system

Abstract

The system according to this embodiment aims to analyze customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an update unit. The collection unit collects customer communication usage data and behavioral patterns. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal pricing plans and promotions based on the analysis results obtained by the analysis unit. The provision unit provides the content proposed by the proposal unit to the user. The update unit periodically re-analyzes the data based on the content provided by the provision unit and updates the proposals.

Description

Technical Field

[0005]

[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, the method including 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 as a 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, it has not been fully carried out to provide an optimal tariff plan or promotion based on customers' communication usage data and behavior patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customers' communication usage data and behavior patterns and provide an optimal tariff plan or promotion.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an update unit. The collection unit collects customer communication usage data and behavioral patterns. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal pricing plans and promotions based on the analysis results obtained by the analysis unit. The provision unit provides the content proposed by the proposal unit to the user. The update unit periodically re-analyzes the data based on the content provided by the provision unit and updates the proposals. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 receiving 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 receiving 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) The personalized AI system according to an embodiment of the present invention is a system in which AI analyzes customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions to individual users. The personalized AI system collects customers' communication usage data and behavioral patterns, and the AI ​​analyzes them to propose optimal pricing plans and promotions for each user. As a result, users can receive optimal information and suggestions based on their usage trends, enabling them to optimize their communication costs and use customized services. This also leads to improved customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. For example, the personalized AI system collects customers' communication usage data and behavioral patterns. Specifically, it collects communication usage data such as call time, data traffic, and applications used in real time. Next, the AI ​​analyzes the collected data to understand each user's usage trends. For example, it analyzes the trends of users who frequently use specific applications such as video streaming or games. Next, based on the analysis results, the AI ​​automatically proposes optimal pricing plans, additional data packages, and promotions for each user. For example, it proposes discounts and additional services to users who frequently use video streaming. The AI ​​also periodically re-analyzes user data and updates its suggestions to match changing usage patterns. This ensures users always receive the optimal service. This system allows users to receive information and suggestions tailored to their usage patterns, enabling optimized communication costs and the use of customized services. For example, users who frequently use video streaming can reduce communication costs through discounts and additional services. Furthermore, it leads to improved customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. For instance, providing services tailored to individual needs can reduce customer churn and cancellation rates. Moreover, the proliferation of next-generation communication technologies is diversifying communication volume and usage patterns, increasing the need to propose optimal plans to users. Advances in AI technology are enabling real-time personalized suggestions, which is expected to improve the customer experience.For example, AI can analyze data in real time and suggest the optimal plan for each user, thereby increasing customer satisfaction. In this way, personalized AI systems dramatically improve the user's communication experience by leveraging customer data to provide optimal services tailored to individual needs. For telecommunications carriers, this leads to increased customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. Now is the perfect time to enter this field, against the backdrop of the spread of next-generation communication technologies and the evolution of AI technology. As a result, personalized AI systems can analyze customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions for each individual user.

[0029] The personalized AI system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an update unit. The collection unit collects customer communication usage data and behavioral patterns. The collection unit collects communication usage data in real time, such as call duration, data traffic, and applications used. For example, the collection unit can collect call duration in real time. The collection unit can also collect data traffic in real time. Furthermore, the collection unit can also collect application usage data in real time. For example, the collection unit can collect call duration, data traffic, and application usage data in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using AI to understand each user's usage trends. For example, the analysis unit can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis unit can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis unit can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. The proposal department proposes the optimal pricing plan and promotions based on the analysis results obtained by the analysis department. The proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. The proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. Furthermore, the proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. The delivery department provides the content proposed by the proposal department to the user. The delivery department provides the proposed content to the user.The provisioning unit can, for example, provide the proposed content to the user. The provisioning unit can also provide the proposed content to the user. Furthermore, the provisioning unit can also provide the proposed content to the user. For example, the provisioning unit can provide the proposed content to the user. The update unit periodically reanalyzes the data based on the content provided by the provisioning unit and updates the proposals. The update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. For example, the update unit can periodically reanalyze the user's data and update the proposals to match the changing usage patterns. Furthermore, the update unit can periodically reanalyze the user's data and update the proposals to match the changing usage patterns. Furthermore, the update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. For example, the update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. As a result, the personalized AI system according to the embodiment can analyze customer communication usage data and behavioral patterns and provide optimal pricing plans and promotions to individual users.

[0030] The data collection unit collects customer communication usage data and behavioral patterns. Specifically, it collects communication usage data such as call duration, data traffic, and applications used in real time. For example, the data collection unit can collect call duration in real time. Call duration is collected by recording the start and end times of each call and calculating call frequency and average call duration. Data traffic is collected by monitoring the amount of data used for each application and service in real time and aggregating monthly and weekly data usage. Application usage data is collected by recording which applications users use and for how long, and analyzing application usage frequency and duration. This allows the data collection unit to understand users' communication usage patterns in detail and clarify the behavioral characteristics of individual users. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the collected data using AI to understand the usage trends of each user. The AI ​​uses machine learning algorithms to analyze users' communication usage data and behavioral patterns. For example, it uses clustering techniques to group users with similar usage patterns and extract the characteristics of each group. It also uses regression analysis to predict fluctuations in users' data usage and call time, and understand future usage trends. Furthermore, it can use natural language processing technology to analyze the types and content of applications used by users and identify their interests and preferences. This allows the analysis department to quickly and accurately analyze the collected data and understand the usage trends of each user in detail. In addition, the analysis department can utilize historical data and statistical information to conduct long-term usage trend analysis. For example, based on historical data, it can predict fluctuations in data usage during specific seasons or events and plan future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis department 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 security of the entire system.

[0032] The Proposal Department proposes optimal pricing plans and promotions based on the analysis results obtained by the Analysis Department. Specifically, it automatically proposes the most suitable pricing plans, additional data packages, and promotions for each user based on the analysis results. For example, it proposes a large data plan to users with high data usage and an unlimited call plan to users with long call times. It can also propose promotions and discounts related to specific applications to users who frequently use those applications. The Proposal Department uses AI to generate optimal suggestions based on user usage trends and past preferences. For example, it uses a recommendation system to analyze plans and promotions previously selected by users and makes similar suggestions to similar users. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to quickly and accurately propose the most suitable pricing plans and promotions to each user, thereby improving user satisfaction. Furthermore, the Proposal Department can continuously modify its suggestions based on real-time updated data to respond to the latest situations. For example, if a user's data usage or call time changes rapidly, the Proposal Department immediately incorporates the new data and updates its suggestions. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, enabling them to respond quickly and appropriately to user needs.

[0033] The service provider delivers the proposed content to users. Specifically, it delivers the proposed content to users. For example, the service provider can deliver the proposed content to users. The service provider notifies users of the details of the best pricing plan and promotions, making it easy for users to select. For example, it can send a notification to the user's smartphone displaying the details of the proposed plan and promotion. The service provider also optimizes the interface so that users can easily select the proposed content. For example, it can enable one-click plan changes and promotion applications. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the proposed content. For example, after a user selects a proposed plan or promotion, the service provider monitors how that selection affects satisfaction and incorporates it into future proposals. This allows the service provider to deliver proposals quickly and reliably to users, improving user satisfaction. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver proposals quickly and reliably to users, improving user satisfaction.

