system

Abstract

The system according to this embodiment aims to analyze mobile user usage data, predict future usage trends and needs, and propose optimal plans and services. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, and a report provision unit. The data collection unit collects user mobile usage data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal plans and services based on the results predicted by the prediction unit. The report provision unit provides the content proposed by the proposal unit to marketing personnel as a report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the usage data of mobile users has not been effectively utilized to predict future usage trends and needs, and to propose optimal plans and services, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the usage data of mobile users, predict future usage trends and needs, and propose optimal plans and services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, and a report provision unit. The data collection unit collects user mobile usage data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal plans and services based on the results predicted by the prediction unit. The report provision unit provides the content proposed by the proposal unit to marketing personnel as a report. [Effects of the Invention]

[0007] The system according to this embodiment can analyze mobile user data, predict future usage trends and needs, and propose optimal plans and services. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that analyzes mobile user usage data and predicts future usage trends and needs. This AI agent system collects and analyzes users' mobile usage data, predicts future usage trends and needs, proposes optimal plans and services, and provides reports to marketing personnel. For example, the AI ​​agent system collects data such as users' data usage, call history, and app usage. Next, the AI ​​agent system analyzes the collected data and identifies usage patterns for each user. For example, it identifies users whose data usage increases during specific time periods or users who frequently use specific apps. Next, the AI ​​agent system predicts future usage trends and needs based on the analysis results. For example, it can propose a data plan upgrade to users whose data usage is increasing. It can also propose additional services related to specific apps to users who frequently use those apps. Furthermore, the AI ​​agent system predicts the timing of contract changes and proposes optimal plans and services at the appropriate time. For example, by proposing new plans and services to users in line with contract renewal timing, it can improve contract renewal rates. Finally, the AI ​​agent system provides marketing personnel with a user usage trend report. This enables marketing professionals to plan and execute effective marketing strategies. This system improves the user experience, increasing user satisfaction and loyalty. Furthermore, it allows marketing professionals to implement more effective marketing strategies by understanding user usage trends. This enables the AI ​​agent system to efficiently collect, analyze, predict, suggest, and report on user mobile usage data.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, and a report provision unit. The data collection unit collects user mobile usage data. The data collection unit collects data such as data usage, call history, and app usage. The data collection unit monitors data usage in real time and records monthly data usage. The data collection unit can also collect call history and obtain information such as call duration, call recipient, and call frequency. Furthermore, the data collection unit can collect app usage data and obtain information such as app usage time, usage frequency, and usage patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI and identifies usage patterns for each user. For example, the analysis unit identifies users whose data usage increases during specific time periods. The analysis unit can also identify users who frequently use specific apps. Furthermore, the analysis unit can analyze a user's call history and identify call patterns. The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The prediction unit uses AI to predict future usage trends and needs. For example, the prediction unit predicts users whose data usage is on an upward trend. The prediction unit can also predict future usage trends for users who frequently use a particular app. Furthermore, the prediction unit can predict future call trends based on users' call patterns. The proposal unit proposes the optimal plan or service based on the results predicted by the prediction unit. For example, the proposal unit uses AI to propose the optimal plan or service. For example, the proposal unit proposes a data plan upgrade to users whose data usage is on an upward trend. Furthermore, the proposal unit can propose additional services related to a particular app to users who frequently use that app. Furthermore, the proposal unit can propose the optimal call plan based on users' call patterns. The reporting unit provides the content proposed by the proposal unit to marketing personnel as a report. For example, the reporting unit generates reports using AI and provides them to marketing personnel.The reporting unit can, for example, generate user usage trend reports and provide them to marketing personnel. The reporting unit can also generate reports evaluating the effectiveness of proposed plans and services. Furthermore, it can generate reports evaluating the effectiveness of marketing initiatives. This enables the AI ​​agent system according to the embodiment to efficiently collect, analyze, predict, propose, and report on user mobile usage data.

[0030] The data collection unit collects user mobile usage data. This includes data such as data usage, call history, and app usage. Specifically, the unit acquires data from users' smartphones and tablets and monitors this data in real time. Regarding data usage, it records in detail which applications and services users are using and when data usage is highest. For example, it tracks the usage of video streaming services and social media apps and records monthly data usage. Regarding call history, it collects information such as the start and end times of calls, the phone number of the person being called, and the frequency of calls. This allows the unit to understand the user's calling patterns. Furthermore, regarding app usage, it records in detail the usage time, frequency, and usage patterns for each app. For example, it can determine how often a user uses a particular game app or at what times of day they use business apps. This allows the data collection unit to comprehensively collect user mobile usage data and understand detailed usage patterns. The collected data is stored on a secure cloud server and made accessible to the analysis and prediction units. Furthermore, the data collection unit implements data encryption and access control to ensure data privacy and security. This allows the collection unit to collect data efficiently and securely, improving the overall performance and reliability of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and identify user usage patterns. Specifically, the AI ​​uses machine learning algorithms to extract user behavior patterns and usage trends from the collected data. For instance, it performs time-series data analysis to identify users whose data usage increases during specific time periods. This allows for understanding when users are using the internet most. Furthermore, it uses clustering algorithms to classify app usage patterns to identify users who frequently use specific apps. This allows for understanding which apps users use and how often. In addition, it performs statistical analysis of call data to analyze users' call history and identify call patterns. This allows for understanding users' call habits. Based on these analysis results, the analysis unit creates detailed usage profiles for each user. This enables the analysis unit to quickly and accurately analyze collected data and understand user usage trends. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term usage trends. For example, it can predict future data usage based on past fluctuations in data usage. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual usage patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to analyze long-term usage trends and detect anomalies, thereby improving the reliability and safety of the entire system.

