System

Abstract

The system according to the embodiment aims to analyze usage data of a member and recommend an optimal communication plan.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects usage data of the member. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends an appropriate communication plan based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend optimal communication plans based on individual members' usage data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze usage data of members and recommend the most suitable communication plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects usage data of members. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends an appropriate communication plan based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the usage data of the member and recommend the most suitable communication plan. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication plan optimization system according to an embodiment of the present invention collects and analyzes member usage data and recommends an optimal communication plan. The communication plan optimization system collects information such as member usage data, usage patterns, lifestyles, and family composition, and then uses AI to analyze the data to recommend the optimal communication plan for each member. For example, the communication plan optimization system collects data such as members' smartphone and internet usage history, call time, and data usage. The communication plan optimization system then analyzes the collected information using AI to recommend the optimal communication plan for each member. For example, the system may recommend a high-capacity data plan for members with heavy data usage, and an unlimited calling plan for members with long call times. The communication plan optimization system can also comprehensively consider the communication usage of each family member and recommend the most cost-effective plan for the entire family. This allows the communication plan optimization system to optimize the communication usage of each family member, improving usability and cost efficiency. This allows each family member to use a communication plan that suits their individual needs, improving usability and reducing communication costs.

[0029] A communication plan optimization system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects member usage data. The member usage data includes, but is not limited to, call history, data usage, and application usage. The collection unit collects, for example, smartphone and internet usage history. The collection unit can also collect call time and data usage. The collection unit can also collect application usage. For example, the collection unit analyzes smartphone usage history to determine the extent to which each application is used. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI and recommends an optimal communication plan for each member. For example, the analysis unit can recommend a high-capacity data plan to a member with heavy data usage. The analysis unit can also recommend an unlimited calling plan to a member with long call duration. The analysis unit can also comprehensively consider the communication usage of all family members and propose the most cost-effective plan for the entire family. The recommendation unit recommends an optimal communication plan based on the analysis results obtained by the analysis unit. The recommendation unit provides an optimal plan based on the analysis results using, for example, AI. For example, the recommendation unit may recommend a high-capacity data plan to a member with heavy data usage. The recommendation unit may also recommend an unlimited calling plan to a member with long call duration. Furthermore, the recommendation unit may comprehensively consider the communication usage status of all family members and propose the most cost-effective plan for the entire family. As a result, the communication plan optimization system according to the embodiment can collect and analyze member usage data and recommend the optimal communication plan, thereby optimizing usability and cost efficiency.

[0030] The collection unit can collect information on the member's usage patterns, lifestyle, and family composition. The collection unit, for example, collects the member's usage patterns. For example, the collection unit can collect the time periods and frequency of data usage. The collection unit can also collect the member's lifestyle. For example, the collection unit can collect information such as the member's work hours, hobbies, and daily activities. The collection unit can also collect the member's family composition. For example, the collection unit collects information such as the number of family members, their ages, and their occupations. By collecting information such as the member's usage patterns, lifestyle, and family composition, more detailed data can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the member's usage patterns into AI and have the AI ​​analyze the usage patterns.

[0031] The collection unit can collect data such as smartphone and internet usage history, call duration, and data usage. The collection unit, for example, collects smartphone usage history. For example, the collection unit can collect application usage history and website browsing history. The collection unit can also collect call duration. For example, the collection unit can collect call frequency and call length. The collection unit can also collect data usage. For example, the collection unit collects monthly data usage and peak data usage. By collecting data such as smartphone and internet usage history, call duration, and data usage, it is possible to grasp the member's communication usage status in detail. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input smartphone usage history into AI and have the AI ​​analyze the usage history.

[0032] The analysis unit can analyze the collected data and recommend an appropriate communication plan to each member. The analysis unit can, for example, analyze the collected data using AI. For example, the analysis unit can recommend a high-capacity data plan to a member with heavy data usage. The analysis unit can also recommend an unlimited calling plan to a member with long call duration. Furthermore, the analysis unit can comprehensively consider the communication usage status of all family members and propose the most cost-effective plan for the entire family. In this way, by analyzing the collected data and recommending the optimal communication plan for each member, usability and cost efficiency can be optimized. Some or all of the above-mentioned processing by the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​recommend the optimal communication plan.

[0033] The analysis unit can comprehensively consider the communication usage status of all family members and propose a cost-effective plan for the entire family. The analysis unit, for example, performs an analysis by comprehensively considering the communication usage status of all family members. For example, the analysis unit proposes the most cost-effective plan based on the data usage and call time of the entire family. The analysis unit can also apply a family discount by having all family members use the same telecommunications company. This makes it possible to reduce communication costs by comprehensively considering the communication usage status of all family members and proposing the most cost-effective plan for the entire family. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the communication usage status of all family members into AI and have the AI ​​propose the optimal plan.

[0034] The collection unit can monitor each member's communication usage status in real time and detect abnormal usage patterns. The collection unit, for example, monitors each member's communication usage status in real time. For example, the collection unit can increase the frequency of data updates to grasp communication usage status in real time. The collection unit can also detect abnormal usage patterns. For example, the collection unit detects anomalies based on the degree of deviation from normal usage patterns. The collection unit can also detect abnormal data usage. The collection unit can also detect abnormal usage patterns of specific apps. For example, the collection unit detects an abnormality when a member suddenly increases their data usage more than usual. The collection unit can also detect an abnormality when a member uses a large amount of data at a time different from normal usage hours. The collection unit can also detect an abnormality when a member uses a specific app abnormally frequently. In this way, by monitoring each member's communication usage status in real time and detecting abnormal usage patterns, abnormal usage can be discovered early and countermeasures can be taken. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected in real time into an AI and have the AI ​​detect abnormal usage patterns.