[0034] The update department periodically reanalyzes data based on the content provided by the service provider and updates its recommendations. Specifically, it periodically reanalyzes user data and updates recommendations to match changing usage patterns. For example, the update department can periodically reanalyze user data and update recommendations to match changing usage patterns. The update department uses AI to detect changes in user usage trends and behavioral patterns and revise recommendations accordingly. For example, if a user's data usage increases, the update department will suggest a larger data plan. Also, if a user starts using a new application frequently, it can suggest promotions related to that application. Furthermore, the update department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, after a user selects a suggested plan or promotion, it monitors how that selection affects satisfaction and incorporates this into future recommendations. This allows the update department to always provide highly accurate recommendations based on the latest information and respond quickly and appropriately to user needs. In addition, the update department can continuously revise its recommendations based on real-time updated data to respond to the latest situations. For example, if a user's data usage or call duration changes drastically, the update unit immediately incorporates the new data and updates the recommendations. This allows the update unit to always provide highly accurate recommendations based on the latest information, enabling it to respond quickly and appropriately to user needs.

[0035] The data collection unit can collect communication usage data such as call duration, data traffic, and applications used in real time. For example, the data collection unit can collect call duration in real time. It can also collect data traffic in real time. Furthermore, the data collection unit can collect application usage data in real time. For example, the data collection unit can collect call duration, data traffic, and application usage data in real time. This allows for analysis based on the latest data by collecting communication usage data in real time. The specific definition and criteria of real time include, for example, the delay time and update frequency of data collection. 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 collect call duration, data traffic, and application usage data in real time and analyze the data using AI.

[0036] The analysis department can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis department can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. This allows for an accurate understanding of each user's usage trends by analyzing the data with AI. Specific AI technologies and algorithms include, for example, machine learning, deep learning, and natural language processing. Some or all of the above-described processes in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input the collected data into a generative AI, which can then analyze the data to understand each user's usage trends.

[0037] The proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. Furthermore, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. This allows for the provision of the best possible service to users by making optimal suggestions based on the analysis results. Specific methods and criteria for automatic suggestions include, for example, the type of algorithm and the timing of the suggestion. Some or all of the above-described processes in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the analysis results into a generative AI, which can then suggest the most suitable pricing plan and promotions.

[0038] The service provider can provide the proposed content to the user. For example, the service provider can provide the proposed content to the user. Furthermore, the service provider can also provide the proposed content to the user. In addition, the service provider can also provide the proposed content to the user. For example, the service provider can provide the proposed content to the user. By providing the proposed content to the user, the user can receive the optimal service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the proposed content into AI, and the AI ​​can provide it to the user.

[0039] The update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. For example, the update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. Furthermore, the update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. This allows for the continuous provision of optimal service by periodically reanalyzing data and updating suggestions. Specific frequencies and timings of "periodically" include, for example, monthly, weekly, or after specific events. Some or all of the above-described processes in the update unit may be performed using AI, or not. For example, the update unit can input user data into AI, which can then reanalyze the data and update suggestions.

[0040] The data collection unit can analyze the user's past communication usage history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. Furthermore, the data collection unit can identify periods of high data traffic from the user's past communication usage history and collect data during those periods. In addition, the data collection unit can analyze the user's past communication usage history and select the most efficient data collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. This allows for the selection of the optimal data collection method by analyzing past communication usage history. Specific criteria and methods for the optimal data collection method include, for example, data type, collection frequency, and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past communication usage history into AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current communication environment and device when collecting communication usage data. For example, if the user is connected to Wi-Fi, the data collection unit can prioritize the collection of large amounts of data. Furthermore, if the user is using mobile data, the data collection unit can select a collection method that minimizes data usage. In addition, the data collection unit can adjust the amount of data collected according to the battery level of the user's device. For example, if the user is connected to Wi-Fi, the data collection unit can prioritize the collection of large amounts of data. This enables efficient data collection by filtering based on the current communication environment and device. Specific criteria and methods for filtering include, for example, the type of device and the conditions of the communication environment. 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 data on the user's current communication environment and device into the AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication usage data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data based on their home usage patterns. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting communication usage data. For example, the data collection unit can collect relevant data based on the content a user frequently posts on a particular social media platform. It can also collect relevant data based on the activity of accounts a user follows on social media. Furthermore, the data collection unit can collect relevant data based on groups and events a user participates in on social media. For example, the data collection unit can collect relevant data based on the content a user frequently posts on a particular social media platform. This allows for the efficient collection of relevant data by analyzing social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the communication usage data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit can perform a detailed analysis on data with high importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the communication usage data. Specific evaluation criteria and methods for importance include, for example, data frequency and impact. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the communication usage data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of communication usage data during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes viewing time and viewing frequency to video streaming data. Similarly, it can apply an analysis algorithm that emphasizes call duration and call frequency to call data. Furthermore, it can apply an analysis algorithm that emphasizes data usage and usage patterns to data communication volume. For example, the analysis unit can apply an analysis algorithm that emphasizes viewing time and viewing frequency to video streaming data. By applying different analysis algorithms depending on the category of communication usage data, more accurate analysis becomes possible. Specific types and classification methods of categories include, for example, data communication, calls, and app usage. 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 categories of communication usage data into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0046] The analysis unit can determine the priority of analysis based on the collection timing of communication usage data. For example, the analysis unit can prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of the most recent data. This enables efficient analysis by determining the priority of analysis based on the collection timing of communication usage data. Specific criteria and methods for collection timing include, for example, regular collection after a specific event. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collection timing of communication usage data into AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0047] The analysis unit can adjust the order of analysis based on the relevance of communication usage data during the analysis process. 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 dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of communication usage data. Specific evaluation criteria and methods for relevance include, for example, data co-occurrence frequency and correlation. 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 relevance of communication usage data into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0048] The proposal department can adjust the level of detail in its proposals based on the importance of the pricing plans and promotions. For example, it can provide detailed proposals for high-priority pricing plans. It can also provide simplified proposals for low-priority promotions. Furthermore, it can provide proposals with a moderate level of detail for medium-priority pricing plans. For example, the proposal department can provide detailed proposals for high-priority pricing plans. By adjusting the level of detail in proposals based on the importance of pricing plans and promotions, it is possible to provide the user with the most suitable proposal. Specific evaluation criteria and methods for importance include, for example, data frequency and impact. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of pricing plans and promotions into the AI, which can then adjust the level of detail in the proposals based on importance.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the pricing plan or promotion. For example, for pricing plans based on data usage, the proposal unit can apply a proposal algorithm that prioritizes data usage. Similarly, for pricing plans based on call duration, the proposal unit can apply a proposal algorithm that prioritizes call duration. Furthermore, for promotions based on specific app usage, the proposal unit can apply a proposal algorithm that prioritizes the frequency of use of that app. For example, for pricing plans based on data usage, the proposal unit can apply a proposal algorithm that prioritizes data usage. By applying different proposal algorithms depending on the category of the pricing plan or promotion, more accurate proposals become possible. Specific categories and classification methods include, for example, data communication, calls, and app usage. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the categories of pricing plans and promotions into the AI, which can then apply different proposal algorithms depending on the category.

