System

Abstract

An object of a system according to an embodiment is to monitor the performance of a mobile network in real time and quickly detect an abnormality.SOLUTION: A system according to an embodiment includes a generated AI, a communication pattern learning unit, a behavior reproduction unit, a real-time monitoring unit, and an anomaly detection unit. The generative AI learns communication patterns of users of various ages and genders. The communication pattern learning unit learns communication patterns of users of various ages and genders. The behavior reproduction unit reproduces user behavior based on the communication pattern learned by the communication pattern learning unit. The real-time monitoring unit monitors the performance of the mobile network in real time based on the behavior of the user reproduced by the behavior reproduction unit. The abnormality detection unit detects an abnormality from the performance monitored by the real-time monitoring 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 have had the problem of making it difficult to monitor mobile network performance in real time and quickly detect abnormalities.

[0005] The system according to the embodiment aims to monitor the performance of a mobile network in real time and quickly detect abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a communication pattern learning unit, a behavior reproduction unit, a real-time monitoring unit, and an anomaly detection unit. The generation AI learns the communication patterns of users of various ages and genders. The communication pattern learning unit learns the communication patterns of users of various ages and genders. The behavior reproduction unit reproduces user behavior based on the communication patterns learned by the communication pattern learning unit. The real-time monitoring unit monitors mobile network performance in real time based on the user behavior reproduced by the behavior reproduction unit. The anomaly detection unit detects anomalies from the performance monitored by the real-time monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the performance of a mobile network in real time and quickly detect abnormalities. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 mobile network monitoring system according to an embodiment of the present invention uses a generative AI to reproduce user behavior and monitors mobile networks using the generative AI embedded in smartphones. This allows the mobile network monitoring system to learn user communication patterns, monitor mobile network performance in real time, and detect anomalies.

[0029] A mobile network monitoring system according to an embodiment includes a generation AI, a communication pattern learning unit, a behavior reproduction unit, a real-time monitoring unit, and an anomaly detection unit. The generation AI includes a communication pattern learning unit that learns the communication patterns of users of various ages and genders. For example, the communication pattern learning unit learns patterns such as frequent social networking site use by young users and frequent news app use by elderly users. The communication pattern learning unit also performs learning based on the user's mobile service usage history and communication data. The behavior reproduction unit reproduces user behavior based on the communication patterns learned by the communication pattern learning unit. For example, it reproduces behavior such as using a specific app during a specific time period or consuming a certain amount of data. The real-time monitoring unit monitors mobile network performance in real time based on the user behavior reproduced by the behavior reproduction unit. For example, it detects anomalies such as a decrease in communication speed or unstable connection. The anomaly detection unit detects anomalies from the performance monitored by the real-time monitoring unit. For example, it issues a notification when communication speed falls below a certain standard or when connection is frequently lost. As a result, the mobile network monitoring system according to the embodiment can learn users' communication patterns, reproduce their behavior, monitor the performance of the mobile network in real time, and detect abnormalities.

[0030] The communication pattern learning unit can learn communication patterns based on the user's location information and movement patterns. For example, the generation AI in the communication pattern learning unit learns communication patterns at specific locations based on the user's location information. For example, it analyzes patterns such as heavy email usage at work and heavy video streaming usage at home. This makes it possible to learn communication patterns based on the user's location information and movement patterns.

[0031] The communication pattern learning unit can learn communication patterns based on the user's device usage history. For example, the generation AI in the communication pattern learning unit learns communication patterns based on the user's app installation history. For example, it analyzes changes in communication volume immediately after installing a new app. This makes it possible to learn communication patterns based on the user's device usage history.

[0032] The communication pattern learning unit can learn communication patterns based on the user's voice data and image data. For example, the generation AI in the communication pattern learning unit learns communication patterns based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in the communication patterns. This makes it possible to learn communication patterns based on the user's voice data and image data.

[0033] The communication pattern learning unit can learn the communication patterns of users in different regions and cultural spheres. For example, the generation AI in the communication pattern learning unit learns the communication patterns of users in different regions and analyzes the differences between regions. For example, it clarifies the differences in communication patterns between urban and rural areas. This makes it possible to learn the communication patterns of users in different regions and cultural spheres.