[0035] When collecting data, the collection unit can select an appropriate collection method depending on the member's device type and OS version. The collection unit, for example, identifies the type of device used by the member. For example, the collection unit can identify the device type, such as a smartphone, tablet, or PC. The collection unit can also identify the OS version of the member's device. For example, the collection unit can identify the iOS or Android version. The collection unit then selects the optimal collection method depending on the device type and OS version. For example, if the member is using an iOS device, the collection unit can select a data collection method optimized for iOS. If the member is using an Android device, the collection unit can select a data collection method optimized for Android. Furthermore, if the member is using a different OS version, the collection unit can select the optimal data collection method for each version. This improves the efficiency and accuracy of data collection by selecting the optimal collection method depending on the member's device type and OS version. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input information about the device type and OS version into AI and have the AI ​​select the optimal collection method.

[0036] The collection unit can customize the collection scope based on the member's privacy settings when collecting data. The collection unit, for example, checks the member's privacy settings. For example, the collection unit can check whether the member has permitted collection of location information. The collection unit can also check whether the member has permitted data collection for a specific app. Furthermore, the collection unit can check whether the member has permitted collection of call history. For example, the collection unit does not collect location information if the member's privacy settings do not permit collection of location information. Furthermore, the collection unit can not collect data for a specific app if the member's privacy settings do not permit data collection for that app. Furthermore, the collection unit can not collect call history if the member has not permitted collection of call history in their privacy settings. In this way, by customizing the collection scope based on the member's privacy settings, data collection can be performed while protecting the member's privacy. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the member's privacy settings into AI and have the AI ​​customize the collection scope.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the member's geographical location information. The collection unit, for example, collects the member's geographical location information. For example, the collection unit can collect the member's geographical location information using GPS data or location information services. Furthermore, if the member is in a specific area, the collection unit can prioritize collecting data related to that area. For example, if the member is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the member is at home, the collection unit can prioritize collecting data related to the member's home. In this way, by prioritizing the collection of highly relevant data by taking into account the member's geographical location information, more useful data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the member's geographical location information into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0038] The collection unit may analyze the member's social media activity and collect related data during data collection. The collection unit, for example, collects the member's social media activity. For example, the collection unit may collect data related to places where the member checked in on social media. The collection unit may also analyze the member's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the member's friends on social media. For example, the collection unit may collect data related to places where the member checked in on social media. The collection unit may also analyze the member's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the member's friends on social media. In this way, by analyzing the member's social media activity and collecting related data, it is possible to understand the member's communication usage status in more detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the member's social media activity data into AI and have the AI ​​collect related data.

[0039] The collection unit can customize the collection method by reflecting the member's past feedback when collecting data. The collection unit, for example, collects the member's past feedback. For example, the collection unit can collect survey results and user comments. The collection unit can also adjust the data collection method based on the member's past feedback. For example, the collection unit can avoid a collection method for which the member has expressed dissatisfaction in the past and select an alternative method. The collection unit can also preferentially adopt a collection method for which the member has given favorable feedback in the past. In this way, the collection method can be customized to reflect the member's past feedback, thereby improving member satisfaction. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the member's past feedback data into AI and have the AI ​​customize the collection method.

[0040] During the analysis, the analysis unit can identify peak periods of communication usage for each member and propose an optimal plan. The analysis unit, for example, identifies peak periods of communication usage for each member. For example, the analysis unit can analyze data usage by time period and call duration by time period. The analysis unit can also propose plans suitable for periods of high data usage based on peak periods of communication usage for each member. Furthermore, the analysis unit can propose cost-effective plans taking into account peak periods of communication usage for each member. This allows for identifying peak periods of communication usage for each member and proposing an optimal plan, thereby improving the efficiency of communication usage. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input each member's communication usage data into AI and have the AI ​​identify peak periods and propose an optimal plan.

[0041] During the analysis, the analysis unit can analyze fluctuation patterns in the member's communication usage and predict future usage. The analysis unit predicts future usage patterns, for example, based on the member's past communication usage data. For example, the analysis unit can analyze temporal and seasonal fluctuations and predict future data usage. The analysis unit can also propose an optimal plan based on the member's communication usage fluctuation patterns. In this way, by analyzing the member's communication usage fluctuation patterns and predicting future usage, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the member's past communication usage data into AI and have the AI ​​perform future usage predictions.

[0042] During analysis, the analysis unit can apply different analysis algorithms based on the member's lifestyle. The analysis unit applies different analysis algorithms based on the member's lifestyle, for example. For example, the analysis unit applies an analysis algorithm for proposing a student discount plan to a student member. The analysis unit can also apply an analysis algorithm for proposing a business plan to a business member. The analysis unit can also apply an analysis algorithm for proposing a family discount plan depending on family composition. In this way, by applying different analysis algorithms based on the member's lifestyle, a more appropriate communication plan can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the member's lifestyle data into AI and have the AI ​​apply different analysis algorithms.

[0043] The analysis unit can perform the analysis taking into account the geographical distribution of members. The analysis unit, for example, collects the geographical distribution of members. For example, the analysis unit can collect data usage and call duration by region. The analysis unit can also perform the analysis taking into account the communication environment in the area where the member lives. For example, the analysis unit can perform the analysis taking into account the communication environment in the area where the member frequently visits. Furthermore, the analysis unit can propose an optimal communication plan based on the geographical distribution of members. In this way, by performing the analysis taking into account the geographical distribution of members, it is possible to propose an optimal communication plan according to the region. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of members into AI and have the AI ​​perform the analysis.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature on the member. The analysis unit, for example, can refer to related literature on the member's communication usage. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers and technical reports. The analysis unit can also refer to related literature on the member's lifestyle. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature on the member's family structure. In this way, by improving the accuracy of the analysis by referring to the member's related literature, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature into AI and have the AI ​​improve the accuracy of the analysis.