[0032] The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. For example, the prediction unit uses AI to predict future usage trends and needs. Specifically, the prediction unit uses machine learning models to predict future usage trends based on collected data and analysis results. For example, it performs regression analysis to predict users whose data usage is increasing. This allows it to predict how much data users will use in the future. It also uses time series forecasting models to predict future usage trends of users who frequently use specific apps. This allows it to predict which apps users will use and how often in the future. Furthermore, it uses clustering algorithms to predict future call trends based on users' call patterns. This allows it to predict what kind of call habits users will have in the future. Based on these prediction results, the prediction unit creates future usage profiles for each user. This allows the prediction unit to predict future usage trends and needs with high accuracy and provide information for taking appropriate measures. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if data usage or app usage changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. Furthermore, the prediction unit can make more accurate predictions by taking into account the characteristics of each region and past usage history. As a result, the prediction unit can always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0033] The Proposal Department proposes optimal plans and services based on the results predicted by the Prediction Department. For example, the Proposal Department uses AI to propose optimal plans and services. Specifically, the Proposal Department uses machine learning algorithms to select the optimal plans and services based on the user's usage profile and prediction results. For example, for users whose data usage is increasing, the Proposal Department proposes an upgrade to their data plan. This allows users to choose the optimal plan according to their data usage. Also, for users who frequently use a particular app, the Proposal Department proposes additional services related to that app. For example, a user who frequently uses a music streaming app can be offered a premium plan for a music streaming service. Furthermore, the Proposal Department can propose the optimal calling plan based on the user's calling patterns. For example, a user who makes long calls can be offered an unlimited calling plan. The Proposal Department delivers these proposals to users in a personalized manner. For example, it notifies users of the proposals through smartphone notifications, email, SMS, etc. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to provide users with optimal plans and services and improve user satisfaction. Furthermore, the proposal department can make multiple proposals simultaneously, helping users make the optimal choice. This allows the proposal department to provide flexible proposals tailored to user needs and maximize the overall effectiveness of the system.

[0034] The reporting department provides marketing personnel with reports based on proposals from the proposal department. For example, the reporting department generates reports using AI and provides them to marketing personnel. Specifically, the reporting department generates reports to evaluate user usage trends and the effectiveness of proposals. For instance, it creates detailed reports on user data usage and app usage and provides them to marketing personnel. This allows marketing personnel to understand user usage trends and develop effective marketing strategies. It can also generate reports to evaluate the effectiveness of proposed plans and services. For example, it creates reports to evaluate how many users accepted data plan upgrade proposals or how many users utilized additional service proposals. Furthermore, it can generate reports to evaluate the effectiveness of marketing initiatives. For example, it creates reports to evaluate how specific campaigns or promotions impacted user behavior. The reporting department regularly generates and provides these reports to marketing personnel, providing information for continuously evaluating and improving the effectiveness of marketing initiatives. Additionally, the reporting department visualizes data using graphs and charts to make the report content visually easy to understand. This allows marketing personnel to intuitively understand the data and make quick decisions. This allows the reporting department to provide valuable information to marketing personnel and support the development and implementation of effective marketing strategies.

[0035] The data collection unit can collect data such as data usage, call history, and app usage. For example, the data collection unit can monitor data usage in real time and record monthly data usage. For example, the data collection unit can collect call history and obtain information such as call duration, call recipient, and call frequency. For example, the data collection unit can collect app usage and obtain information such as app usage time, usage frequency, and usage patterns. This allows for the comprehensive collection of user mobile usage data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as data usage, call history, and app usage into a generating AI and have the generating AI perform the data collection.

[0036] The analysis unit can analyze the collected data and identify usage patterns for each user. For example, the analysis unit can use AI to analyze the data and identify usage patterns for each user. For example, the analysis unit can identify users whose data usage increases during specific time periods. The analysis unit can also identify users who frequently use specific applications. Furthermore, the analysis unit can analyze users' call history and identify call patterns. By identifying usage patterns for each user, it is possible to address individual needs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0037] The prediction unit can predict future usage trends and needs based on the analysis results. For example, the prediction unit can use AI to predict future usage trends and needs. For example, the prediction unit can predict users whose data usage is on an increasing trend. The prediction unit can also predict the future usage trends of users who frequently use a particular app. Furthermore, the prediction unit can predict future call trends based on the user's call patterns. This makes it possible to make appropriate suggestions by predicting future usage trends and needs. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the analysis results into a generating AI and have the generating AI perform predictions of future usage trends and needs.

