System

Abstract

An object of a system according to an embodiment is to appropriately grasp a mental state and an indoor environment of a child and notify a parent of the mental state and the indoor environment.SOLUTION: A system includes a collection unit, an analysis unit, a notification unit, a sensor unit, and an adjustment unit. The collection unit collects statement contents of the child. The analysis unit analyzes the remark contents collected by the collection unit and estimates an emotion. The notification unit notifies the parent based on the emotion estimated by the analysis unit. The sensor unit monitors an indoor environment. The adjusting unit adjusts the indoor environment based on the data monitored by the sensor unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately grasp a child's mental state and indoor environment when they are alone and notify parents of this, so there is room for improvement.

[0005] The system according to the embodiment aims to properly grasp the mental state and indoor environment of a child and notify the parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a notification unit, a sensor unit, and an adjustment unit. The collection unit collects the child's speech content. The analysis unit analyzes the speech content collected by the collection unit and infers emotions. The notification unit notifies the parent based on the emotions inferred by the analysis unit. The sensor unit monitors the indoor environment. The adjustment unit adjusts the indoor environment based on the data monitored by the sensor unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately grasp the mental state and indoor environment of a child and notify the parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes a generative AI to support children's alone time. This system collects the child's speech, analyzes it, infers their emotions, and notifies their parents. It also monitors the indoor environment and automatically adjusts it. For example, the generative AI can serve as an educational assistant by reading stories to children or studying with them. The generative AI analyzes the child's speech, infers their emotions, and notifies their parents. For example, if a child says, "I had a fight with a friend at school today," the generative AI notifies the parent. Furthermore, the system uses sensors to monitor the indoor environment and automatically adjusts it. For example, if the room temperature is too high, the system adjusts the air conditioner to maintain an appropriate temperature. It also has a function that immediately notifies parents if a danger signal is being signaled or if a child is in danger. For example, if a child is in danger of falling from a high place, the generative AI notifies the parent. This allows parents, even those with working parents, to enjoy their children's alone time with peace of mind. This system supports children's alone time and provides parents with a sense of security. For example, by collecting what a child says and inferring their emotions and notifying parents, parents can understand their child's mental state. It also automatically adjusts the indoor environment to provide a comfortable environment for children. Furthermore, it provides powerful support in terms of safety by immediately notifying parents if a dangerous situation occurs.

[0029] A child support system according to an embodiment includes a collection unit, an analysis unit, a notification unit, a sensor unit, and an adjustment unit. The collection unit collects the child's speech. For example, the collection unit collects the child's voice using a microphone and converts it into text data. The collection unit can also directly collect text data entered by the child. The collection unit can also collect the child's gestures using a camera and analyze them. The analysis unit analyzes the speech collected by the collection unit and infers the child's emotions. For example, the analysis unit analyzes voice data and infers the child's emotions from the child's tone of voice and choice of words. The analysis unit can also analyze text data and infer the child's emotions. The analysis unit can also analyze gesture data and infer the child's emotions. The notification unit notifies the parent based on the emotions inferred by the analysis unit. For example, the notification unit notifies the parent in real time via a smartphone app. The notification unit can also send periodic reports. The sensor unit monitors the indoor environment. For example, the sensor unit monitors the indoor environment using a temperature sensor, a humidity sensor, and a motion sensor. The adjustment unit adjusts the indoor environment based on the data monitored by the sensor unit. For example, the adjustment unit adjusts the air conditioner to maintain an appropriate room temperature. The adjustment unit can also adjust the humidifier to maintain appropriate humidity. As a result, the child support system according to the embodiment can support children's time alone and provide parents with a sense of security. For example, by collecting the content of a child's comments, inferring their emotions, and notifying the parents, parents can understand their child's mental state. Furthermore, by automatically adjusting the indoor environment, an environment in which the child can spend time comfortably is provided. Furthermore, by immediately notifying the parents in the event of a dangerous situation, strong support is also provided in terms of safety.

[0030] The collection unit can analyze the child's past speech history and select an appropriate collection method. For example, the collection unit prioritizes collection of topics that the child has talked about frequently in the past. The collection unit can also analyze the language patterns used by the child in the past and adjust the collection method. Furthermore, if the collection unit determines from the child's past speech history that there is a lot of speech during a specific time period, it can increase the collection frequency during that time period. In this way, the optimal collection method can be selected by analyzing the past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past speech history into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting the utterances, the collection unit can filter them based on the child's current activity status. For example, if the child is studying, the collection unit prioritizes collecting utterances related to studying. Also, if the child is playing, the collection unit can collect utterances related to playing. Furthermore, if the child is eating, the collection unit can collect utterances related to eating. In this way, by filtering the utterances based on the child's current activity status, highly relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's current activity status to the generation AI and cause the generation AI to filter the utterances.

[0032] When collecting utterances, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child speaks aloud, the collection unit collects the utterances using voice recognition technology. In addition, if the child inputs text, the collection unit can also collect the utterances using text analysis technology. Furthermore, if the child expresses themselves using gestures, the collection unit can also collect the utterances using gesture recognition technology. This allows for efficient collection of utterances by selecting the optimal collection means depending on the child's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0033] When collecting utterance content, the collection unit can prioritize collecting highly relevant utterances by taking into account the child's geographical location information. For example, when the child is at school, the collection unit can prioritize collecting utterances related to the school. Furthermore, when the child is at a park, the collection unit can also prioritize collecting utterances related to the park. Furthermore, when the child is at home, the collection unit can also prioritize collecting utterances related to the home. In this way, by taking the child's geographical location information into account, highly relevant utterances can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant utterances.

[0034] When collecting the content of comments, the collection unit can analyze the child's social media activities and collect related comments. For example, the collection unit analyzes content posted by the child on social media and collects related comments. The collection unit can also collect related comments by referring to the activities of the child's friends on social media. Furthermore, the collection unit can collect related comments based on the child's check-in information on social media. In this way, related comments can be collected by analyzing the child's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's social media activities into the generation AI and cause the generation AI to collect related comments.