[0045] The analysis unit can perform the analysis taking into account the market value of the member. The analysis unit, for example, evaluates the market value of the member. For example, the analysis unit can evaluate the market value based on income, occupation, purchase history, etc. The analysis unit can also propose an optimal communication plan based on the market value of the member. For example, the analysis unit can propose a cost-effective plan taking into account the market value of the member. In this way, by performing the analysis taking into account the market value of the member, it is possible to propose a cost-effective communication plan. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the market value data of the member into AI and have the AI ​​perform the analysis.

[0046] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of each member's communication usage. The recommendation unit, for example, evaluates the importance of each member's communication usage. For example, the recommendation unit can evaluate the importance of communication usage based on data usage, call time, application usage frequency, etc. The recommendation unit can also adjust the level of detail of the recommendation based on the importance of communication usage. For example, the recommendation unit can recommend a detailed data plan to a member with a high data usage. The recommendation unit can also recommend a detailed call plan to a member with a long call time. Furthermore, the recommendation unit can recommend a simple plan to a member for whom the importance of communication usage is low. In this way, by adjusting the level of detail of the recommendation based on the importance of each member's communication usage, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input each member's communication usage data into AI and have the AI ​​adjust the level of detail of the recommendation.

[0047] The recommendation unit can apply different recommendation algorithms depending on the member category when making recommendations. The recommendation unit, for example, classifies member categories. For example, the recommendation unit can classify member categories based on age, occupation, hobbies, etc. The recommendation unit can also apply different recommendation algorithms depending on the member category. For example, the recommendation unit can apply an algorithm for recommending a student discount plan to a student member. The recommendation unit can also apply an algorithm for recommending a business plan to a business member. The recommendation unit can also apply an algorithm for recommending a family discount plan depending on family composition. In this way, by applying different recommendation algorithms depending on the member category, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input member category data into AI and cause the AI ​​to apply different recommendation algorithms.

[0048] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the member's past recommendation results. The recommendation unit, for example, collects the member's past recommendation results. For example, the recommendation unit can collect recommendation history and user feedback. The recommendation unit can also recommend the optimal plan based on plans the member has previously selected. For example, the recommendation unit can avoid plans that the member has previously rejected and recommend a different plan. The recommendation unit can also improve the accuracy of the recommendation based on feedback the member has previously provided. In this way, by improving the accuracy of the recommendation by referring to the member's past recommendation results, it is possible to propose a more appropriate communication plan. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the member's past recommendation result data into AI and have the AI ​​improve the accuracy of the recommendations.

[0049] When making recommendations, the recommendation unit can determine the priority of recommendations based on the time of submission of the member's communication usage. The recommendation unit, for example, collects the time of submission of the member's communication usage. For example, the recommendation unit can collect submission times such as the beginning or end of the month, or during specific events. The recommendation unit can also preferentially recommend the most appropriate plan based on the time when the member wishes to change their communication plan. For example, if the member's data usage increases during a specific period, the recommendation unit can preferentially recommend a plan suitable for that period. The recommendation unit can also preferentially recommend an unlimited calling plan based on the time when the member's call time increases. In this way, by determining the priority of recommendations based on the time of submission of the member's communication usage, a more appropriate communication plan can be proposed. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the member's submission time data into AI and have the AI ​​determine the priority of recommendations.

[0050] The recommendation unit can adjust the order of recommendations based on the member's relevance when making recommendations. The recommendation unit, for example, analyzes the member's communication usage patterns. For example, the recommendation unit can evaluate the relevance of data and the relevance of usage patterns. The recommendation unit can also prioritize recommending plans that are highly relevant based on the member's lifestyle. For example, the recommendation unit can prioritize recommending plans that are highly relevant based on the member's family composition. The recommendation unit can also first recommend the most relevant plan based on the member's communication usage patterns. This makes it possible to propose more appropriate communication plans by adjusting the order of recommendations based on the member's relevance. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the member's relevance data into AI and have the AI ​​adjust the order of recommendations.

[0051] The recommendation unit may adjust the use of recommended terminology depending on the member's level of expertise when making recommendations. The recommendation unit may, for example, evaluate the member's level of expertise. For example, the recommendation unit may evaluate the member's level of expertise as beginner, intermediate, advanced, or the like. The recommendation unit may also adjust the use of recommended terminology depending on the member's level of expertise. For example, if the member is knowledgeable in communications technology, the recommendation unit may provide recommendations that use a lot of technical terminology. If the member is not knowledgeable in communications technology, the recommendation unit may provide concise recommendations that avoid technical terminology. The recommendation unit may also provide recommendations that use appropriate terminology depending on the member's level of expertise. In this way, by adjusting the use of recommended terminology depending on the member's level of expertise, more understandable recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit may input the member's level of expertise data into AI and have the AI ​​execute the use of recommended terminology.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can collect data based on the member's hobbies and interests. For example, the collection unit can analyze the categories of applications and websites frequently used by the member to identify the member's hobbies and interests. This allows the collection unit to prioritize data related to the member's hobbies. The collection unit can also collect information on events and communities in which the member participates and provide data based on the member's hobbies and interests. Furthermore, the collection unit can customize communication plans based on the member's hobbies and interests. For example, for a member who enjoys sports, the collection unit can prioritize the collection of sports-related data and recommend a plan suitable for live streaming of sporting events. This allows the collection unit to provide the optimal communication plan based on the member's hobbies and interests.

