, the analysis unit performs a detailed analysis on information in which the customer is particularly interested based on information on the customer's call time, data usage, and regional coverage. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the information into the generation AI, which can adjust the level of detail of the analysis based on this information.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a call analysis algorithm to call data. For example, the analysis unit applies a call analysis algorithm based on customer call data. The analysis unit can also apply a data analysis algorithm to data usage. For example, the analysis unit applies a data analysis algorithm based on customer data usage. Furthermore, the analysis unit can also apply a geographic information analysis algorithm to regional coverage. For example, the analysis unit applies a geographic information analysis algorithm based on customer regional coverage information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of information into the generation AI, which then applies different analysis algorithms based on this information.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, corrects the current analysis result based on the customer's past analysis results. For example, the analysis unit analyzes the customer's past analysis results and corrects the current analysis result. The analysis unit can also adjust the analysis algorithm by referring to the customer's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the customer's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the customer's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the customer's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis based on this information.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time of submission of the information. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit postpones information that was submitted earlier based on the time of submission of the information. Furthermore, the analysis unit can also prioritize analysis of information that was submitted recently. For example, the analysis unit prioritizes analysis of information that was submitted recently based on the time of submission of the information. This enables more effective analysis by determining the priority of analysis based on the time of submission of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of submission of information to the generation AI, and the generation AI can determine the priority of analysis based on this information.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of information most relevant to the customer's needs. For example, the analysis unit prioritizes analysis of information relevant to the customer's needs. The analysis unit can also postpone less relevant information. For example, the analysis unit postpones less relevant information. Furthermore, the analysis unit can also adjust the order of analysis based on the customer's interests. For example, the analysis unit adjusts the order of analysis based on the customer's interests. In this way, adjusting the order of analysis based on the relevance of information enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of information into the generation AI, and the generation AI can adjust the order of analysis based on this information.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the customer's level of expertise. For example, the analysis unit uses more technical terms for customers with high levels of expertise. For example, the analysis unit uses more technical terms based on the customer's level of expertise. The analysis unit can also provide analysis results in simpler language for customers with low levels of expertise. For example, the analysis unit provides analysis results in simpler language based on the customer's level of expertise. Furthermore, the analysis unit can adjust the expression of the analysis results according to the customer's level of expertise. For example, the analysis unit adjusts the expression of the analysis results based on the customer's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the customer's level of expertise, thereby providing more effective analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the customer's level of expertise into the generation AI, which can adjust the use of technical terms in the analysis based on this information.

[0048] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. For example, the proposal unit makes a detailed proposal for an important plan. For example, the proposal unit makes a detailed proposal for an important plan based on customer needs and business impact. The proposal unit can also make a concise proposal for a general plan. For example, the proposal unit makes a concise proposal for a general plan based on customer needs and business impact. The proposal unit can also make a detailed proposal for a plan in which the customer is particularly interested. For example, the proposal unit makes a detailed proposal for a plan in which the customer is particularly interested based on customer needs and business impact. This allows for more effective proposals by adjusting the level of detail of the proposal based on the importance of the plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the plan into the generation AI, and the generation AI can adjust the level of detail of the proposal based on this information.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the plan category. For example, the proposal unit applies a call proposal algorithm to a call plan. For example, the proposal unit applies a call proposal algorithm based on the customer's call data. The proposal unit can also apply a data proposal algorithm to a data plan. For example, the proposal unit applies a data proposal algorithm based on the customer's data usage. The proposal unit can also apply a geographic information proposal algorithm to a regional coverage plan. For example, the proposal unit applies a geographic information proposal algorithm based on the customer's regional coverage information. This enables more effective proposals by applying different proposal algorithms depending on the plan category. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the plan category into the generation AI, which can then apply different proposal algorithms based on this information.

[0050] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit, for example, corrects the current proposal based on the customer's past proposal results. For example, the proposal unit analyzes the customer's past proposal results and corrects the current proposal. The proposal unit can also adjust the proposal algorithm by referring to the customer's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the customer's past proposal results. Furthermore, the proposal unit can also improve the accuracy of the proposal by using the customer's past proposal results. For example, the proposal unit improves the accuracy of the proposal based on the customer's past proposal results. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the customer's past proposal results into the generation AI, and the generation AI can improve the accuracy of the proposal based on this information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze customers' social media activities to understand their interests. For example, the collection unit can analyze the accounts that customers follow on social media and the posts that they like to identify their interests. The collection unit can also analyze the content that customers share to understand their areas of interest. Furthermore, the collection unit can analyze customers' comments and replies to understand their opinions and emotions. This makes it possible to propose more personalized plans based on customers' social media activities.