[0050] The proposal department can prioritize proposals based on the timing of the delivery of pricing plans and promotions. For example, the proposal department can prioritize proposals for pricing plans that are about to be delivered. It can also postpone proposals that are far away. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the delivery timing. For example, it can prioritize proposals for pricing plans that are about to be delivered. This enables efficient proposals by prioritizing proposals based on the timing of the delivery of pricing plans and promotions. Specific criteria and methods for delivery timing include, for example, delivery after a specific event or periodic delivery. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the delivery timing of pricing plans and promotions into AI, and the AI ​​can determine the priority of proposals based on the delivery timing.

[0051] The proposal department can adjust the order of proposals based on the relevance of pricing plans and promotions during the proposal process. For example, the proposal department can prioritize proposing highly relevant pricing plans. It can also postpone less relevant promotions. Furthermore, the proposal department can dynamically adjust the order of proposals according to the relevance of pricing plans and promotions. For example, the proposal department can prioritize proposing highly relevant pricing plans. This allows for efficient proposals by adjusting the order of proposals based on the relevance of pricing plans and promotions. Specific criteria and methods for evaluating relevance include, for example, data co-occurrence frequency and correlation. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the relevance of pricing plans and promotions into AI, which can then adjust the order of proposals based on that relevance.

[0052] The service provider can select the optimal service delivery method by referring to the user's past communication usage history at the time of delivery. For example, the service provider can prioritize the delivery method the user has used in the past. Furthermore, the service provider can select the most effective delivery method based on the user's past communication usage history. In addition, the service provider can dynamically select the optimal delivery method by analyzing the user's past communication usage history. For example, the service provider can prioritize the delivery method the user has used in the past. This allows the service provider to select the optimal delivery method by referring to past communication usage history. Specific criteria and methods for the optimal delivery method include, for example, notification methods and delivery timing. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past communication usage history into AI, which can then select the optimal delivery method.

[0053] The service provider can customize the delivery method based on the user's current communication environment at the time of delivery. For example, if the user is connected to Wi-Fi, the service provider can provide a large amount of data. Furthermore, if the user is using mobile data, the service provider can select a delivery method that minimizes data usage. In addition, the service provider can adjust the amount of data provided according to the battery level of the user's device. For example, if the user is connected to Wi-Fi, the service provider can provide a large amount of data. This allows for efficient information delivery by customizing the delivery method based on the current communication environment. Specific conditions and evaluation criteria for the current communication environment include, for example, communication speed and connection status. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's current communication environment data into the AI, which can then customize the delivery method.

[0054] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. Similarly, if the user is traveling, the service provider can prioritize providing information related to their travel destination. Furthermore, if the user is at home, the service provider can prioritize providing information based on their home usage patterns. For example, if the service provider is in a specific region, the service provider can prioritize providing information related to that region. This allows the service provider to select the optimal delivery method by considering geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input the user's geographical location information into AI, which can then select the optimal delivery method.

[0055] The service provider can analyze the user's social media activity and propose a means of providing information at the time of delivery. For example, the service provider can provide relevant information based on the content that the user frequently posts on a particular social media platform. It can also provide relevant information based on the activity of accounts that the user follows on social media. Furthermore, it can provide relevant information based on groups and events that the user participates in on social media. For example, the service provider can provide relevant information based on the content that the user frequently posts on a particular social media platform. This allows for the efficient provision of relevant information by analyzing social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media activity data into AI, which can then provide relevant information.

[0056] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit can analyze past update data and select the optimal update algorithm. It can also identify effective update methods from past update data and optimize the algorithm accordingly. Furthermore, the update unit can dynamically adjust the update algorithm based on past update data. For example, it can analyze past update data and select the optimal update algorithm. This allows for the selection of the optimal update algorithm by referring to past update data. Specific criteria and methods for optimization include, for example, algorithm adjustments and parameter settings. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, which can then optimize the update algorithm.

[0057] The update unit can detect changes in the user's communication usage patterns in real time during updates and immediately update the suggestions. For example, the update unit can detect changes in the user's communication usage patterns in real time and immediately update the suggestions. The update unit can also dynamically update the optimal suggestions in response to changes in the user's communication usage patterns. Furthermore, the update unit can optimize suggestions in real time based on changes in the user's communication usage patterns. For example, the update unit can detect changes in the user's communication usage patterns in real time and immediately update the suggestions. This allows for immediate updates of suggestions by detecting changes in communication usage patterns in real time. Specific definitions and criteria for "real time" include, for example, data collection delay time and update frequency. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input changes in the user's communication usage patterns into the AI, which can detect them in real time and update the suggestions.

[0058] The update unit can weight the update data based on the collection timing of communication usage data during the update process. For example, the update unit can prioritize the most recent data during the update. Alternatively, the update unit can prioritize the most recent data while also referring to past data. Furthermore, the update unit can prioritize data collected during a specific period during the update. For example, the update unit can prioritize the most recent data during the update. This enables efficient updates by weighting the update data based on the collection timing of communication usage data. Specific criteria and methods for collection timing include, for example, periodic collection after a specific event. Some or all of the above processing in the update unit may be performed using AI, or without AI. For example, the update unit can input the collection timing of communication usage data into the AI, which can then weight the update data based on the collection timing.

[0059] The update unit can predict changes in the user's communication usage patterns and prepare future suggestions during updates. For example, the update unit can predict changes in the user's communication usage patterns and prepare future suggestions. Furthermore, the update unit can dynamically prepare future suggestions based on changes in the user's communication usage patterns. In addition, the update unit can optimize future suggestions based on changes in the user's communication usage patterns. For example, the update unit can predict changes in the user's communication usage patterns and prepare future suggestions. This allows for the preparation of future suggestions by predicting changes in communication usage patterns. Specific methods and criteria for prediction include, for example, trend analysis of historical data and machine learning models. Some or all of the above processing in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input changes in the user's communication usage patterns into AI, which can then predict and prepare future suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The personalized AI system can also include a health management unit that collects and analyzes user health data. This unit can collect health data such as the user's steps, heart rate, and sleep patterns. Furthermore, it can analyze the collected data to understand the user's health status. In addition, the health management unit can suggest optimal communication plans and promotions based on the user's health status. For example, if a user is not getting enough exercise, the health management unit can suggest promotions to encourage exercise. This enables personalized suggestions based on the user's health status, supporting their health management.

[0062] The personalized AI system can also include a purchase analysis unit that collects and analyzes the user's purchase history. For example, the purchase analysis unit can collect data on products and services the user has purchased in the past. Furthermore, the purchase analysis unit can analyze the collected purchase data to understand the user's purchasing trends. In addition, the purchase analysis unit can suggest optimal communication plans and promotions based on the user's purchasing trends. For example, if the purchase analysis unit frequently purchases products from a particular brand, it can suggest promotions related to that brand. This enables personalized suggestions based on the user's purchasing trends, improving the user's purchasing experience.

