system

Abstract

The system according to this embodiment aims to appropriately manage children's smartphone use and maintain good parent-child relationships. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an inference unit, an operation unit, and a setting unit. The collection unit collects data on the child's smartphone usage. The analysis unit analyzes the data collected by the collection unit. The inference unit performs inferences based on the analysis results obtained by the analysis unit. The operation unit performs information manipulation based on the inference results obtained by the inference unit. The setting unit allows the parent to perform settings remotely.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to appropriately manage children's use of smartphones, and there are also problems in the parent-child relationship.

[0005] The system according to the embodiment aims to appropriately manage children's use of smartphones and maintain a good parent-child relationship.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an inference unit, an operation unit, and a setting unit. The data collection unit collects data on the child's smartphone usage. The analysis unit analyzes the data collected by the data collection unit. The inference unit performs inferences based on the analysis results obtained by the analysis unit. The operation unit performs information manipulation based on the inference results obtained by the inference unit. The setting unit allows the parent to perform settings remotely. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage children's smartphone use and maintain good parent-child relationships. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. [[ID=1于2]]

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

[0018] [[ID=于7]] The data processing device 12 includes a computer 于2, a database 24, and a communication I / F 26. The computer 于2 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smartphone usage control system according to an embodiment of the present invention is a system that uses a generating AI to safely and appropriately control a child's smartphone use. This smartphone usage control system analyzes the child's smartphone usage trends and makes inferences based on those trends. Next, it compares this with an ideal smartphone usage pattern set remotely by the parent, and the generating AI manipulates the information in real time. This makes it possible to guide the child's smartphone use to be safe and appropriate. Furthermore, this system aims to maintain a good parent-child relationship. For example, the generating AI analyzes the child's smartphone usage trends. In this process, it collects detailed data such as which apps the child uses, for how long, and what kind of content they access. For example, if a child uses a game app for a long time, the data is analyzed to understand the usage trend. Next, the generating AI makes inferences based on the analyzed usage trend. For example, if a child frequently uses a particular app during a specific time period, the AI ​​infers the usage pattern during that time period. This makes it possible to understand the characteristics of the child's smartphone use. Furthermore, it compares this with an ideal smartphone usage pattern set remotely by the parent. For example, if the parent sets "game apps can be used for up to one hour per day on weekdays," this setting is compared with the child's actual usage trends. This allows for an understanding of the gap between the ideal and reality. Finally, the generating AI manipulates information in real time. For example, if a child tries to use a game app beyond the set time, the generating AI will display a warning message or restrict app use. In this way, children's smartphone use can be guided towards safety and appropriateness. This system allows parents to remotely control their children's smartphone use, enabling children to use smartphones safely and appropriately. It can also help maintain a good parent-child relationship. For example, since the generating AI automatically controls the use without the parent having to directly intervene, friction between parents and children can be reduced. Thus, the smartphone use control system can safely and appropriately control children's smartphone use and maintain a good parent-child relationship.

[0029] The smartphone usage control system according to this embodiment comprises a collection unit, an analysis unit, an inference unit, an operation unit, and a setting unit. The collection unit collects data on the child's smartphone usage. This data includes, but is not limited to, app usage time, accessed websites, and call history. The collection unit also collects, for example, smartphone log data to provide data for understanding usage trends. The collection unit can also collect detailed data on the child's smartphone usage. For example, the collection unit collects data such as which apps the child uses, for how long, and what kind of content they access. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, statistical analysis of the data or the application of machine learning algorithms, but is not limited to these methods. The analysis unit uses the collected data to understand the child's smartphone usage trends. The analysis unit can also analyze data patterns to identify characteristics of the child's smartphone usage. The inference unit performs inferences based on the analysis results obtained by the analysis unit. The inference is performed by, for example, rule-based inference or Bayesian inference, but is not limited to these methods. The inference unit, for example, infers a child's smartphone usage patterns based on analysis results. The inference unit can also predict future trends in a child's smartphone usage based on the analysis results. The operation unit performs information manipulation based on the inference results obtained by the inference unit. Information manipulation is performed by methods such as restricting app usage or displaying warning messages, but is not limited to these examples. For example, the operation unit displays a warning message if a child attempts to use an app beyond a set time. The operation unit can also restrict app usage if a child attempts to use an app beyond a set time. The settings unit is for parents to make settings remotely. Settings are made by methods such as settings via a smartphone app or settings via a web interface, but is not limited to these examples. For example, the settings unit allows parents to set their ideal smartphone usage patterns. The settings unit also allows parents to change settings remotely. As a result, the smartphone usage control system according to this embodiment can safely and appropriately control a child's smartphone usage and maintain a good parent-child relationship.

[0030] The control unit includes a warning unit that displays a warning message if a child attempts to use the app beyond the set time limit. The warning message may include, but is not limited to, text messages or pop-up notifications. For example, the warning unit might display a message such as "You have exceeded your usage time. Take a break." if a child attempts to use the app beyond the set time limit. The warning unit can also display a warning via a pop-up notification if a child attempts to use the app beyond the set time limit. For example, the warning unit might display a warning message on the app screen to alert the child. This allows for the display of a warning message when a child attempts to use the app beyond the set time limit, thereby encouraging appropriate smartphone use.

[0031] The control unit includes a restriction unit that limits app usage if a child attempts to use an app beyond a set time limit. Methods for restricting app usage include, but are not limited to, limiting usage time or blocking specific apps. For example, the restriction unit temporarily stops app usage if a child attempts to use an app beyond a set time limit. The restriction unit can also completely block the use of specific apps. For instance, the restriction unit restricts app usage if a child attempts to use a game app beyond a set time limit. This encourages appropriate smartphone use by limiting app usage when children attempt to use apps beyond a set time limit.