[0034] The behavior reproduction unit can reproduce behavior based on the user's device settings. For example, the behavior reproduction unit reproduces behavior according to the settings based on the user's device settings using a generation AI. For example, it reproduces the difference in behavior when notification settings are on and off. This makes it possible to reproduce behavior based on the user's device settings.

[0035] The behavior reproduction unit can reproduce behavior based on the user's past communication history and activity log. For example, the behavior reproduction unit uses a generation AI to reproduce past behavior patterns based on the user's past communication history. For example, it reproduces a pattern of using a specific app during a specific time period. This makes it possible to reproduce behavior based on the user's past communication history and activity log.

[0036] The behavior reproduction unit can reproduce behavior based on the user's voice commands and gesture operations. For example, the behavior reproduction unit reproduces natural behavior based on the user's voice commands using a generation AI. For example, it reproduces the behavior of launching an app with a voice command. This makes it possible to reproduce behavior based on the user's voice commands and gesture operations.

[0037] The behavior reproduction unit can reproduce user behavior on different devices. For example, the generation AI reproduces user behavior on a tablet and analyzes the differences between that and a smartphone. For example, it analyzes differences such as whether users watch more videos on a tablet and use social media more on a smartphone. This makes it possible to reproduce user behavior on different devices.

[0038] The real-time monitoring unit can monitor network performance based on the user's location information and movement patterns. For example, the generation AI monitors network performance at specific locations based on the user's location information. For example, it monitors performance such as whether communication speeds are fast at work and communication is stable at home. This makes it possible to monitor network performance based on the user's location information and movement patterns.

[0039] The real-time monitoring unit can monitor network performance based on the user's device settings and usage status. For example, the generation AI monitors network performance according to the settings based on the user's device settings. For example, it monitors the difference in performance when notification settings are on and off. This makes it possible to monitor network performance based on the user's device settings and usage status.

[0040] The real-time monitoring unit can monitor network performance based on the user's voice data and image data. For example, the generation AI monitors network performance based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in communication performance. This makes it possible to monitor network performance based on the user's voice data and image data.

[0041] The real-time monitoring unit can monitor network performance in different regions and cultural spheres. For example, the generation AI monitors network performance in different regions and analyzes the differences between regions. For example, it can clarify the differences in communication performance between urban and rural areas. This makes it possible to monitor network performance in different regions and cultural spheres.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The mobile network monitoring system can also be equipped with a health monitoring unit that monitors the user's health status. For example, it can measure the user's heart rate and blood pressure in real time and notify the user if any abnormalities are detected. It can also analyze the user's exercise volume and sleep patterns to monitor changes in the user's health status. This allows for comprehensive monitoring of the user's health status and allows for prompt action if an abnormality occurs.

[0044] The communication pattern learning unit can learn communication patterns based on a user's hobbies and interests. For example, the generation AI analyzes a user's web browsing history and app usage history to learn communication patterns related to specific hobbies and interests. For example, users who are interested in sports tend to frequently use sports-related apps and websites. Similarly, users who are interested in music and movies often use streaming services. This makes it possible to learn communication patterns based on the user's hobbies and interests.

[0045] The mobile network monitoring system can further include an energy consumption monitoring unit that monitors the user's energy consumption. For example, it can analyze the battery consumption and charging patterns of the user's smartphone to optimize energy consumption. It can also predict energy consumption based on the user's device usage status, enabling efficient energy management. This allows for comprehensive monitoring of the user's energy consumption and efficient energy management.

[0046] The communication pattern learning unit can learn communication patterns based on the user's voice data and image data. For example, the generation AI learns communication patterns based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in the communication patterns. This makes it possible to learn communication patterns based on the user's voice data and image data.

[0047] The mobile network monitoring system can further include a communication pattern learning unit that learns communication patterns based on the user's device usage history. For example, the generation AI can learn communication patterns based on the user's app installation history. For example, it can analyze changes in communication volume immediately after installing a new app. This makes it possible to learn communication patterns based on the user's device usage history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The communication pattern learning unit learns the communication patterns of users of various ages and genders. For example, it learns the patterns of young users who frequently use social media, and the patterns of elderly users who frequently use news apps. It also learns based on users' mobile service usage history and communication data. Step 2: The behavior reproducing unit reproduces user behavior based on the communication patterns learned by the communication pattern learning unit. For example, it reproduces behavior such as using a specific app at a specific time of day or consuming a certain amount of data. Step 3: The real-time monitoring unit monitors the performance of the mobile network in real time based on the user behavior reproduced by the behavior reproduction unit, detecting abnormalities such as slow communication speeds and unstable connections. Step 4: The anomaly detection unit detects anomalies in the performance monitored by the real-time monitoring unit. For example, it notifies you if the communication speed drops below a certain standard or if the connection is frequently dropped.