[0063] The personalized AI system may also include a location information analysis unit that collects and analyzes the user's location information. This unit can, for example, collect the user's current location and past travel history. It can also analyze the collected location data to understand the user's movement patterns. Furthermore, based on the user's movement patterns, the unit can suggest optimal communication plans and promotions. For example, if a user frequently visits a particular area, the unit can suggest promotions related to that area. This enables personalized suggestions based on the user's location information, improving user convenience.

[0064] The personalized AI system can also include a device analysis unit that collects and analyzes the user's device usage. For example, the device analysis unit can collect information such as the type and frequency of use of the device the user is using. Furthermore, the device analysis unit can analyze the collected device usage data to understand the user's device usage trends. In addition, based on the user's device usage trends, the device analysis unit can suggest optimal communication plans and promotions. For example, if a user frequently uses a smartphone, the device analysis unit can suggest smartphone-specific promotions. This enables personalized suggestions based on the user's device usage trends, improving user convenience.

[0065] A personalized AI system can also include a hobby analysis unit that collects and analyzes the user's hobbies and interests. For example, the hobby analysis unit can collect data on topics and hobbies that the user is interested in. Furthermore, it can analyze the collected hobby data to understand the user's interests and hobbies. In addition, the hobby analysis unit can suggest optimal communication plans and promotions based on the user's interests and hobbies. For example, if the user is interested in sports, the hobby analysis unit can suggest sports-related promotions. This enables personalized suggestions based on the user's hobbies and interests, thereby improving user satisfaction.

[0066] The personalized AI system can also include a social network analysis unit that collects and analyzes the user's social network data. For example, the social network analysis unit can collect data on accounts the user follows and groups they participate in. Furthermore, it can analyze the collected social network data to understand the user's activity on social networks. In addition, the social network analysis unit can suggest optimal communication plans and promotions based on the user's social network activity. For example, if a user frequently participates in a particular group, the social network analysis unit can suggest promotions related to that group. This enables personalized suggestions based on the user's social network activity, thereby improving user satisfaction.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects customer communication usage data and behavioral patterns. For example, it collects communication usage data such as call duration, data usage, and applications used in real time. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they use AI to analyze the collected data and understand each user's usage trends. Step 3: The proposal department proposes the optimal pricing plan and promotions based on the analysis results obtained by the analysis department. For example, it automatically proposes the most suitable pricing plan, additional data package, and promotions for each user. Step 4: The providing department provides the user with the content proposed by the proposing department. For example, it provides the user with the proposed content. Step 5: The update department periodically reanalyzes the data based on the content provided by the provision department and updates the recommendations. For example, they periodically reanalyze user data and update the recommendations to match changing usage patterns.

[0069] (Example of form 2) The personalized AI system according to an embodiment of the present invention is a system in which AI analyzes customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions to individual users. The personalized AI system collects customers' communication usage data and behavioral patterns, and the AI ​​analyzes them to propose optimal pricing plans and promotions for each user. As a result, users can receive optimal information and suggestions based on their usage trends, enabling them to optimize their communication costs and use customized services. This also leads to improved customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. For example, the personalized AI system collects customers' communication usage data and behavioral patterns. Specifically, it collects communication usage data such as call time, data traffic, and applications used in real time. Next, the AI ​​analyzes the collected data to understand each user's usage trends. For example, it analyzes the trends of users who frequently use specific applications such as video streaming or games. Next, based on the analysis results, the AI ​​automatically proposes optimal pricing plans, additional data packages, and promotions for each user. For example, it proposes discounts and additional services to users who frequently use video streaming. The AI ​​also periodically re-analyzes user data and updates its suggestions to match changing usage patterns. This ensures users always receive the optimal service. This system allows users to receive information and suggestions tailored to their usage patterns, enabling optimized communication costs and the use of customized services. For example, users who frequently use video streaming can reduce communication costs through discounts and additional services. Furthermore, it leads to improved customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. For instance, providing services tailored to individual needs can reduce customer churn and cancellation rates. Moreover, the proliferation of next-generation communication technologies is diversifying communication volume and usage patterns, increasing the need to propose optimal plans to users. Advances in AI technology are enabling real-time personalized suggestions, which is expected to improve the customer experience.For example, AI can analyze data in real time and suggest the optimal plan for each user, thereby increasing customer satisfaction. In this way, personalized AI systems dramatically improve the user's communication experience by leveraging customer data to provide optimal services tailored to individual needs. For telecommunications carriers, this leads to increased customer satisfaction and strengthened loyalty, enabling differentiation in a highly competitive market. Now is the perfect time to enter this field, against the backdrop of the spread of next-generation communication technologies and the evolution of AI technology. As a result, personalized AI systems can analyze customers' communication usage data and behavioral patterns to provide optimal pricing plans and promotions for each individual user.

[0070] The personalized AI system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, and an update unit. The collection unit collects customer communication usage data and behavioral patterns. The collection unit collects communication usage data in real time, such as call duration, data traffic, and applications used. For example, the collection unit can collect call duration in real time. The collection unit can also collect data traffic in real time. Furthermore, the collection unit can also collect application usage data in real time. For example, the collection unit can collect call duration, data traffic, and application usage data in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data using AI to understand each user's usage trends. For example, the analysis unit can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis unit can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis unit can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. The proposal department proposes the optimal pricing plan and promotions based on the analysis results obtained by the analysis department. The proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. The proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. Furthermore, the proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal department can automatically propose the optimal pricing plan, additional data package, and promotions for each user based on the analysis results. The delivery department provides the content proposed by the proposal department to the user. The delivery department provides the proposed content to the user.The provisioning unit can, for example, provide the proposed content to the user. The provisioning unit can also provide the proposed content to the user. Furthermore, the provisioning unit can also provide the proposed content to the user. For example, the provisioning unit can provide the proposed content to the user. The update unit periodically reanalyzes the data based on the content provided by the provisioning unit and updates the proposals. The update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. For example, the update unit can periodically reanalyze the user's data and update the proposals to match the changing usage patterns. Furthermore, the update unit can periodically reanalyze the user's data and update the proposals to match the changing usage patterns. Furthermore, the update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. For example, the update unit periodically reanalyzes the user's data and updates the proposals to match the changing usage patterns. As a result, the personalized AI system according to the embodiment can analyze customer communication usage data and behavioral patterns and provide optimal pricing plans and promotions to individual users.

[0071] The data collection unit collects customer communication usage data and behavioral patterns. Specifically, it collects communication usage data such as call duration, data traffic, and applications used in real time. For example, the data collection unit can collect call duration in real time. Call duration is collected by recording the start and end times of each call and calculating call frequency and average call duration. Data traffic is collected by monitoring the amount of data used for each application and service in real time and aggregating monthly and weekly data usage. Application usage data is collected by recording which applications users use and for how long, and analyzing application usage frequency and duration. This allows the data collection unit to understand users' communication usage patterns in detail and clarify the behavioral characteristics of individual users. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0072] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the collected data using AI to understand the usage trends of each user. The AI ​​uses machine learning algorithms to analyze users' communication usage data and behavioral patterns. For example, it uses clustering techniques to group users with similar usage patterns and extract the characteristics of each group. It also uses regression analysis to predict fluctuations in users' data usage and call time, and understand future usage trends. Furthermore, it can use natural language processing technology to analyze the types and content of applications used by users and identify their interests and preferences. This allows the analysis department to quickly and accurately analyze the collected data and understand the usage trends of each user in detail. In addition, the analysis department can utilize historical data and statistical information to conduct long-term usage trend analysis. For example, based on historical data, it can predict fluctuations in data usage during specific seasons or events and plan future countermeasures. It can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis department 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 security of the entire system.

