system

Abstract

The system according to this embodiment aims to provide an optimal communication plan based on the user's communication patterns and needs. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a change proposal unit. The collection unit collects data on the user's past communication history, application usage, and lifestyle. The analysis unit analyzes the data collected by the collection unit to identify the user's communication patterns and needs. The proposal unit proposes an optimal communication plan based on the needs identified by the analysis unit. The change proposal unit dynamically proposes a plan change if a change in the communication pattern occurs during use.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the selection of a user's communication plan is complicated and it is difficult to find a plan optimal for individual needs.

[0005] The system according to the embodiment aims to provide an optimal communication plan based on a user's communication pattern and needs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a change proposal unit. The data collection unit collects data on the user's past communication history, application usage, and lifestyle. The analysis unit analyzes the data collected by the data collection unit to identify the user's communication patterns and needs. The proposal unit proposes an optimal communication plan based on the needs identified by the analysis unit. The change proposal unit dynamically proposes a plan change if a change in communication patterns occurs during use. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal communication plan based on the user's communication patterns and needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 communication plan suggestion system according to an embodiment of the present invention is a new service for solving the challenges individual users face in selecting communication plans. This communication plan suggestion system utilizes generative AI to analyze data on the user's past communication history, app usage, and lifestyle, and provides an optimal communication plan tailored to individual needs. Furthermore, even if communication patterns change during use, the AI ​​dynamically suggests plan changes, ensuring that the user always maintains the optimal plan. This service maximizes the use of robust communication infrastructure and customer data to provide users with simple and effective communication plan selection, thereby improving customer satisfaction, reducing churn rates, promoting migration to high-profit plans, and contributing to increased revenue. For example, it collects data on the user's past communication history, app usage, and lifestyle. For instance, it collects detailed data such as which apps the user uses, how often, and call and data usage. This data is input into the generative AI. Next, the generative AI analyzes the collected data to identify the user's communication patterns and needs. For example, if a user frequently uses video streaming, the AI ​​determines that a plan with high data usage is suitable for that user. In this way, the generative AI suggests the optimal communication plan to the user. Furthermore, if communication patterns change during use, the generating AI dynamically suggests plan changes. For example, if a user starts frequently using a new app, the AI ​​will suggest a plan change based on the data usage of that app. This ensures that users always have access to the optimal communication plan. This service leverages robust communication infrastructure and customer data to provide users with a simple and effective communication plan selection. For instance, it utilizes communication infrastructure to provide a high-quality communication environment and customer data to suggest plans tailored to user needs. This improves customer satisfaction, reduces churn rates, and promotes migration to high-profit plans, contributing to increased revenue. This system allows users to easily select the communication plan best suited to them, reducing wasted communication costs. It also flexibly adapts to changes in communication patterns, ensuring users always have access to the optimal plan.For example, if a user starts frequently using a new app, the service can suggest changing their plan based on the data usage of that app, thereby reducing wasted communication costs. This service not only provides users with a simple and effective communication plan selection, but also contributes to increased revenue for telecommunications carriers. For instance, improved customer satisfaction reduces churn rates, and the shift to higher-profit plans is encouraged, leading to increased revenue. In this way, a service that benefits both users and telecommunications carriers can be provided. As a result, the communication plan suggestion system can provide the optimal communication plan based on the user's communication history and usage, and can flexibly adapt to changes in communication patterns.

[0029] The communication plan proposal system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a change proposal unit. The collection unit collects data on the user's past communication history, app usage, and lifestyle. For example, the collection unit collects detailed data such as which apps the user uses and how often, and the amount of calls and data communication used. For example, the collection unit records the types and frequency of apps used by the user. The collection unit can also record the user's call history and data communication volume. Furthermore, the collection unit can also collect data on the user's lifestyle. For example, it collects data on the user's hobbies, interests, and daily behavior patterns. The analysis unit analyzes the data collected by the collection unit to identify the user's communication patterns and needs. For example, the analysis unit uses a generative AI to analyze the collected data. The generative AI analyzes the user's communication history and app usage to identify the user's communication patterns. The analysis unit can also use the generative AI to identify needs based on the user's lifestyle. For example, the generative AI proposes an optimal communication plan based on the user's hobbies and interests. The proposal unit proposes the optimal communication plan based on the needs identified by the analysis unit. The proposal unit proposes the optimal communication plan to the user, for example, using a generative AI. The generative AI selects the optimal communication plan based on the user's communication patterns and needs, for example. The proposal unit can also use the generative AI to propose plan changes to the user. For example, if a user starts using a new app frequently, the proposal unit will propose a plan change according to the data usage of that app. The change proposal unit dynamically proposes plan changes when changes in communication patterns occur during use. The change proposal unit detects changes in the user's communication patterns and proposes the optimal plan change, for example. The change proposal unit detects changes in the user's communication patterns in real time, for example, using AI. The change proposal unit can also use AI to propose plan changes in response to changes in the user's communication patterns. As a result, the communication plan proposal system according to the embodiment provides the optimal communication plan based on the user's communication history and usage status, and can flexibly respond to changes in communication patterns.

[0030] The data collection unit collects data on users' past communication history, app usage, and lifestyle. Specifically, it collects detailed data such as which apps users use, how often they use them, and their call and data usage. For example, to record the types and frequency of apps used by users, it analyzes smartphone app usage logs and records the launch time and usage time of each app in detail. The data collection unit can also record users' call history and data usage. This includes the number of calls made and received, call duration, and data usage. Furthermore, the data collection unit can collect data on users' lifestyles. For example, to collect data on users' hobbies, interests, and daily behavior patterns, it collects information such as events users attend, places they visit, newsletters they subscribe to, and social media accounts they follow. This allows the data collection unit to comprehensively collect diverse user behavior data and gain a detailed understanding of users' communication patterns and lifestyles. In addition, the data collection unit centrally manages this data and makes it accessible to the analysis and proposal units. For example, the collected data is stored on a cloud server, allowing the analysis and proposal units to access it in real time. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to identify user communication patterns and needs. This analysis uses generative AI to process data in real time and identify user communication patterns. Specifically, the generative AI analyzes user communication history and app usage to identify which apps users use most frequently at what times, call frequency, and peak data communication times. The generative AI can also identify needs based on user lifestyles. For example, it analyzes the content of websites frequently accessed and newsletters subscribed to by users to suggest optimal communication plans based on their hobbies and interests. Furthermore, the analysis unit can utilize historical data and statistics to predict long-term fluctuations and trends in communication patterns. For instance, it can predict fluctuations in communication volume during specific seasons or events based on past communication data, optimizing future communication plans. The analysis unit can also use anomaly detection algorithms to detect unusual communication patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also manage long-term communication patterns and detect anomalies, improving the overall reliability and security of the system.