[0035] When collecting utterance content, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting topics that the child liked in the past. The collection unit can also avoid collecting topics that the child avoided in the past. Furthermore, the collection unit can adjust the collection method based on the child's past feedback. In this way, the collection method can be customized by reflecting the child's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the statement content. For example, the analysis unit performs a detailed analysis of important statement content. The analysis unit can also perform a concise analysis of general statement content. Furthermore, the analysis unit can also perform a detailed analysis of statement content in which emotional changes are observed. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the statement content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the statement content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the utterance content. For example, the analysis unit can apply an educational analysis algorithm to utterance content related to learning. The analysis unit can also apply an algorithm that analyzes play patterns to utterance content related to play. Furthermore, the analysis unit can apply an emotion analysis algorithm to utterance content related to emotions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the utterance content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of the utterance content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, analyzes the content of the child's current utterances more accurately based on the child's past analysis results. The analysis unit can also identify patterns of emotional changes from the past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time when the comment content was submitted. For example, the analysis unit prioritizes analysis of recent comment content. The analysis unit can also prioritize analysis of comment content submitted within a specific time period. Furthermore, the analysis unit can also prioritize analysis of comment content submitted immediately after an important event. This enables efficient analysis by determining the priority of analysis based on the time when the comment content was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the comment content was submitted to the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the utterance content. For example, the analysis unit prioritizes analysis of highly relevant utterance content. The analysis unit can also postpone analysis of less relevant utterance content. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the utterance content. This enables efficient analysis by adjusting the order of analysis based on the relevance of the utterance content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the utterance content to the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Also, if the child's level of expertise is low, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the child's level of expertise. This allows appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the child's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise into the generation AI and have the generation AI use technical terminology.

[0042] The notification unit can adjust the level of detail of the notification based on the importance of the emotion when notifying. For example, the notification unit sends a detailed notification to the parent for an important emotional change. The notification unit can also send a concise notification to the parent for a general emotional change. Furthermore, the notification unit can dynamically adjust the level of detail of the notification based on the importance of the emotion. This makes it possible to provide appropriate information to the parent by adjusting the level of detail of the notification based on the importance of the emotion. Some or all of the above-described processing in the notification unit may be performed using, or without, AI, for example. For example, the notification unit can input the importance of the emotion to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0043] The notification unit can apply different notification algorithms depending on the emotion category when notifying. For example, the notification unit can apply an educational notification algorithm to emotions related to learning. The notification unit can also apply an algorithm that notifies play patterns to emotions related to play. The notification unit can also apply an emotion analysis algorithm to emotion-related utterances. In this way, by applying different notification algorithms depending on the emotion category, appropriate information can be provided to parents. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the emotion category to the generation AI and cause the generation AI to apply the notification algorithm.

[0044] The notification unit can improve the accuracy of notifications by referring to the parent's past notification results when sending notifications. The notification unit, for example, adjusts the content of the current notification more accurately based on the parent's past notification results. The notification unit can also identify the parent's reaction pattern from the past notification results and reflect it in the current notification. Furthermore, the notification unit can adjust the notification algorithm by referring to the past notification results. In this way, the accuracy of notifications can be improved by referring to the parent's past notification results. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the parent's past notification results into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0045] The notification unit can determine the priority of notifications based on the time when the emotion occurred when notifying. For example, the notification unit can prioritize notification of recent emotional changes. The notification unit can also prioritize notification of emotional changes that occurred during a specific time period. Furthermore, the notification unit can prioritize notification of emotional changes that occurred immediately after an important event. In this way, by determining the priority of notifications based on the time when the emotion occurred, appropriate information can be provided to parents. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the time when the emotion occurred to the generation AI and have the generation AI determine the priority of notifications.

[0046] The notification unit can adjust the order of notifications based on the relevance of emotions when notifying. For example, the notification unit prioritizes notification of highly relevant emotional changes. The notification unit can also postpone notification of less relevant emotional changes. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of emotions. This makes it possible to provide appropriate information to parents by adjusting the order of notifications based on the relevance of emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of emotions to a generation AI and cause the generation AI to adjust the order of notifications.

[0047] The notification unit can adjust the use of technical terminology in the notification depending on the parent's level of expertise. For example, if the parent's level of expertise is high, the notification unit can provide a notification that uses a lot of technical terminology. Alternatively, if the parent's level of expertise is low, the notification unit can provide a notification in simple language. Furthermore, the notification unit can adjust the way the notification content is expressed depending on the parent's level of expertise. This allows appropriate information to be provided to the parent by adjusting the use of technical terminology in the notification depending on the parent's level of expertise. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the parent's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0048] The sensor unit can appropriately position sensors by referring to past data. For example, the sensor unit determines the most effective sensor position based on past data. The sensor unit can also position additional sensors in specific locations based on past data. Furthermore, the sensor unit can dynamically adjust the sensor position by referring to past data. In this way, the sensor position can be optimized by referring to past data. Some or all of the above-described processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input past data into a generation AI and cause the generation AI to optimize the sensor position.

[0049] The sensor unit can switch the sensor's operation mode according to different environmental conditions. For example, when the room temperature is high, the sensor unit can switch the sensor's operation mode to cooling mode. Furthermore, when the humidity is high, the sensor unit can switch the sensor's operation mode to dehumidification mode. Furthermore, when the lighting is low, the sensor unit can switch the sensor's operation mode to lighting mode. This enables optimal data collection by switching the sensor's operation mode according to different environmental conditions. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input environmental conditions to the generation AI and have the generation AI switch the operation mode.

[0050] The sensor unit can analyze the child's activity pattern and adjust the sensor operation timing. For example, if the child is active, the sensor unit can increase the frequency of the sensor operation. Also, if the child is quiet, the sensor unit can reduce the frequency of the sensor operation. Furthermore, the sensor unit can dynamically adjust the sensor operation timing based on the child's activity pattern. This enables efficient data collection by adjusting the sensor operation timing based on the child's activity pattern. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the child's activity pattern to a generation AI and have the generation AI adjust the operation timing.

[0051] The sensor unit can improve data accuracy by combining different types of sensors. For example, the sensor unit can combine a temperature sensor and a humidity sensor to collect more accurate indoor environment data. The sensor unit can also combine a motion sensor and a sound sensor to obtain detailed information about a child's activity status. Furthermore, the sensor unit can combine a lighting sensor and a temperature sensor to maintain an optimal indoor environment. This allows for improving data accuracy by combining different types of sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input different types of sensor data into the generation AI and have the generation AI improve the data accuracy.

[0052] The sensor unit can complement environmental data in cooperation with an external data source. The sensor unit can, for example, optimize the indoor environment in cooperation with weather forecast data. The sensor unit can also adjust the indoor temperature in cooperation with external temperature data. Furthermore, the sensor unit can also adjust the indoor humidity in cooperation with external humidity data. In this way, by cooperating with an external data source, environmental data can be complemented and more accurate data can be collected. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input external data sources into the generation AI and cause the generation AI to complement the environmental data.