[0073] The Proposal Department proposes optimal pricing plans and promotions based on the analysis results obtained by the Analysis Department. Specifically, it automatically proposes the most suitable pricing plans, additional data packages, and promotions for each user based on the analysis results. For example, it proposes a large data plan to users with high data usage and an unlimited call plan to users with long call times. It can also propose promotions and discounts related to specific applications to users who frequently use those applications. The Proposal Department uses AI to generate optimal suggestions based on user usage trends and past preferences. For example, it uses a recommendation system to analyze plans and promotions previously selected by users and makes similar suggestions to similar users. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Proposal Department to quickly and accurately propose the most suitable pricing plans and promotions to each user, thereby improving user satisfaction. Furthermore, the Proposal Department can continuously modify its suggestions based on real-time updated data to respond to the latest situations. For example, if a user's data usage or call time changes rapidly, the Proposal Department immediately incorporates the new data and updates its suggestions. This allows the proposal department to consistently provide highly accurate proposals based on the latest information, enabling them to respond quickly and appropriately to user needs.

[0074] The service provider delivers the proposed content to users. Specifically, it delivers the proposed content to users. For example, the service provider can deliver the proposed content to users. The service provider notifies users of the details of the best pricing plan and promotions, making it easy for users to select. For example, it can send a notification to the user's smartphone displaying the details of the proposed plan and promotion. The service provider also optimizes the interface so that users can easily select the proposed content. For example, it can enable one-click plan changes and promotion applications. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the proposed content. For example, after a user selects a proposed plan or promotion, the service provider monitors how that selection affects satisfaction and incorporates it into future proposals. This allows the service provider to deliver proposals quickly and reliably to users, improving user satisfaction. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to deliver proposals quickly and reliably to users, improving user satisfaction.

[0075] The update department periodically reanalyzes data based on the content provided by the service provider and updates its recommendations. Specifically, it periodically reanalyzes user data and updates recommendations to match changing usage patterns. For example, the update department can periodically reanalyze user data and update recommendations to match changing usage patterns. The update department uses AI to detect changes in user usage trends and behavioral patterns and revise recommendations accordingly. For example, if a user's data usage increases, the update department will suggest a larger data plan. Also, if a user starts using a new application frequently, it can suggest promotions related to that application. Furthermore, the update department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, after a user selects a suggested plan or promotion, it monitors how that selection affects satisfaction and incorporates this into future recommendations. This allows the update department to always provide highly accurate recommendations based on the latest information and respond quickly and appropriately to user needs. In addition, the update department can continuously revise its recommendations based on real-time updated data to respond to the latest situations. For example, if a user's data usage or call duration changes drastically, the update unit immediately incorporates the new data and updates the recommendations. This allows the update unit to always provide highly accurate recommendations based on the latest information, enabling it to respond quickly and appropriately to user needs.

[0076] The data collection unit can collect communication usage data such as call duration, data traffic, and applications used in real time. For example, the data collection unit can collect call duration in real time. It can also collect data traffic in real time. Furthermore, the data collection unit can collect application usage data in real time. For example, the data collection unit can collect call duration, data traffic, and application usage data in real time. This allows for analysis based on the latest data by collecting communication usage data in real time. The specific definition and criteria of real time include, for example, the delay time and update frequency of data collection. 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 collect call duration, data traffic, and application usage data in real time and analyze the data using AI.

[0077] The analysis department can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. Furthermore, the analysis department can analyze the collected data using AI to understand each user's usage trends. For example, the analysis department can analyze the collected data using AI to understand each user's usage trends. This allows for an accurate understanding of each user's usage trends by analyzing the data with AI. Specific AI technologies and algorithms include, for example, machine learning, deep learning, and natural language processing. Some or all of the above-described processes in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input the collected data into a generative AI, which can then analyze the data to understand each user's usage trends.

[0078] The proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. Furthermore, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. For example, the proposal unit can automatically suggest the most suitable pricing plan, additional data package, and promotions for each user based on the analysis results. This allows for the provision of the best possible service to users by making optimal suggestions based on the analysis results. Specific methods and criteria for automatic suggestions include, for example, the type of algorithm and the timing of the suggestion. Some or all of the above-described processes in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the analysis results into a generative AI, which can then suggest the most suitable pricing plan and promotions.

[0079] The service provider can provide the proposed content to the user. For example, the service provider can provide the proposed content to the user. Furthermore, the service provider can also provide the proposed content to the user. In addition, the service provider can also provide the proposed content to the user. For example, the service provider can provide the proposed content to the user. By providing the proposed content to the user, the user can receive the optimal service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the proposed content into AI, and the AI ​​can provide it to the user.

[0080] The update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. For example, the update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. Furthermore, the update unit can periodically reanalyze user data and update suggestions to match changing usage patterns. This allows for the continuous provision of optimal service by periodically reanalyzing data and updating suggestions. Specific frequencies and timings of "periodically" include, for example, monthly, weekly, or after specific events. Some or all of the above-described processes in the update unit may be performed using AI, or not. For example, the update unit can input user data into AI, which can then reanalyze the data and update suggestions.

[0081] 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 delay the data collection timing to reduce the user's burden. Conversely, if the user is relaxed, the data collection unit can advance the data collection timing to collect data in real time. Furthermore, if the user is in a hurry, the data collection unit can optimize the data collection timing to collect the necessary data in a short time. For example, if the user is stressed, the data collection unit can delay the data collection timing to reduce the user's burden. This reduces the user's burden by adjusting the data collection timing based on 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 without AI. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and adjust the data collection timing.

[0082] The data collection unit can analyze the user's past communication usage history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. Furthermore, the data collection unit can identify periods of high data traffic from the user's past communication usage history and collect data during those periods. In addition, the data collection unit can analyze the user's past communication usage history and select the most efficient data collection method. For example, the data collection unit can prioritize collecting data from apps that the user has frequently used in the past. This allows for the selection of the optimal data collection method by analyzing past communication usage history. Specific criteria and methods for the optimal data collection method include, for example, data type, collection frequency, and collection means. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's past communication usage history into AI, which can then select the optimal data collection method.

[0083] The data collection unit can filter data based on the user's current communication environment and device when collecting communication usage data. For example, if the user is connected to Wi-Fi, the data collection unit can prioritize the collection of large amounts of data. Furthermore, if the user is using mobile data, the data collection unit can select a collection method that minimizes data usage. In addition, the data collection unit can adjust the amount of data collected according to the battery level of the user's device. For example, if the user is connected to Wi-Fi, the data collection unit can prioritize the collection of large amounts of data. This enables efficient data collection by filtering based on the current communication environment and device. Specific criteria and methods for filtering include, for example, the type of device and the conditions of the communication environment. 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 data on the user's current communication environment and device into the AI, which can then perform the filtering.