[0053] The analysis unit can analyze a customer's purchase history and understand the customer's consumption patterns. For example, the analysis unit can analyze products and services that the customer has purchased in the past and identify the customer's consumption trends. The analysis unit can also analyze a customer's purchase frequency and purchase amount to understand the customer's consumption patterns. Furthermore, the analysis unit can identify products and services that customers tend to purchase at specific times and understand seasonal consumption patterns. This makes it possible to propose more appropriate plans based on the customer's purchase history.

[0054] The proposal unit can analyze the customer's health data and propose a plan based on their health condition. For example, the proposal unit can analyze data obtained from the customer's fitness tracker and propose a plan based on their health condition. The proposal unit can also analyze the customer's food records and propose a plan to support a healthy lifestyle. Furthermore, the proposal unit can analyze the customer's sleep data and propose a plan to improve the quality of sleep. This makes it possible to propose plans that support a healthier lifestyle through the customer's health data.

[0055] The Interaction Department can collect customer feedback in real time and respond immediately. For example, the Interaction Department can analyze feedback provided by customers and immediately propose solutions to resolve problems. The Interaction Department can also identify areas for service improvement based on customer feedback and respond quickly. Furthermore, the Interaction Department can propose new services and features through customer feedback. This enables faster and more effective responses through customer feedback.

[0056] The analysis unit can grasp a customer's life events and propose plans and services that correspond to them. For example, the analysis unit can grasp a customer's life events such as marriage or childbirth and propose plans that correspond to them. The analysis unit can also grasp a customer's life events such as moving or changing jobs and propose services to help them adapt to their new environment. Furthermore, the analysis unit can grasp a customer's life events such as retirement or retirement planning and propose plans to support their future lifestyle. This makes it possible to propose more appropriate plans that correspond to the customer's life events.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects customer information, including call duration, data usage, and area coverage. The collection unit measures the customer's call duration, monitors data usage, and collects area coverage information. Step 2: The analysis department analyzes the collected information to determine customer needs and usage patterns. The analysis department uses data mining techniques, statistical analysis, and machine learning algorithms to analyze customer usage patterns and determine needs. Step 3: The proposal unit proposes a personalized smartphone plan based on the results of the analysis unit. For example, it proposes an unlimited call plan if the user has a lot of call time, a high-capacity data plan if the user has a lot of data usage, or a plan with good coverage if regional coverage is important. Step 4: The Interaction Department interacts with customers based on the plan proposed by the Proposal Department. The Interaction Department provides prompt answers to customer questions and offers customized service proposals to address customer complaints. It can also interact with customers through chatbots. Step 5: The Analytics Department analyzes customer usage based on the information obtained by the Interaction Department and proposes the most appropriate plan or upgrade. The Analytics Department understands customer calling, messaging, and data usage patterns and uses trend analysis and predictive models to predict future customer usage. Step 6: The introduction department introduces new features and services based on the findings of the analysis department. The introduction department provides information on new applications, additional services, upgrade options, and the latest technologies.

[0059] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to propose smartphone plans, thereby improving customer satisfaction and promoting business growth. This system promotes improved customer satisfaction and business growth by collecting and analyzing customer information and proposing personalized plans. For example, the system collects information such as the customer's call time, data usage, and regional coverage, and then proposes the optimal plan. Next, the system proposes strategies to enhance new customer acquisition and existing customer retention through customer interactions. Furthermore, the system provides support to help customers resolve issues and questions about their smartphone plans. For example, when a customer encounters a problem, the system provides real-time support and solutions via generative AI. The system also analyzes customer usage and proposes optimal plans and upgrades. Finally, the system introduces new features and services and provides customers with information about the latest technology. This system can promote improved customer satisfaction and business growth. For example, by providing personalized proposals and prompt support tailored to customer needs, the system strengthens customer relationships and achieves long-term business success.

[0060] A smartphone plan proposal system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an interaction unit, an analysis unit, and an introduction unit. The collection unit collects customer information. The customer information includes, but is not limited to, call time, data usage, and regional coverage. For example, the collection unit may measure the customer's call time, monitor data usage, and collect regional coverage information. The analysis unit analyzes the collected information to determine the customer's needs and usage patterns. For example, the analysis unit may analyze the customer's usage patterns using data mining technology. The analysis unit may also determine the customer's needs using statistical analysis. The analysis unit may also predict the customer's usage patterns using a machine learning algorithm. The proposal unit proposes a personalized smartphone plan based on the determination result obtained by the analysis unit. For example, the proposal unit may propose an unlimited calling plan if the customer has a high call time. The proposal unit may also propose a high-capacity data plan if the customer has a high data usage. The proposal unit may also propose a plan with good coverage if regional coverage is important. The interaction unit interacts with the customer based on the plan proposed by the proposal unit. The interaction unit, for example, provides quick responses to customer questions. The interaction unit can also provide customized service proposals in response to customer complaints. The interaction unit can also interact with the customer through a chatbot. The analysis unit analyzes customer usage based on the information obtained by the interaction unit and proposes optimal plans and upgrades. The analysis unit, for example, understands the customer's calling, messaging, and data usage patterns. The analysis unit can also analyze usage patterns and perform trend analysis. The analysis unit can also predict the customer's future usage using a predictive model. The introduction unit introduces new features and services based on the results obtained by the analysis unit. The introduction unit can introduce, for example, new applications and additional services. The introduction unit can also provide upgrade options.Furthermore, the introduction unit can also provide information about the latest technology, which allows the smartphone plan proposal system according to the embodiment to improve customer satisfaction and promote business growth.