[0032] The data collection unit can analyze a child's past smartphone usage history and select the optimal data collection method. For example, the data collection unit uses a generating AI to determine the timing of data collection based on the usage time of apps that the child frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the child's past usage history. For example, the data collection unit analyzes the child's past usage patterns, and the generating AI selects the optimal data collection method. This allows for efficient data collection by selecting the optimal data collection method based on past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past usage history data into the generating AI and have the generating AI select the optimal data collection method.

[0033] The data collection unit can filter data based on the child's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data on the use of learning-related apps when the child is at school. It can also prioritize collecting data on the use of entertainment-related apps when the child is at home. For example, the data collection unit can filter and collect data on the use of specific apps based on the child's areas of interest. This allows for the collection of more relevant data by filtering the data based on the child's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation into a generating AI and have the generating AI perform the filtering.

[0034] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data on the use of learning-related apps. Similarly, if the child is in a park, the data collection unit can prioritize the collection of data on the use of exercise-related apps. For example, if the child is at home, the data collection unit can prioritize the collection of data on the use of entertainment-related apps. This allows for the collection of more relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0035] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, if a child frequently uses a particular social media platform, the data collection unit will prioritize collecting usage data for that platform. The data collection unit can also prioritize collecting usage data for specific time periods if a child uses social media during those times. For example, the data collection unit can collect app usage data related to a child's interests from their social media activity. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's social media activity data into a generating AI and have the generating AI collect relevant data.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can use the generating AI to perform a detailed analysis on high-importance data. Conversely, the analysis unit can use the generating AI to perform a simplified analysis on low-importance data. For example, the analysis unit can use the generating AI to determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for learning-related data, the analysis unit can apply an algorithm in which the generating AI evaluates the learning effect. Similarly, for entertainment-related data, the analysis unit can apply an algorithm in which the generating AI evaluates usage time. For example, for social media-related data, the analysis unit can apply an algorithm in which the generating AI evaluates the frequency of interaction. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data category into the generating AI and have the generating AI apply the appropriate analysis algorithm.

[0038] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the generation AI may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data, with the generation AI performing the analysis later. For example, the generation AI determines the priority of analysis according to the data collection timing. This allows for efficient analysis by determining the priority of analysis according to the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection timing to the generation AI and have the generation AI determine the priority of analysis.

[0039] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data using the generating AI. Conversely, the analysis unit can also postpone the analysis of less relevant data using the generating AI. For example, the analysis unit can determine the order of analysis based on the relevance of the data using the generating AI. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the generating AI and have the generating AI adjust the order of analysis.

[0040] The inference unit can improve the accuracy of its inferences by considering the interrelationships between data during inference. For example, the inference unit can perform inferences by considering the interrelationships between learning-related data and entertainment-related data. It can also perform inferences by considering the interrelationships between social media-related data and exercise-related data. For example, the inference unit can use the generative AI to improve the accuracy of its inferences based on the interrelationships of the data. This improves the accuracy of inferences by considering the interrelationships of the data. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the interrelationships of the data into the generative AI and have the generative AI perform the improvement of the inference accuracy.

[0041] The inference unit can perform inferences while considering the attribute information of the data submitter. For example, the inference unit can use the generating AI to perform inferences based on the child's age and gender. The inference unit can also use the generating AI to perform inferences based on the child's grade level and type of school. For example, the inference unit can use the generating AI to perform inferences based on the child's hobbies and interests. This allows for the provision of more appropriate inference results by considering the submitter's attribute information. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the submitter's attribute information into the generating AI and have the generating AI perform the inference.

[0042] The inference unit can perform inference while considering the geographical distribution of the data. For example, the inference unit can use the generative AI to perform inference while considering the characteristics of the area where the child lives. The inference unit can also use the generative AI to perform inference while considering the characteristics of the area where the child attends school. For example, the inference unit can use the generative AI to perform inference while considering the geographical characteristics of places the child frequently visits. By considering geographical distribution, more appropriate inference results can be provided. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input geographical distribution data into the generative AI and have the generative AI perform the inference.

[0043] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit can perform inferences on learning-related data by referring to relevant academic papers. It can also perform inferences on entertainment-related data by referring to relevant market research reports. For example, the inference unit can perform inferences on social media-related data by referring to relevant research reports. This improves the accuracy of the inferences by referring to relevant literature. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature data into a generating AI and have the generating AI perform the inference accuracy improvement.

[0044] The control unit can analyze a child's past smartphone usage behavior during operation and select the optimal operation method. For example, the control unit's generating AI can suggest the optimal operation method based on the operation methods the child has frequently used in the past. The control unit can also prioritize suggesting specific operation methods based on the child's past usage behavior. For example, the control unit analyzes the child's past usage patterns, and the generating AI selects the optimal operation method. This allows for efficient operation by selecting the optimal operation method based on past usage behavior. Some or all of the above processing in the control unit may be performed using AI, or without AI. For example, the control unit can input past usage behavior data into the generating AI and have the generating AI select the optimal operation method.