[0084] 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 can postpone the collection of less important data. Conversely, if the user is relaxed, the data collection unit can collect all data equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. This allows for the priority collection of important data by determining data priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 using AI. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and determine the data priority.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication usage data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of data based on their home usage patterns. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data when collecting communication usage data. For example, the data collection unit can collect relevant data based on the content a user frequently posts on a particular social media platform. It can also collect relevant data based on the activity of accounts a user follows on social media. Furthermore, the data collection unit can collect relevant data based on groups and events a user participates in on social media. For example, the data collection unit can collect relevant data based on the content a user frequently posts on a particular social media platform. This allows for the efficient collection of relevant data by analyzing social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0087] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. By adjusting the presentation of the analysis based on the user's emotions, the analysis results can be made easier 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 not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust the presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of its analysis based on the importance of the communication usage data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit can perform a detailed analysis on data with high importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the communication usage data. Specific evaluation criteria and methods for importance include, for example, data frequency and impact. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the communication usage data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0089] The analysis unit can apply different analysis algorithms depending on the category of communication usage data during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes viewing time and viewing frequency to video streaming data. Similarly, it can apply an analysis algorithm that emphasizes call duration and call frequency to call data. Furthermore, it can apply an analysis algorithm that emphasizes data usage and usage patterns to data communication volume. For example, the analysis unit can apply an analysis algorithm that emphasizes viewing time and viewing frequency to video streaming data. By applying different analysis algorithms depending on the category of communication usage data, more accurate analysis becomes possible. Specific types and classification methods of categories include, for example, data communication, calls, and app usage. 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 categories of communication usage data into the AI, and the AI ​​can apply different analysis algorithms depending on the category.

[0090] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. By adjusting the length of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. 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 not using AI. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the collection timing of communication usage data. For example, the analysis unit can prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the most recent data while also referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of the most recent data. This enables efficient analysis by determining the priority of analysis based on the collection timing of communication usage data. Specific criteria and methods for collection timing include, for example, regular collection after a specific event. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collection timing of communication usage data into AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0092] The analysis unit can adjust the order of analysis based on the relevance of communication usage data during the analysis process. 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 dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of communication usage data. Specific evaluation criteria and methods for relevance include, for example, data co-occurrence frequency and correlation. 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 relevance of communication usage data into AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions in a format that can be quickly understood. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. By adjusting the way suggestions are presented based on the user's emotions, suggestions 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotion and adjust the way suggestions are presented.

[0094] The proposal department can adjust the level of detail in its proposals based on the importance of the pricing plans and promotions. For example, it can provide detailed proposals for high-priority pricing plans. It can also provide simplified proposals for low-priority promotions. Furthermore, it can provide proposals with a moderate level of detail for medium-priority pricing plans. For example, the proposal department can provide detailed proposals for high-priority pricing plans. By adjusting the level of detail in proposals based on the importance of pricing plans and promotions, it is possible to provide the user with the most suitable proposal. Specific evaluation criteria and methods for importance include, for example, data frequency and impact. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of pricing plans and promotions into the AI, which can then adjust the level of detail in the proposals based on importance.

[0095] The proposal unit can apply different proposal algorithms depending on the category of the pricing plan or promotion. For example, for pricing plans based on data usage, the proposal unit can apply a proposal algorithm that prioritizes data usage. Similarly, for pricing plans based on call duration, the proposal unit can apply a proposal algorithm that prioritizes call duration. Furthermore, for promotions based on specific app usage, the proposal unit can apply a proposal algorithm that prioritizes the frequency of use of that app. For example, for pricing plans based on data usage, the proposal unit can apply a proposal algorithm that prioritizes data usage. By applying different proposal algorithms depending on the category of the pricing plan or promotion, more accurate proposals become possible. Specific categories and classification methods include, for example, data communication, calls, and app usage. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the categories of pricing plans and promotions into the AI, which can then apply different proposal algorithms depending on the category.

[0096] 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 relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions in a format that can be quickly understood. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. This allows the suggestion unit to provide the most suitable suggestions for the user by adjusting the length of the suggestions based on 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the suggestions.

[0097] The proposal department can prioritize proposals based on the timing of the delivery of pricing plans and promotions. For example, the proposal department can prioritize proposals for pricing plans that are about to be delivered. It can also postpone proposals that are far away. Furthermore, the proposal department can dynamically adjust the priority of proposals according to the delivery timing. For example, it can prioritize proposals for pricing plans that are about to be delivered. This enables efficient proposals by prioritizing proposals based on the timing of the delivery of pricing plans and promotions. Specific criteria and methods for delivery timing include, for example, delivery after a specific event or periodic delivery. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the delivery timing of pricing plans and promotions into AI, and the AI ​​can determine the priority of proposals based on the delivery timing.

[0098] The proposal department can adjust the order of proposals based on the relevance of pricing plans and promotions during the proposal process. For example, the proposal department can prioritize proposing highly relevant pricing plans. It can also postpone less relevant promotions. Furthermore, the proposal department can dynamically adjust the order of proposals according to the relevance of pricing plans and promotions. For example, the proposal department can prioritize proposing highly relevant pricing plans. This allows for efficient proposals by adjusting the order of proposals based on the relevance of pricing plans and promotions. Specific criteria and methods for evaluating relevance include, for example, data co-occurrence frequency and correlation. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input the relevance of pricing plans and promotions into AI, which can then adjust the order of proposals based on that relevance.

[0099] The service provider can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is stressed, the service provider can provide concise and to-the-point information. Furthermore, if the user is in a hurry, the service provider can provide information in a format that can be quickly understood. For example, if the service provider is relaxed, the service provider can provide detailed information. By adjusting the delivery method based on the user's emotions, information that is 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust the delivery method.

[0100] The service provider can select the optimal service delivery method by referring to the user's past communication usage history at the time of delivery. For example, the service provider can prioritize the delivery method the user has used in the past. Furthermore, the service provider can select the most effective delivery method based on the user's past communication usage history. In addition, the service provider can dynamically select the optimal delivery method by analyzing the user's past communication usage history. For example, the service provider can prioritize the delivery method the user has used in the past. This allows the service provider to select the optimal delivery method by referring to past communication usage history. Specific criteria and methods for the optimal delivery method include, for example, notification methods and delivery timing. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past communication usage history into AI, which can then select the optimal delivery method.

[0101] The service provider can customize the delivery method based on the user's current communication environment at the time of delivery. For example, if the user is connected to Wi-Fi, the service provider can provide a large amount of data. Furthermore, if the user is using mobile data, the service provider can select a delivery method that minimizes data usage. In addition, the service provider can adjust the amount of data provided according to the battery level of the user's device. For example, if the user is connected to Wi-Fi, the service provider can provide a large amount of data. This allows for efficient information delivery by customizing the delivery method based on the current communication environment. Specific conditions and evaluation criteria for the current communication environment include, for example, communication speed and connection status. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's current communication environment data into the AI, which can then customize the delivery method.

[0102] The service provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the service provider can postpone the delivery of less important information. Conversely, if the user is relaxed, the service provider can deliver all information equally. Furthermore, if the user is in a hurry, the service provider can prioritize the delivery of highly important information. For example, if the service provider is stressed, the service provider can postpone the delivery of less important information. This allows for the priority delivery of important information by determining the priority of information delivery based on 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into an AI, which can estimate the emotions and determine the priority of information delivery.