[0053] The sensor unit can dynamically change the placement of the sensors to improve the efficiency of data collection. The sensor unit dynamically changes the placement of the sensors depending on, for example, the location of a child's activity. The sensor unit can also dynamically adjust the placement of the sensors depending on indoor environmental conditions. Furthermore, the sensor unit can dynamically change the placement of the sensors to improve the efficiency of data collection. This makes it possible to improve the efficiency of data collection by dynamically changing the placement of the sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the placement of the sensors to a generation AI and cause the generation AI to dynamically change the placement.

[0054] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data. For example, the adjustment unit develops an algorithm that maintains an optimal indoor environment based on the past adjustment data. The adjustment unit can also identify an optimal adjustment method under specific conditions from the past adjustment data. Furthermore, the adjustment unit can dynamically adjust the adjustment algorithm by referring to the past adjustment data. In this way, the adjustment algorithm can be optimized by referring to the past adjustment data. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the past adjustment data into the generation AI and cause the generation AI to optimize the algorithm.

[0055] The adjustment unit can dynamically change the adjustment parameters according to different environmental conditions. For example, when the room temperature is high, the adjustment unit dynamically changes the cooling parameters. Furthermore, when the humidity is high, the adjustment unit can also dynamically change the dehumidification parameters. Furthermore, when the lighting is dim, the adjustment unit can dynamically change the lighting parameters. In this way, by dynamically changing the adjustment parameters according to different environmental conditions, an optimal environment can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input environmental conditions to the generation AI and cause the generation AI to dynamically change the parameters.

[0056] The adjustment unit can analyze the child's activity pattern and adjust the timing of the adjustment appropriately. For example, if the child is active, the adjustment unit can make the adjustment timing more frequent. Also, if the child is quiet, the adjustment unit can reduce the timing of the adjustment. Furthermore, the adjustment unit can dynamically adjust the timing of the adjustment based on the child's activity pattern. This enables efficient environmental adjustment by optimizing the timing of the adjustment based on the child's activity pattern. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the child's activity pattern to the generation AI and cause the generation AI to adjust the timing.

[0057] The adjustment unit can adjust the indoor environment by coordinating different types of devices. For example, the adjustment unit can coordinate an air conditioner and a humidifier to maintain an optimal indoor environment. The adjustment unit can also coordinate lighting and a music player to create a relaxing environment. Furthermore, the adjustment unit can coordinate a temperature sensor and a humidity sensor to maintain a comfortable indoor environment. In this way, an optimal indoor environment can be maintained by coordinating different types of devices. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input different types of devices into the generation AI and cause the generation AI to execute device coordination.

[0058] The adjustment unit can improve the accuracy of the adjustment in cooperation with an external data source. The adjustment unit can, for example, optimize the indoor environment in cooperation with weather forecast data. The adjustment unit can also adjust the indoor temperature in cooperation with external temperature data. Furthermore, the adjustment unit can also adjust the indoor humidity in cooperation with external humidity data. This allows the adjustment accuracy to be improved by cooperation with an external data source. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input an external data source into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0059] The adjustment unit can feed back the results of the adjustment and reflect them in the next adjustment. The adjustment unit, for example, optimizes the next adjustment method based on the results of the adjustment. The adjustment unit can also identify the optimal adjustment method under specific conditions from the results of the adjustment. Furthermore, the adjustment unit can dynamically adjust the adjustment algorithm by referring to the results of the adjustment. In this way, by feeding back the results of the adjustment, it is possible to reflect them in the next adjustment and provide a more optimal environment. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the results of the adjustment to a generation AI and reflect them in the next adjustment.

[0060] The notification unit can provide real-time notifications through a smartphone app. For example, the notification unit can notify parents in real time of changes in the child's statements and emotions. The notification unit can also notify parents in real time of changes in the indoor environment or dangerous situations. This allows parents to immediately understand the child's situation by providing real-time notifications through a smartphone app. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the child's statements and changes in emotions into a generation AI and have the generation AI execute real-time notifications.

[0061] The notification unit can send periodic reports. For example, the notification unit can periodically send a report to the parent on the content of the child's statements and changes in emotions. The notification unit can also periodically send a report to the parent on changes in the indoor environment and dangerous situations. By sending periodic reports, the parent can continuously understand the situation of the child. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the content of the child's statements and changes in emotions into the generation AI and have the generation AI execute periodic reports.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The notification unit can adjust the timing of notifications taking into account the parent's schedule. For example, if the parent is in a meeting, the notification unit can delay notifications until the meeting is over. Also, if the parent is driving, the notification unit can hold off notifications until the parent finishes driving. Furthermore, if the parent is on vacation, the notification unit can adjust to send only important notifications. This allows the parent to receive information at an appropriate time by adjusting the timing of notifications according to the parent's schedule.

[0064] The adjustment unit can monitor the child's health condition and adjust the indoor environment based on the child's health condition. For example, if the child has a cold, the adjustment unit can maintain the room temperature appropriately and adjust the humidity. If the child has allergies, the adjustment unit can also operate an air purifier to improve air quality. Furthermore, if the child is tired, the adjustment unit can dim the lights to provide a relaxing environment. In this way, adjusting the indoor environment based on the child's health condition can provide a more comfortable environment.

[0065] When collecting a child's speech content, the collection unit can adjust the collection method according to the child's age. For example, for a young child, the collection unit can prioritize collecting simple words and gestures. For an elementary school student, the collection unit can also collect more complex speech content. Furthermore, for a middle school student, the collection unit can prioritize collecting speech content related to learning. In this way, by adjusting the collection method according to the child's age, more appropriate information can be collected.

[0066] When analyzing a child's utterances, the analysis unit can evaluate the child's learning progress and adjust the content of the analysis based on the evaluation results. For example, if a child is struggling with a particular subject, the analysis unit can analyze utterances related to that subject in detail. Alternatively, if a child shows a high level of understanding of a particular topic, the analysis unit can analyze utterances related to that topic briefly. Furthermore, if the child's learning progress changes, the analysis unit can dynamically adjust the content of the analysis in response to those changes. This allows for more appropriate analysis results to be provided by adjusting the content of the analysis based on the child's learning progress.