[0061] The collection unit can collect information on customer call duration, data usage, and regional coverage. The collection unit, for example, measures customer call duration. For example, the collection unit records the start and end times of calls and calculates the call duration. The collection unit can also monitor data usage. For example, the collection unit records the amount of data sent and received and calculates the data usage. The collection unit can also collect regional coverage information. For example, the collection unit obtains customer location information and collects regional coverage information. This allows for more detailed customer usage information to be collected, enabling more accurate plan proposals. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input customer call duration, data usage, and regional coverage information into a generation AI, which can then analyze and collect this information.

[0062] The analysis unit can analyze the collected information and determine customer needs and usage patterns. The analysis unit can analyze the collected information using, for example, data mining technology. For example, the analysis unit can analyze information on customer call duration, data usage, and regional coverage to determine customer usage patterns. The analysis unit can also determine customer needs using statistical analysis. For example, the analysis unit can analyze statistical data on customer call duration and data usage to determine customer needs. The analysis unit can also predict customer usage patterns using machine learning algorithms. For example, the analysis unit can predict customer future usage patterns based on past data. This makes it possible to accurately determine customer needs and usage patterns and propose optimal plans. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI, which can analyze the information to determine customer needs and usage patterns.

[0063] The proposal unit can propose a personalized smartphone plan based on the determination result. For example, the proposal unit can propose an unlimited calling plan if the customer has a large amount of call time. For example, the proposal unit can propose an unlimited calling plan based on data on the customer's call time. The proposal unit can also propose a large-capacity data plan if the customer has a large amount of data usage. For example, the proposal unit can propose a large-capacity data plan based on data on the customer's data usage. Furthermore, the proposal unit can also propose a plan with good coverage if regional coverage is important. For example, the proposal unit can propose a plan with good coverage based on coverage information by the customer's region. This improves customer satisfaction by proposing the optimal plan to the customer. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input the determination result into a generation AI, which can then propose a personalized smartphone plan based on this information.

[0064] The interaction unit can provide quick responses to customer questions and complaints and offer customized services. The interaction unit, for example, provides quick responses to customer questions. For example, when a customer inquires about a pricing plan, the interaction unit immediately suggests the most suitable plan. The interaction unit can also offer customized services in response to customer complaints. For example, when a customer experiences a data communication problem, the interaction unit immediately identifies the cause and proposes a solution. Furthermore, the interaction unit can interact with customers through a chatbot. For example, the interaction unit automatically answers customer questions using a chatbot. This improves customer satisfaction by quickly responding to customer questions and complaints. Some or all of the above-described processing in the interaction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the interaction unit inputs customer questions and complaints into a generation AI, which can then provide quick responses and offer customized services based on this information.

[0065] The analysis unit can understand a customer's calling, messaging, and data usage patterns and recommend appropriate plans and upgrades. The analysis unit, for example, understands a customer's calling patterns. For example, the analysis unit analyzes the frequency and duration of a customer's calls to understand the calling patterns. The analysis unit can also understand a customer's messaging patterns. For example, the analysis unit analyzes the amount of messages sent and received by a customer and the time of day to understand the messaging patterns. The analysis unit can also understand a customer's data usage patterns. For example, the analysis unit analyzes a customer's data usage amount and the time of day to understand the data usage patterns. This allows for analyzing the customer's usage status and making recommendations for optimal plans and upgrades. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit inputs a customer's calling, messaging, and data usage patterns into a generation AI, which can then recommend optimal plans and upgrades based on this information.

[0066] The introduction unit can introduce new features and services and provide customers with information about the latest technology. The introduction unit, for example, introduces new applications and additional services. For example, the introduction unit introduces new smartphone features and applications. The introduction unit can also provide upgrade options. For example, the introduction unit introduces the latest upgrade options to customers. The introduction unit can also provide information about the latest technology. For example, the introduction unit provides news and information about the latest technology to customers. This attracts customer interest and improves customer satisfaction by introducing new features and services. Some or all of the above-mentioned processing in the introduction unit may be performed using, or without, the generation AI. For example, the introduction unit can input information about new features and services into the generation AI, which can then introduce the features and services to customers based on this information.