[0032] The proposal department proposes the optimal communication plan based on the needs identified by the analysis department. Specifically, it uses generative AI to propose the best communication plan for the user. The generative AI compares multiple communication plans to select the optimal plan based on the user's communication patterns and needs, and selects the plan that best suits the user's usage. For example, if a user uses a lot of data, it will propose a plan with a large data allowance, and if a user makes a lot of calls, it will propose a plan with a large call time. The proposal department can also use generative AI to suggest plan changes to users. For example, if a user starts using a new app frequently, it will suggest changing the plan according to the data usage of that app. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can monitor user satisfaction with the proposed plan and actual usage, and reflect this in future suggestions. The proposal department can also simulate multiple communication plans and propose the plan that offers the best cost performance for the user. In this way, the proposal department can provide users with the optimal communication plan, helping them reduce communication costs and improve usage efficiency.

[0033] The Change Proposal Department dynamically proposes plan changes when communication patterns change during use. Specifically, it uses AI to detect changes in the user's communication patterns in real time and propose the optimal plan change. For example, if a user starts using a new app frequently, it will propose a plan change based on the data usage of that app. It can also propose the optimal plan for a period when a user's communication patterns temporarily change due to travel or business trips. Furthermore, the Change Proposal Department continuously monitors changes in the user's communication patterns and proposes plan changes as needed. For example, if a user's data usage increases sharply, it will propose a change to a plan with higher data usage, and if call time increases, it will propose a change to a plan with higher call time. The Change Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it monitors user satisfaction with proposed plan changes and actual usage, and reflects this in future proposals. This allows the Change Proposal Department to flexibly respond to changes in the user's communication patterns and provide the optimal communication plan. In addition, the Change Proposal Department can also propose long-term plan changes in response to changes in the user's lifestyle and communication needs. This allows the change proposal department to provide users with the most suitable plan for their communication needs, helping them reduce communication costs and improve utilization efficiency.

[0034] The data collection unit can collect detailed data such as which apps a user uses and how often, as well as call and data usage. For example, the data collection unit can record which apps a user uses and how often. It can also record the user's call history and data usage. Furthermore, the data collection unit can collect data related to the user's lifestyle. For example, it can collect data on the user's hobbies, interests, and daily behavior patterns. This allows for a detailed understanding of the user's usage and enables more accurate analysis. Detailed data includes, but is not limited to, app usage frequency, call duration, and data usage. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's app usage data into AI, which can analyze the data to understand detailed usage.

[0035] The analysis unit can analyze data collected by the generative AI to identify user communication patterns and needs. For example, the analysis unit uses the generative AI to analyze the collected data. The generative AI, for example, analyzes the user's communication history and app usage to identify the user's communication patterns. The analysis unit can also use the generative AI to identify needs based on the user's lifestyle. For example, the generative AI proposes an optimal communication plan based on the user's hobbies and interests. In this way, the use of the generative AI allows for highly accurate identification of user communication patterns and needs. The generative AI includes, but is not limited to, natural language processing, image recognition, and predictive models. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit inputs the user's communication history data into the generative AI, which then analyzes the data to identify communication patterns and needs.

[0036] The proposal unit can propose the optimal communication plan to the user using generative AI. For example, the proposal unit uses generative AI to propose the optimal communication plan to the user. The generative AI selects the optimal communication plan based on the user's communication patterns and needs, for example. The proposal unit can also use generative AI to suggest plan changes to the user. For example, if a user starts using a new app frequently, the proposal unit can suggest changing the plan according to the data usage of that app. In this way, the optimal communication plan can be proposed to the user using generative AI. The optimal communication plan includes, but is not limited to, price plans, data usage, and call time. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's communication pattern data into the generative AI, and the generative AI analyzes the data and proposes the optimal communication plan.

[0037] The change suggestion unit can suggest a plan change based on the data usage of a new app if the user starts using that app frequently. For example, if the user starts using a new app frequently, the change suggestion unit can analyze the data usage of that app and suggest the optimal plan change. The change suggestion unit can also detect changes in the user's communication patterns and suggest the optimal plan change. For example, if the user starts using a new app frequently, it can suggest a plan change based on the data usage of that app. This allows for the suggestion of the optimal plan change in response to changes in the user's usage. Examples of new apps include, but are not limited to, the release date and the user's installation date. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit can input the data usage of the user's new app into the AI, which can then analyze the data and suggest the optimal plan change.

[0038] The proposal department can provide a high-quality communication environment by utilizing communication infrastructure and propose plans tailored to user needs by utilizing customer data. For example, the proposal department can provide a high-quality communication environment by utilizing communication infrastructure. Communication infrastructure includes, but is not limited to, high-speed internet connection, extensive coverage, and stable connection. The proposal department can also propose plans tailored to user needs by utilizing customer data. Customer data includes, but is not limited to, past communication history, app usage, and lifestyle data. By providing a high-quality communication environment and plans tailored to user needs, customer satisfaction is improved. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs customer data into the generative AI, which analyzes the data and proposes the optimal communication plan.

[0039] The data collection unit can analyze the user's past communication history and select the optimal data collection method. For example, the data collection unit can analyze the user's past communication 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. The data collection unit can also adjust the frequency of data collection based on the user's past communication volume. Furthermore, the data collection unit can analyze the user's past communication patterns and determine the optimal data collection timing. This enables efficient data collection by selecting the optimal data collection method based on past communication history. The optimal data collection method includes, but is not limited to, the frequency of data collection, the means of collection, and the timing of collection. 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 the user's past communication history data into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is health-conscious, the data collection unit can prioritize collecting data from health-related apps. Similarly, if the user enjoys traveling, the data collection unit can prioritize collecting data from travel-related apps. Furthermore, if the user is a business professional, the data collection unit can prioritize collecting data from business-related apps. This allows for the collection of more relevant data by filtering data based on the user's lifestyle and areas of interest. Lifestyle includes, but is not limited to, daily behavioral patterns, hobbies, and interests. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's lifestyle data into an AI, which then analyzes and filters the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes 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. In addition, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of more relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location services, and the user's current location. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's geographical location information data into AI, and the AI ​​analyzes the data and prioritizes the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on the content a user frequently posts on social media. The data collection unit can also collect relevant data based on the activity of accounts a user follows. Furthermore, the data collection unit can collect relevant data based on the activity of groups and communities a user participates in. This makes it possible to collect data based on user interests by analyzing social media activity. Social media activity includes, but is not limited to, posts, followed accounts, and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's social media data into AI, and the AI ​​analyzes the data and collects relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication history during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the communication history. For example, the analysis unit performs a detailed analysis on important communication history. The analysis unit can also perform a simplified analysis on less important communication history. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the communication history. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the communication history. Importance includes, but is not limited to, data frequency, user needs, and communication volume. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs communication history data into the generation AI, which analyzes the data, evaluates its importance, and adjusts the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the communication history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the communication history. For example, for call history, the analysis unit can apply an algorithm that analyzes call duration and frequency. The analysis unit can also apply an algorithm that analyzes data usage and usage patterns for data communication history. Furthermore, the analysis unit can apply an algorithm that analyzes app usage frequency and duration for app usage history. This allows for more accurate analysis by applying analysis algorithms according to the category of the communication history. Categories include, but are not limited to, call history, message history, and data communication history. Analysis algorithms include, but are not limited to, clustering, regression analysis, and classification algorithms. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs communication history data into the generative AI, which analyzes the data and applies an algorithm according to the category.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the communication history during analysis. For example, the analysis unit may prioritize the analysis based on the submission date of the communication history. The analysis unit may, for example, prioritize the analysis of recent communication history. The analysis unit may also postpone the analysis of past communication history. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the communication history. The submission date includes, but is not limited to, the data collection date, submission deadline, and user-specified date. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the communication history data into the generation AI, which analyzes the data and determines the priority based on the submission date.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the communication history during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the communication history. For example, the analysis unit prioritizes the analysis of highly relevant communication history. The analysis unit can also postpone the analysis of less relevant communication history. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the communication history. This enables efficient analysis by adjusting the order of analysis based on the relevance of the communication history. Relevance includes, but is not limited to, commonalities in data, correlations, and user needs. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs communication history data into the generative AI, which analyzes the data and adjusts the order based on relevance.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the communication plans. For example, the proposal unit can adjust the level of detail of its proposals based on the importance of the communication plans. For example, the proposal unit can provide detailed proposals for important communication plans. It can also provide simplified proposals for less important communication plans. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the communication plans. This allows for efficient proposals by adjusting the level of detail of proposals according to the importance of the communication plans. Importance includes, but is not limited to, data frequency, user needs, and communication volume. Some or all of the above processing in the proposal unit is performed using a generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data, evaluates importance, and adjusts the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the communication plan when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the communication plan. For example, for data communication plans, the proposal unit can make proposals based on data usage. The proposal unit can also make proposals based on call duration for call plans. Furthermore, the proposal unit can make proposals based on the frequency of app usage for app usage plans. By applying proposal algorithms according to the category of the communication plan, more accurate proposals become possible. Categories include, but are not limited to, call plans, data plans, and messaging plans. Proposal algorithms include, but are not limited to, recommendation systems and optimization algorithms. Some or all of the above processing in the proposal unit is performed using a generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data and applies an algorithm according to the category.