[0045] The control unit can customize the means of operation based on the child's current living situation during operation. For example, if the child is at school, the control unit may prioritize providing learning-related means of operation. Similarly, if the child is at home, the control unit may prioritize providing entertainment-related means of operation. For example, the control unit may use a generating AI to customize the means of operation based on the child's current living situation. This allows for the provision of more appropriate operation methods by customizing the means of operation based on the current living situation. Some or all of the above-described processes in the control unit may be performed using AI, or not. For example, the control unit may input child living situation data into a generating AI and have the generating AI perform the customization of the means of operation.

[0046] The control unit can select the optimal control method by considering the child's geographical location information during operation. For example, if the child is at school, the control unit can prioritize providing learning-related control methods. Similarly, if the child is in a park, it can prioritize providing exercise-related control methods. For example, if the child is at home, it can prioritize providing entertainment-related control methods. This allows for the provision of more appropriate control methods by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, or without AI. For example, the control unit can input the child's geographical location data into a generating AI and have the generating AI select the optimal control method.

[0047] The control unit can analyze a child's social media activity during operation and suggest actions to take. For example, if a child frequently uses a particular social media platform, the control unit can suggest actions related to that platform. Furthermore, if a child uses social media during a specific time period, the control unit can suggest actions appropriate for that time period. For example, the control unit can suggest actions related to the child's interests based on their social media activity. This allows for the provision of more appropriate actions by analyzing social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the child's social media activity data into a generating AI and have the generating AI execute the suggestions for actions.

[0048] The configuration unit can select the optimal configuration method by referring to the parent's past configuration history during configuration. For example, the configuration unit's generating AI can suggest the optimal configuration method based on the configuration methods the parent has frequently used in the past. The configuration unit can also preferentially suggest specific configuration methods based on the parent's past configuration history. For example, the configuration unit can analyze the parent's past configuration patterns, and the generating AI can select the optimal configuration method. This allows for efficient configuration by selecting the optimal configuration method based on past configuration history. Some or all of the above-described processes in the configuration unit may be performed using AI, or they may not. For example, the configuration unit can input past configuration history data into the generating AI and have the generating AI select the optimal configuration method.

[0049] The configuration unit can customize the configuration process based on the parent's current living situation during configuration. For example, if the parent is at work, the generating AI can provide a simplified configuration method. Alternatively, if the parent is at home, the generating AI can provide a more detailed configuration method. For instance, the configuration unit can customize the configuration method based on the parent's current living situation. This allows for a more appropriate configuration method to be provided by customizing the configuration method based on the current living situation. Some or all of the above-described processes in the configuration unit may be performed using AI, or not. For example, the configuration unit can input parent's living situation data into the generating AI and have the generating AI perform the customization of the configuration method.

[0050] The configuration unit can select the optimal configuration method by considering the parent's geographical location information during configuration. For example, if the parent is at work, the generating AI can provide a simplified configuration method. Alternatively, if the parent is at home, the generating AI can provide a more detailed configuration method. For example, the configuration unit can select the optimal configuration method based on the parent's geographical location information. This allows for the provision of a more appropriate configuration method by considering geographical location information. Some or all of the above-described processes in the configuration unit may be performed using AI, or without AI. For example, the configuration unit can input the parent's geographical location data into the generating AI and have the generating AI select the optimal configuration method.

[0051] The configuration unit can analyze the parent's social media activity during configuration and suggest configuration options. For example, if the parent frequently uses a particular social media platform, the configuration unit will suggest configuration options related to that platform. Furthermore, if the parent uses social media during a specific time period, the configuration unit can suggest configuration options appropriate for that time period. For example, the configuration unit will suggest configuration options related to the parent's interests based on their social media activity. This allows for the provision of more appropriate configuration options by analyzing social media activity. Some or all of the above-described processes in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the parent's social media activity data into a generating AI and have the generating AI propose configuration options.

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

[0053] The analysis unit can analyze children's smartphone usage data while taking into account the child's learning progress. For example, if a child is using a specific learning app, it can analyze the usage time of that app in relation to learning outcomes. The analysis unit can also compare the time periods when a child is using learning apps with the time periods when they are using other apps to identify apps that are hindering learning. Furthermore, the analysis unit can analyze the optimal timing for using learning apps based on the child's learning progress. This allows for the identification of smartphone usage patterns that maximize the effectiveness of a child's learning.

[0054] The control unit can include a notification unit that sends a notification to the parent if the child attempts to use an app for longer than the set time. For example, if the child attempts to use a game app for longer than the set time, a notification will be sent to the parent's smartphone. The notification unit can also send a notification to the parent if the child is using a specific app for an extended period of time. Furthermore, the notification unit can send a weekly report to the parent regarding the child's smartphone usage. This allows the parent to understand their child's smartphone usage in real time and take appropriate action.

[0055] The control unit can include a suggestion unit that proposes alternative activities if a child attempts to use an app for longer than the set time. For example, if a child is using a game app for an extended period, the suggestion unit can suggest alternative activities such as reading or exercise. The suggestion unit can also suggest appropriate activities based on the child's interests. Furthermore, the suggestion unit can select and suggest activities from a list set by the parent. This helps children develop interests in activities other than smartphones and lead a balanced life.

[0056] The data collection unit can analyze a child's past smartphone usage history and select the optimal data collection method. For example, the generating AI determines the timing of data collection based on the usage time of apps the child frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the child's past usage history. Furthermore, the data collection unit can analyze the child's past usage patterns, and the generating AI can select the optimal data collection method. This allows for efficient data collection by selecting the optimal data collection method based on past usage history.