[0067] The collection unit can estimate a customer's emotions and adjust the timing of information collection based on the estimated customer emotions. For example, if a customer is feeling stressed, the collection unit collects information during a relaxed time. For example, the collection unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. Furthermore, if a customer is relaxed, the collection unit can immediately start collecting information. For example, the collection unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if a customer is busy, the collection unit can collect information during a less busy time. For example, the collection unit collects the customer's biometric data (heart rate and electrodermal activity) using a sensor and estimates the customer's emotions using an emotion estimation algorithm. This allows for more effective information collection by adjusting the timing of information collection according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input customer emotion data into the generation AI, and the generation AI may adjust the timing of information collection based on this information.

[0068] The collection unit can analyze the customer's past call history and data usage history and select the optimal information collection method. For example, if the customer has used a lot of data in the past, the collection unit collects information based on the amount of data usage. For example, the collection unit analyzes the customer's past data usage history and selects the optimal information collection method. Furthermore, if the customer's call duration is long, the collection unit can also collect information based on the call history. For example, the collection unit analyzes the customer's past call history and selects the optimal information collection method. Furthermore, if the customer uses a lot of data during a specific time period, the collection unit can also collect information during that time period. For example, the collection unit collects information during a specific time period based on the customer's past data usage history. This enables more effective information collection by analyzing the past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the customer's past call history and data usage history into the generation AI, which can select the optimal information collection method based on this information.

[0069] When collecting information, the collection unit can filter the information based on the customer's current lifestyle and areas of interest. For example, if the customer is traveling, the collection unit prioritizes collecting travel-related information. For example, the collection unit acquires the customer's location information and collects travel-related information. Furthermore, if the customer has a new hobby, the collection unit can also collect information related to the hobby. For example, the collection unit analyzes the customer's social media activity and collects information related to the new hobby. Furthermore, if the customer is at work, the collection unit can also collect work-related information. For example, the collection unit acquires the customer's calendar information and collects work-related information. This allows more relevant information to be collected by filtering the information based on the customer's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input information about the customer's current lifestyle and areas of interest into the generation AI, which can then perform filtering based on this information.

[0070] When collecting information, the collection unit can select the optimal collection means depending on the customer's input method. For example, if the customer uses voice input, the collection unit prioritizes collecting voice data. For example, the collection unit acquires the customer's voice data and collects information using voice recognition technology. Furthermore, if the customer uses text input, the collection unit can also prioritize collecting text data. For example, the collection unit acquires the customer's text data and collects information using text analysis technology. Furthermore, if the customer uses image input, the collection unit can also prioritize collecting image data. For example, the collection unit acquires the customer's image data and collects information using image analysis technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the customer's input method. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input data depending on the customer's input method into the generation AI, which can select the optimal collection means based on this information.

[0071] The collection unit can estimate the customer's emotions and prioritize the information to be collected based on the estimated customer emotions. For example, if the customer is feeling stressed, the collection unit prioritizes collecting information that will help the customer relax. For example, the collection unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. Furthermore, if the customer is relaxed, the collection unit can prioritize collecting information of interest. For example, the collection unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if the customer is busy, the collection unit can prioritize collecting important information. For example, the collection unit collects the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimates the customer's emotions using an emotion estimation algorithm. This enables more effective information collection by prioritizing information based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input customer emotion data into the generation AI, and the generation AI may determine the priority of the information to be collected based on this information.

[0072] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the collection unit prioritizes collecting information related to that area. For example, the collection unit acquires the customer's location information and collects information related to that area. Furthermore, if the customer is traveling, the collection unit can prioritize collecting information related to the customer's travel destination. For example, the collection unit acquires the customer's location information and collects information related to the travel destination. Furthermore, if the customer is at home, the collection unit can prioritize collecting information around the customer's home. For example, the collection unit acquires the customer's location information and collects information around the customer's home. In this way, more relevant information can be collected by taking the customer's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the customer's geographical location information into the generation AI, which can then prioritize collecting highly relevant information based on this information.

[0073] When collecting information, the collection unit can analyze the customer's social media activities and collect related information. For example, the collection unit can collect information related to topics in which the customer expressed interest on social media. For example, the collection unit can analyze the customer's social media activities and collect information related to the topics in which the customer expressed interest. The collection unit can also collect information related to accounts the customer follows on social media. For example, the collection unit can analyze the accounts the customer follows and collect related information. The collection unit can also collect information related to content the customer shared on social media. For example, the collection unit can analyze the content the customer shared and collect related information. This allows for analyzing the customer's social media activities to collect more relevant information. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input data on the customer's social media activities into the generation AI, which can then collect related information based on this information.