[0038] The suggestion unit can propose the optimal plan or service based on predicted results. For example, the suggestion unit can use AI to propose the optimal plan or service. For example, the suggestion unit can propose a data plan upgrade to a user whose data usage is increasing. The suggestion unit can also propose additional services related to a particular app to a user who frequently uses that app. Furthermore, the suggestion unit can propose the optimal calling plan based on the user's calling patterns. This improves the user experience by proposing the optimal plan or service. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input predicted results into a generating AI and have the generating AI execute a proposal for the optimal plan or service.

[0039] The reporting department can provide the proposed content to marketing personnel as a report. The reporting department can, for example, generate reports using AI and provide them to marketing personnel. The reporting department can, for example, generate user usage trend reports and provide them to marketing personnel. The reporting department can also generate reports that evaluate the effectiveness of proposed plans and services. Furthermore, the reporting department can generate reports that evaluate the effectiveness of marketing measures. This allows marketing personnel to understand user usage trends and formulate effective measures. Some or all of the above processes in the reporting department may be performed using AI, for example, or without AI. For example, the reporting department can input the proposed content into a generating AI and have the generating AI execute the report generation.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also adjust the amount of data collected based on the user's past data usage. Furthermore, the data collection unit can prioritize collecting important call data by referring to the user's past call history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related data. Similarly, if the user is interested in health, the data collection unit can prioritize collecting data from health-related apps. Furthermore, if the user is at work, the data collection unit can prioritize collecting work-related data. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI, and have the generating AI perform the data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information about their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of information about their home area. This allows for the acquisition of more useful data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of accounts that the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activity of groups that the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a call pattern analysis algorithm to call history data. It can also apply an app usage frequency analysis algorithm to app usage data. Furthermore, it can apply a data consumption pattern analysis algorithm to data usage data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationship between data usage and call history. It can also make predictions by considering the interrelationship between app usage and data usage. Furthermore, it can make predictions by considering the interrelationship between call history and app usage. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0049] The prediction unit can make predictions while considering the attribute information of the data submitter. For example, the prediction unit can make predictions while considering the user's age. It can also make predictions while considering the user's gender. Furthermore, it can also make predictions while considering the user's place of residence. This makes it possible to make more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the prediction.

[0050] The prediction unit can make predictions while considering the geographical distribution of the data. For example, the prediction unit can make predictions based on the user's place of residence. It can also make predictions based on the user's travel history. Furthermore, it can make predictions based on the user's travel destinations. This makes it possible to make predictions that are tailored to regional characteristics by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit can predict data usage based on relevant literature. It can also predict call history based on relevant literature. Furthermore, it can predict app usage based on relevant literature. In this way, the accuracy of the prediction is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the prediction.

[0052] The proposal department can adjust the level of detail in its proposals based on the importance of the plans and services. For example, it can provide detailed proposals for important plans and services, and simplified proposals for less important plans and services. Furthermore, it can prioritize proposals according to the importance of the plans and services. This allows for efficient proposals by adjusting the level of detail based on the importance of the plans and services. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the plans and services into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0053] The proposal unit can apply different proposal algorithms depending on the plan or service category when making a proposal. For example, the proposal unit can apply a proposal algorithm based on data usage to data plans. It can also apply a proposal algorithm based on call history to call plans. Furthermore, it can apply a proposal algorithm based on app usage to app-related services. This improves the accuracy of proposals by applying the appropriate proposal algorithm according to the plan or service category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the plan or service category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0054] The proposal department can prioritize proposals based on the timing of plan and service submissions. For example, the proposal department can propose the optimal plan to coincide with contract renewal. It can also propose relevant services immediately after a user installs a new app. Furthermore, it can propose a data plan upgrade when a user's data usage increases. This enables timely proposals by prioritizing proposals based on the timing of plan and service submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the timing of plan and service submissions into a generating AI and have the generating AI determine the priority of proposals.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of plans and services. For example, it may prioritize suggesting services related to apps that the user frequently uses. It can also prioritize suggesting the optimal data plan based on the user's data usage. Furthermore, it can prioritize suggesting the optimal call plan based on the user's call history. By adjusting the order of suggestions based on the relevance of plans and services, it becomes possible to make the best possible suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the relevance of plans and services into a generating AI and have the generating AI adjust the order of suggestions.