[0049] The proposal department can determine the priority of proposals based on the submission timing of the communication plans. For example, the proposal department may prioritize proposals based on the submission timing of the communication plans. The proposal department may, for example, prioritize recent communication plans. It may also postpone proposals for older communication plans. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of communication plans. The submission timing includes, but is not limited to, the data collection date, submission deadline, and user-specified date. Some or all of the above processing in the proposal department is performed using a generative AI. For example, the proposal department inputs communication plan data into the generative AI, which analyzes the data and determines the priority based on the submission timing.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the communication plans during the proposal process. For example, the proposal unit can adjust the order of proposals based on the relevance of the communication plans. For example, the proposal unit can prioritize proposing highly relevant communication plans. The proposal unit can also postpone proposing less relevant communication plans. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the communication plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the communication plans. Relevance includes, but is not limited to, commonalities in data, correlations, and user needs. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data and adjusts the order based on relevance.

[0051] The change proposal unit can analyze the user's past communication patterns and select the optimal change proposal when a plan is changed. For example, the change proposal unit analyzes the user's past communication patterns and selects the optimal change proposal when a plan is changed. For example, the change proposal unit makes the optimal change proposal based on the communication plan the user has frequently used in the past. The change proposal unit can also make change proposals that take into account data usage and call time based on the user's past communication patterns. Furthermore, the change proposal unit can analyze the user's past communication patterns and make the most efficient plan change proposal. This makes it possible to make efficient plan changes by making the optimal plan change proposal based on past communication patterns. Past communication patterns include, but are not limited to, call history, message history, and data usage. Some or all of the above processing in the change proposal unit may be performed using AI or not. For example, the change proposal unit inputs the user's past communication pattern data into AI, and the AI ​​analyzes the data and selects the optimal change proposal.

[0052] The change suggestion unit can customize the means of suggesting changes based on the user's current lifestyle when a plan is changed. For example, the change suggestion unit customizes the means of suggesting changes based on the user's current lifestyle when a plan is changed. For example, if the user is health-conscious, the change suggestion unit will suggest a plan change that takes into account the data usage of health-related apps. Also, if the user enjoys traveling, the change suggestion unit can suggest a plan change that takes into account the data usage at travel destinations. Furthermore, if the user is a business person, the change suggestion unit can suggest a plan change that takes into account the data usage of business-related apps. By customizing the plan change suggestion based on the user's lifestyle, more appropriate suggestions become possible. Lifestyle includes, but is not limited to, daily behavior patterns, hobbies, and interests. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's lifestyle data into AI, and the AI ​​analyzes the data to customize the means of suggesting changes.

[0053] The change suggestion unit can select the most appropriate change suggestion when a plan is changed, taking into account the user's geographical location information. For example, when a plan is changed, the change suggestion unit selects the most appropriate change suggestion by considering the user's geographical location information. For example, if the user is in a specific region, the change suggestion unit will prioritize suggesting plan changes related to that region. Also, if the user is traveling, the change suggestion unit can prioritize suggesting plan changes related to the travel destination. Furthermore, if the user is at home, the change suggestion unit can prioritize suggesting plan changes around the user's home. This makes it possible to suggest more appropriate plan changes by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location services, and the user's current location. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's geographical location information data into AI, and the AI ​​analyzes the data to select the most appropriate change suggestion.

[0054] The change suggestion unit can analyze the user's social media activity and propose means of suggesting changes when a plan is changed. For example, the change suggestion unit can analyze the user's social media activity and propose means of suggesting changes when a plan is changed. For example, the change suggestion unit can propose relevant plan changes based on the content that the user frequently posts on social media. The change suggestion unit can also propose relevant plan changes based on the activity of accounts that the user follows. Furthermore, the change suggestion unit can propose relevant plan changes based on the activity of groups and communities that the user participates in. This makes it possible to propose plan changes based on the user's interests by analyzing social media activity. Social media activity includes, but is not limited to, the content of posts, accounts followed, and the number of likes. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's social media data into AI, and the AI ​​analyzes the data and proposes relevant plan changes.

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

[0056] The communication plan suggestion system can propose the optimal communication plan considering the user's geographical location. For example, if the user is traveling, it can suggest a plan with a large data allowance at their travel destination. Specifically, it can suggest plans that include international roaming or plans that make internet access easy at the travel destination. Furthermore, if the user is in a specific region, it can suggest a plan that includes services related to that region. For example, it can suggest a plan that includes region-specific discounts or benefits. In addition, if the user is at home, it can suggest a plan with a large data allowance for the area around their home. In this way, the system can provide the optimal communication plan based on the user's geographical location.

[0057] The communication plan suggestion system can analyze a user's social media activity and suggest a relevant communication plan. For example, it can suggest a plan based on the content a user frequently posts on social media. Specifically, it can suggest a plan with a large data allowance to users who frequently post photos and videos. It can also suggest a plan based on the activity of accounts a user follows. For example, it can suggest a plan with a large data allowance for travel destinations to users who follow many travel-related accounts. Furthermore, it can suggest a plan based on the activity of groups and communities a user participates in. This allows the system to provide the optimal communication plan based on social media activity.