[0074] When collecting information, the collection unit can customize the collection method by reflecting the customer's past feedback. The collection unit, for example, adjusts the information collection method based on feedback provided by the customer in the past. For example, the collection unit analyzes the customer's past feedback and customizes the information collection method. The collection unit can also prioritize collecting information that the customer has previously preferred. For example, the collection unit prioritizes collecting information that the customer has preferred based on the customer's past feedback. Furthermore, the collection unit can avoid collecting information about which the customer has previously expressed dissatisfaction. For example, the collection unit avoids collecting information about which the customer has previously expressed dissatisfaction based on the customer's past feedback. This enables more effective information collection by reflecting the customer's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the customer's past feedback into the generation AI, which can customize the collection method based on this information.

[0075] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated customer emotions. For example, if the customer is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. Furthermore, if the customer is in a hurry, the analysis unit can provide concise analysis results that focus on the key points. For example, the analysis unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if the customer is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit collects the customer's biometric data (heart rate and electrodermal activity) using a sensor and estimates the customer's emotions using an emotion estimation algorithm. This allows for more effective analysis results to be provided by adjusting the presentation of the analysis based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input customer emotion data into the generation AI, and the generation AI may adjust the way the analysis is presented based on this information.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. For example, the analysis unit performs a detailed analysis on important information based on information on the customer's call time, data usage, and regional coverage. The analysis unit can also perform a concise analysis on general information. For example, the analysis unit performs a concise analysis on general information based on information on the customer's call time, data usage, and regional coverage. The analysis unit can also perform a detailed analysis on information in which the customer is particularly interested. For example, the analysis unit performs a detailed analysis on information in which the customer is particularly interested based on information on the customer's call time, data usage, and regional coverage. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the information into the generation AI, which can adjust the level of detail of the analysis based on this information.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a call analysis algorithm to call data. For example, the analysis unit applies a call analysis algorithm based on customer call data. The analysis unit can also apply a data analysis algorithm to data usage. For example, the analysis unit applies a data analysis algorithm based on customer data usage. Furthermore, the analysis unit can also apply a geographic information analysis algorithm to regional coverage. For example, the analysis unit applies a geographic information analysis algorithm based on customer regional coverage information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of information into the generation AI, which then applies different analysis algorithms based on this information.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the customer's past analysis results. The analysis unit, for example, corrects the current analysis result based on the customer's past analysis results. For example, the analysis unit analyzes the customer's past analysis results and corrects the current analysis result. The analysis unit can also adjust the analysis algorithm by referring to the customer's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the customer's past analysis results. Furthermore, the analysis unit can also improve the accuracy of the analysis by using the customer's past analysis results. For example, the analysis unit improves the accuracy of the analysis based on the customer's past analysis results. In this way, the accuracy of the analysis is improved by referring to the customer's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the customer's past analysis results into the generation AI, and the generation AI can improve the accuracy of the analysis based on this information.

[0079] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. For example, if the customer is in a hurry, the analysis unit provides a short analysis result. For example, the analysis unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis result if the customer is relaxed. For example, the analysis unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, the analysis unit can provide a visually appealing analysis result if the customer is excited. For example, the analysis unit collects the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimates the customer's emotions using an emotion estimation algorithm. This allows for adjusting the length of the analysis based on the customer's emotions, thereby providing more effective analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input customer emotion data into the generation AI, and the generation AI may adjust the length of the analysis based on this information.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit, for example, prioritizes analysis of the most recent information. For example, the analysis unit prioritizes analysis of the most recent information based on the time of submission of the information. The analysis unit can also postpone information that was submitted earlier. For example, the analysis unit postpones information that was submitted earlier based on the time of submission of the information. Furthermore, the analysis unit can also prioritize analysis of information that was submitted recently. For example, the analysis unit prioritizes analysis of information that was submitted recently based on the time of submission of the information. This enables more effective analysis by determining the priority of analysis based on the time of submission of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of submission of information to the generation AI, and the generation AI can determine the priority of analysis based on this information.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit prioritizes analysis of information most relevant to the customer's needs. For example, the analysis unit prioritizes analysis of information relevant to the customer's needs. The analysis unit can also postpone less relevant information. For example, the analysis unit postpones less relevant information. Furthermore, the analysis unit can also adjust the order of analysis based on the customer's interests. For example, the analysis unit adjusts the order of analysis based on the customer's interests. In this way, adjusting the order of analysis based on the relevance of information enables more effective analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of information into the generation AI, and the generation AI can adjust the order of analysis based on this information.