[0057] The data collection unit can filter data based on the child's current lifestyle and areas of interest. For example, it can prioritize collecting data on learning-related apps while the child is at school. It can also prioritize collecting data on entertainment-related apps while the child is at home. Furthermore, the unit can filter and collect data on specific apps based on the child's areas of interest. This allows for the collection of more relevant data by filtering data based on the child's lifestyle and areas of interest.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, it can prioritize the collection of data on the use of learning-related apps. Similarly, if the child is in a park, it can prioritize the collection of data on the use of exercise-related apps. Furthermore, if the child is at home, it can prioritize the collection of data on the use of entertainment-related apps. This allows for the collection of more relevant data by considering geographical location.

[0059] The data collection unit can analyze children's social media activity and collect relevant data during the data collection process. For example, if a child frequently uses a particular social media platform, it can prioritize collecting usage data for that platform. The unit can also prioritize collecting usage data for specific time periods if a child uses social media during those times. Furthermore, it can collect app usage data related to a child's interests from their social media activity. This allows for the collection of more relevant data by analyzing social media activity.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects data on the child's smartphone usage. This data includes app usage time, websites visited, and call history. The collection unit collects smartphone log data to provide information for understanding usage trends. The collection unit also collects detailed data such as which apps the child uses, for how long, and what kind of content they access. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as statistical analysis of the data and the application of machine learning algorithms. Based on the collected data, the analysis unit grasps the trends in children's smartphone usage and identifies the characteristics of children's smartphone usage by analyzing the patterns in the data. Step 3: The inference unit performs inference based on the analysis results obtained by the analysis unit. Inference is performed using methods such as rule-based inference or Bayesian inference. Based on the analysis results, the inference unit infers the child's smartphone usage patterns and predicts future trends in the child's smartphone usage based on the analysis results. Step 4: The control unit manipulates information based on the inference results obtained by the inference unit. This manipulation can be done in ways such as restricting app usage or displaying warning messages. The control unit can also display a warning message and restrict app usage if a child attempts to use the app beyond a set time limit. Step 5: The settings section is where parents can remotely configure the device. Configuration can be done through a smartphone app or a web interface. The settings section allows parents to set their ideal smartphone usage patterns and remotely change the settings.

[0062] (Example of form 2) The smartphone usage control system according to an embodiment of the present invention is a system that uses a generating AI to safely and appropriately control a child's smartphone use. This smartphone usage control system analyzes the child's smartphone usage trends and makes inferences based on those trends. Next, it compares this with an ideal smartphone usage pattern set remotely by the parent, and the generating AI manipulates the information in real time. This makes it possible to guide the child's smartphone use to be safe and appropriate. Furthermore, this system aims to maintain a good parent-child relationship. For example, the generating AI analyzes the child's smartphone usage trends. In this process, it collects detailed data such as which apps the child uses, for how long, and what kind of content they access. For example, if a child uses a game app for a long time, the data is analyzed to understand the usage trend. Next, the generating AI makes inferences based on the analyzed usage trend. For example, if a child frequently uses a particular app during a specific time period, the AI ​​infers the usage pattern during that time period. This makes it possible to understand the characteristics of the child's smartphone use. Furthermore, it compares this with an ideal smartphone usage pattern set remotely by the parent. For example, if the parent sets "game apps can be used for up to one hour per day on weekdays," this setting is compared with the child's actual usage trends. This allows for an understanding of the gap between the ideal and reality. Finally, the generating AI manipulates information in real time. For example, if a child tries to use a game app beyond the set time, the generating AI will display a warning message or restrict app use. In this way, children's smartphone use can be guided towards safety and appropriateness. This system allows parents to remotely control their children's smartphone use, enabling children to use smartphones safely and appropriately. It can also help maintain a good parent-child relationship. For example, since the generating AI automatically controls the use without the parent having to directly intervene, friction between parents and children can be reduced. Thus, the smartphone use control system can safely and appropriately control children's smartphone use and maintain a good parent-child relationship.

[0063] The smartphone usage control system according to this embodiment comprises a collection unit, an analysis unit, an inference unit, an operation unit, and a setting unit. The collection unit collects data on the child's smartphone usage. This data includes, but is not limited to, app usage time, accessed websites, and call history. The collection unit also collects, for example, smartphone log data to provide data for understanding usage trends. The collection unit can also collect detailed data on the child's smartphone usage. For example, the collection unit collects data such as which apps the child uses, for how long, and what kind of content they access. The analysis unit analyzes the data collected by the collection unit. The analysis is performed by, for example, statistical analysis of the data or the application of machine learning algorithms, but is not limited to these methods. The analysis unit uses the collected data to understand the child's smartphone usage trends. The analysis unit can also analyze data patterns to identify characteristics of the child's smartphone usage. The inference unit performs inferences based on the analysis results obtained by the analysis unit. The inference is performed by, for example, rule-based inference or Bayesian inference, but is not limited to these methods. The inference unit, for example, infers a child's smartphone usage patterns based on analysis results. The inference unit can also predict future trends in a child's smartphone usage based on the analysis results. The operation unit performs information manipulation based on the inference results obtained by the inference unit. Information manipulation is performed by methods such as restricting app usage or displaying warning messages, but is not limited to these examples. For example, the operation unit displays a warning message if a child attempts to use an app beyond a set time. The operation unit can also restrict app usage if a child attempts to use an app beyond a set time. The settings unit is for parents to make settings remotely. Settings are made by methods such as settings via a smartphone app or settings via a web interface, but is not limited to these examples. For example, the settings unit allows parents to set their ideal smartphone usage patterns. The settings unit also allows parents to change settings remotely. As a result, the smartphone usage control system according to this embodiment can safely and appropriately control a child's smartphone usage and maintain a good parent-child relationship.