[0058] The communication plan suggestion system can analyze a user's past communication patterns and select the optimal data collection method. For example, it can prioritize collecting data from apps that the user has frequently used in the past. Specifically, it selects the optimal data collection method based on the data usage of apps that the user has used most often in the past. It can also adjust the frequency of data collection based on the user's past communication usage. For example, for users who have used a lot of data in the past, it can select a method to collect data more frequently. Furthermore, it can analyze the user's past communication patterns and determine the optimal timing for data collection. This enables efficient data collection based on past communication patterns.

[0059] The communication plan suggestion system can customize communication plans based on the user's lifestyle. For example, if a user is health-conscious, it can suggest a plan with high data usage for health-related apps. Specifically, it can suggest plans with high data usage for fitness apps and health management apps. If a user enjoys traveling, it can suggest a plan with high data usage for travel destinations. For example, it can suggest a plan that includes international roaming or a plan that makes internet access easy while traveling. Furthermore, if a user is a business person, it can suggest a plan with high data usage for business-related apps. This allows the system to provide the optimal communication plan based on the user's lifestyle.

[0060] The communication plan suggestion system can adjust the level of detail in its analysis based on the importance of the user's communication history. For example, it can perform a detailed analysis on important communication history. Specifically, it will perform a detailed analysis on the communication history of apps that the user frequently uses and suggest the optimal plan. It can also perform a simplified analysis on less important communication history. For example, it will perform a simplified analysis on the communication history of apps that the user rarely uses. Furthermore, it can determine the priority of the analysis according to the importance of the communication history. This enables efficient analysis based on the importance of the communication history.

[0061] The communication plan suggestion system can apply different analysis algorithms depending on the category of the user's communication history. For example, for call history, it can apply algorithms that analyze call duration and frequency. Specifically, it analyzes the user's call history and suggests the optimal plan based on call duration and frequency. Similarly, for data communication history, it can apply algorithms that analyze data usage and usage patterns. For example, it analyzes the user's data communication history and suggests the optimal plan based on data usage and usage patterns. Furthermore, for app usage history, it can apply algorithms that analyze app usage frequency and duration. This enables optimal analysis tailored to each category of communication history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data on the user's past communication history, app usage, and lifestyle. For example, it collects detailed data such as which apps the user uses and how often, as well as call and data usage. It also collects data on the user's hobbies, interests, and daily behavior patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's communication patterns and needs. For example, it uses generative AI to analyze the user's communication history and app usage to identify the user's communication patterns. It also identifies needs based on the user's lifestyle. Step 3: The proposal unit proposes the optimal communication plan based on the needs identified by the analysis unit. For example, it uses generative AI to select the optimal communication plan based on the user's communication patterns and needs, and proposes it to the user. Step 4: The change proposal unit dynamically proposes plan changes when communication patterns change during use. For example, it uses AI to detect changes in the user's communication patterns in real time and proposes the optimal plan change.

[0064] (Example of form 2) The communication plan suggestion system according to an embodiment of the present invention is a new service for solving the challenges individual users face in selecting communication plans. This communication plan suggestion system utilizes generative AI to analyze data on the user's past communication history, app usage, and lifestyle, and provides an optimal communication plan tailored to individual needs. Furthermore, even if communication patterns change during use, the AI ​​dynamically suggests plan changes, ensuring that the user always maintains the optimal plan. This service maximizes the use of robust communication infrastructure and customer data to provide users with simple and effective communication plan selection, thereby improving customer satisfaction, reducing churn rates, promoting migration to high-profit plans, and contributing to increased revenue. For example, it collects data on the user's past communication history, app usage, and lifestyle. For instance, it collects detailed data such as which apps the user uses, how often, and call and data usage. This data is input into the generative AI. Next, the generative AI analyzes the collected data to identify the user's communication patterns and needs. For example, if a user frequently uses video streaming, the AI ​​determines that a plan with high data usage is suitable for that user. In this way, the generative AI suggests the optimal communication plan to the user. Furthermore, if communication patterns change during use, the generating AI dynamically suggests plan changes. For example, if a user starts frequently using a new app, the AI ​​will suggest a plan change based on the data usage of that app. This ensures that users always have access to the optimal communication plan. This service leverages robust communication infrastructure and customer data to provide users with a simple and effective communication plan selection. For instance, it utilizes communication infrastructure to provide a high-quality communication environment and customer data to suggest plans tailored to user needs. This improves customer satisfaction, reduces churn rates, and promotes migration to high-profit plans, contributing to increased revenue. This system allows users to easily select the communication plan best suited to them, reducing wasted communication costs. It also flexibly adapts to changes in communication patterns, ensuring users always have access to the optimal plan.For example, if a user starts frequently using a new app, the service can suggest changing their plan based on the data usage of that app, thereby reducing wasted communication costs. This service not only provides users with a simple and effective communication plan selection, but also contributes to increased revenue for telecommunications carriers. For instance, improved customer satisfaction reduces churn rates, and the shift to higher-profit plans is encouraged, leading to increased revenue. In this way, a service that benefits both users and telecommunications carriers can be provided. As a result, the communication plan suggestion system can provide the optimal communication plan based on the user's communication history and usage, and can flexibly adapt to changes in communication patterns.

[0065] The communication plan proposal system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a change proposal unit. The collection unit collects data on the user's past communication history, app usage, and lifestyle. For example, the collection unit collects detailed data such as which apps the user uses and how often, and the amount of calls and data communication used. For example, the collection unit records the types and frequency of apps used by the user. The collection unit can also record the user's call history and data communication volume. Furthermore, the collection unit can also collect data on the user's lifestyle. For example, it collects data on the user's hobbies, interests, and daily behavior patterns. The analysis unit analyzes the data collected by the collection unit to identify the user's communication patterns and needs. For example, the analysis unit uses a generative AI to analyze the collected data. The generative AI analyzes the user's communication history and app usage to identify the user's communication patterns. The analysis unit can also use the generative AI to identify needs based on the user's lifestyle. For example, the generative AI proposes an optimal communication plan based on the user's hobbies and interests. The proposal unit proposes the optimal communication plan based on the needs identified by the analysis unit. The proposal unit proposes the optimal communication plan to the user, for example, using a generative AI. The generative AI selects the optimal communication plan based on the user's communication patterns and needs, for example. The proposal unit can also use the generative AI to propose plan changes to the user. For example, if a user starts using a new app frequently, the proposal unit will propose a plan change according to the data usage of that app. The change proposal unit dynamically proposes plan changes when changes in communication patterns occur during use. The change proposal unit detects changes in the user's communication patterns and proposes the optimal plan change, for example. The change proposal unit detects changes in the user's communication patterns in real time, for example, using AI. The change proposal unit can also use AI to propose plan changes in response to changes in the user's communication patterns. As a result, the communication plan proposal system according to the embodiment provides the optimal communication plan based on the user's communication history and usage status, and can flexibly respond to changes in communication patterns.