[0054] The collection unit can adjust the timing of data collection taking into account the remaining battery life of the member's device. For example, the collection unit can monitor the remaining battery life of the member's smartphone or tablet and pause data collection when the battery is low. The collection unit can also resume data collection when the battery life is sufficient. Furthermore, the collection unit can adjust the frequency and method of data collection to minimize battery consumption of the device. For example, the collection unit can collect data in a way that consumes less battery. This enables efficient data collection that takes into account the remaining battery life of the member's device.

[0055] The analysis unit can take the member's social network into consideration when analyzing the member's communication usage data. For example, the analysis unit can analyze the member's social media activity and contact list to identify the member's social network. This can then recommend the optimal communication plan based on the person or group with whom the member frequently communicates. The analysis unit can also customize the communication plan based on the member's social network. For example, if a member frequently makes calls or sends messages to a specific group, the analysis unit can recommend a plan specifically tailored to that group. Furthermore, the analysis unit can automatically adjust the communication plan in response to changes in the member's social network. This can then provide the optimal communication plan that takes the member's social network into consideration.

[0056] The collection unit can take into account the security status of the member's device when collecting the member's communication usage data. For example, the collection unit can check whether the member's device has the latest security patches installed. The collection unit can also check whether the device is infected with a virus or malware. Furthermore, the collection unit can suspend data collection if the security status is insufficient. This allows data to be collected safely while ensuring the security of the member's device.

[0057] The collection unit can take into account the network connection status of the member's device when collecting the member's communication usage data. For example, the collection unit can check whether the member's device is connected to Wi-Fi or is using mobile data. The collection unit can also pause data collection if the network connection is unstable. Furthermore, the collection unit can resume data collection if the network connection is stable. This enables efficient data collection that takes into account the member's network connection status.

[0058] The collection unit may take into account the storage capacity of the member's device when collecting the member's communication usage data. For example, the collection unit may check whether the storage capacity of the member's device is sufficient. The collection unit may also suspend data collection if the storage capacity is insufficient. Furthermore, the collection unit may resume data collection when the storage capacity becomes sufficient. This enables efficient data collection that takes into account the storage capacity of the member's device.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the member's usage data. The member's usage data includes call history, data usage, application usage, etc. The collection unit collects smartphone and internet usage history, call time, data usage, and application usage. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and recommend the optimal communication plan for each member. For example, it may recommend a high-capacity data plan for members with heavy data usage, and an unlimited calling plan for members with long call times. It can also comprehensively consider the communication usage of each family member to propose the most cost-effective plan for the entire family. Step 3: The recommendation unit recommends the optimal communication plan based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to provide the optimal plan based on the analysis results. For example, it may recommend a high-capacity data plan to members with heavy data usage, and an unlimited calling plan to members with long call times. It can also comprehensively consider the communication usage of each family member to propose the most cost-effective plan for the entire family.

[0061] (Example 2) A communication plan optimization system according to an embodiment of the present invention collects and analyzes member usage data and recommends an optimal communication plan. The communication plan optimization system collects information such as member usage data, usage patterns, lifestyles, and family composition, and then uses AI to analyze the data to recommend the optimal communication plan for each member. For example, the communication plan optimization system collects data such as members' smartphone and internet usage history, call time, and data usage. The communication plan optimization system then analyzes the collected information using AI to recommend the optimal communication plan for each member. For example, the system may recommend a high-capacity data plan for members with heavy data usage, and an unlimited calling plan for members with long call times. The communication plan optimization system can also comprehensively consider the communication usage of each family member and recommend the most cost-effective plan for the entire family. This allows the communication plan optimization system to optimize the communication usage of each family member, improving usability and cost efficiency. This allows each family member to use a communication plan that suits their individual needs, improving usability and reducing communication costs.

[0062] A communication plan optimization system according to an embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects member usage data. The member usage data includes, but is not limited to, call history, data usage, and application usage. The collection unit collects, for example, smartphone and internet usage history. The collection unit can also collect call time and data usage. The collection unit can also collect application usage. For example, the collection unit analyzes smartphone usage history to determine the extent to which each application is used. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI and recommends an optimal communication plan for each member. For example, the analysis unit can recommend a high-capacity data plan to a member with heavy data usage. The analysis unit can also recommend an unlimited calling plan to a member with long call duration. The analysis unit can also comprehensively consider the communication usage of all family members and propose the most cost-effective plan for the entire family. The recommendation unit recommends an optimal communication plan based on the analysis results obtained by the analysis unit. The recommendation unit provides an optimal plan based on the analysis results using, for example, AI. For example, the recommendation unit may recommend a high-capacity data plan to a member with heavy data usage. The recommendation unit may also recommend an unlimited calling plan to a member with long call duration. Furthermore, the recommendation unit may comprehensively consider the communication usage status of all family members and propose the most cost-effective plan for the entire family. As a result, the communication plan optimization system according to the embodiment can collect and analyze member usage data and recommend the optimal communication plan, thereby optimizing usability and cost efficiency.

[0063] The collection unit can collect information on the member's usage patterns, lifestyle, and family composition. The collection unit, for example, collects the member's usage patterns. For example, the collection unit can collect the time periods and frequency of data usage. The collection unit can also collect the member's lifestyle. For example, the collection unit can collect information such as the member's work hours, hobbies, and daily activities. The collection unit can also collect the member's family composition. For example, the collection unit collects information such as the number of family members, their ages, and their occupations. By collecting information such as the member's usage patterns, lifestyle, and family composition, more detailed data can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the member's usage patterns into AI and have the AI ​​analyze the usage patterns.