[0067] When collecting the content of a child's statements, the collection unit can adjust the collection method taking into account the child's friendships. For example, if the child is with friends, the collection unit will prioritize collecting conversations with friends. Also, if the child is alone, the collection unit can collect monologue and self-expression. Furthermore, if the child makes a new friend, the collection unit can prioritize collecting statements about that friend. In this way, by adjusting the collection method according to the child's friendships, more appropriate information can be collected.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects what the child says. For example, the collection unit collects the child's voice using a microphone and converts it into text data. The collection unit can also directly collect text data entered by the child. Furthermore, the collection unit can collect the child's gestures using a camera and analyze them. Step 2: The analysis unit analyzes the speech content collected by the collection unit and infers emotions. For example, the analysis unit analyzes voice data and infers emotions from a child's tone of voice and choice of words. The analysis unit can also analyze text data and infer emotions. Furthermore, the analysis unit can analyze gesture data and infer emotions. Step 3: The notification unit notifies the parent based on the emotion inferred by the analysis unit. For example, the notification unit may notify the parent in real time via a smartphone app. The notification unit may also send periodic reports. Step 4: The sensor unit monitors the indoor environment. For example, the sensor unit monitors the indoor environment using a temperature sensor, a humidity sensor, and a motion sensor. Step 5: The adjustment unit adjusts the indoor environment based on the data monitored by the sensor unit. For example, the adjustment unit may adjust the air conditioner to maintain an appropriate room temperature. The adjustment unit may also adjust the humidifier to maintain an appropriate humidity level.

[0070] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to support children's alone time. This system collects the child's speech, analyzes it, infers their emotions, and notifies their parents. It also monitors the indoor environment and automatically adjusts it. For example, the generative AI can serve as an educational assistant by reading stories to children or studying with them. The generative AI analyzes the child's speech, infers their emotions, and notifies their parents. For example, if a child says, "I had a fight with a friend at school today," the generative AI notifies the parent. Furthermore, the system uses sensors to monitor the indoor environment and automatically adjusts it. For example, if the room temperature is too high, the system adjusts the air conditioner to maintain an appropriate temperature. It also has a function that immediately notifies parents if a danger signal is being signaled or if a child is in danger. For example, if a child is in danger of falling from a high place, the generative AI notifies the parent. This allows parents, even those with working parents, to enjoy their children's alone time with peace of mind. This system supports children's alone time and provides parents with a sense of security. For example, by collecting what a child says and inferring their emotions and notifying parents, parents can understand their child's mental state. It also automatically adjusts the indoor environment to provide a comfortable environment for children. Furthermore, it provides powerful support in terms of safety by immediately notifying parents if a dangerous situation occurs.

[0071] A child support system according to an embodiment includes a collection unit, an analysis unit, a notification unit, a sensor unit, and an adjustment unit. The collection unit collects the child's speech. For example, the collection unit collects the child's voice using a microphone and converts it into text data. The collection unit can also directly collect text data entered by the child. The collection unit can also collect the child's gestures using a camera and analyze them. The analysis unit analyzes the speech collected by the collection unit and infers the child's emotions. For example, the analysis unit analyzes voice data and infers the child's emotions from the child's tone of voice and choice of words. The analysis unit can also analyze text data and infer the child's emotions. The analysis unit can also analyze gesture data and infer the child's emotions. The notification unit notifies the parent based on the emotions inferred by the analysis unit. For example, the notification unit notifies the parent in real time via a smartphone app. The notification unit can also send periodic reports. The sensor unit monitors the indoor environment. For example, the sensor unit monitors the indoor environment using a temperature sensor, a humidity sensor, and a motion sensor. The adjustment unit adjusts the indoor environment based on the data monitored by the sensor unit. For example, the adjustment unit adjusts the air conditioner to maintain an appropriate room temperature. The adjustment unit can also adjust the humidifier to maintain appropriate humidity. As a result, the child support system according to the embodiment can support children's time alone and provide parents with a sense of security. For example, by collecting the content of a child's comments, inferring their emotions, and notifying the parents, parents can understand their child's mental state. Furthermore, by automatically adjusting the indoor environment, an environment in which the child can spend time comfortably is provided. Furthermore, by immediately notifying the parents in the event of a dangerous situation, strong support is also provided in terms of safety.

[0072] The collection unit can estimate the child's emotions and adjust the frequency of collection of speech content based on the estimated emotions. For example, when the child is excited, the collection unit can collect speech content frequently to obtain detailed information. Furthermore, when the child is calm, the collection unit can reduce the frequency of collection of speech content and collect only necessary information. Furthermore, when the child is sad, the collection unit can collect speech content at an appropriate frequency to track changes in the child's emotions. In this way, more appropriate information can be collected by adjusting the frequency of collection of speech content according to the child's emotions. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input voice data to a generation AI to estimate the child's emotions and cause the generation AI to estimate the emotions.

[0073] The collection unit can analyze the child's past speech history and select an appropriate collection method. For example, the collection unit prioritizes collection of topics that the child has talked about frequently in the past. The collection unit can also analyze the language patterns used by the child in the past and adjust the collection method. Furthermore, if the collection unit determines from the child's past speech history that there is a lot of speech during a specific time period, it can increase the collection frequency during that time period. In this way, the optimal collection method can be selected by analyzing the past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past speech history into the generation AI and have the generation AI select the optimal collection method.

[0074] When collecting the utterances, the collection unit can filter them based on the child's current activity status. For example, if the child is studying, the collection unit prioritizes collecting utterances related to studying. Also, if the child is playing, the collection unit can collect utterances related to playing. Furthermore, if the child is eating, the collection unit can collect utterances related to eating. In this way, by filtering the utterances based on the child's current activity status, highly relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's current activity status to the generation AI and cause the generation AI to filter the utterances.

[0075] When collecting utterances, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child speaks aloud, the collection unit collects the utterances using voice recognition technology. In addition, if the child inputs text, the collection unit can also collect the utterances using text analysis technology. Furthermore, if the child expresses themselves using gestures, the collection unit can also collect the utterances using gesture recognition technology. This allows for efficient collection of utterances by selecting the optimal collection means depending on the child's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0076] The collection unit can estimate the child's emotions and determine the priority of the utterance content to be collected based on the estimated emotions. For example, if the child is excited, the collection unit can prioritize collecting the utterance content to track changes in the child's emotions. Furthermore, if the child is calm, the collection unit can prioritize collecting important utterance content. Furthermore, if the child is sad, the collection unit can prioritize collecting related utterance content to identify the cause of the emotion. Thus, by prioritizing the utterance content based on the child's emotions, important information can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data to a generation AI to estimate the child's emotions and cause the generation AI to estimate the emotions.