[0082] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the customer's level of expertise. For example, the analysis unit uses more technical terms for customers with high levels of expertise. For example, the analysis unit uses more technical terms based on the customer's level of expertise. The analysis unit can also provide analysis results in simpler language for customers with low levels of expertise. For example, the analysis unit provides analysis results in simpler language based on the customer's level of expertise. Furthermore, the analysis unit can adjust the expression of the analysis results according to the customer's level of expertise. For example, the analysis unit adjusts the expression of the analysis results based on the customer's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the customer's level of expertise, thereby providing more effective analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the customer's level of expertise into the generation AI, which can adjust the use of technical terms in the analysis based on this information.

[0083] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, if the customer is relaxed, the suggestion unit can provide detailed suggestions. For example, the suggestion unit can capture the customer's facial expressions with a camera and estimate the customer's emotions using an emotion estimation algorithm. Furthermore, if the customer is in a hurry, the suggestion unit can provide concise suggestions. For example, the suggestion unit can record the customer's voice and estimate the customer's emotions using voice analysis technology. Furthermore, if the customer is excited, the suggestion unit can provide visually appealing suggestions. For example, the suggestion unit can collect the customer's biometric data (heart rate and electrodermal activity) using a sensor and estimate the customer's emotions using an emotion estimation algorithm. This enables more effective suggestions by adjusting the way the suggestion is presented based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input customer emotion data into the generation AI, and the generation AI may adjust the way the suggestion is presented based on this information.

[0084] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the plan. For example, the proposal unit makes a detailed proposal for an important plan. For example, the proposal unit makes a detailed proposal for an important plan based on customer needs and business impact. The proposal unit can also make a concise proposal for a general plan. For example, the proposal unit makes a concise proposal for a general plan based on customer needs and business impact. The proposal unit can also make a detailed proposal for a plan in which the customer is particularly interested. For example, the proposal unit makes a detailed proposal for a plan in which the customer is particularly interested based on customer needs and business impact. This allows for more effective proposals by adjusting the level of detail of the proposal based on the importance of the plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the importance of the plan into the generation AI, and the generation AI can adjust the level of detail of the proposal based on this information.

[0085] When making a proposal, the proposal unit can apply different proposal algorithms depending on the plan category. For example, the proposal unit applies a call proposal algorithm to a call plan. For example, the proposal unit applies a call proposal algorithm based on the customer's call data. The proposal unit can also apply a data proposal algorithm to a data plan. For example, the proposal unit applies a data proposal algorithm based on the customer's data usage. The proposal unit can also apply a geographic information proposal algorithm to a regional coverage plan. For example, the proposal unit applies a geographic information proposal algorithm based on the customer's regional coverage information. This enables more effective proposals by applying different proposal algorithms depending on the plan category. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the plan category into the generation AI, which can then apply different proposal algorithms based on this information.

[0086] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the customer's past proposal results. The proposal unit, for example, corrects the current proposal based on the customer's past proposal results. For example, the proposal unit analyzes the customer's past proposal results and corrects the current proposal. The proposal unit can also adjust the proposal algorithm by referring to the customer's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the customer's past proposal results. Furthermore, the proposal unit can also improve the accuracy of the proposal by using the customer's past proposal results. For example, the proposal unit improves the accuracy of the proposal based on the customer's past proposal results. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the customer's past proposal results into the generation AI, and the generation AI can improve the accuracy of the proposal based on this information.