[0066] The data collection unit collects data on users' past communication history, app usage, and lifestyle. Specifically, it collects detailed data such as which apps users use, how often they use them, and their call and data usage. For example, to record the types and frequency of apps used by users, it analyzes smartphone app usage logs and records the launch time and usage time of each app in detail. The data collection unit can also record users' call history and data usage. This includes the number of calls made and received, call duration, and data usage. Furthermore, the data collection unit can collect data on users' lifestyles. For example, to collect data on users' hobbies, interests, and daily behavior patterns, it collects information such as events users attend, places they visit, newsletters they subscribe to, and social media accounts they follow. This allows the data collection unit to comprehensively collect diverse user behavior data and gain a detailed understanding of users' communication patterns and lifestyles. In addition, the data collection unit centrally manages this data and makes it accessible to the analysis and proposal units. For example, the collected data is stored on a cloud server, allowing the analysis and proposal units to access it in real time. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes data collected by the data collection unit to identify user communication patterns and needs. This analysis uses generative AI to process data in real time and identify user communication patterns. Specifically, the generative AI analyzes user communication history and app usage to identify which apps users use most frequently at what times, call frequency, and peak data communication times. The generative AI can also identify needs based on user lifestyles. For example, it analyzes the content of websites frequently accessed and newsletters subscribed to by users to suggest optimal communication plans based on their hobbies and interests. Furthermore, the analysis unit can utilize historical data and statistics to predict long-term fluctuations and trends in communication patterns. For instance, it can predict fluctuations in communication volume during specific seasons or events based on past communication data, optimizing future communication plans. The analysis unit can also use anomaly detection algorithms to detect unusual communication patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also manage long-term communication patterns and detect anomalies, improving the overall reliability and security of the system.

[0068] The proposal department proposes the optimal communication plan based on the needs identified by the analysis department. Specifically, it uses generative AI to propose the best communication plan for the user. The generative AI compares multiple communication plans to select the optimal plan based on the user's communication patterns and needs, and selects the plan that best suits the user's usage. For example, if a user uses a lot of data, it will propose a plan with a large data allowance, and if a user makes a lot of calls, it will propose a plan with a large call time. The proposal department can also use generative AI to suggest plan changes to users. For example, if a user starts using a new app frequently, it will suggest changing the plan according to the data usage of that app. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can monitor user satisfaction with the proposed plan and actual usage, and reflect this in future suggestions. The proposal department can also simulate multiple communication plans and propose the plan that offers the best cost performance for the user. In this way, the proposal department can provide users with the optimal communication plan, helping them reduce communication costs and improve usage efficiency.

[0069] The Change Proposal Department dynamically proposes plan changes when communication patterns change during use. Specifically, it uses AI to detect changes in the user's communication patterns in real time and propose the optimal plan change. For example, if a user starts using a new app frequently, it will propose a plan change based on the data usage of that app. It can also propose the optimal plan for a period when a user's communication patterns temporarily change due to travel or business trips. Furthermore, the Change Proposal Department continuously monitors changes in the user's communication patterns and proposes plan changes as needed. For example, if a user's data usage increases sharply, it will propose a change to a plan with higher data usage, and if call time increases, it will propose a change to a plan with higher call time. The Change Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it monitors user satisfaction with proposed plan changes and actual usage, and reflects this in future proposals. This allows the Change Proposal Department to flexibly respond to changes in the user's communication patterns and provide the optimal communication plan. In addition, the Change Proposal Department can also propose long-term plan changes in response to changes in the user's lifestyle and communication needs. This allows the change proposal department to provide users with the most suitable plan for their communication needs, helping them reduce communication costs and improve utilization efficiency.

[0070] The data collection unit can collect detailed data such as which apps a user uses and how often, as well as call and data usage. For example, the data collection unit can record which apps a user uses and how often. It can also record the user's call history and data usage. Furthermore, the data collection unit can collect data related to the user's lifestyle. For example, it can collect data on the user's hobbies, interests, and daily behavior patterns. This allows for a detailed understanding of the user's usage and enables more accurate analysis. Detailed data includes, but is not limited to, app usage frequency, call duration, and data usage. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's app usage data into AI, which can analyze the data to understand detailed usage.

[0071] The analysis unit can analyze data collected by the generative AI to identify user communication patterns and needs. For example, the analysis unit uses the generative AI to analyze the collected data. The generative AI, for example, analyzes the user's communication history and app usage to identify the user's communication patterns. The analysis unit can also use the generative AI to identify needs based on the user's lifestyle. For example, the generative AI proposes an optimal communication plan based on the user's hobbies and interests. In this way, the use of the generative AI allows for highly accurate identification of user communication patterns and needs. The generative AI includes, but is not limited to, natural language processing, image recognition, and predictive models. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit inputs the user's communication history data into the generative AI, which then analyzes the data to identify communication patterns and needs.

[0072] The proposal unit can propose the optimal communication plan to the user using generative AI. For example, the proposal unit uses generative AI to propose the optimal communication plan to the user. The generative AI selects the optimal communication plan based on the user's communication patterns and needs, for example. The proposal unit can also use generative AI to suggest plan changes to the user. For example, if a user starts using a new app frequently, the proposal unit can suggest changing the plan according to the data usage of that app. In this way, the optimal communication plan can be proposed to the user using generative AI. The optimal communication plan includes, but is not limited to, price plans, data usage, and call time. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs the user's communication pattern data into the generative AI, and the generative AI analyzes the data and proposes the optimal communication plan.

[0073] The change suggestion unit can suggest a plan change based on the data usage of a new app if the user starts using that app frequently. For example, if the user starts using a new app frequently, the change suggestion unit can analyze the data usage of that app and suggest the optimal plan change. The change suggestion unit can also detect changes in the user's communication patterns and suggest the optimal plan change. For example, if the user starts using a new app frequently, it can suggest a plan change based on the data usage of that app. This allows for the suggestion of the optimal plan change in response to changes in the user's usage. Examples of new apps include, but are not limited to, the release date and the user's installation date. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit can input the data usage of the user's new app into the AI, which can then analyze the data and suggest the optimal plan change.

[0074] The proposal department can provide a high-quality communication environment by utilizing communication infrastructure and propose plans tailored to user needs by utilizing customer data. For example, the proposal department can provide a high-quality communication environment by utilizing communication infrastructure. Communication infrastructure includes, but is not limited to, high-speed internet connection, extensive coverage, and stable connection. The proposal department can also propose plans tailored to user needs by utilizing customer data. Customer data includes, but is not limited to, past communication history, app usage, and lifestyle data. By providing a high-quality communication environment and plans tailored to user needs, customer satisfaction is improved. Some or all of the above processing in the proposal department is performed using generative AI. For example, the proposal department inputs customer data into the generative AI, which analyzes the data and proposes the optimal communication plan.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, 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 refrain from collecting data and collect it when the user is relaxed. Also, if the user is relaxed, the data collection unit can collect more detailed data and obtain more information. Furthermore, if the user is in a hurry, the data collection unit can collect only the minimum necessary data to reduce the user's burden. In this way, the user's burden is reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit inputs the user's emotion data into the generative AI, the generative AI analyzes the data to estimate the emotions, and adjusts the timing of data collection.