[0103] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing information related to that region. Similarly, if the user is traveling, the service provider can prioritize providing information related to their travel destination. Furthermore, if the user is at home, the service provider can prioritize providing information based on their home usage patterns. For example, if the service provider is in a specific region, the service provider can prioritize providing information related to that region. This allows the service provider to select the optimal delivery method by considering geographical location information. Specific types and methods of acquiring geographical location information include, for example, GPS data and Wi-Fi location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or without AI. For example, the service provider can input the user's geographical location information into AI, which can then select the optimal delivery method.

[0104] The service provider can analyze the user's social media activity and propose a means of providing information at the time of delivery. For example, the service provider can provide relevant information based on the content that the user frequently posts on a particular social media platform. It can also provide relevant information based on the activity of accounts that the user follows on social media. Furthermore, it can provide relevant information based on groups and events that the user participates in on social media. For example, the service provider can provide relevant information based on the content that the user frequently posts on a particular social media platform. This allows for the efficient provision of relevant information by analyzing social media activity. Specific types of social media activity and methods of analysis include, for example, post content, number of likes, and number of followers. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's social media activity data into AI, which can then provide relevant information.

[0105] The update unit can estimate the user's emotions and select update data based on the estimated emotions. For example, if the user is relaxed, the update unit can provide detailed update data. If the user is stressed, the update unit can provide concise and to-the-point update data. Furthermore, if the user is in a hurry, the update unit can provide update data in a format that can be quickly understood. For example, if the user is relaxed, the update unit can provide detailed update data. This allows the system to provide the most suitable update data for the user by selecting update data based on their 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 update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into an AI, which can estimate the emotions and select update data.

[0106] The update unit can optimize the update algorithm by referring to past update data during the update process. For example, the update unit can analyze past update data and select the optimal update algorithm. It can also identify effective update methods from past update data and optimize the algorithm accordingly. Furthermore, the update unit can dynamically adjust the update algorithm based on past update data. For example, it can analyze past update data and select the optimal update algorithm. This allows for the selection of the optimal update algorithm by referring to past update data. Specific criteria and methods for optimization include, for example, algorithm adjustments and parameter settings. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input past update data into AI, which can then optimize the update algorithm.

[0107] The update unit can detect changes in the user's communication usage patterns in real time during updates and immediately update the suggestions. For example, the update unit can detect changes in the user's communication usage patterns in real time and immediately update the suggestions. The update unit can also dynamically update the optimal suggestions in response to changes in the user's communication usage patterns. Furthermore, the update unit can optimize suggestions in real time based on changes in the user's communication usage patterns. For example, the update unit can detect changes in the user's communication usage patterns in real time and immediately update the suggestions. This allows for immediate updates of suggestions by detecting changes in communication usage patterns in real time. Specific definitions and criteria for "real time" include, for example, data collection delay time and update frequency. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input changes in the user's communication usage patterns into the AI, which can detect them in real time and update the suggestions.

[0108] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated emotions. For example, if the user is relaxed, the update unit can update frequently. Conversely, if the user is stressed, the update unit can reduce the update frequency. Furthermore, if the user is in a hurry, the update unit can quickly update only the necessary information. For example, if the update unit is relaxed, it can update frequently. This allows for updates at an optimal frequency for the user by adjusting the update frequency based on their 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 update unit may be performed using AI, for example, or without AI. For example, the update unit can input user emotion data into an AI, which can estimate the emotions and adjust the update frequency.

[0109] The update unit can weight the update data based on the collection timing of communication usage data during the update process. For example, the update unit can prioritize the most recent data during the update. Alternatively, the update unit can prioritize the most recent data while also referring to past data. Furthermore, the update unit can prioritize data collected during a specific period during the update. For example, the update unit can prioritize the most recent data during the update. This enables efficient updates by weighting the update data based on the collection timing of communication usage data. Specific criteria and methods for collection timing include, for example, periodic collection after a specific event. Some or all of the above processing in the update unit may be performed using AI, or without AI. For example, the update unit can input the collection timing of communication usage data into the AI, which can then weight the update data based on the collection timing.

[0110] The update unit can predict changes in the user's communication usage patterns and prepare future suggestions during updates. For example, the update unit can predict changes in the user's communication usage patterns and prepare future suggestions. Furthermore, the update unit can dynamically prepare future suggestions based on changes in the user's communication usage patterns. In addition, the update unit can optimize future suggestions based on changes in the user's communication usage patterns. For example, the update unit can predict changes in the user's communication usage patterns and prepare future suggestions. This allows for the preparation of future suggestions by predicting changes in communication usage patterns. Specific methods and criteria for prediction include, for example, trend analysis of historical data and machine learning models. Some or all of the above processing in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input changes in the user's communication usage patterns into AI, which can then predict and prepare future suggestions.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The personalized AI system can also include a health management unit that collects and analyzes user health data. This unit can collect health data such as the user's steps, heart rate, and sleep patterns. Furthermore, it can analyze the collected data to understand the user's health status. In addition, the health management unit can suggest optimal communication plans and promotions based on the user's health status. For example, if a user is not getting enough exercise, the health management unit can suggest promotions to encourage exercise. This enables personalized suggestions based on the user's health status, supporting their health management.

[0113] The personalized AI system can also include an emotion analysis unit that estimates the user's emotions and suggests communication plans and promotions based on those estimated emotions. For example, the emotion analysis unit can estimate emotions from the user's social media posts and messages. Furthermore, based on the estimated emotions, the emotion analysis unit can suggest the most suitable communication plans and promotions to the user. In addition, the emotion analysis unit can dynamically update its suggestions in response to changes in the user's emotions. For example, if the user is feeling stressed, the emotion analysis unit can suggest promotions that provide relaxing content. This enables personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0114] The personalized AI system can also include a purchase analysis unit that collects and analyzes the user's purchase history. For example, the purchase analysis unit can collect data on products and services the user has purchased in the past. Furthermore, the purchase analysis unit can analyze the collected purchase data to understand the user's purchasing trends. In addition, the purchase analysis unit can suggest optimal communication plans and promotions based on the user's purchasing trends. For example, if the purchase analysis unit frequently purchases products from a particular brand, it can suggest promotions related to that brand. This enables personalized suggestions based on the user's purchasing trends, improving the user's purchasing experience.

[0115] The personalized AI system may also include a location information analysis unit that collects and analyzes the user's location information. This unit can, for example, collect the user's current location and past travel history. It can also analyze the collected location data to understand the user's movement patterns. Furthermore, based on the user's movement patterns, the unit can suggest optimal communication plans and promotions. For example, if a user frequently visits a particular area, the unit can suggest promotions related to that area. This enables personalized suggestions based on the user's location information, improving user convenience.