[0050] (Example 2) A mobile network monitoring system according to an embodiment of the present invention uses a generative AI to reproduce user behavior and monitors mobile networks using the generative AI embedded in smartphones. This allows the mobile network monitoring system to learn user communication patterns, monitor mobile network performance in real time, and detect anomalies.

[0051] A mobile network monitoring system according to an embodiment includes a generation AI, a communication pattern learning unit, a behavior reproduction unit, a real-time monitoring unit, and an anomaly detection unit. The generation AI includes a communication pattern learning unit that learns the communication patterns of users of various ages and genders. For example, the communication pattern learning unit learns patterns such as frequent social networking site use by young users and frequent news app use by elderly users. The communication pattern learning unit also performs learning based on the user's mobile service usage history and communication data. The behavior reproduction unit reproduces user behavior based on the communication patterns learned by the communication pattern learning unit. For example, it reproduces behavior such as using a specific app during a specific time period or consuming a certain amount of data. The real-time monitoring unit monitors mobile network performance in real time based on the user behavior reproduced by the behavior reproduction unit. For example, it detects anomalies such as a decrease in communication speed or unstable connection. The anomaly detection unit detects anomalies from the performance monitored by the real-time monitoring unit. For example, it issues a notification when communication speed falls below a certain standard or when connection is frequently lost. As a result, the mobile network monitoring system according to the embodiment can learn users' communication patterns, reproduce their behavior, monitor the performance of the mobile network in real time, and detect abnormalities.

[0052] The communication pattern learning unit can classify communication patterns based on the user's emotional state and learn patterns based on emotions. For example, when the generation AI learns a user's communication patterns, the communication pattern learning unit analyzes the user's emotional state in real time and classifies communication patterns into those when stress is high and those when stress is low. For example, it can learn patterns such as increased use of social media when stress is high. This makes it possible to classify communication patterns based on the user's emotional state and learn patterns based on emotions.

[0053] The communication pattern learning unit can learn communication patterns based on the user's location information and movement patterns. For example, the generation AI in the communication pattern learning unit learns communication patterns at specific locations based on the user's location information. For example, it analyzes patterns such as heavy email usage at work and heavy video streaming usage at home. This makes it possible to learn communication patterns based on the user's location information and movement patterns.

[0054] The communication pattern learning unit can learn communication patterns based on the user's device usage history. For example, the generation AI in the communication pattern learning unit learns communication patterns based on the user's app installation history. For example, it analyzes changes in communication volume immediately after installing a new app. This makes it possible to learn communication patterns based on the user's device usage history.

[0055] The communication pattern learning unit can learn communication patterns based on the user's voice data and image data. For example, the generation AI in the communication pattern learning unit learns communication patterns based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in the communication patterns. This makes it possible to learn communication patterns based on the user's voice data and image data.

[0056] The communication pattern learning unit can learn the communication patterns of users in different regions and cultural spheres. For example, the generation AI in the communication pattern learning unit learns the communication patterns of users in different regions and analyzes the differences between regions. For example, it clarifies the differences in communication patterns between urban and rural areas. This makes it possible to learn the communication patterns of users in different regions and cultural spheres.

[0057] The communication pattern learning unit can learn a communication pattern based on the emotional state of the user using the emotion estimation function. The communication pattern learning unit, for example, uses the emotion estimation function to learn a communication pattern based on the emotional state of the user. For example, the communication pattern when the user is emotionally excited is analyzed. In this way, the emotion estimation function can be used to learn a communication pattern based on the emotional state of the user.

[0058] The behavior reproduction unit can reproduce behavior based on the user's emotional state. For example, the behavior reproduction unit uses a generation AI to analyze the user's emotional state in real time and reproduce changes in behavior according to the emotion. For example, it can reproduce behavior such as increased use of social media when stress is high. This makes it possible to reproduce behavior based on the user's emotional state.