[0077] When collecting utterance content, the collection unit can prioritize collecting highly relevant utterances by taking into account the child's geographical location information. For example, when the child is at school, the collection unit can prioritize collecting utterances related to the school. Furthermore, when the child is at a park, the collection unit can also prioritize collecting utterances related to the park. Furthermore, when the child is at home, the collection unit can also prioritize collecting utterances related to the home. In this way, by taking the child's geographical location information into account, highly relevant utterances can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant utterances.

[0078] When collecting the content of comments, the collection unit can analyze the child's social media activities and collect related comments. For example, the collection unit analyzes content posted by the child on social media and collects related comments. The collection unit can also collect related comments by referring to the activities of the child's friends on social media. Furthermore, the collection unit can collect related comments based on the child's check-in information on social media. In this way, related comments can be collected by analyzing the child's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's social media activities into the generation AI and cause the generation AI to collect related comments.

[0079] When collecting utterance content, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting topics that the child liked in the past. The collection unit can also avoid collecting topics that the child avoided in the past. Furthermore, the collection unit can adjust the collection method based on the child's past feedback. In this way, the collection method can be customized by reflecting the child's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the child's emotions and adjust the way the analysis is expressed based on the estimated emotions. For example, if the child is excited, the analysis unit can provide a detailed analysis result. Also, if the child is calm, the analysis unit can provide a concise analysis result. Furthermore, if the child is sad, the analysis unit can perform a detailed analysis to identify the cause of the emotion. This allows for adjusting the way the analysis is expressed based on the child's emotions to provide a more appropriate analysis result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI to estimate the child's emotions and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the statement content. For example, the analysis unit performs a detailed analysis of important statement content. The analysis unit can also perform a concise analysis of general statement content. Furthermore, the analysis unit can also perform a detailed analysis of statement content in which emotional changes are observed. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the statement content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the statement content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the utterance content. For example, the analysis unit can apply an educational analysis algorithm to utterance content related to learning. The analysis unit can also apply an algorithm that analyzes play patterns to utterance content related to play. Furthermore, the analysis unit can apply an emotion analysis algorithm to utterance content related to emotions. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the utterance content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the category of the utterance content to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, analyzes the content of the child's current utterances more accurately based on the child's past analysis results. The analysis unit can also identify patterns of emotional changes from the past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can also adjust the analysis algorithm by referring to the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] 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 excited, the analysis unit can perform a detailed analysis and create a longer report. Alternatively, if the child is calm, the analysis unit can perform a concise analysis and create a shorter report. Furthermore, if the child is sad, the analysis unit can perform a detailed analysis to identify the cause of the emotion and create a longer report. This allows appropriate analysis results to be provided by adjusting the length of the analysis based on the child's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI to estimate the child's emotions and have the generation AI perform emotion estimation.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time when the comment content was submitted. For example, the analysis unit prioritizes analysis of recent comment content. The analysis unit can also prioritize analysis of comment content submitted within a specific time period. Furthermore, the analysis unit can also prioritize analysis of comment content submitted immediately after an important event. This enables efficient analysis by determining the priority of analysis based on the time when the comment content was submitted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the comment content was submitted to the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the utterance content. For example, the analysis unit prioritizes analysis of highly relevant utterance content. The analysis unit can also postpone analysis of less relevant utterance content. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the utterance content. This enables efficient analysis by adjusting the order of analysis based on the relevance of the utterance content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the utterance content to the generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terminology. Also, if the child's level of expertise is low, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the child's level of expertise. This allows appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the child's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise into the generation AI and have the generation AI use technical terminology.

[0088] The notification unit can estimate the child's emotions and adjust the way the notification is expressed based on the estimated emotions. For example, if the child is excited, the notification unit can send a detailed notification to the parent. Also, if the child is calm, the notification unit can send a concise notification to the parent. Furthermore, if the child is sad, the notification unit can send a detailed notification to the parent to identify the cause of the emotion. In this way, by adjusting the way the notification is expressed based on the child's emotions, appropriate information can be provided to the parent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input voice data to a generation AI to estimate the child's emotions and have the generation AI perform emotion estimation.

[0089] The notification unit can adjust the level of detail of the notification based on the importance of the emotion when notifying. For example, the notification unit sends a detailed notification to the parent for an important emotional change. The notification unit can also send a concise notification to the parent for a general emotional change. Furthermore, the notification unit can dynamically adjust the level of detail of the notification based on the importance of the emotion. This makes it possible to provide appropriate information to the parent by adjusting the level of detail of the notification based on the importance of the emotion. Some or all of the above-described processing in the notification unit may be performed using, or without, AI, for example. For example, the notification unit can input the importance of the emotion to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0090] The notification unit can apply different notification algorithms depending on the emotion category when notifying. For example, the notification unit can apply an educational notification algorithm to emotions related to learning. The notification unit can also apply an algorithm that notifies play patterns to emotions related to play. The notification unit can also apply an emotion analysis algorithm to emotion-related utterances. In this way, by applying different notification algorithms depending on the emotion category, appropriate information can be provided to parents. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the emotion category to the generation AI and cause the generation AI to apply the notification algorithm.

[0091] The notification unit can improve the accuracy of notifications by referring to the parent's past notification results when sending notifications. The notification unit, for example, adjusts the content of the current notification more accurately based on the parent's past notification results. The notification unit can also identify the parent's reaction pattern from the past notification results and reflect it in the current notification. Furthermore, the notification unit can adjust the notification algorithm by referring to the past notification results. In this way, the accuracy of notifications can be improved by referring to the parent's past notification results. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the parent's past notification results into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0092] The notification unit can estimate the child's emotion and adjust the length of the notification based on the estimated emotion. For example, if the child is excited, the notification unit can send a detailed notification to the parent. If the child is calm, the notification unit can also send a brief notification to the parent. Furthermore, if the child is sad, the notification unit can send a detailed notification to the parent to identify the cause of the emotion. In this way, by adjusting the length of the notification based on the child's emotion, appropriate information can be provided to the parent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input voice data to a generation AI to estimate the child's emotion and have the generation AI perform emotion estimation.