[0064] The control unit includes a warning unit that displays a warning message if a child attempts to use the app beyond the set time limit. The warning message may include, but is not limited to, text messages or pop-up notifications. For example, the warning unit might display a message such as "You have exceeded your usage time. Take a break." if a child attempts to use the app beyond the set time limit. The warning unit can also display a warning via a pop-up notification if a child attempts to use the app beyond the set time limit. For example, the warning unit might display a warning message on the app screen to alert the child. This allows for the display of a warning message when a child attempts to use the app beyond the set time limit, thereby encouraging appropriate smartphone use.

[0065] The control unit includes a restriction unit that limits app usage if a child attempts to use an app beyond a set time limit. Methods for restricting app usage include, but are not limited to, limiting usage time or blocking specific apps. For example, the restriction unit temporarily stops app usage if a child attempts to use an app beyond a set time limit. The restriction unit can also completely block the use of specific apps. For instance, the restriction unit restricts app usage if a child attempts to use a game app beyond a set time limit. This encourages appropriate smartphone use by limiting app usage when children attempt to use apps beyond a set time limit.

[0066] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, if a child is stressed, the data collection unit can use a generative AI to reduce the frequency of data collection, thereby reducing the child's burden. Conversely, if a child is relaxed, the data collection unit can use a generative AI to increase the frequency of data collection, collecting more detailed usage data. For example, if a child is excited, the data collection unit can use a generative AI to adjust the timing of data collection, collecting data at the appropriate time. This allows for the collection of detailed usage data while reducing the child's burden by adjusting the timing of data collection according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze a child's past smartphone usage history and select the optimal data collection method. For example, the data collection unit uses a generating AI to determine the timing of data collection based on the usage time of apps that the child frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the child's past usage history. For example, the data collection unit analyzes the child's past usage patterns, and the generating AI selects the optimal data collection method. This allows for efficient data collection by selecting the optimal data collection method based on past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past usage history data into the generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter data based on the child's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data on the use of learning-related apps when the child is at school. It can also prioritize collecting data on the use of entertainment-related apps when the child is at home. For example, the data collection unit can filter and collect data on the use of specific apps based on the child's areas of interest. This allows for the collection of more relevant data by filtering the data based on the child's living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation into a generating AI and have the generating AI perform the filtering.

[0069] The data collection unit can estimate a child's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a child is stressed, the data collection unit's generating AI will prioritize the collection of data on the use of stress-reducing apps. Similarly, if a child is relaxed, the data collection unit's generating AI can prioritize the collection of data on the use of entertainment-related apps. For example, if a child is excited, the data collection unit's generating AI will prioritize the collection of data on the use of apps to calm the child. This allows for the collection of more appropriate data by prioritizing data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input child emotion data into a generating AI and have the generating AI determine the data priority.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data on the use of learning-related apps. Similarly, if the child is in a park, the data collection unit can prioritize the collection of data on the use of exercise-related apps. For example, if the child is at home, the data collection unit can prioritize the collection of data on the use of entertainment-related apps. This allows for the collection of more relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location data into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0071] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, if a child frequently uses a particular social media platform, the data collection unit will prioritize collecting usage data for that platform. The data collection unit can also prioritize collecting usage data for specific time periods if a child uses social media during those times. For example, the data collection unit can collect app usage data related to a child's interests from their social media activity. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's social media activity data into a generating AI and have the generating AI collect relevant data.

[0072] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is stressed, the generation AI can provide a simple and visually clear analysis result. The analysis unit can also provide a detailed analysis result if the child is relaxed. For example, if the child is excited, the generation AI can provide a visually stimulating analysis result. By adjusting the presentation of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can use the generating AI to perform a detailed analysis on high-importance data. Conversely, the analysis unit can use the generating AI to perform a simplified analysis on low-importance data. For example, the analysis unit can use the generating AI to determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, for learning-related data, the analysis unit can apply an algorithm in which the generating AI evaluates the learning effect. Similarly, for entertainment-related data, the analysis unit can apply an algorithm in which the generating AI evaluates usage time. For example, for social media-related data, the analysis unit can apply an algorithm in which the generating AI evaluates the frequency of interaction. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data category into the generating AI and have the generating AI apply the appropriate analysis algorithm.

[0075] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is stressed, the generating AI can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the child is relaxed. For example, if the child is excited, the generating AI can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the generation AI may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data, with the generation AI performing the analysis later. For example, the generation AI determines the priority of analysis according to the data collection timing. This allows for efficient analysis by determining the priority of analysis according to the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection timing to the generation AI and have the generation AI determine the priority of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data using the generating AI. Conversely, the analysis unit can also postpone the analysis of less relevant data using the generating AI. For example, the analysis unit can determine the order of analysis based on the relevance of the data using the generating AI. This allows for efficient analysis by adjusting the order of analysis according to the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the generating AI and have the generating AI adjust the order of analysis.

[0078] The inference unit can estimate a child's emotions and adjust its inference criteria based on the estimated emotions. For example, if a child is stressed, the generating AI will perform inferences that prioritize stress reduction. Similarly, if a child is relaxed, the generating AI can perform inferences to maintain that relaxation. For example, if a child is excited, the generating AI can perform inferences to suppress the excitement. By adjusting the inference criteria according to the child's emotions, more appropriate inference results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may 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 processes in the inference unit may be performed using AI, or not. For example, the inference unit can input child emotion data into the generating AI and have the generating AI adjust the inference criteria.