[0064] The collection unit can collect data such as smartphone and internet usage history, call duration, and data usage. The collection unit, for example, collects smartphone usage history. For example, the collection unit can collect application usage history and website browsing history. The collection unit can also collect call duration. For example, the collection unit can collect call frequency and call length. The collection unit can also collect data usage. For example, the collection unit collects monthly data usage and peak data usage. By collecting data such as smartphone and internet usage history, call duration, and data usage, it is possible to grasp the member's communication usage status in detail. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input smartphone usage history into AI and have the AI ​​analyze the usage history.

[0065] The analysis unit can analyze the collected data and recommend an appropriate communication plan to each member. The analysis unit can, for example, analyze the collected data using AI. For example, the analysis unit can recommend a high-capacity data plan to a member with heavy data usage. The analysis unit can also recommend an unlimited calling plan to a member with long call duration. Furthermore, the analysis unit can comprehensively consider the communication usage status of all family members and propose the most cost-effective plan for the entire family. In this way, by analyzing the collected data and recommending the optimal communication plan for each member, usability and cost efficiency can be optimized. Some or all of the above-mentioned processing by the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​recommend the optimal communication plan.

[0066] The analysis unit can comprehensively consider the communication usage status of all family members and propose a cost-effective plan for the entire family. The analysis unit, for example, performs an analysis by comprehensively considering the communication usage status of all family members. For example, the analysis unit proposes the most cost-effective plan based on the data usage and call time of the entire family. The analysis unit can also apply a family discount by having all family members use the same telecommunications company. This makes it possible to reduce communication costs by comprehensively considering the communication usage status of all family members and proposing the most cost-effective plan for the entire family. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the communication usage status of all family members into AI and have the AI ​​propose the optimal plan.

[0067] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. The collection unit can also analyze the user's text data and estimate the emotions. This allows the timing of data collection to be adjusted according to the user's emotions, thereby reducing the user's stress and collecting more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0068] The collection unit can monitor each member's communication usage status in real time and detect abnormal usage patterns. The collection unit, for example, monitors each member's communication usage status in real time. For example, the collection unit can increase the frequency of data updates to grasp communication usage status in real time. The collection unit can also detect abnormal usage patterns. For example, the collection unit detects anomalies based on the degree of deviation from normal usage patterns. The collection unit can also detect abnormal data usage. The collection unit can also detect abnormal usage patterns of specific apps. For example, the collection unit detects an abnormality when a member suddenly increases their data usage more than usual. The collection unit can also detect an abnormality when a member uses a large amount of data at a time different from normal usage hours. The collection unit can also detect an abnormality when a member uses a specific app abnormally frequently. In this way, by monitoring each member's communication usage status in real time and detecting abnormal usage patterns, abnormal usage can be discovered early and countermeasures can be taken. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data collected in real time into an AI and have the AI ​​detect abnormal usage patterns.

[0069] When collecting data, the collection unit can select an appropriate collection method depending on the member's device type and OS version. The collection unit, for example, identifies the type of device used by the member. For example, the collection unit can identify the device type, such as a smartphone, tablet, or PC. The collection unit can also identify the OS version of the member's device. For example, the collection unit can identify the iOS or Android version. The collection unit then selects the optimal collection method depending on the device type and OS version. For example, if the member is using an iOS device, the collection unit can select a data collection method optimized for iOS. If the member is using an Android device, the collection unit can select a data collection method optimized for Android. Furthermore, if the member is using a different OS version, the collection unit can select the optimal data collection method for each version. This improves the efficiency and accuracy of data collection by selecting the optimal collection method depending on the member's device type and OS version. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input information about the device type and OS version into AI and have the AI ​​select the optimal collection method.

[0070] The collection unit can customize the collection scope based on the member's privacy settings when collecting data. The collection unit, for example, checks the member's privacy settings. For example, the collection unit can check whether the member has permitted collection of location information. The collection unit can also check whether the member has permitted data collection for a specific app. Furthermore, the collection unit can check whether the member has permitted collection of call history. For example, the collection unit does not collect location information if the member's privacy settings do not permit collection of location information. Furthermore, the collection unit can not collect data for a specific app if the member's privacy settings do not permit data collection for that app. Furthermore, the collection unit can not collect call history if the member has not permitted collection of call history in their privacy settings. In this way, by customizing the collection scope based on the member's privacy settings, data collection can be performed while protecting the member's privacy. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the member's privacy settings into AI and have the AI ​​customize the collection scope.

[0071] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. The collection unit can also analyze the user's text data and estimate the emotions. This allows the priority of data to be collected based on the user's emotions, thereby enabling important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI and have the AI ​​determine the priority of the data.

[0072] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the member's geographical location information. The collection unit, for example, collects the member's geographical location information. For example, the collection unit can collect the member's geographical location information using GPS data or location information services. Furthermore, if the member is in a specific area, the collection unit can prioritize collecting data related to that area. For example, if the member is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the member is at home, the collection unit can prioritize collecting data related to the member's home. In this way, by prioritizing the collection of highly relevant data by taking into account the member's geographical location information, more useful data can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the member's geographical location information into AI and cause the AI ​​to prioritize the collection of highly relevant data.