[0093] The notification unit can determine the priority of notifications based on the time when the emotion occurred when notifying. For example, the notification unit can prioritize notification of recent emotional changes. The notification unit can also prioritize notification of emotional changes that occurred during a specific time period. Furthermore, the notification unit can prioritize notification of emotional changes that occurred immediately after an important event. In this way, by determining the priority of notifications based on the time when the emotion occurred, appropriate information can be provided to parents. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the time when the emotion occurred to the generation AI and have the generation AI determine the priority of notifications.

[0094] The notification unit can adjust the order of notifications based on the relevance of emotions when notifying. For example, the notification unit prioritizes notification of highly relevant emotional changes. The notification unit can also postpone notification of less relevant emotional changes. Furthermore, the notification unit can dynamically adjust the order of notifications based on the relevance of emotions. This makes it possible to provide appropriate information to parents by adjusting the order of notifications based on the relevance of emotions. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the relevance of emotions to a generation AI and cause the generation AI to adjust the order of notifications.

[0095] The notification unit can adjust the use of technical terminology in the notification depending on the parent's level of expertise. For example, if the parent's level of expertise is high, the notification unit can provide a notification that uses a lot of technical terminology. Alternatively, if the parent's level of expertise is low, the notification unit can provide a notification in simple language. Furthermore, the notification unit can adjust the way the notification content is expressed depending on the parent's level of expertise. This allows appropriate information to be provided to the parent by adjusting the use of technical terminology in the notification depending on the parent's level of expertise. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the parent's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0096] The sensor unit can estimate the child's emotions and adjust the sensitivity of the sensor based on the estimated emotions. For example, if the child is excited, the sensor unit increases the sensitivity of the sensor to collect more detailed data. Furthermore, if the child is calm, the sensor unit can decrease the sensitivity of the sensor to collect only necessary data. Furthermore, if the child is sad, the sensor unit can appropriately adjust the sensitivity of the sensor to track changes in emotions. By adjusting the sensitivity of the sensor based on the child's emotions, more detailed data can be collected. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input sensor data to a generation AI to estimate the child's emotions, and have the generation AI perform emotion estimation.

[0097] The sensor unit can appropriately position sensors by referring to past data. For example, the sensor unit determines the most effective sensor position based on past data. The sensor unit can also position additional sensors in specific locations based on past data. Furthermore, the sensor unit can dynamically adjust the sensor position by referring to past data. In this way, the sensor position can be optimized by referring to past data. Some or all of the above-described processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input past data into a generation AI and cause the generation AI to optimize the sensor position.

[0098] The sensor unit can switch the sensor's operation mode according to different environmental conditions. For example, when the room temperature is high, the sensor unit can switch the sensor's operation mode to cooling mode. Furthermore, when the humidity is high, the sensor unit can switch the sensor's operation mode to dehumidification mode. Furthermore, when the lighting is low, the sensor unit can switch the sensor's operation mode to lighting mode. This enables optimal data collection by switching the sensor's operation mode according to different environmental conditions. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input environmental conditions to the generation AI and have the generation AI switch the operation mode.

[0099] The sensor unit can analyze the child's activity pattern and adjust the sensor operation timing. For example, if the child is active, the sensor unit can increase the frequency of the sensor operation. Also, if the child is quiet, the sensor unit can reduce the frequency of the sensor operation. Furthermore, the sensor unit can dynamically adjust the sensor operation timing based on the child's activity pattern. This enables efficient data collection by adjusting the sensor operation timing based on the child's activity pattern. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the child's activity pattern to a generation AI and have the generation AI adjust the operation timing.

[0100] The sensor unit can estimate the child's emotions and adjust the data collection frequency of the sensor based on the estimated emotions. For example, if the child is excited, the sensor unit can increase the data collection frequency to collect detailed data. Furthermore, if the child is calm, the sensor unit can decrease the data collection frequency to collect only necessary data. Furthermore, if the child is sad, the sensor unit can appropriately adjust the data collection frequency to track changes in the child's emotions. Thus, by adjusting the data collection frequency of the sensor based on the child's emotions, more detailed data can be collected. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input sensor data to a generation AI to estimate the child's emotions and have the generation AI perform emotion estimation.

[0101] The sensor unit can improve data accuracy by combining different types of sensors. For example, the sensor unit can combine a temperature sensor and a humidity sensor to collect more accurate indoor environment data. The sensor unit can also combine a motion sensor and a sound sensor to obtain detailed information about a child's activity status. Furthermore, the sensor unit can combine a lighting sensor and a temperature sensor to maintain an optimal indoor environment. This allows for improving data accuracy by combining different types of sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input different types of sensor data into the generation AI and have the generation AI improve the data accuracy.

[0102] The sensor unit can complement environmental data in cooperation with an external data source. The sensor unit can, for example, optimize the indoor environment in cooperation with weather forecast data. The sensor unit can also adjust the indoor temperature in cooperation with external temperature data. Furthermore, the sensor unit can also adjust the indoor humidity in cooperation with external humidity data. In this way, by cooperating with an external data source, environmental data can be complemented and more accurate data can be collected. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input external data sources into the generation AI and cause the generation AI to complement the environmental data.

[0103] The sensor unit can dynamically change the placement of the sensors to improve the efficiency of data collection. The sensor unit dynamically changes the placement of the sensors depending on, for example, the location of a child's activity. The sensor unit can also dynamically adjust the placement of the sensors depending on indoor environmental conditions. Furthermore, the sensor unit can dynamically change the placement of the sensors to improve the efficiency of data collection. This makes it possible to improve the efficiency of data collection by dynamically changing the placement of the sensors. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the placement of the sensors to a generation AI and cause the generation AI to dynamically change the placement.

[0104] The adjustment unit can estimate the child's emotions and change the method of adjusting the indoor environment based on the estimated emotions. For example, if the child is excited, the adjustment unit can change the indoor lighting to a calm color. If the child is calm, the adjustment unit can also adjust the indoor temperature to a comfortable range. Furthermore, if the child is sad, the adjustment unit can change the indoor music to something relaxing. In this way, by changing the method of adjusting the indoor environment based on the child's emotions, a more comfortable environment can be provided. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input sensor data to the generation AI to estimate the child's emotions and cause the generation AI to estimate the emotions.

[0105] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data. For example, the adjustment unit develops an algorithm that maintains an optimal indoor environment based on the past adjustment data. The adjustment unit can also identify an optimal adjustment method under specific conditions from the past adjustment data. Furthermore, the adjustment unit can dynamically adjust the adjustment algorithm by referring to the past adjustment data. In this way, the adjustment algorithm can be optimized by referring to the past adjustment data. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the past adjustment data into the generation AI and cause the generation AI to optimize the algorithm.