[0087] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. For example, if the customer is in a hurry, the suggestion unit can make a short suggestion. For example, the suggestion unit can capture the customer's facial expressions with a camera and estimate the customer's emotions using an emotion estimation algorithm. Furthermore, if the customer is relaxed, the suggestion unit can make a detailed suggestion. For example, the suggestion unit can record the customer's voice and estimate the customer's emotions using voice analysis technology. Furthermore, if the customer is excited, the suggestion unit can make a visually appealing suggestion. For example, the suggestion unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the customer's emotions using an emotion estimation algorithm. This enables more effective suggestions by adjusting the length of the suggestion based on the customer'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 suggestion unit can be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input customer emotional data into the generation AI, which can then adjust the length of the suggestion based on this information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, interaction unit, analysis unit, and introduction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the customer's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14 and estimate their emotions using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 to determine the customer's needs and usage patterns. For example, the proposal unit proposes a personalized smartphone plan using the specific processing unit 290 of the data processing device 12. For example, the interaction unit interacts with the customer using the control unit 46A of the smart device 14. For example, the analysis unit analyzes the customer's usage status using the specific processing unit 290 of the data processing device 12 and proposes an optimal plan or upgrade. For example, the introduction unit introduces new functions and services using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, interaction unit, analysis unit, and introduction unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect a customer's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate their emotions using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 to determine the customer's needs and usage patterns. For example, the suggestion unit suggests a personalized smartphone plan using the specific processing unit 290 of the data processing device 12. For example, the interaction unit interacts with the customer using the control unit 46A of the smart glasses 214. For example, the analysis unit analyzes the customer's usage status using the specific processing unit 290 of the data processing device 12 and suggests an optimal plan or upgrade. For example, the introduction unit introduces new functions and services using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, interaction unit, analysis unit, and introduction unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect the customer's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314 and estimate the customer's emotions using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 to determine the customer's needs and usage patterns. For example, the proposal unit proposes a personalized smartphone plan using the specific processing unit 290 of the data processing device 12. For example, the interaction unit interacts with the customer using the control unit 46A of the headset terminal 314. For example, the analysis unit analyzes the customer's usage status using the specific processing unit 290 of the data processing device 12 and proposes an optimal plan or upgrade. For example, the introduction unit introduces new functions and services using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, interaction unit, analysis unit, and introduction unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the customer's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and estimate the customer's emotions using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 to determine the customer's needs and usage patterns. For example, the proposal unit proposes a personalized smartphone plan using the specific processing unit 290 of the data processing device 12. For example, the interaction unit interacts with the customer using the control unit 46A of the robot 414. For example, the analysis unit analyzes the customer's usage status using the specific processing unit 290 of the data processing device 12 and proposes an optimal plan or upgrade. For example, the introduction unit introduces new functions and services using the control unit 46A of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can analyze customers' social media activities to understand their interests. For example, the collection unit can analyze the accounts that customers follow on social media and the posts that they like to identify their interests. The collection unit can also analyze the content that customers share to understand their areas of interest. Furthermore, the collection unit can analyze customers' comments and replies to understand their opinions and emotions. This makes it possible to propose more personalized plans based on customers' social media activities.

[0090] The analysis unit can analyze a customer's purchase history and understand the customer's consumption patterns. For example, the analysis unit can analyze products and services that the customer has purchased in the past and identify the customer's consumption trends. The analysis unit can also analyze a customer's purchase frequency and purchase amount to understand the customer's consumption patterns. Furthermore, the analysis unit can identify products and services that customers tend to purchase at specific times and understand seasonal consumption patterns. This makes it possible to propose more appropriate plans based on the customer's purchase history.

[0091] The proposal unit can analyze the customer's health data and propose a plan based on their health condition. For example, the proposal unit can analyze data obtained from the customer's fitness tracker and propose a plan based on their health condition. The proposal unit can also analyze the customer's food records and propose a plan to support a healthy lifestyle. Furthermore, the proposal unit can analyze the customer's sleep data and propose a plan to improve the quality of sleep. This makes it possible to propose plans that support a healthier lifestyle through the customer's health data.

[0092] The Interaction Department can collect customer feedback in real time and respond immediately. For example, the Interaction Department can analyze feedback provided by customers and immediately propose solutions to resolve problems. The Interaction Department can also identify areas for service improvement based on customer feedback and respond quickly. Furthermore, the Interaction Department can propose new services and features through customer feedback. This enables faster and more effective responses through customer feedback.

[0093] The analysis unit can grasp a customer's life events and propose plans and services that correspond to them. For example, the analysis unit can grasp a customer's life events such as marriage or childbirth and propose plans that correspond to them. The analysis unit can also grasp a customer's life events such as moving or changing jobs and propose services to help them adapt to their new environment. Furthermore, the analysis unit can grasp a customer's life events such as retirement or retirement planning and propose plans to support their future lifestyle. This makes it possible to propose more appropriate plans that correspond to the customer's life events.

[0094] The collection unit can estimate the customer's emotions and adjust the timing of information collection based on the estimated customer emotions. For example, if the customer is feeling stressed, the collection unit collects information during a relaxed time period. For example, the collection unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. The collection unit can also immediately start collecting information when the customer is relaxed. For example, the collection unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if the customer is busy, the collection unit can collect information during a less busy time period. For example, the collection unit collects the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimates the customer's emotions using an emotion estimation algorithm. This allows for more effective information collection by adjusting the timing of information collection according to the customer's emotions.

[0095] The analysis unit can estimate the customer's emotions and adjust the way the analysis is presented based on the estimated customer emotions. For example, if the customer is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. In addition, if the customer is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if the customer is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit collects the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimates the customer's emotions using an emotion estimation algorithm. This allows the analysis unit to adjust the way the analysis is presented based on the customer's emotions, thereby providing more effective analysis results.