[0079] The inference unit can improve the accuracy of its inferences by considering the interrelationships between data during inference. For example, the inference unit can perform inferences by considering the interrelationships between learning-related data and entertainment-related data. It can also perform inferences by considering the interrelationships between social media-related data and exercise-related data. For example, the inference unit can use the generative AI to improve the accuracy of its inferences based on the interrelationships of the data. This improves the accuracy of inferences by considering the interrelationships of the data. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the interrelationships of the data into the generative AI and have the generative AI perform the improvement of the inference accuracy.

[0080] The inference unit can perform inferences while considering the attribute information of the data submitter. For example, the inference unit can use the generating AI to perform inferences based on the child's age and gender. The inference unit can also use the generating AI to perform inferences based on the child's grade level and type of school. For example, the inference unit can use the generating AI to perform inferences based on the child's hobbies and interests. This allows for the provision of more appropriate inference results by considering the submitter's attribute information. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input the submitter's attribute information into the generating AI and have the generating AI perform the inference.

[0081] The inference unit can estimate a child's emotions and adjust the order in which the inference results are displayed based on the estimated emotions. For example, if the child is stressed, the generating AI can display important results first. The inference unit can also have the generating AI display detailed results sequentially if the child is relaxed. For example, if the child is excited, the generating AI can display visually stimulating results first. This allows for the provision of more appropriate information by adjusting the order in which the inference results are displayed according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the inference unit may be performed using AI, or not using AI. For example, the inference unit can input child emotion data into the generating AI and have the generating AI adjust the order in which the results are displayed.

[0082] The inference unit can perform inference while considering the geographical distribution of the data. For example, the inference unit can use the generative AI to perform inference while considering the characteristics of the area where the child lives. The inference unit can also use the generative AI to perform inference while considering the characteristics of the area where the child attends school. For example, the inference unit can use the generative AI to perform inference while considering the geographical characteristics of places the child frequently visits. By considering geographical distribution, more appropriate inference results can be provided. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input geographical distribution data into the generative AI and have the generative AI perform the inference.

[0083] The inference unit can improve the accuracy of its inferences by referring to relevant literature during the inference process. For example, the inference unit can perform inferences on learning-related data by referring to relevant academic papers. It can also perform inferences on entertainment-related data by referring to relevant market research reports. For example, the inference unit can perform inferences on social media-related data by referring to relevant research reports. This improves the accuracy of the inferences by referring to relevant literature. Some or all of the above processing in the inference unit may be performed using AI, for example, or without AI. For example, the inference unit can input relevant literature data into a generating AI and have the generating AI perform the inference accuracy improvement.

[0084] The control unit can estimate the child's emotions and adjust the operation method based on the estimated emotions. For example, if the child is stressed, the control unit can use a generative AI to provide a simple and visually clear operation method. The control unit can also use a generative AI to provide a more detailed operation method if the child is relaxed. For example, if the child is excited, the control unit can use a generative AI to provide a visually stimulating operation method. This allows for more appropriate operation methods to be provided by adjusting the operation method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may 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 processes in the control unit may be performed using AI, or not. For example, the control unit can input child emotion data into the generative AI and have the generative AI adjust the operation method.

[0085] The control unit can analyze a child's past smartphone usage behavior during operation and select the optimal operation method. For example, the control unit's generating AI can suggest the optimal operation method based on the operation methods the child has frequently used in the past. The control unit can also prioritize suggesting specific operation methods based on the child's past usage behavior. For example, the control unit analyzes the child's past usage patterns, and the generating AI selects the optimal operation method. This allows for efficient operation by selecting the optimal operation method based on past usage behavior. Some or all of the above processing in the control unit may be performed using AI, or without AI. For example, the control unit can input past usage behavior data into the generating AI and have the generating AI select the optimal operation method.

[0086] The control unit can customize the means of operation based on the child's current living situation during operation. For example, if the child is at school, the control unit may prioritize providing learning-related means of operation. Similarly, if the child is at home, the control unit may prioritize providing entertainment-related means of operation. For example, the control unit may use a generating AI to customize the means of operation based on the child's current living situation. This allows for the provision of more appropriate operation methods by customizing the means of operation based on the current living situation. Some or all of the above-described processes in the control unit may be performed using AI, or not. For example, the control unit may input child living situation data into a generating AI and have the generating AI perform the customization of the means of operation.

[0087] The control unit can estimate the child's emotions and determine the priority of operations based on the estimated emotions. For example, if the child is stressed, the control unit's generating AI will prioritize operations related to stress reduction. Similarly, if the child is relaxed, the control unit's generating AI can prioritize operations related to entertainment. For example, if the child is excited, the control unit's generating AI will prioritize operations to calm the excitement. This allows for more appropriate operation methods by prioritizing operations according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may 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 processes in the control unit may be performed using AI, or not. For example, the control unit can input the child's emotion data into the generating AI and have the generating AI determine the priority of operations.

[0088] The control unit can select the optimal control method by considering the child's geographical location information during operation. For example, if the child is at school, the control unit can prioritize providing learning-related control methods. Similarly, if the child is in a park, it can prioritize providing exercise-related control methods. For example, if the child is at home, it can prioritize providing entertainment-related control methods. This allows for the provision of more appropriate control methods by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, or without AI. For example, the control unit can input the child's geographical location data into a generating AI and have the generating AI select the optimal control method.