[0056] The report delivery unit can select the optimal display method by referring to the user's past operation history when providing reports. For example, the report delivery unit may prioritize display formats that the user has previously preferred. The report delivery unit can also suggest the optimal report display method based on the user's past operation history. Furthermore, the report delivery unit can automatically apply filtering options that the user has previously used. This allows the optimal display method to be provided by referring to the user's past operation history. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0057] The report delivery unit can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the report delivery unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the report delivery unit can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the report delivery unit can provide a display method that includes detailed information. This allows the system to provide the optimal display method by considering the user's device information. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

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

[0059] The data collection unit can analyze a user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from apps the user has frequently used in the past. It can also adjust the amount of data collected based on the user's past data usage. Furthermore, it can prioritize collecting important call data by referring to the user's past call history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0060] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if the user is traveling, it can prioritize collecting travel-related data. Similarly, if the user is interested in health, it can prioritize collecting data from health-related apps. Furthermore, if the user is at work, it can prioritize collecting work-related data. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest.

[0061] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting data related to that region. If a user is traveling, it can also prioritize collecting information about their travel destination. Furthermore, if a user is at home, it can prioritize collecting information about their surroundings. This allows for the collection of more relevant data based on the user's geographical location, resulting in the acquisition of more useful data.

[0062] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect relevant data based on information users share on social media. It can also analyze the activity of accounts users follow and collect relevant data. Furthermore, it can analyze the activity of groups users participate in and collect relevant data. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects the user's mobile usage data. The data collection unit collects data such as data usage, call history, and app usage. The data collection unit monitors data usage in real time and records monthly data usage. It can also collect call history and obtain information such as call duration, call recipient, and call frequency. Furthermore, it can collect app usage data and obtain information such as app usage time, usage frequency, and usage patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify usage patterns for each user. For example, it can identify users whose data usage increases during specific time periods, users who frequently use specific apps, and call patterns. Step 3: The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The prediction unit uses AI to predict future usage trends and needs, and can predict future usage trends of users whose data usage is on the rise, users who frequently use specific apps, and future call trends based on call patterns. Step 4: The proposal unit proposes the optimal plan or service based on the results predicted by the prediction unit. The proposal unit uses AI to propose the optimal plan or service, for example, suggesting a data plan upgrade to a user whose data usage is on the rise. It can also suggest additional services related to a specific app to a user who frequently uses that app, and suggest the optimal calling plan based on the user's calling patterns. Step 5: The reporting department provides the marketing team with reports based on the proposals made by the proposal department. The reporting department uses AI to generate reports and provide them to the marketing team. For example, it can generate reports on user usage trends, reports evaluating the effectiveness of proposed plans and services, and reports evaluating the effectiveness of marketing measures.

[0066] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that analyzes mobile user usage data and predicts future usage trends and needs. This AI agent system collects and analyzes users' mobile usage data, predicts future usage trends and needs, proposes optimal plans and services, and provides reports to marketing personnel. For example, the AI ​​agent system collects data such as users' data usage, call history, and app usage. Next, the AI ​​agent system analyzes the collected data and identifies usage patterns for each user. For example, it identifies users whose data usage increases during specific time periods or users who frequently use specific apps. Next, the AI ​​agent system predicts future usage trends and needs based on the analysis results. For example, it can propose a data plan upgrade to users whose data usage is increasing. It can also propose additional services related to specific apps to users who frequently use those apps. Furthermore, the AI ​​agent system predicts the timing of contract changes and proposes optimal plans and services at the appropriate time. For example, by proposing new plans and services to users in line with contract renewal timing, it can improve contract renewal rates. Finally, the AI ​​agent system provides marketing personnel with a user usage trend report. This enables marketing professionals to plan and execute effective marketing strategies. This system improves the user experience, increasing user satisfaction and loyalty. Furthermore, it allows marketing professionals to implement more effective marketing strategies by understanding user usage trends. This enables the AI ​​agent system to efficiently collect, analyze, predict, suggest, and report on user mobile usage data.

[0067] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, a proposal unit, and a report provision unit. The data collection unit collects user mobile usage data. The data collection unit collects data such as data usage, call history, and app usage. The data collection unit monitors data usage in real time and records monthly data usage. The data collection unit can also collect call history and obtain information such as call duration, call recipient, and call frequency. Furthermore, the data collection unit can collect app usage data and obtain information such as app usage time, usage frequency, and usage patterns. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using AI and identifies usage patterns for each user. For example, the analysis unit identifies users whose data usage increases during specific time periods. The analysis unit can also identify users who frequently use specific apps. Furthermore, the analysis unit can analyze a user's call history and identify call patterns. The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The prediction unit uses AI to predict future usage trends and needs. For example, the prediction unit predicts users whose data usage is on an upward trend. The prediction unit can also predict future usage trends for users who frequently use a particular app. Furthermore, the prediction unit can predict future call trends based on users' call patterns. The proposal unit proposes the optimal plan or service based on the results predicted by the prediction unit. For example, the proposal unit uses AI to propose the optimal plan or service. For example, the proposal unit proposes a data plan upgrade to users whose data usage is on an upward trend. Furthermore, the proposal unit can propose additional services related to a particular app to users who frequently use that app. Furthermore, the proposal unit can propose the optimal call plan based on users' call patterns. The reporting unit provides the content proposed by the proposal unit to marketing personnel as a report. For example, the reporting unit generates reports using AI and provides them to marketing personnel.The reporting unit can, for example, generate user usage trend reports and provide them to marketing personnel. The reporting unit can also generate reports evaluating the effectiveness of proposed plans and services. Furthermore, it can generate reports evaluating the effectiveness of marketing initiatives. This enables the AI ​​agent system according to the embodiment to efficiently collect, analyze, predict, propose, and report on user mobile usage data.