[0073] The collection unit may analyze the member's social media activity and collect related data during data collection. The collection unit, for example, collects the member's social media activity. For example, the collection unit may collect data related to places where the member checked in on social media. The collection unit may also analyze the member's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the member's friends on social media. For example, the collection unit may collect data related to places where the member checked in on social media. The collection unit may also analyze the member's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the member's friends on social media. In this way, by analyzing the member's social media activity and collecting related data, it is possible to understand the member's communication usage status in more detail. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the member's social media activity data into AI and have the AI ​​collect related data.

[0074] The collection unit can customize the collection method by reflecting the member's past feedback when collecting data. The collection unit, for example, collects the member's past feedback. For example, the collection unit can collect survey results and user comments. The collection unit can also adjust the data collection method based on the member's past feedback. For example, the collection unit can avoid a collection method for which the member has expressed dissatisfaction in the past and select an alternative method. The collection unit can also preferentially adopt a collection method for which the member has given favorable feedback in the past. In this way, the collection method can be customized to reflect the member's past feedback, thereby improving member satisfaction. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the member's past feedback data into AI and have the AI ​​customize the collection method.

[0075] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the user's text data and estimate the emotion. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the expression method of the analysis according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the way the analysis is expressed.

[0076] During the analysis, the analysis unit can identify peak periods of communication usage for each member and propose an optimal plan. The analysis unit, for example, identifies peak periods of communication usage for each member. For example, the analysis unit can analyze data usage by time period and call duration by time period. The analysis unit can also propose plans suitable for periods of high data usage based on peak periods of communication usage for each member. Furthermore, the analysis unit can propose cost-effective plans taking into account peak periods of communication usage for each member. This allows for identifying peak periods of communication usage for each member and proposing an optimal plan, thereby improving the efficiency of communication usage. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input each member's communication usage data into AI and have the AI ​​identify peak periods and propose an optimal plan.

[0077] During the analysis, the analysis unit can analyze fluctuation patterns in the member's communication usage and predict future usage. The analysis unit predicts future usage patterns, for example, based on the member's past communication usage data. For example, the analysis unit can analyze temporal and seasonal fluctuations and predict future data usage. The analysis unit can also propose an optimal plan based on the member's communication usage fluctuation patterns. In this way, by analyzing the member's communication usage fluctuation patterns and predicting future usage, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the member's past communication usage data into AI and have the AI ​​perform future usage predictions.

[0078] During analysis, the analysis unit can apply different analysis algorithms based on the member's lifestyle. The analysis unit applies different analysis algorithms based on the member's lifestyle, for example. For example, the analysis unit applies an analysis algorithm for proposing a student discount plan to a student member. The analysis unit can also apply an analysis algorithm for proposing a business plan to a business member. The analysis unit can also apply an analysis algorithm for proposing a family discount plan depending on family composition. In this way, by applying different analysis algorithms based on the member's lifestyle, a more appropriate communication plan can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the member's lifestyle data into AI and have the AI ​​apply different analysis algorithms.

[0079] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also analyze the user's text data and estimate the emotion. This makes it possible to adjust the display method of the analysis results according to the user's emotion, thereby providing a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0080] The analysis unit can perform the analysis taking into account the geographical distribution of members. The analysis unit, for example, collects the geographical distribution of members. For example, the analysis unit can collect data usage and call duration by region. The analysis unit can also perform the analysis taking into account the communication environment in the area where the member lives. For example, the analysis unit can perform the analysis taking into account the communication environment in the area where the member frequently visits. Furthermore, the analysis unit can propose an optimal communication plan based on the geographical distribution of members. In this way, by performing the analysis taking into account the geographical distribution of members, it is possible to propose an optimal communication plan according to the region. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of members into AI and have the AI ​​perform the analysis.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature on the member. The analysis unit, for example, can refer to related literature on the member's communication usage. For example, the analysis unit can improve the accuracy of the analysis by referring to academic papers and technical reports. The analysis unit can also refer to related literature on the member's lifestyle. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature on the member's family structure. In this way, by improving the accuracy of the analysis by referring to the member's related literature, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature into AI and have the AI ​​improve the accuracy of the analysis.

[0082] The analysis unit can perform the analysis taking into account the market value of the member. The analysis unit, for example, evaluates the market value of the member. For example, the analysis unit can evaluate the market value based on income, occupation, purchase history, etc. The analysis unit can also propose an optimal communication plan based on the market value of the member. For example, the analysis unit can propose a cost-effective plan taking into account the market value of the member. In this way, by performing the analysis taking into account the market value of the member, it is possible to propose a cost-effective communication plan. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the market value data of the member into AI and have the AI ​​perform the analysis.

[0083] The recommendation unit can estimate the user's emotion and adjust the way in which recommendations are expressed based on the estimated emotion. The recommendation unit, for example, estimates the user's emotion. For example, the recommendation unit can estimate the user's emotion using facial expression recognition technology. The recommendation unit can also estimate the user's emotion using voice analysis technology. The recommendation unit can also estimate the user's emotion using text analysis technology. For example, the recommendation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The recommendation unit can also record the user's voice and estimate the emotion using voice analysis technology. The recommendation unit can also analyze the user's text data and estimate the emotion. This makes it possible to provide recommendations that are easy for the user to understand by adjusting the way recommendations are expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit may input user emotion data into AI and have the AI ​​adjust the recommended expression method.

[0084] When making a recommendation, the recommendation unit can adjust the level of detail of the recommendation based on the importance of each member's communication usage. The recommendation unit, for example, evaluates the importance of each member's communication usage. For example, the recommendation unit can evaluate the importance of communication usage based on data usage, call time, application usage frequency, etc. The recommendation unit can also adjust the level of detail of the recommendation based on the importance of communication usage. For example, the recommendation unit can recommend a detailed data plan to a member with a high data usage. The recommendation unit can also recommend a detailed call plan to a member with a long call time. Furthermore, the recommendation unit can recommend a simple plan to a member for whom the importance of communication usage is low. In this way, by adjusting the level of detail of the recommendation based on the importance of each member's communication usage, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input each member's communication usage data into AI and have the AI ​​adjust the level of detail of the recommendation.