[0059] The behavior reproduction unit can reproduce behavior based on the user's device settings. For example, the behavior reproduction unit reproduces behavior according to the settings based on the user's device settings using a generation AI. For example, it reproduces the difference in behavior when notification settings are on and off. This makes it possible to reproduce behavior based on the user's device settings.

[0060] The behavior reproduction unit can reproduce behavior based on the user's past communication history and activity log. For example, the behavior reproduction unit uses a generation AI to reproduce past behavior patterns based on the user's past communication history. For example, it reproduces a pattern of using a specific app during a specific time period. This makes it possible to reproduce behavior based on the user's past communication history and activity log.

[0061] The behavior reproduction unit can reproduce behavior based on the user's voice commands and gesture operations. For example, the behavior reproduction unit reproduces natural behavior based on the user's voice commands using a generation AI. For example, it reproduces the behavior of launching an app with a voice command. This makes it possible to reproduce behavior based on the user's voice commands and gesture operations.

[0062] The behavior reproduction unit can reproduce user behavior on different devices. For example, the generation AI reproduces user behavior on a tablet and analyzes the differences between that and a smartphone. For example, it analyzes differences such as whether users watch more videos on a tablet and use social media more on a smartphone. This makes it possible to reproduce user behavior on different devices.

[0063] The behavior reproduction unit can reproduce behavior based on the emotional state of the user using the emotion estimation function. The behavior reproduction unit, for example, reproduces behavior based on the emotional state of the user using the emotion estimation function. For example, it reproduces behavior such as increased use of social media when the user is emotionally excited. In this way, it is possible to reproduce behavior based on the emotional state of the user using the emotion estimation function.

[0064] The real-time monitoring unit can monitor network performance based on the user's emotional state. For example, the real-time monitoring unit uses a generation AI to analyze the user's emotional state in real time and monitor changes in network performance according to the emotion. For example, it monitors performance changes such as a decrease in communication speed when stress is high. This makes it possible to monitor network performance based on the user's emotional state.

[0065] The real-time monitoring unit can monitor network performance based on the user's location information and movement patterns. For example, the generation AI monitors network performance at specific locations based on the user's location information. For example, it monitors performance such as whether communication speeds are fast at work and communication is stable at home. This makes it possible to monitor network performance based on the user's location information and movement patterns.

[0066] The real-time monitoring unit can monitor network performance based on the user's device settings and usage status. For example, the generation AI monitors network performance according to the settings based on the user's device settings. For example, it monitors the difference in performance when notification settings are on and off. This makes it possible to monitor network performance based on the user's device settings and usage status.

[0067] The real-time monitoring unit can monitor network performance based on the user's voice data and image data. For example, the generation AI monitors network performance based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in communication performance. This makes it possible to monitor network performance based on the user's voice data and image data.

[0068] The real-time monitoring unit can monitor network performance in different regions and cultural spheres. For example, the generation AI monitors network performance in different regions and analyzes the differences between regions. For example, it can clarify the differences in communication performance between urban and rural areas. This makes it possible to monitor network performance in different regions and cultural spheres.

[0069] The real-time monitoring unit can monitor network performance based on the emotional state of a user using the emotion estimation function. The real-time monitoring unit, for example, uses the emotion estimation function to monitor network performance based on the emotional state of a user. For example, it monitors performance changes such as a decrease in communication speed when the user is emotionally excited. In this way, it is possible to monitor network performance based on the emotional state of a user using the emotion estimation function.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The mobile network monitoring system can also be equipped with a health monitoring unit that monitors the user's health status. For example, it can measure the user's heart rate and blood pressure in real time and notify the user if any abnormalities are detected. It can also analyze the user's exercise volume and sleep patterns to monitor changes in the user's health status. This allows for comprehensive monitoring of the user's health status and allows for prompt action if an abnormality occurs.

[0072] The communication pattern learning unit can learn communication patterns based on a user's hobbies and interests. For example, the generation AI analyzes a user's web browsing history and app usage history to learn communication patterns related to specific hobbies and interests. For example, users who are interested in sports tend to frequently use sports-related apps and websites. Similarly, users who are interested in music and movies often use streaming services. This makes it possible to learn communication patterns based on the user's hobbies and interests.