[0116] The personalized AI system can also include an emotion estimation unit that estimates the user's emotions and suggests communication plans and promotions based on those estimated emotions. The emotion estimation unit can, for example, estimate emotions from the user's voice tone and facial expressions. Furthermore, based on the estimated emotions, the emotion estimation unit can suggest the most suitable communication plans and promotions to the user. In addition, the emotion estimation unit can dynamically update its suggestions in response to changes in the user's emotions. For example, if the user is happy, the emotion estimation unit can suggest entertainment-related promotions. This enables personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0117] The personalized AI system can also include a device analysis unit that collects and analyzes the user's device usage. For example, the device analysis unit can collect information such as the type and frequency of use of the device the user is using. Furthermore, the device analysis unit can analyze the collected device usage data to understand the user's device usage trends. In addition, based on the user's device usage trends, the device analysis unit can suggest optimal communication plans and promotions. For example, if a user frequently uses a smartphone, the device analysis unit can suggest smartphone-specific promotions. This enables personalized suggestions based on the user's device usage trends, improving user convenience.

[0118] The personalized AI system can also include an emotional feedback unit that estimates the user's emotions and proposes communication plans and promotions based on those estimated emotions. The emotional feedback unit can estimate emotions from, for example, user feedback or survey results. Furthermore, based on the estimated emotions, the emotional feedback unit can propose the most suitable communication plans and promotions to the user. In addition, the emotional feedback unit can dynamically update its suggestions in response to changes in the user's emotions. For example, if the user is dissatisfied, the emotional feedback unit can offer promotions that suggest improvements. This enables personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0119] A personalized AI system can also include a hobby analysis unit that collects and analyzes the user's hobbies and interests. For example, the hobby analysis unit can collect data on topics and hobbies that the user is interested in. Furthermore, it can analyze the collected hobby data to understand the user's interests and hobbies. In addition, the hobby analysis unit can suggest optimal communication plans and promotions based on the user's interests and hobbies. For example, if the user is interested in sports, the hobby analysis unit can suggest sports-related promotions. This enables personalized suggestions based on the user's hobbies and interests, thereby improving user satisfaction.

[0120] The personalized AI system can also include an emotion monitoring unit that estimates the user's emotions and suggests communication plans and promotions based on those estimated emotions. The emotion monitoring unit can, for example, estimate emotions using data from the user's biosensors. Furthermore, based on the estimated emotions, the emotion monitoring unit can suggest the most suitable communication plans and promotions to the user. In addition, the emotion monitoring unit can dynamically update its suggestions in response to changes in the user's emotions. For example, if the user is tired, the emotion monitoring unit can suggest promotions that provide relaxing content. This enables personalized suggestions based on the user's emotions, thereby improving user satisfaction.

[0121] The personalized AI system can also include a social network analysis unit that collects and analyzes the user's social network data. For example, the social network analysis unit can collect data on accounts the user follows and groups they participate in. Furthermore, it can analyze the collected social network data to understand the user's activity on social networks. In addition, the social network analysis unit can suggest optimal communication plans and promotions based on the user's social network activity. For example, if a user frequently participates in a particular group, the social network analysis unit can suggest promotions related to that group. This enables personalized suggestions based on the user's social network activity, thereby improving user satisfaction.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects customer communication usage data and behavioral patterns. For example, it collects communication usage data such as call duration, data usage, and applications used in real time. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they use AI to analyze the collected data and understand each user's usage trends. Step 3: The proposal department proposes the optimal pricing plan and promotions based on the analysis results obtained by the analysis department. For example, it automatically proposes the most suitable pricing plan, additional data package, and promotions for each user. Step 4: The providing department provides the user with the content proposed by the proposing department. For example, it provides the user with the proposed content. Step 5: The update department periodically reanalyzes the data based on the content provided by the provision department and updates the recommendations. For example, they periodically reanalyze user data and update the recommendations to match changing usage patterns.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects communication usage data using the camera 42 and communication I / F 44 of the smart device 14 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates optimal pricing plans and promotions based on the analysis results. The provision unit provides the proposed content to the user through the output device 40 of the smart device 14. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and periodically reanalyzes the data and updates the proposals. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects communication usage data using the camera 42 and communication I / F 44 of the smart glasses 214 and manages the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and generates optimal pricing plans and promotions based on the analysis results. The provision unit provides the proposal content to the user, for example, through the speaker 240 of the smart glasses 214. The update unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and periodically reanalyzes the data and updates the proposals. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects communication usage data using the camera 42 and communication I / F 44 of the headset terminal 314 and manages the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates optimal pricing plans and promotions based on the analysis results. The provision unit provides the proposed content to the user through the display 343 of the headset terminal 314. The update unit is implemented in the specific processing unit 290 of the data processing unit 12 and periodically reanalyzes the data and updates the proposals. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and update unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects communication usage data using the camera 42 and communication I / F 44 of the robot 414 and manages the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates optimal pricing plans and promotions based on the analysis results. The provision unit provides the proposal content to the user, for example, through the speaker 240 of the robot 414. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and periodically reanalyzes the data and updates the proposals. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A data collection unit that collects customer communication usage data and behavioral patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable pricing plan and promotions. A provisioning unit that provides the content proposed by the proposal unit to the user, The system includes an update unit that periodically reanalyzes the data based on the content provided by the aforementioned provision unit and updates the proposals accordingly. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects communication usage data in real time, such as call duration, data usage, and apps used. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed using AI to understand each user's usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, the system automatically suggests the most suitable pricing plan, additional data packages, and promotions for each user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the proposed content to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is, We regularly reanalyze user data and update our suggestions to match changing usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts 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 communication usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting communication usage data, filtering is performed based on the user's current communication environment and device. 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 When collecting communication usage data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting communication usage data, 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 is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the communication usage data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of communication usage data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is 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 is During analysis, the priority of the analysis is determined based on when the communication usage data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relevance of communication usage data. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the pricing plan and the importance of the promotion. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the pricing plan and promotion category. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned proposal section is, When making proposals, prioritize them based on pricing plans and promotional timing. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of pricing plans and promotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past communication usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the method of delivery will be customized based on the user's current communication environment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is, The system estimates the user's emotions and selects update data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is, During updates, the update algorithm is optimized by referring to past update data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is, During updates, changes in the user's communication usage patterns are detected in real time, and suggestions are updated immediately. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is, It estimates user sentiment and adjusts the update frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is, During updates, update data is weighted based on the timing of data collection for communication usage. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is, During updates, we predict changes in users' communication usage patterns and prepare future recommendations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 data collection unit that collects customer communication usage data and behavioral patterns, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the most suitable pricing plan and promotions. A provisioning unit that provides the content proposed by the proposal unit to the user, The system includes an update unit that periodically reanalyzes the data based on the content provided by the aforementioned provision unit and updates the proposals accordingly. A system characterized by the following features.

2. The aforementioned collection unit is It collects communication usage data in real time, such as call duration, data usage, and apps used. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed using AI to understand each user's usage trends. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, the system automatically suggests the most suitable pricing plan, additional data packages, and promotions for each user. The system according to feature 1.

5. The aforementioned supply unit is, Provide the proposed content to the user. The system according to feature 1.

6. The aforementioned update unit is, We regularly reanalyze user data and update our suggestions to match changing usage patterns. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts 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 communication usage history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting communication usage data, filtering is performed based on the user's current communication environment and device. The system according to feature 1.

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 according to feature 1.

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