[0085] The recommendation unit can apply different recommendation algorithms depending on the member category when making recommendations. The recommendation unit, for example, classifies member categories. For example, the recommendation unit can classify member categories based on age, occupation, hobbies, etc. The recommendation unit can also apply different recommendation algorithms depending on the member category. For example, the recommendation unit can apply an algorithm for recommending a student discount plan to a student member. The recommendation unit can also apply an algorithm for recommending a business plan to a business member. The recommendation unit can also apply an algorithm for recommending a family discount plan depending on family composition. In this way, by applying different recommendation algorithms depending on the member category, a more appropriate communication plan can be proposed. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input member category data into AI and cause the AI ​​to apply different recommendation algorithms.

[0086] When making a recommendation, the recommendation unit can improve the accuracy of the recommendation by referring to the member's past recommendation results. The recommendation unit, for example, collects the member's past recommendation results. For example, the recommendation unit can collect recommendation history and user feedback. The recommendation unit can also recommend the optimal plan based on plans the member has previously selected. For example, the recommendation unit can avoid plans that the member has previously rejected and recommend a different plan. The recommendation unit can also improve the accuracy of the recommendation based on feedback the member has previously provided. In this way, by improving the accuracy of the recommendation by referring to the member's past recommendation results, it is possible to propose a more appropriate communication plan. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the member's past recommendation result data into AI and have the AI ​​improve the accuracy of the recommendations.

[0087] The recommendation unit can estimate the user's emotion and adjust the length of the recommendation based on the estimated emotion. The recommendation unit, for example, estimates the user's emotion. For example, the recommendation unit can estimate the user's emotion using facial expression recognition technology. The recommendation unit can also estimate the user's emotion using voice analysis technology. The recommendation unit can also estimate the user's emotion using text analysis technology. For example, the recommendation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The recommendation unit can also record the user's voice and estimate the emotion using voice analysis technology. The recommendation unit can also analyze the user's text data and estimate the emotion. This allows the length of the recommendation to be adjusted according to the user's emotion, thereby providing recommendations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the recommended length.

[0088] When making recommendations, the recommendation unit can determine the priority of recommendations based on the time of submission of the member's communication usage. The recommendation unit, for example, collects the time of submission of the member's communication usage. For example, the recommendation unit can collect submission times such as the beginning or end of the month, or during specific events. The recommendation unit can also preferentially recommend the most appropriate plan based on the time when the member wishes to change their communication plan. For example, if the member's data usage increases during a specific period, the recommendation unit can preferentially recommend a plan suitable for that period. The recommendation unit can also preferentially recommend an unlimited calling plan based on the time when the member's call time increases. In this way, by determining the priority of recommendations based on the time of submission of the member's communication usage, a more appropriate communication plan can be proposed. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the member's submission time data into AI and have the AI ​​determine the priority of recommendations.

[0089] The recommendation unit can adjust the order of recommendations based on the member's relevance when making recommendations. The recommendation unit, for example, analyzes the member's communication usage patterns. For example, the recommendation unit can evaluate the relevance of data and the relevance of usage patterns. The recommendation unit can also prioritize recommending plans that are highly relevant based on the member's lifestyle. For example, the recommendation unit can prioritize recommending plans that are highly relevant based on the member's family composition. The recommendation unit can also first recommend the most relevant plan based on the member's communication usage patterns. This makes it possible to propose more appropriate communication plans by adjusting the order of recommendations based on the member's relevance. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the member's relevance data into AI and have the AI ​​adjust the order of recommendations.

[0090] The recommendation unit may adjust the use of recommended terminology depending on the member's level of expertise when making recommendations. The recommendation unit may, for example, evaluate the member's level of expertise. For example, the recommendation unit may evaluate the member's level of expertise as beginner, intermediate, advanced, or the like. The recommendation unit may also adjust the use of recommended terminology depending on the member's level of expertise. For example, if the member is knowledgeable in communications technology, the recommendation unit may provide recommendations that use a lot of technical terminology. If the member is not knowledgeable in communications technology, the recommendation unit may provide concise recommendations that avoid technical terminology. The recommendation unit may also provide recommendations that use appropriate terminology depending on the member's level of expertise. In this way, by adjusting the use of recommended terminology depending on the member's level of expertise, more understandable recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit may input the member's level of expertise data into AI and have the AI ​​execute the use of recommended terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects member usage data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends an optimal communication plan based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects member usage data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends an optimal communication plan based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects member usage data using the camera 42 and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends an optimal communication plan based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects member usage data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the collected data. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and recommends an optimal communication plan based on the analysis results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can take into account the member's health condition when analyzing the member's communication usage data. For example, the analysis unit can collect the member's health data and analyze the correlation between communication usage patterns and health condition. This can recommend a communication plan to reduce stress to a member whose health condition is deteriorating. The analysis unit can also adjust the frequency and time of communication usage based on the member's health condition. For example, if communication usage at night has a negative impact on health, a plan that limits communication at night can be recommended. Furthermore, the analysis unit can monitor the member's health condition and automatically adjust the communication plan according to changes in the member's health condition. This can provide the optimal communication plan that takes the member's health condition into consideration.