[0076] The data collection unit can analyze the user's past communication history and select the optimal data collection method. For example, the data collection unit can analyze the user's past communication 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. The data collection unit can also adjust the frequency of data collection based on the user's past communication volume. Furthermore, the data collection unit can analyze the user's past communication patterns and determine the optimal data collection timing. This enables efficient data collection by selecting the optimal data collection method based on past communication history. The optimal data collection method includes, but is not limited to, the frequency of data collection, the means of collection, and the timing of collection. 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 the user's past communication history data into AI, and the AI ​​can analyze the data and select the optimal data collection method.

[0077] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is health-conscious, the data collection unit can prioritize collecting data from health-related apps. Similarly, if the user enjoys traveling, the data collection unit can prioritize collecting data from travel-related apps. Furthermore, if the user is a business professional, the data collection unit can prioritize collecting data from business-related apps. This allows for the collection of more relevant data by filtering data based on the user's lifestyle and areas of interest. Lifestyle includes, but is not limited to, daily behavioral patterns, hobbies, and interests. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's lifestyle data into an AI, which then analyzes and filters the data.

[0078] 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 may prioritize collecting data related to stress reduction. If the user is relaxed, the data collection unit may also prioritize collecting entertainment-related data. Furthermore, if the user is in a hurry, the data collection unit may prioritize collecting only essential data. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs user emotion data into a generative AI, which analyzes the data to estimate emotions and determines the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes 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. In addition, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. This allows for the collection of more relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location services, and the user's current location. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's geographical location information data into AI, and the AI ​​analyzes the data and prioritizes the collection of highly relevant data.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on the content a user frequently posts on social media. The data collection unit can also collect relevant data based on the activity of accounts a user follows. Furthermore, the data collection unit can collect relevant data based on the activity of groups and communities a user participates in. This makes it possible to collect data based on user interests by analyzing social media activity. Social media activity includes, but is not limited to, posts, followed accounts, and the number of likes. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the user's social media data into AI, and the AI ​​analyzes the data and collects relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, 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 in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, the generative AI analyzes the data to estimate emotions, and adjusts the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication history during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the communication history. For example, the analysis unit performs a detailed analysis on important communication history. The analysis unit can also perform a simplified analysis on less important communication history. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the communication history. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the communication history. Importance includes, but is not limited to, data frequency, user needs, and communication volume. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs communication history data into the generation AI, which analyzes the data, evaluates its importance, and adjusts the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the communication history during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the communication history. For example, for call history, the analysis unit can apply an algorithm that analyzes call duration and frequency. The analysis unit can also apply an algorithm that analyzes data usage and usage patterns for data communication history. Furthermore, the analysis unit can apply an algorithm that analyzes app usage frequency and duration for app usage history. This allows for more accurate analysis by applying analysis algorithms according to the category of the communication history. Categories include, but are not limited to, call history, message history, and data communication history. Analysis algorithms include, but are not limited to, clustering, regression analysis, and classification algorithms. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs communication history data into the generative AI, which analyzes the data and applies an algorithm according to the category.

[0084] 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 in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is stressed, the analysis unit can provide a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results 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 analysis unit is performed using generative AI. For example, the analysis unit inputs user emotion data into the generative AI, the generative AI analyzes the data to estimate emotions, and adjusts the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the submission date of the communication history during analysis. For example, the analysis unit may prioritize the analysis based on the submission date of the communication history. The analysis unit may, for example, prioritize the analysis of recent communication history. The analysis unit may also postpone the analysis of past communication history. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the communication history. The submission date includes, but is not limited to, the data collection date, submission deadline, and user-specified date. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the communication history data into the generation AI, which analyzes the data and determines the priority based on the submission date.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the communication history during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the communication history. For example, the analysis unit prioritizes the analysis of highly relevant communication history. The analysis unit can also postpone the analysis of less relevant communication history. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the communication history. This enables efficient analysis by adjusting the order of analysis based on the relevance of the communication history. Relevance includes, but is not limited to, commonalities in data, correlations, and user needs. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs communication history data into the generative AI, which analyzes the data and adjusts the order based on relevance.

[0087] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. Furthermore, if the user is stressed, the suggestion unit can provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, more easily understandable suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit are performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI, which analyzes the data to estimate emotions and adjusts the way suggestions are presented.

[0088] The proposal unit can adjust the level of detail of its proposals based on the importance of the communication plans. For example, the proposal unit can adjust the level of detail of its proposals based on the importance of the communication plans. For example, the proposal unit can provide detailed proposals for important communication plans. It can also provide simplified proposals for less important communication plans. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the communication plans. This allows for efficient proposals by adjusting the level of detail of proposals according to the importance of the communication plans. Importance includes, but is not limited to, data frequency, user needs, and communication volume. Some or all of the above processing in the proposal unit is performed using a generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data, evaluates importance, and adjusts the level of detail of the proposals.

[0089] The proposal unit can apply different proposal algorithms depending on the category of the communication plan when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the communication plan. For example, for data communication plans, the proposal unit can make proposals based on data usage. The proposal unit can also make proposals based on call duration for call plans. Furthermore, the proposal unit can make proposals based on the frequency of app usage for app usage plans. By applying proposal algorithms according to the category of the communication plan, more accurate proposals become possible. Categories include, but are not limited to, call plans, data plans, and messaging plans. Proposal algorithms include, but are not limited to, recommendation systems and optimization algorithms. Some or all of the above processing in the proposal unit is performed using a generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data and applies an algorithm according to the category.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a more detailed suggestion. Furthermore, if the user is stressed, the suggestion unit can provide a visually easy-to-understand suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as 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 is performed using generative AI. For example, the suggestion unit inputs user emotion data into the generative AI, which analyzes the data to estimate the emotions and adjusts the length of the suggestion.

[0091] The proposal department can determine the priority of proposals based on the submission timing of the communication plans. For example, the proposal department may prioritize proposals based on the submission timing of the communication plans. The proposal department may, for example, prioritize recent communication plans. It may also postpone proposals for older communication plans. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This enables efficient proposals by prioritizing proposals based on the submission timing of communication plans. The submission timing includes, but is not limited to, the data collection date, submission deadline, and user-specified date. Some or all of the above processing in the proposal department is performed using a generative AI. For example, the proposal department inputs communication plan data into the generative AI, which analyzes the data and determines the priority based on the submission timing.