[0106] The adjustment unit can dynamically change the adjustment parameters according to different environmental conditions. For example, when the room temperature is high, the adjustment unit dynamically changes the cooling parameters. Furthermore, when the humidity is high, the adjustment unit can also dynamically change the dehumidification parameters. Furthermore, when the lighting is dim, the adjustment unit can dynamically change the lighting parameters. In this way, by dynamically changing the adjustment parameters according to different environmental conditions, an optimal environment can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input environmental conditions to the generation AI and cause the generation AI to dynamically change the parameters.

[0107] The adjustment unit can analyze the child's activity pattern and adjust the timing of the adjustment appropriately. For example, if the child is active, the adjustment unit can make the adjustment timing more frequent. Also, if the child is quiet, the adjustment unit can reduce the timing of the adjustment. Furthermore, the adjustment unit can dynamically adjust the timing of the adjustment based on the child's activity pattern. This enables efficient environmental adjustment by optimizing the timing of the adjustment based on the child's activity pattern. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the child's activity pattern to the generation AI and cause the generation AI to adjust the timing.

[0108] The adjustment unit can estimate the child's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the child is excited, the adjustment unit can prioritize adjusting the lighting in the room. Furthermore, if the child is calm, the adjustment unit can also prioritize adjusting the temperature in the room. Furthermore, if the child is sad, the adjustment unit can also prioritize adjusting the music in the room. In this way, by determining the priority of adjustments based on the child's emotions, a more comfortable environment can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input sensor data to the generation AI to estimate the child's emotions and cause the generation AI to estimate the emotions.

[0109] The adjustment unit can adjust the indoor environment by coordinating different types of devices. For example, the adjustment unit can coordinate an air conditioner and a humidifier to maintain an optimal indoor environment. The adjustment unit can also coordinate lighting and a music player to create a relaxing environment. Furthermore, the adjustment unit can coordinate a temperature sensor and a humidity sensor to maintain a comfortable indoor environment. In this way, an optimal indoor environment can be maintained by coordinating different types of devices. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input different types of devices into the generation AI and cause the generation AI to execute device coordination.

[0110] The adjustment unit can improve the accuracy of the adjustment in cooperation with an external data source. The adjustment unit can, for example, optimize the indoor environment in cooperation with weather forecast data. The adjustment unit can also adjust the indoor temperature in cooperation with external temperature data. Furthermore, the adjustment unit can also adjust the indoor humidity in cooperation with external humidity data. This allows the adjustment accuracy to be improved by cooperation with an external data source. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input an external data source into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0111] The adjustment unit can feed back the results of the adjustment and reflect them in the next adjustment. The adjustment unit, for example, optimizes the next adjustment method based on the results of the adjustment. The adjustment unit can also identify the optimal adjustment method under specific conditions from the results of the adjustment. Furthermore, the adjustment unit can dynamically adjust the adjustment algorithm by referring to the results of the adjustment. In this way, by feeding back the results of the adjustment, it is possible to reflect them in the next adjustment and provide a more optimal environment. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the adjustment unit can input the results of the adjustment to a generation AI and reflect them in the next adjustment.

[0112] The notification unit can provide real-time notifications through a smartphone app. For example, the notification unit can notify parents in real time of changes in the child's statements and emotions. The notification unit can also notify parents in real time of changes in the indoor environment or dangerous situations. This allows parents to immediately understand the child's situation by providing real-time notifications through a smartphone app. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the child's statements and changes in emotions into a generation AI and have the generation AI execute real-time notifications.

[0113] The notification unit can send periodic reports. For example, the notification unit can periodically send a report to the parent on the content of the child's statements and changes in emotions. The notification unit can also periodically send a report to the parent on changes in the indoor environment and dangerous situations. By sending periodic reports, the parent can continuously understand the situation of the child. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the content of the child's statements and changes in emotions into the generation AI and have the generation AI execute periodic reports. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, sensor unit, and adjustment unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the child's speech and gestures using the microphone 38B and camera 42 of the smart device 14 and convert them into text data using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the collected data and infer emotions using the specific processing unit 290 of the data processing device 12. The notification unit can send notifications to the parent's smartphone via the control unit 46A of the smart device 14. The sensor unit can monitor the indoor environment using, for example, a temperature sensor and a humidity sensor of the smart device 14. The adjustment unit can maintain an appropriate indoor environment by controlling, for example, an air conditioner or a humidifier using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, sensor unit, and adjustment unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the child's speech and gestures using the microphone 238 and camera 42 of the smart glasses 214 and convert them into text data by the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12 and infer emotions. The notification unit can send notifications to the parent's smartphone via the control unit 46A of the smart glasses 214, for example. The sensor unit can monitor the indoor environment using, for example, a temperature sensor and a humidity sensor of the smart glasses 214. The adjustment unit can maintain an appropriate indoor environment by, for example, controlling an air conditioner or a humidifier via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, sensor unit, and adjustment unit, described above, is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect the child's speech and gestures using the microphone 238 and camera 42 of the headset-type terminal 314 and convert them into text data using the specific processing unit 290 of the data processing device 12. The analysis unit can analyze the collected data using the specific processing unit 290 of the data processing device 12 and infer emotions. The notification unit can send a notification to the parent's smartphone via the control unit 46A of the headset-type terminal 314, for example. The sensor unit can monitor the indoor environment using, for example, a temperature sensor and a humidity sensor of the headset-type terminal 314. The adjustment unit can maintain an appropriate indoor environment by controlling, for example, an air conditioner or a humidifier using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, notification unit, sensor unit, and adjustment unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the child's speech and gestures using the microphone 238 and camera 42 of the robot 414 and convert them into text data by the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the collected data by the specific processing unit 290 of the data processing device 12 and infer emotions. The notification unit can send a notification to the parent's smartphone via the control unit 46A of the robot 414. The sensor unit can monitor the indoor environment using, for example, a temperature sensor and a humidity sensor of the robot 414. The adjustment unit can maintain an appropriate indoor environment by, for example, controlling an air conditioner or a humidifier by the specific processing unit 290 of the data processing device 12.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] When analyzing the content of a child's comments, the analysis unit can estimate the child's interests and adjust the depth of the analysis based on the estimated interests and concerns. For example, if a child shows a strong interest in a particular topic, the analysis unit can analyze comments related to that topic in detail. Also, if a child begins to become interested in a new topic, it can prioritize the analysis of comments related to that topic. Furthermore, if the child's interests change, the analysis unit can dynamically adjust the depth of the analysis in response to those changes. This allows for more appropriate analysis results to be provided by adjusting the depth of the analysis based on the child's interests and concerns.