[0068] The data collection unit collects user mobile usage data. This includes data such as data usage, call history, and app usage. Specifically, the unit acquires data from users' smartphones and tablets and monitors this data in real time. Regarding data usage, it records in detail which applications and services users are using and when data usage is highest. For example, it tracks the usage of video streaming services and social media apps and records monthly data usage. Regarding call history, it collects information such as the start and end times of calls, the phone number of the person being called, and the frequency of calls. This allows the unit to understand the user's calling patterns. Furthermore, regarding app usage, it records in detail the usage time, frequency, and usage patterns for each app. For example, it can determine how often a user uses a particular game app or at what times of day they use business apps. This allows the data collection unit to comprehensively collect user mobile usage data and understand detailed usage patterns. The collected data is stored on a secure cloud server and made accessible to the analysis and prediction units. Furthermore, the data collection unit implements data encryption and access control to ensure data privacy and security. This allows the collection unit to collect data efficiently and securely, improving the overall performance and reliability of the system.

[0069] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and identify user usage patterns. Specifically, the AI ​​uses machine learning algorithms to extract user behavior patterns and usage trends from the collected data. For instance, it performs time-series data analysis to identify users whose data usage increases during specific time periods. This allows for understanding when users are using the internet most. Furthermore, it uses clustering algorithms to classify app usage patterns to identify users who frequently use specific apps. This allows for understanding which apps users use and how often. In addition, it performs statistical analysis of call data to analyze users' call history and identify call patterns. This allows for understanding users' call habits. Based on these analysis results, the analysis unit creates detailed usage profiles for each user. This enables the analysis unit to quickly and accurately analyze collected data and understand user usage trends. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term usage trends. For example, it can predict future data usage based on past fluctuations in data usage. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual usage patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to analyze long-term usage trends and detect anomalies, thereby improving the reliability and safety of the entire system.

[0070] The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. For example, the prediction unit uses AI to predict future usage trends and needs. Specifically, the prediction unit uses machine learning models to predict future usage trends based on collected data and analysis results. For example, it performs regression analysis to predict users whose data usage is increasing. This allows it to predict how much data users will use in the future. It also uses time series forecasting models to predict future usage trends of users who frequently use specific apps. This allows it to predict which apps users will use and how often in the future. Furthermore, it uses clustering algorithms to predict future call trends based on users' call patterns. This allows it to predict what kind of call habits users will have in the future. Based on these prediction results, the prediction unit creates future usage profiles for each user. This allows the prediction unit to predict future usage trends and needs with high accuracy and provide information for taking appropriate measures. Furthermore, the prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situation. For example, if data usage or app usage changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. Furthermore, the prediction unit can make more accurate predictions by taking into account the characteristics of each region and past usage history. As a result, the prediction unit can always provide highly accurate predictions based on the latest information, supporting quick and appropriate responses.

[0071] The Proposal Department proposes optimal plans and services based on the results predicted by the Prediction Department. For example, the Proposal Department uses AI to propose optimal plans and services. Specifically, the Proposal Department uses machine learning algorithms to select the optimal plans and services based on the user's usage profile and prediction results. For example, for users whose data usage is increasing, the Proposal Department proposes an upgrade to their data plan. This allows users to choose the optimal plan according to their data usage. Also, for users who frequently use a particular app, the Proposal Department proposes additional services related to that app. For example, a user who frequently uses a music streaming app can be offered a premium plan for a music streaming service. Furthermore, the Proposal Department can propose the optimal calling plan based on the user's calling patterns. For example, a user who makes long calls can be offered an unlimited calling plan. The Proposal Department delivers these proposals to users in a personalized manner. For example, it notifies users of the proposals through smartphone notifications, email, SMS, etc. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to provide users with optimal plans and services and improve user satisfaction. Furthermore, the proposal department can make multiple proposals simultaneously, helping users make the optimal choice. This allows the proposal department to provide flexible proposals tailored to user needs and maximize the overall effectiveness of the system.