[0089] The control unit can analyze a child's social media activity during operation and suggest actions to take. For example, if a child frequently uses a particular social media platform, the control unit can suggest actions related to that platform. Furthermore, if a child uses social media during a specific time period, the control unit can suggest actions appropriate for that time period. For example, the control unit can suggest actions related to the child's interests based on their social media activity. This allows for the provision of more appropriate actions by analyzing social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the child's social media activity data into a generating AI and have the generating AI execute the suggestions for actions.

[0090] The settings unit can estimate the parent's emotions and adjust the settings method based on the estimated emotions. For example, if the parent is stressed, the settings unit can use a generating AI to provide a simple and visually clear settings method. If the parent is relaxed, the settings unit can also use a generating AI to provide a more detailed settings method. For example, if the parent is excited, the settings unit can use a generating AI to provide a visually stimulating settings method. This allows for the provision of a more appropriate settings method by adjusting the settings method according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input parent emotion data into the generating AI and have the generating AI adjust the settings method.

[0091] The configuration unit can select the optimal configuration method by referring to the parent's past configuration history during configuration. For example, the configuration unit's generating AI can suggest the optimal configuration method based on the configuration methods the parent has frequently used in the past. The configuration unit can also preferentially suggest specific configuration methods based on the parent's past configuration history. For example, the configuration unit can analyze the parent's past configuration patterns, and the generating AI can select the optimal configuration method. This allows for efficient configuration by selecting the optimal configuration method based on past configuration history. Some or all of the above-described processes in the configuration unit may be performed using AI, or they may not. For example, the configuration unit can input past configuration history data into the generating AI and have the generating AI select the optimal configuration method.

[0092] The configuration unit can customize the configuration process based on the parent's current living situation during configuration. For example, if the parent is at work, the generating AI can provide a simplified configuration method. Alternatively, if the parent is at home, the generating AI can provide a more detailed configuration method. For instance, the configuration unit can customize the configuration method based on the parent's current living situation. This allows for a more appropriate configuration method to be provided by customizing the configuration method based on the current living situation. Some or all of the above-described processes in the configuration unit may be performed using AI, or not. For example, the configuration unit can input parent's living situation data into the generating AI and have the generating AI perform the customization of the configuration method.

[0093] The settings unit can estimate the parent's emotions and determine the priority of settings based on the estimated parent's emotions. For example, if the parent is stressed, the settings unit's generating AI will prioritize settings related to stress reduction. The settings unit can also prioritize detailed settings if the parent is relaxed. For example, if the parent is agitated, the settings unit's generating AI will prioritize visually stimulating settings. This allows for a more appropriate setting method by determining the priority of settings according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input parent emotion data into the generating AI and have the generating AI determine the priority of settings.

[0094] The configuration unit can select the optimal configuration method by considering the parent's geographical location information during configuration. For example, if the parent is at work, the generating AI can provide a simplified configuration method. Alternatively, if the parent is at home, the generating AI can provide a more detailed configuration method. For example, the configuration unit can select the optimal configuration method based on the parent's geographical location information. This allows for the provision of a more appropriate configuration method by considering geographical location information. Some or all of the above-described processes in the configuration unit may be performed using AI, or without AI. For example, the configuration unit can input the parent's geographical location data into the generating AI and have the generating AI select the optimal configuration method.

[0095] The configuration unit can analyze the parent's social media activity during configuration and suggest configuration options. For example, if the parent frequently uses a particular social media platform, the configuration unit will suggest configuration options related to that platform. Furthermore, if the parent uses social media during a specific time period, the configuration unit can suggest configuration options appropriate for that time period. For example, the configuration unit will suggest configuration options related to the parent's interests based on their social media activity. This allows for the provision of more appropriate configuration options by analyzing social media activity. Some or all of the above-described processes in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the parent's social media activity data into a generating AI and have the generating AI propose configuration options. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, operation unit, and setting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects log data from the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The inference unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs inference based on the analysis results. The operation unit is implemented by the control unit 46A of the smart device 14 and performs information manipulation based on the inference results. The setting unit is implemented by the control unit 46A of the smart device 14 and allows the parent to perform settings remotely. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, operation unit, and setting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects log data from the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The inference unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs inference based on the analysis results. The operation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs information manipulation based on the inference results. The setting unit is implemented, for example, by the control unit 46A of the smart glasses 214 and is configured remotely by the parent. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, operation unit, and setting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects log data from the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The inference unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs inference based on the analysis results. The operation unit is implemented by the control unit 46A of the headset terminal 314 and performs information operations based on the inference results. The setting unit is implemented by the control unit 46A of the headset terminal 314 and allows the parent to perform settings remotely. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, inference unit, operation unit, and setting unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects log data of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The inference unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs inference based on the analysis results. The operation unit is implemented, for example, by the control unit 46A of the robot 414 and performs information manipulation based on the inference results. The setting unit is implemented, for example, by the control unit 46A of the robot 414 and is configured remotely by the master.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can analyze children's smartphone usage data while taking into account the child's learning progress. For example, if a child is using a specific learning app, it can analyze the usage time of that app in relation to learning outcomes. The analysis unit can also compare the time periods when a child is using learning apps with the time periods when they are using other apps to identify apps that are hindering learning. Furthermore, the analysis unit can analyze the optimal timing for using learning apps based on the child's learning progress. This allows for the identification of smartphone usage patterns that maximize the effectiveness of a child's learning.