[0073] The communication pattern learning unit can classify communication patterns based on the user's emotional state and learn patterns based on emotions. For example, when the generation AI learns a user's communication patterns, it analyzes the user's emotional state in real time and classifies communication patterns into those when stress is high and those when stress is low. For example, it can learn patterns such as increased use of social media when stress is high. This makes it possible to classify communication patterns based on the user's emotional state and learn patterns based on emotions.

[0074] The mobile network monitoring system can further include an energy consumption monitoring unit that monitors the user's energy consumption. For example, it can analyze the battery consumption and charging patterns of the user's smartphone to optimize energy consumption. It can also predict energy consumption based on the user's device usage status, enabling efficient energy management. This allows for comprehensive monitoring of the user's energy consumption and efficient energy management.

[0075] The communication pattern learning unit can learn communication patterns based on the user's voice data and image data. For example, the generation AI learns communication patterns based on the user's voice data. For example, it analyzes the frequency and content of voice commands and reflects this in the communication patterns. This makes it possible to learn communication patterns based on the user's voice data and image data.

[0076] The mobile network monitoring system can further include a real-time monitoring unit that monitors network performance based on the user's emotional state. For example, the generative AI can analyze the user's emotional state in real time and monitor changes in network performance according to the emotion. For example, it can monitor performance changes such as a decrease in communication speed when stress is high. This makes it possible to monitor network performance based on the user's emotional state.

[0077] The mobile network monitoring system can also be equipped with an anomaly detection unit that detects anomalies based on the user's emotional state. For example, the generative AI can analyze the user's emotional state in real time and detect the occurrence of anomalies according to the emotion. For example, it can detect anomalies such as a decrease in communication speed when stress is high. This makes it possible to detect anomalies based on the user's emotional state.

[0078] The mobile network monitoring system can also be equipped with a notification unit that sends notifications based on the user's emotional state. For example, the generation AI can analyze the user's emotional state in real time and send notifications according to the emotion. For example, when stress is high, a notification can be sent recommending a relaxation app. This allows appropriate notifications to be sent based on the user's emotional state.

[0079] The mobile network monitoring system can further include an optimization unit that optimizes communication patterns based on the user's emotional state. For example, the generation AI can analyze the user's emotional state in real time and optimize communication patterns according to the emotion. For example, optimization can be performed by prioritizing communication speed when stress is high. This makes it possible to optimize communication patterns based on the user's emotional state.

[0080] The mobile network monitoring system can further include a communication pattern learning unit that learns communication patterns based on the user's device usage history. For example, the generation AI can learn communication patterns based on the user's app installation history. For example, it can analyze changes in communication volume immediately after installing a new app. This makes it possible to learn communication patterns based on the user's device usage history.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The communication pattern learning unit learns the communication patterns of users of various ages and genders. For example, it learns the patterns of young users who frequently use social media, and the patterns of elderly users who frequently use news apps. It also learns based on users' mobile service usage history and communication data. Step 2: The behavior reproducing unit reproduces user behavior based on the communication patterns learned by the communication pattern learning unit. For example, it reproduces behavior such as using a specific app at a specific time of day or consuming a certain amount of data. Step 3: The real-time monitoring unit monitors the performance of the mobile network in real time based on the user behavior reproduced by the behavior reproduction unit, detecting abnormalities such as slow communication speeds and unstable connections. Step 4: The anomaly detection unit detects anomalies in the performance monitored by the real-time monitoring unit. For example, it notifies you if the communication speed drops below a certain standard or if the connection is frequently dropped.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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. Equipped with generative AI, The generated AI is a communication pattern learning unit that learns communication patterns of users of various ages and genders; a behavior reproducing unit that reproduces user behavior based on the communication pattern learned by the communication pattern learning unit; a real-time monitoring unit that monitors the performance of a mobile network in real time based on the user behavior reproduced by the behavior reproduction unit; an anomaly detection unit that detects anomalies from the performance monitored by the real-time monitoring unit. A system characterized by:

2. The communication pattern learning unit The communication pattern is learned based on the user's voice data and image data.

2. The system of claim 1.

3. The behavior reproduction unit Reproduce the behavior based on the user's past communication history and activity log 2. The system of claim 1.

4. The real-time monitoring unit Monitor network performance based on the user's location and movement patterns 2. The system of claim 1.

5. The communication pattern learning unit Classifying the communication patterns based on the emotional state of the user and learning emotion-based patterns.

2. The system of claim 1.

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