[0072] The reporting department provides marketing personnel with reports based on proposals from the proposal department. For example, the reporting department generates reports using AI and provides them to marketing personnel. Specifically, the reporting department generates reports to evaluate user usage trends and the effectiveness of proposals. For instance, it creates detailed reports on user data usage and app usage and provides them to marketing personnel. This allows marketing personnel to understand user usage trends and develop effective marketing strategies. It can also generate reports to evaluate the effectiveness of proposed plans and services. For example, it creates reports to evaluate how many users accepted data plan upgrade proposals or how many users utilized additional service proposals. Furthermore, it can generate reports to evaluate the effectiveness of marketing initiatives. For example, it creates reports to evaluate how specific campaigns or promotions impacted user behavior. The reporting department regularly generates and provides these reports to marketing personnel, providing information for continuously evaluating and improving the effectiveness of marketing initiatives. Additionally, the reporting department visualizes data using graphs and charts to make the report content visually easy to understand. This allows marketing personnel to intuitively understand the data and make quick decisions. This allows the reporting department to provide valuable information to marketing personnel and support the development and implementation of effective marketing strategies.

[0073] The data collection unit can collect data such as data usage, call history, and app usage. For example, the data collection unit can monitor data usage in real time and record monthly data usage. For example, the data collection unit can collect call history and obtain information such as call duration, call recipient, and call frequency. For example, the data collection unit can collect app usage and obtain information such as app usage time, usage frequency, and usage patterns. This allows for the comprehensive collection of user mobile usage data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data such as data usage, call history, and app usage into a generating AI and have the generating AI perform the data collection.

[0074] The analysis unit can analyze the collected data and identify usage patterns for each user. For example, the analysis unit can use AI to analyze the data and identify usage patterns for each user. For example, the analysis unit can identify users whose data usage increases during specific time periods. The analysis unit can also identify users who frequently use specific applications. Furthermore, the analysis unit can analyze users' call history and identify call patterns. By identifying usage patterns for each user, it is possible to address individual needs. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0075] The prediction unit can predict future usage trends and needs based on the analysis results. For example, the prediction unit can use AI to predict future usage trends and needs. For example, the prediction unit can predict users whose data usage is on an increasing trend. The prediction unit can also predict the future usage trends of users who frequently use a particular app. Furthermore, the prediction unit can predict future call trends based on the user's call patterns. This makes it possible to make appropriate suggestions by predicting future usage trends and needs. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the analysis results into a generating AI and have the generating AI perform predictions of future usage trends and needs.

[0076] The suggestion unit can propose the optimal plan or service based on predicted results. For example, the suggestion unit can use AI to propose the optimal plan or service. For example, the suggestion unit can propose a data plan upgrade to a user whose data usage is increasing. The suggestion unit can also propose additional services related to a particular app to a user who frequently uses that app. Furthermore, the suggestion unit can propose the optimal calling plan based on the user's calling patterns. This improves the user experience by proposing the optimal plan or service. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input predicted results into a generating AI and have the generating AI execute a proposal for the optimal plan or service.

[0077] The reporting department can provide the proposed content to marketing personnel as a report. The reporting department can, for example, generate reports using AI and provide them to marketing personnel. The reporting department can, for example, generate user usage trend reports and provide them to marketing personnel. The reporting department can also generate reports that evaluate the effectiveness of proposed plans and services. Furthermore, the reporting department can generate reports that evaluate the effectiveness of marketing measures. This allows marketing personnel to understand user usage trends and formulate effective measures. Some or all of the above processes in the reporting department may be performed using AI, for example, or without AI. For example, the reporting department can input the proposed content into a generating AI and have the generating AI execute the report generation.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the data collection unit can perform more detailed data collection to obtain more information. Furthermore, if the user is busy, the data collection unit can perform data collection in a shorter time, saving the user's time. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0079] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also adjust the amount of data collected based on the user's past data usage. Furthermore, the data collection unit can prioritize collecting important call data by referring to the user's past call history. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0080] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related data. Similarly, if the user is interested in health, the data collection unit can prioritize collecting data from health-related apps. Furthermore, if the user is at work, the data collection unit can prioritize collecting work-related data. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI, and have the generating AI perform the data filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. It can also prioritize collecting entertainment-related data if the user is relaxed. Furthermore, if the user is busy, the data collection unit can prioritize collecting work-related data. This allows for the collection of more appropriate data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of information about their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of information about their home area. This allows for the acquisition of more useful data by collecting highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze the activity of accounts that the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activity of groups that the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a call pattern analysis algorithm to call history data. It can also apply an app usage frequency analysis algorithm to app usage data. Furthermore, it can apply a data consumption pattern analysis algorithm to data usage data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a brief analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an appropriate result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection timing. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is relaxed, the prediction unit can make a detailed prediction. If the user is in a hurry, the prediction unit can also make a concise prediction. Furthermore, if the user is excited, the prediction unit can make a visually stimulating prediction. By adjusting the prediction criteria according to the user's emotions, more appropriate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction criteria.

[0091] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationship between data usage and call history. It can also make predictions by considering the interrelationship between app usage and data usage. Furthermore, it can make predictions by considering the interrelationship between call history and app usage. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0092] The prediction unit can make predictions while considering the attribute information of the data submitter. For example, the prediction unit can make predictions while considering the user's age. It can also make predictions while considering the user's gender. Furthermore, it can also make predictions while considering the user's place of residence. This makes it possible to make more personalized predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the prediction.