[0098] The control unit can include a notification unit that sends a notification to the parent if the child attempts to use an app for longer than the set time. For example, if the child attempts to use a game app for longer than the set time, a notification will be sent to the parent's smartphone. The notification unit can also send a notification to the parent if the child is using a specific app for an extended period of time. Furthermore, the notification unit can send a weekly report to the parent regarding the child's smartphone usage. This allows the parent to understand their child's smartphone usage in real time and take appropriate action.

[0099] The control unit can include a suggestion unit that proposes alternative activities if a child attempts to use an app for longer than the set time. For example, if a child is using a game app for an extended period, the suggestion unit can suggest alternative activities such as reading or exercise. The suggestion unit can also suggest appropriate activities based on the child's interests. Furthermore, the suggestion unit can select and suggest activities from a list set by the parent. This helps children develop interests in activities other than smartphones and lead a balanced life.

[0100] The data collection unit can estimate a child's emotions and adjust the content of data collection based on the estimated emotions. For example, if a child is stressed, the generating AI will prioritize collecting data on the use of stress-reducing apps. Similarly, if a child is relaxed, the generating AI can prioritize collecting data on the use of entertainment-related apps. Furthermore, if a child is excited, the generating AI can prioritize collecting data on apps that help calm the child. This allows for the collection of more relevant data by adjusting the content of data collection according to the child's emotions.

[0101] The data collection unit can analyze a child's past smartphone usage history and select the optimal data collection method. For example, the generating AI determines the timing of data collection based on the usage time of apps the child frequently used in the past. The data collection unit can also concentrate data collection during specific time periods based on the child's past usage history. Furthermore, the data collection unit can analyze the child's past usage patterns, and the generating AI can select the optimal data collection method. This allows for efficient data collection by selecting the optimal data collection method based on past usage history.

[0102] The data collection unit can filter data based on the child's current lifestyle and areas of interest. For example, it can prioritize collecting data on learning-related apps while the child is at school. It can also prioritize collecting data on entertainment-related apps while the child is at home. Furthermore, the unit can filter and collect data on specific apps based on the child's areas of interest. This allows for the collection of more relevant data by filtering data based on the child's lifestyle and areas of interest.

[0103] The data collection unit can estimate a child's emotions and prioritize the data to collect based on those estimates. For example, if a child is stressed, the generating AI will prioritize collecting data on the use of stress-reducing apps. Similarly, if a child is relaxed, the generating AI can prioritize collecting data on the use of entertainment-related apps. Furthermore, if a child is agitated, the generating AI can prioritize collecting data on apps that help calm the agitation. This allows for the collection of more relevant data by prioritizing data according to the child's emotions.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, it can prioritize the collection of data on the use of learning-related apps. Similarly, if the child is in a park, it can prioritize the collection of data on the use of exercise-related apps. Furthermore, if the child is at home, it can prioritize the collection of data on the use of entertainment-related apps. This allows for the collection of more relevant data by considering geographical location.

[0105] The data collection unit can analyze children's social media activity and collect relevant data during the data collection process. For example, if a child frequently uses a particular social media platform, it can prioritize collecting usage data for that platform. The unit can also prioritize collecting usage data for specific time periods if a child uses social media during those times. Furthermore, it can collect app usage data related to a child's interests from their social media activity. This allows for the collection of more relevant data by analyzing social media activity.

[0106] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a child is stressed, the generating AI provides a simple and easy-to-understand analysis result. The analysis unit can also provide a more detailed analysis result if the child is relaxed. Furthermore, if the child is excited, the generating AI can provide a visually stimulating analysis result. By adjusting the presentation of the analysis according to the child's emotions, more appropriate analysis results can be provided.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects data on the child's smartphone usage. This data includes app usage time, websites visited, and call history. The collection unit collects smartphone log data to provide information for understanding usage trends. The collection unit also collects detailed data such as which apps the child uses, for how long, and what kind of content they access. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as statistical analysis of the data and the application of machine learning algorithms. Based on the collected data, the analysis unit grasps the trends in children's smartphone usage and identifies the characteristics of children's smartphone usage by analyzing the patterns in the data. Step 3: The inference unit performs inference based on the analysis results obtained by the analysis unit. Inference is performed using methods such as rule-based inference or Bayesian inference. Based on the analysis results, the inference unit infers the child's smartphone usage patterns and predicts future trends in the child's smartphone usage based on the analysis results. Step 4: The control unit manipulates information based on the inference results obtained by the inference unit. This manipulation can be done in ways such as restricting app usage or displaying warning messages. The control unit can also display a warning message and restrict app usage if a child attempts to use the app beyond a set time limit. Step 5: The settings section is where parents can remotely configure the device. Configuration can be done through a smartphone app or a web interface. The settings section allows parents to set their ideal smartphone usage patterns and remotely change the settings.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on children's smartphone usage, An analysis unit analyzes the data collected by the aforementioned collection unit, An inference unit that performs inference based on the analysis results obtained by the analysis unit, An operation unit that performs information manipulation based on the inference results obtained by the inference unit, It includes a setting unit that allows the parent to configure settings remotely. A system characterized by the following features.

2. The aforementioned operating unit is It includes a warning section that displays a warning message if a child attempts to use the app beyond the set time limit. The system according to feature 1.

3. The aforementioned operating unit is It includes a restriction section that limits app usage if a child attempts to use the app beyond a set time limit. The system according to feature 1.

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

5. The aforementioned collection unit is Analyze children's past smartphone usage history to select the optimal data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the child's geographical location. The system according to feature 1.

9. The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system according to feature 1.

10. The aforementioned analysis unit, We estimate the child's emotions and adjust the representation of the analysis based on the estimated emotions. The system according to feature 1.

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