[0093] The collection unit can collect data based on the member's hobbies and interests. For example, the collection unit can analyze the categories of applications and websites frequently used by the member to identify the member's hobbies and interests. This allows the collection unit to prioritize data related to the member's hobbies. The collection unit can also collect information on events and communities in which the member participates and provide data based on the member's hobbies and interests. Furthermore, the collection unit can customize communication plans based on the member's hobbies and interests. For example, for a member who enjoys sports, the collection unit can prioritize the collection of sports-related data and recommend a plan suitable for live streaming of sporting events. This allows the collection unit to provide the optimal communication plan based on the member's hobbies and interests.

[0094] The collection unit can adjust the timing of data collection taking into account the remaining battery life of the member's device. For example, the collection unit can monitor the remaining battery life of the member's smartphone or tablet and pause data collection when the battery is low. The collection unit can also resume data collection when the battery life is sufficient. Furthermore, the collection unit can adjust the frequency and method of data collection to minimize battery consumption of the device. For example, the collection unit can collect data in a way that consumes less battery. This enables efficient data collection that takes into account the remaining battery life of the member's device.

[0095] The analysis unit can take into account the member's psychological stress level when analyzing the member's communication usage data. For example, the analysis unit can estimate the member's stress level based on the member's communication usage patterns and lifestyle. This can then recommend a communication plan that helps members with high stress levels to relax. The analysis unit can also adjust the frequency and time periods of communication usage based on the member's stress level. For example, it can recommend a plan that reduces communication usage during times when stress tends to build up. Furthermore, the analysis unit can monitor the member's stress level and automatically adjust the communication plan according to changes in stress level. This makes it possible to provide the member with an optimal communication plan that takes into account their psychological stress level.

[0096] The analysis unit can take the member's social network into consideration when analyzing the member's communication usage data. For example, the analysis unit can analyze the member's social media activity and contact list to identify the member's social network. This can then recommend the optimal communication plan based on the person or group with whom the member frequently communicates. The analysis unit can also customize the communication plan based on the member's social network. For example, if a member frequently makes calls or sends messages to a specific group, the analysis unit can recommend a plan specifically tailored to that group. Furthermore, the analysis unit can automatically adjust the communication plan in response to changes in the member's social network. This can then provide the optimal communication plan that takes the member's social network into consideration.

[0097] The collection unit can estimate the member's emotions and adjust the frequency of data collection based on the estimated emotions. For example, the collection unit can reduce the frequency of data collection when the member is feeling stressed. Also, the collection unit can increase the frequency of data collection when the member is relaxed. Furthermore, the collection unit can change the method of data collection according to the member's emotions. For example, when the member is tired, data collection can be performed in the background, thereby reducing the burden on the member. This enables flexible data collection according to the member's emotions.

[0098] The collection unit can take into account the security status of the member's device when collecting the member's communication usage data. For example, the collection unit can check whether the member's device has the latest security patches installed. The collection unit can also check whether the device is infected with a virus or malware. Furthermore, the collection unit can suspend data collection if the security status is insufficient. This allows data to be collected safely while ensuring the security of the member's device.

[0099] The collection unit can take into account the network connection status of the member's device when collecting the member's communication usage data. For example, the collection unit can check whether the member's device is connected to Wi-Fi or is using mobile data. The collection unit can also pause data collection if the network connection is unstable. Furthermore, the collection unit can resume data collection if the network connection is stable. This enables efficient data collection that takes into account the member's network connection status.

[0100] The collection unit may take into account the storage capacity of the member's device when collecting the member's communication usage data. For example, the collection unit may check whether the storage capacity of the member's device is sufficient. The collection unit may also suspend data collection if the storage capacity is insufficient. Furthermore, the collection unit may resume data collection when the storage capacity becomes sufficient. This enables efficient data collection that takes into account the storage capacity of the member's device.

[0101] The collection unit can estimate the member's emotions and adjust the type of data to be collected based on the estimated emotions. For example, if the member is feeling stressed, the collection unit can prioritize collecting data related to stress. Also, if the member is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, the collection unit can change the type of data to be collected depending on the member's emotions. For example, if the member is tired, the collection unit can prioritize collecting data related to fatigue. This enables flexible data collection according to the member's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects the member's usage data. The member's usage data includes call history, data usage, application usage, etc. The collection unit collects smartphone and internet usage history, call time, data usage, and application usage. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and recommend the optimal communication plan for each member. For example, it may recommend a high-capacity data plan for members with heavy data usage, and an unlimited calling plan for members with long call times. It can also comprehensively consider the communication usage of each family member to propose the most cost-effective plan for the entire family. Step 3: The recommendation unit recommends the optimal communication plan based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to provide the optimal plan based on the analysis results. For example, it may recommend a high-capacity data plan to members with heavy data usage, and an unlimited calling plan to members with long call times. It can also comprehensively consider the communication usage of each family member to propose the most cost-effective plan for the entire family.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects member usage data; an analysis unit that analyzes the data collected by the collection unit; a recommendation unit that recommends an appropriate communication plan based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect information about members' usage patterns, lifestyles, and family structures 2. The system of claim 1.

3. The collecting unit Collect data such as smartphone and internet usage history, call duration, and data usage 2. The system of claim 1.

4. The analysis unit Analyze the collected data and recommend appropriate communication plans for each member 2. The system of claim 1.

5. The analysis unit Comprehensively considers the communication usage of each family member and proposes cost-effective plans for the entire family 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the inferred emotions 2. The system of claim 1.

7. The collecting unit Monitor each member's communication usage in real time and detect abnormal usage patterns 2. The system of claim 1.

8. The collecting unit When collecting data, select the appropriate collection method depending on the member's device type and OS version.

2. The system of claim 1.

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