[0092] The proposal unit can adjust the order of proposals based on the relevance of the communication plans during the proposal process. For example, the proposal unit can adjust the order of proposals based on the relevance of the communication plans. For example, the proposal unit can prioritize proposing highly relevant communication plans. The proposal unit can also postpone proposing less relevant communication plans. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the communication plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the communication plans. Relevance includes, but is not limited to, commonalities in data, correlations, and user needs. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit inputs communication plan data into the generative AI, which analyzes the data and adjusts the order based on relevance.

[0093] The change suggestion unit can estimate the user's emotions and adjust the method of suggesting plan changes based on the estimated emotions. For example, the change suggestion unit can estimate the user's emotions and adjust the method of suggesting plan changes based on the estimated emotions. For example, if the user is relaxed, the change suggestion unit can make detailed plan change suggestions. If the user is in a hurry, the change suggestion unit can also make concise plan change suggestions. Furthermore, if the user is stressed, the change suggestion unit can make visually easy-to-understand plan change suggestions. By adjusting the method of suggesting plan changes according to the user's emotions, it becomes possible to make suggestions that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the change suggestion unit may be performed using AI or not using AI. For example, the change suggestion unit inputs user emotion data into the generative AI, the generative AI analyzes the data to estimate emotions, and adjusts the method of suggesting plan changes.

[0094] The change proposal unit can analyze the user's past communication patterns and select the optimal change proposal when a plan is changed. For example, the change proposal unit analyzes the user's past communication patterns and selects the optimal change proposal when a plan is changed. For example, the change proposal unit makes the optimal change proposal based on the communication plan the user has frequently used in the past. The change proposal unit can also make change proposals that take into account data usage and call time based on the user's past communication patterns. Furthermore, the change proposal unit can analyze the user's past communication patterns and make the most efficient plan change proposal. This makes it possible to make efficient plan changes by making the optimal plan change proposal based on past communication patterns. Past communication patterns include, but are not limited to, call history, message history, and data usage. Some or all of the above processing in the change proposal unit may be performed using AI or not. For example, the change proposal unit inputs the user's past communication pattern data into AI, and the AI ​​analyzes the data and selects the optimal change proposal.

[0095] The change suggestion unit can customize the means of suggesting changes based on the user's current lifestyle when a plan is changed. For example, the change suggestion unit customizes the means of suggesting changes based on the user's current lifestyle when a plan is changed. For example, if the user is health-conscious, the change suggestion unit will suggest a plan change that takes into account the data usage of health-related apps. Also, if the user enjoys traveling, the change suggestion unit can suggest a plan change that takes into account the data usage at travel destinations. Furthermore, if the user is a business person, the change suggestion unit can suggest a plan change that takes into account the data usage of business-related apps. By customizing the plan change suggestion based on the user's lifestyle, more appropriate suggestions become possible. Lifestyle includes, but is not limited to, daily behavior patterns, hobbies, and interests. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's lifestyle data into AI, and the AI ​​analyzes the data to customize the means of suggesting changes.

[0096] The change suggestion unit can estimate the user's emotions and determine the priority of plan changes based on the estimated emotions. For example, if the user is feeling stressed, the change suggestion unit will prioritize suggesting plan changes related to stress reduction. If the user is relaxed, the change suggestion unit may also prioritize suggesting entertainment-related plan changes. Furthermore, if the user is in a hurry, the change suggestion unit may prioritize suggesting only important plan changes. This allows for more appropriate plan changes by prioritizing plan changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change proposal unit inputs user emotion data into a generating AI, which analyzes the data to estimate emotions and determines the priority of plan changes.

[0097] The change suggestion unit can select the most appropriate change suggestion when a plan is changed, taking into account the user's geographical location information. For example, when a plan is changed, the change suggestion unit selects the most appropriate change suggestion by considering the user's geographical location information. For example, if the user is in a specific region, the change suggestion unit will prioritize suggesting plan changes related to that region. Also, if the user is traveling, the change suggestion unit can prioritize suggesting plan changes related to the travel destination. Furthermore, if the user is at home, the change suggestion unit can prioritize suggesting plan changes around the user's home. This makes it possible to suggest more appropriate plan changes by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location services, and the user's current location. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's geographical location information data into AI, and the AI ​​analyzes the data to select the most appropriate change suggestion.

[0098] The change suggestion unit can analyze the user's social media activity and propose means of suggesting changes when a plan is changed. For example, the change suggestion unit can analyze the user's social media activity and propose means of suggesting changes when a plan is changed. For example, the change suggestion unit can propose relevant plan changes based on the content that the user frequently posts on social media. The change suggestion unit can also propose relevant plan changes based on the activity of accounts that the user follows. Furthermore, the change suggestion unit can propose relevant plan changes based on the activity of groups and communities that the user participates in. This makes it possible to propose plan changes based on the user's interests by analyzing social media activity. Social media activity includes, but is not limited to, the content of posts, accounts followed, and the number of likes. Some or all of the above processing in the change suggestion unit may be performed using AI or not. For example, the change suggestion unit inputs the user's social media data into AI, and the AI ​​analyzes the data and proposes relevant plan changes.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The communication plan suggestion system can estimate the user's emotions and customize the suggested communication plans based on those emotions. For example, if the user is feeling stressed, the system can suggest a plan that helps reduce stress. Specifically, it might suggest a plan that includes a relaxing music streaming service or a plan with high data usage for stress-relieving apps. If the user is relaxed, it can also suggest an entertainment-related plan, such as one that includes movie or game streaming services. Furthermore, if the user is in a hurry, the system can suggest a concise and to-the-point plan. This allows the system to provide the optimal communication plan tailored to the user's emotions.

[0101] The communication plan suggestion system can propose the optimal communication plan considering the user's geographical location. For example, if the user is traveling, it can suggest a plan with a large data allowance at their travel destination. Specifically, it can suggest plans that include international roaming or plans that make internet access easy at the travel destination. Furthermore, if the user is in a specific region, it can suggest a plan that includes services related to that region. For example, it can suggest a plan that includes region-specific discounts or benefits. In addition, if the user is at home, it can suggest a plan with a large data allowance for the area around their home. In this way, the system can provide the optimal communication plan based on the user's geographical location.

[0102] The communication plan suggestion system can analyze a user's social media activity and suggest a relevant communication plan. For example, it can suggest a plan based on the content a user frequently posts on social media. Specifically, it can suggest a plan with a large data allowance to users who frequently post photos and videos. It can also suggest a plan based on the activity of accounts a user follows. For example, it can suggest a plan with a large data allowance for travel destinations to users who follow many travel-related accounts. Furthermore, it can suggest a plan based on the activity of groups and communities a user participates in. This allows the system to provide the optimal communication plan based on social media activity.

[0103] The communication plan suggestion system can analyze a user's past communication patterns and select the optimal data collection method. For example, it can prioritize collecting data from apps that the user has frequently used in the past. Specifically, it selects the optimal data collection method based on the data usage of apps that the user has used most often in the past. It can also adjust the frequency of data collection based on the user's past communication usage. For example, for users who have used a lot of data in the past, it can select a method to collect data more frequently. Furthermore, it can analyze the user's past communication patterns and determine the optimal timing for data collection. This enables efficient data collection based on past communication patterns.