[0116] The notification unit can adjust the timing of notifications taking into account the parent's schedule. For example, if the parent is in a meeting, the notification unit can delay notifications until the meeting is over. Also, if the parent is driving, the notification unit can hold off notifications until the parent finishes driving. Furthermore, if the parent is on vacation, the notification unit can adjust to send only important notifications. This allows the parent to receive information at an appropriate time by adjusting the timing of notifications according to the parent's schedule.

[0117] The adjustment unit can monitor the child's health condition and adjust the indoor environment based on the child's health condition. For example, if the child has a cold, the adjustment unit can maintain the room temperature appropriately and adjust the humidity. If the child has allergies, the adjustment unit can also operate an air purifier to improve air quality. Furthermore, if the child is tired, the adjustment unit can dim the lights to provide a relaxing environment. In this way, adjusting the indoor environment based on the child's health condition can provide a more comfortable environment.

[0118] When collecting a child's speech content, the collection unit can adjust the collection method according to the child's age. For example, for a young child, the collection unit can prioritize collecting simple words and gestures. For an elementary school student, the collection unit can also collect more complex speech content. Furthermore, for a middle school student, the collection unit can prioritize collecting speech content related to learning. In this way, by adjusting the collection method according to the child's age, more appropriate information can be collected.

[0119] When analyzing a child's utterances, the analysis unit can evaluate the child's learning progress and adjust the content of the analysis based on the evaluation results. For example, if a child is struggling with a particular subject, the analysis unit can analyze utterances related to that subject in detail. Alternatively, if a child shows a high level of understanding of a particular topic, the analysis unit can analyze utterances related to that topic briefly. Furthermore, if the child's learning progress changes, the analysis unit can dynamically adjust the content of the analysis in response to those changes. This allows for more appropriate analysis results to be provided by adjusting the content of the analysis based on the child's learning progress.

[0120] The notification unit can estimate the child's emotion and adjust the urgency of the notification based on the estimated emotion. For example, if the child is very excited, the notification unit can send a high-urgency notification to the parent. If the child is calm, the notification unit can also send a low-urgency notification to the parent. Furthermore, if the child is sad, the notification unit can also send a detailed notification to the parent to identify the cause of the emotion. In this way, by adjusting the urgency of the notification based on the child's emotion, appropriate information can be provided to the parent.

[0121] The adjustment unit can estimate the child's emotions and select music for the room based on the estimated emotions. For example, if the child is excited, the adjustment unit can play relaxing music. If the child is calm, the adjustment unit can play music that helps the child concentrate. Furthermore, if the child is sad, the adjustment unit can play music that brightens the mood. In this way, a more comfortable environment can be provided by selecting music for the room based on the child's emotions.

[0122] When collecting the content of a child's statements, the collection unit can adjust the collection method taking into account the child's friendships. For example, if the child is with friends, the collection unit will prioritize collecting conversations with friends. Also, if the child is alone, the collection unit can collect monologue and self-expression. Furthermore, if the child makes a new friend, the collection unit can prioritize collecting statements about that friend. In this way, by adjusting the collection method according to the child's friendships, more appropriate information can be collected.

[0123] The analysis unit can estimate the child's emotions and adjust the analysis feedback based on the estimated emotions. For example, if the child is excited, the analysis unit can provide detailed feedback. If the child is calm, the analysis unit can provide brief feedback. Furthermore, if the child is sad, the analysis unit can provide detailed feedback to identify the cause of the emotion. This allows for more appropriate information to be provided by adjusting the analysis feedback based on the child's emotions.

[0124] The notification unit can estimate the child's emotion and customize the content of the notification based on the estimated emotion. For example, if the child is excited, the notification unit can send detailed information to the parent. If the child is calm, the notification unit can send brief information to the parent. Furthermore, if the child is sad, the notification unit can send detailed information to the parent to identify the cause of the emotion. In this way, by customizing the content of the notification based on the child's emotion, it is possible to provide appropriate information to the parent.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects what the child says. For example, the collection unit collects the child's voice using a microphone and converts it into text data. The collection unit can also directly collect text data entered by the child. Furthermore, the collection unit can collect the child's gestures using a camera and analyze them. Step 2: The analysis unit analyzes the speech content collected by the collection unit and infers emotions. For example, the analysis unit analyzes voice data and infers emotions from a child's tone of voice and choice of words. The analysis unit can also analyze text data and infer emotions. Furthermore, the analysis unit can analyze gesture data and infer emotions. Step 3: The notification unit notifies the parent based on the emotion inferred by the analysis unit. For example, the notification unit may notify the parent in real time via a smartphone app. The notification unit may also send periodic reports. Step 4: The sensor unit monitors the indoor environment. For example, the sensor unit monitors the indoor environment using a temperature sensor, a humidity sensor, and a motion sensor. Step 5: The adjustment unit adjusts the indoor environment based on the data monitored by the sensor unit. For example, the adjustment unit may adjust the air conditioner to maintain an appropriate room temperature. The adjustment unit may also adjust the humidifier to maintain an appropriate humidity level.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0172] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] 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.

[0190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects the contents of children's statements; an analysis unit that analyzes the content of the utterances collected by the collection unit and infers emotions; a notification unit that notifies a parent based on the emotion estimated by the analysis unit; a sensor unit that monitors the indoor environment; and an adjustment unit that adjusts the indoor environment based on the data monitored by the sensor unit. A system characterized by:

2. The collecting unit To estimate a child's emotions and adjust the frequency of collecting speech content based on the estimated emotions. The system of claim 1 .

3. The collecting unit Analyze the child's past speech history and select the appropriate collection method 2. The system of claim 1.

4. The collecting unit When collecting speech, filtering should be done based on the child's current activity. The system of claim 1 .

5. The collecting unit When collecting speech, choose the appropriate collection method depending on the child's input method.

2. The system of claim 1.

6. The collecting unit Estimating the child's emotions and prioritizing the content of their comments based on the estimated emotions The system of claim 1 .

7. The collecting unit When collecting speech content, prioritize collection of relevant speech by taking into account the child's geographic location information. The system of claim 1 .

8. The collecting unit When collecting comments, analyze the child's social media activity and collect related comments. The system of claim 1 .

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