[0093] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit can display important prediction results first. If the user is relaxed, the prediction unit can also display detailed prediction results sequentially. Furthermore, if the user is in a hurry, the prediction unit can display concise prediction results first. By adjusting the display order of prediction results according to the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display order of prediction results.

[0094] The prediction unit can make predictions while considering the geographical distribution of the data. For example, the prediction unit can make predictions based on the user's place of residence. It can also make predictions based on the user's travel history. Furthermore, it can make predictions based on the user's travel destinations. This makes it possible to make predictions that are tailored to regional characteristics by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0095] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on the data during the prediction process. For example, the prediction unit can predict data usage based on relevant literature. It can also predict call history based on relevant literature. Furthermore, it can predict app usage based on relevant literature. In this way, the accuracy of the prediction is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the prediction.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, it can present more detailed suggestions. Furthermore, if the user is in a hurry, it can present concise suggestions. By adjusting the presentation of suggestions according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0097] The proposal department can adjust the level of detail in its proposals based on the importance of the plans and services. For example, it can provide detailed proposals for important plans and services, and simplified proposals for less important plans and services. Furthermore, it can prioritize proposals according to the importance of the plans and services. This allows for efficient proposals by adjusting the level of detail based on the importance of the plans and services. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of the plans and services into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0098] The proposal unit can apply different proposal algorithms depending on the plan or service category when making a proposal. For example, the proposal unit can apply a proposal algorithm based on data usage to data plans. It can also apply a proposal algorithm based on call history to call plans. Furthermore, it can apply a proposal algorithm based on app usage to app-related services. This improves the accuracy of proposals by applying the appropriate proposal algorithm according to the plan or service category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the plan or service category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide brief suggestions. In this way, by adjusting the length of suggestions according to the user's emotions, appropriate suggestions can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0100] The proposal department can prioritize proposals based on the timing of plan and service submissions. For example, the proposal department can propose the optimal plan to coincide with contract renewal. It can also propose relevant services immediately after a user installs a new app. Furthermore, it can propose a data plan upgrade when a user's data usage increases. This enables timely proposals by prioritizing proposals based on the timing of plan and service submissions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the timing of plan and service submissions into a generating AI and have the generating AI determine the priority of proposals.

[0101] The suggestion unit can adjust the order of suggestions based on the relevance of plans and services. For example, it may prioritize suggesting services related to apps that the user frequently uses. It can also prioritize suggesting the optimal data plan based on the user's data usage. Furthermore, it can prioritize suggesting the optimal call plan based on the user's call history. By adjusting the order of suggestions based on the relevance of plans and services, it becomes possible to make the best possible suggestions for the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the relevance of plans and services into a generating AI and have the generating AI adjust the order of suggestions.

[0102] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide a simple and easy-to-read report. If the user is relaxed, it can provide a detailed report. Furthermore, if the user is in a hurry, it can provide a concise report. By adjusting how the report is displayed according to the user's emotions, the system can provide reports that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust how the report is displayed.

[0103] The report delivery unit can select the optimal display method by referring to the user's past operation history when providing reports. For example, the report delivery unit may prioritize display formats that the user has previously preferred. The report delivery unit can also suggest the optimal report display method based on the user's past operation history. Furthermore, the report delivery unit can automatically apply filtering options that the user has previously used. This allows the optimal display method to be provided by referring to the user's past operation history. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0104] The report delivery unit can estimate the user's emotions and adjust the report's operation procedures based on the estimated emotions. For example, if the user is nervous, the report delivery unit can provide simplified operation procedures. If the user is relaxed, the report delivery unit can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the report delivery unit can provide concise operation procedures. In this way, by adjusting the report's operation procedures according to the user's emotions, a user-friendly report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report delivery unit may be performed using AI or not using AI. For example, the report delivery unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operation procedures.

[0105] The report delivery unit can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the report delivery unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the report delivery unit can provide a display method optimized for a larger screen. In addition, if the user is using a desktop, the report delivery unit can provide a display method that includes detailed information. This allows the system to provide the optimal display method by considering the user's device information. Some or all of the above processing in the report delivery unit may be performed using AI, for example, or without AI. For example, the report delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, more detailed data collection can be performed to obtain more information. Furthermore, if the user is busy, data collection can be performed in a shorter time, saving the user time. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide analysis results that are easy for the user to understand.

[0109] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on those emotions. For example, if the user is relaxed, it can make a detailed prediction. If the user is in a hurry, it can make a concise prediction. Furthermore, if the user is excited, it can make a visually stimulating prediction. By adjusting the prediction criteria according to the user's emotions, more accurate predictions become possible.

[0110] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is nervous, it can present a simple and highly visual proposal. If the user is relaxed, it can present a more detailed proposal. Furthermore, if the user is in a hurry, it can present a concise proposal. By adjusting the way the proposal is presented according to the user's emotions, it can provide proposals that are easy for the user to understand.