[0104] The communication plan suggestion system can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, data collection can be reduced and resumed when the user is relaxed. Specifically, data collection is paused when the user is stressed and resumed when they are relaxed. Furthermore, when the user is relaxed, more detailed data collection can be performed to obtain more information. In addition, if the user is in a hurry, only the minimum necessary data can be collected to reduce the user's burden. This enables optimal data collection tailored to the user's emotions.

[0105] The communication plan suggestion system can customize communication plans based on the user's lifestyle. For example, if a user is health-conscious, it can suggest a plan with high data usage for health-related apps. Specifically, it can suggest plans with high data usage for fitness apps and health management apps. If a user enjoys traveling, it can suggest a plan with high data usage for travel destinations. For example, it can suggest a plan that includes international roaming or a plan that makes internet access easy while traveling. Furthermore, if a user is a business person, it can suggest a plan with high data usage for business-related apps. This allows the system to provide the optimal communication plan based on the user's lifestyle.

[0106] The communication plan suggestion system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. Specifically, when the user is relaxed, it will provide detailed explanations of communication plans and their benefits. If the user is in a hurry, it can provide concise suggestions. For example, when the user is in a hurry, it will provide concise suggestions that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand suggestions. This makes it possible to provide optimal suggestions tailored to the user's emotions.

[0107] The communication plan suggestion system can adjust the level of detail in its analysis based on the importance of the user's communication history. For example, it can perform a detailed analysis on important communication history. Specifically, it will perform a detailed analysis on the communication history of apps that the user frequently uses and suggest the optimal plan. It can also perform a simplified analysis on less important communication history. For example, it will perform a simplified analysis on the communication history of apps that the user rarely uses. Furthermore, it can determine the priority of the analysis according to the importance of the communication history. This enables efficient analysis based on the importance of the communication history.

[0108] The communication plan suggestion system can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point suggestion. Specifically, when the user is in a hurry, it will offer a short, to-the-point communication plan suggestion. Conversely, if the user is relaxed, it can provide a more detailed suggestion. For example, when the user is relaxed, it will provide a detailed explanation of the communication plan and its benefits. Furthermore, if the user is stressed, it can provide a visually easy-to-understand suggestion. This enables the system to provide the most appropriate suggestion based on the user's emotions.

[0109] The communication plan suggestion system can apply different analysis algorithms depending on the category of the user's communication history. For example, for call history, it can apply algorithms that analyze call duration and frequency. Specifically, it analyzes the user's call history and suggests the optimal plan based on call duration and frequency. Similarly, for data communication history, it can apply algorithms that analyze data usage and usage patterns. For example, it analyzes the user's data communication history and suggests the optimal plan based on data usage and usage patterns. Furthermore, for app usage history, it can apply algorithms that analyze app usage frequency and duration. This enables optimal analysis tailored to each category of communication history.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data on the user's past communication history, app usage, and lifestyle. For example, it collects detailed data such as which apps the user uses and how often, as well as call and data usage. It also collects data on the user's hobbies, interests, and daily behavior patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the user's communication patterns and needs. For example, it uses generative AI to analyze the user's communication history and app usage to identify the user's communication patterns. It also identifies needs based on the user's lifestyle. Step 3: The proposal unit proposes the optimal communication plan based on the needs identified by the analysis unit. For example, it uses generative AI to select the optimal communication plan based on the user's communication patterns and needs, and proposes it to the user. Step 4: The change proposal unit dynamically proposes plan changes when communication patterns change during use. For example, it uses AI to detect changes in the user's communication patterns in real time and proposes the optimal plan change.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and change proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's communication history and application usage using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's communication pattern. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and proposes an optimal communication plan based on the analysis results. The change proposal unit is implemented in the control unit 46A of the smart device 14, for example, and proposes a plan change in response to a change in the communication pattern. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and change proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's communication history and application usage using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's communication pattern. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and proposes an optimal communication plan based on the analysis results. The change proposal unit is implemented in the control unit 46A of the smart glasses 214, for example, and proposes a plan change in response to a change in the communication pattern. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and change proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's communication history and application usage using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's communication pattern. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and proposes an optimal communication plan based on the analysis results. The change proposal unit is implemented in the control unit 46A of the headset terminal 314, for example, and proposes a plan change in response to a change in the communication pattern. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and change proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the user's communication history and application usage using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to identify the user's communication pattern. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and proposes an optimal communication plan based on the analysis results. The change proposal unit is implemented in the control unit 46A of the robot 414, for example, and proposes a plan change in response to a change in the communication pattern. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit collects data on the user's past communication history, app usage, and lifestyle, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's communication patterns and needs, A proposal unit that proposes an optimal communication plan based on the needs identified by the analysis unit, It includes a change proposal unit that dynamically proposes a plan change when a change in the communication pattern occurs during use. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect detailed data such as which apps users use, how often they use them, and their call and data usage. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generated AI analyzes the collected data to identify user communication patterns and needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The AI ​​generates and proposes the optimal communication plan for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned change proposal unit is: If a user starts using a new app frequently, we will suggest changing their plan based on the data usage of that app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide a high-quality communication environment by utilizing communication infrastructure, and we propose plans tailored to user needs by utilizing customer data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past communication 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 data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes 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 During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the communication history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the communication history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the communication history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the communication history. 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 importance of the communication plan. 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 communication plan 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 submitting proposals, the priority of proposals will be determined based on the timing of the submission of communication plans. 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 the communication plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned change proposal unit is: It estimates the user's emotions and adjusts how it suggests plan changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned change proposal unit is: When changing plans, the system analyzes the user's past communication patterns to select the most suitable change proposal. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned change proposal unit is: When a user changes their plan, the method of suggesting the change will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned change proposal unit is: The system estimates user sentiment and prioritizes plan changes based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned change proposal unit is: When changing plans, the system selects the most suitable change proposal by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned change proposal unit is: When changing plans, we analyze the user's social media activity and suggest ways to propose changes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 collects data on the user's past communication history, app usage, and lifestyle, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's communication patterns and needs, A proposal unit that proposes an optimal communication plan based on the needs identified by the analysis unit, It includes a change proposal unit that dynamically proposes a plan change when a change in the communication pattern occurs during use. A system characterized by the following features.

2. The aforementioned collection unit is We collect detailed data such as which apps users use, how often they use them, and their call and data usage. The system according to feature 1.

3. The aforementioned analysis unit, The AI ​​generates data and analyzes it to identify user communication patterns and needs. The system according to feature 1.

4. The aforementioned proposal section is, The AI ​​generates and proposes the optimal communication plan for the user. The system according to feature 1.

5. The aforementioned change proposal unit is: If a user starts using a new app frequently, we will suggest changing their plan based on the data usage of that app. The system according to feature 1.

6. The aforementioned proposal section is, We provide a high-quality communication environment by utilizing communication infrastructure, and we propose plans tailored to user needs by utilizing customer data. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current lifestyle and areas of interest. 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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