[0096] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer's emotions. For example, the suggestion unit can provide detailed suggestions when the customer is relaxed. For example, the suggestion unit can capture the customer's facial expressions with a camera and estimate the customer's emotions using an emotion estimation algorithm. The suggestion unit can also provide concise suggestions when the customer is in a hurry. For example, the suggestion unit can record the customer's voice and estimate the customer's emotions using voice analysis technology. Furthermore, the suggestion unit can provide visually appealing suggestions when the customer is excited. For example, the suggestion unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the customer's emotions using an emotion estimation algorithm. This enables more effective suggestions to be presented based on the customer's emotions.

[0097] The interaction unit can estimate a customer's emotions and adjust a response method based on the estimated customer emotions. For example, if a customer is feeling stressed, the interaction unit can respond using gentle language. For example, the interaction unit can capture the customer's facial expressions with a camera and estimate the customer's emotions using an emotion estimation algorithm. Furthermore, if the customer is relaxed, the interaction unit can provide a detailed explanation. For example, the interaction unit can record the customer's voice and estimate the customer's emotions using voice analysis technology. Furthermore, if the customer is excited, the interaction unit can respond quickly. For example, the interaction unit can collect the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimate the customer's emotions using an emotion estimation algorithm. This allows for more effective response by adjusting a response method based on the customer's emotions.

[0098] The analysis unit can estimate the customer's emotions and determine the priorities of analysis based on the estimated customer emotions. For example, if the customer is feeling stressed, the analysis unit prioritizes analysis of information that will help the customer relax. For example, the analysis unit captures the customer's facial expressions with a camera and estimates the customer's emotions using an emotion estimation algorithm. In addition, if the customer is relaxed, the analysis unit can prioritize analysis of information of interest to the customer. For example, the analysis unit records the customer's voice and estimates the customer's emotions using voice analysis technology. Furthermore, if the customer is busy, the analysis unit can prioritize analysis of important information. For example, the analysis unit collects the customer's biometric data (heart rate and electrodermal activity) with a sensor and estimates the customer's emotions using an emotion estimation algorithm. This enables more effective analysis by determining the priorities of analysis based on the customer's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects customer information, including call duration, data usage, and area coverage. The collection unit measures the customer's call duration, monitors data usage, and collects area coverage information. Step 2: The analysis department analyzes the collected information to determine customer needs and usage patterns. The analysis department uses data mining techniques, statistical analysis, and machine learning algorithms to analyze customer usage patterns and determine needs. Step 3: The proposal unit proposes a personalized smartphone plan based on the results of the analysis unit. For example, it proposes an unlimited call plan if the user has a lot of call time, a high-capacity data plan if the user has a lot of data usage, or a plan with good coverage if regional coverage is important. Step 4: The Interaction Department interacts with customers based on the plan proposed by the Proposal Department. The Interaction Department provides prompt answers to customer questions and offers customized service proposals to address customer complaints. It can also interact with customers through chatbots. Step 5: The Analytics Department analyzes customer usage based on the information obtained by the Interaction Department and proposes the most appropriate plan or upgrade. The Analytics Department understands customer calling, messaging, and data usage patterns and uses trend analysis and predictive models to predict future customer usage. Step 6: The introduction department introduces new features and services based on the findings of the analysis department. The introduction department provides information on new applications, additional services, upgrade options, and the latest technologies.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

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

[0124] The 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.

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

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

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

[0128] Fig. 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.

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

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

[0131] In the 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.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

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

[0140] The 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.

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

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting customer information; an analysis unit that analyzes the information collected by the collection unit and determines customer needs and usage patterns; a proposal unit that proposes a personalized smartphone plan based on the determination result obtained by the analysis unit; an interaction unit that interacts with a customer based on the plan proposed by the proposal unit; an analysis unit that analyzes customer usage status based on the information obtained by the interaction unit and proposes appropriate plans and upgrades; an introduction unit that introduces new functions and services based on the results obtained by the analysis unit; A system characterized by:

2. The collecting unit Collect customer talk time, data usage, and geographic coverage information 2. The system of claim 1.

3. The analysis unit Analyzing collected information to determine customer needs and usage patterns 2. The system of claim 1.

4. The proposal unit Propose a personalized smartphone plan based on the results 2. The system of claim 1.

5. The interaction unit includes: Providing prompt responses and customized service recommendations to customer questions and complaints 2. The system of claim 1.

6. The analysis unit Understand customer calling, messaging, and data usage patterns to recommend appropriate plans and upgrades 2. The system of claim 1.

7. The introduction unit Introduce new features and services and keep customers up to date with the latest technology 2. The system of claim 1.

8. The collecting unit Estimate customer sentiment and adjust the timing of information gathering based on the estimated sentiment 2. The system of claim 1.

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