[0111] The reporting system can estimate the user's emotions and adjust how the report is displayed based on that estimation. For example, if the user is stressed, it can provide a simple, easy-to-read report. If the user is relaxed, it can provide a more detailed report. Furthermore, if the user is in a hurry, it can provide a concise report. By adjusting the report display according to the user's emotions, the system can provide reports that are easy for the user to understand.

[0112] The data collection unit can analyze a user's past data collection history and select the optimal collection method. For example, it can prioritize collecting data from apps the user has frequently used in the past. It can also adjust the amount of data collected based on the user's past data usage. Furthermore, it can prioritize collecting important call data by referring to the user's past call history. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0113] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if the user is traveling, it can prioritize collecting travel-related data. Similarly, if the user is interested in health, it can prioritize collecting data from health-related apps. Furthermore, if the user is at work, it can prioritize collecting work-related data. This allows for the collection of highly relevant data by filtering it based on the user's lifestyle and areas of interest.

[0114] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location. For example, if a user is in a specific region, it will prioritize collecting data related to that region. If a user is traveling, it can also prioritize collecting information about their travel destination. Furthermore, if a user is at home, it can prioritize collecting information about their surroundings. This allows for the collection of more relevant data based on the user's geographical location, resulting in the acquisition of more useful data.

[0115] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect relevant data based on information users share on social media. It can also analyze the activity of accounts users follow and collect relevant data. Furthermore, it can analyze the activity of groups users participate in and collect relevant data. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0116] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data, and a simplified analysis on less important data. Furthermore, it can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects the user's mobile usage data. The data collection unit collects data such as data usage, call history, and app usage. The data collection unit monitors data usage in real time and records monthly data usage. It can also collect call history and obtain information such as call duration, call recipient, and call frequency. Furthermore, it can collect app usage data and obtain information such as app usage time, usage frequency, and usage patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and identify usage patterns for each user. For example, it can identify users whose data usage increases during specific time periods, users who frequently use specific apps, and call patterns. Step 3: The prediction unit predicts future usage trends and needs based on the analysis results obtained by the analysis unit. The prediction unit uses AI to predict future usage trends and needs, and can predict future usage trends of users whose data usage is on the rise, users who frequently use specific apps, and future call trends based on call patterns. Step 4: The proposal unit proposes the optimal plan or service based on the results predicted by the prediction unit. The proposal unit uses AI to propose the optimal plan or service, for example, suggesting a data plan upgrade to a user whose data usage is on the rise. It can also suggest additional services related to a specific app to a user who frequently uses that app, and suggest the optimal calling plan based on the user's calling patterns. Step 5: The reporting department provides the marketing team with reports based on the proposals made by the proposal department. The reporting department uses AI to generate reports and provide them to the marketing team. For example, it can generate reports on user usage trends, reports evaluating the effectiveness of proposed plans and services, and reports evaluating the effectiveness of marketing measures.

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, and report provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user mobile usage data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future usage trends and needs based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal plan or service based on the prediction results. The report provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides the proposed content to the marketing personnel as a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, and report provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's mobile usage data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts future usage trends and needs based on the analysis results. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes the optimal plan or service based on the prediction results. The report provision unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides the proposed content to the marketing personnel as a report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, and report provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user mobile usage data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future usage trends and needs based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal plan or service based on the prediction results. The report provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides the proposed content to the marketing personnel as a report. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, and report provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user mobile usage data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts future usage trends and needs based on the analysis results. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal plan or service based on the prediction results. The report provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides the proposed content to the marketing personnel as a report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0172] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A collection unit that collects user mobile usage data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit predicts future usage trends and needs based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes the optimal plan or service based on the results predicted by the prediction unit, The system includes a report provision unit that provides the content proposed by the proposal unit to marketing personnel as a report. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as data usage, call history, and app usage. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify usage patterns for each user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Based on the analysis results, we predict future usage trends and needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal plan and services based on the predicted results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned report provision department, Provide the proposed content to the marketing team as a report. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, consider the interrelationships between data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the attribute information of the data submitters is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, refer to relevant literature to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the plan or service. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the plan or service category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of plan and service submissions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the plans and services. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned report provision department, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned report provision department, When providing reports, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned report provision department, It estimates the user's emotions and adjusts the report's operation steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned report provision department, When providing reports, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user mobile usage data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit predicts future usage trends and needs based on the analysis results obtained by the aforementioned analysis unit, A proposal unit that proposes the optimal plan or service based on the results predicted by the prediction unit, The system includes a report provision unit that provides the content proposed by the proposal unit to marketing personnel as a report. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as data usage, call history, and app usage. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify usage patterns for each user. The system according to feature 1.

4. The prediction unit, Based on the analysis results, we predict future usage trends and needs. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal plan and services based on the predicted results. The system according to feature 1.

6. The aforementioned report provision department, Provide the proposed content to the marketing team as a report. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

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