System

Abstract

An object of a system according to an embodiment is to detect problematic behavior in a nursery at an early stage and propose an appropriate countermeasure.SOLUTION: A system includes a collection unit, an analysis unit, a detection unit, and a proposal unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit. The detection unit detects a problematic behavior based on the data analyzed by the analysis unit. The proposal unit proposes the layout and the arrangement of the monitoring cameras based on the result detected by the detection 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 technology has the drawback of making it difficult to detect problematic behavior in daycare centers early and take appropriate measures.

[0005] The system according to the embodiment aims to detect problematic behaviors in daycare centers at an early stage and propose appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a detection unit, and a proposal unit. The collection unit collects audio data. The analysis unit analyzes the audio data collected by the collection unit. The detection unit detects problematic behavior based on the data analyzed by the analysis unit. The proposal unit proposes layouts and surveillance camera placements based on the results of detection by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect problematic behaviors in a nursery school at an early stage and propose appropriate countermeasures. [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 childcare environment safety system according to an embodiment of the present invention collects audio data from within a nursery school, analyzes it using a generation AI, detects problematic behavior early, and proposes optimization of the layout and surveillance cameras. The childcare environment safety system collects audio data from within a nursery school, and analyzes the audio data using a generation AI to detect problematic behavior among children early. For example, the childcare environment safety system analyzes children's crying, screaming, and fighting sounds using a generation AI to identify problematic behavior. Furthermore, the childcare environment safety system makes proposals to optimize the layout and surveillance camera placement within the nursery school. The generation AI analyzes the audio data and video data from surveillance cameras within the nursery school and proposes optimal layouts and surveillance camera placements, thereby improving the safety of the childcare environment. For example, the childcare environment safety system collects audio data from within the nursery school. For example, the childcare environment safety system collects audio data using microphones within the nursery school. Next, the childcare environment safety system uses a generation AI to analyze the collected audio data and detect problematic behavior. The input to the generation AI is the collected audio data itself, and the generation AI identifies problematic behavior based on the content of the audio data. For example, the generating AI receives a prompt saying, "Please detect problematic behavior from this voice data," and analyzes the patterns of the voice data to identify problematic behavior. The childcare environment safety system then makes suggestions to optimize the layout and placement of surveillance cameras within the nursery based on the results of the generating AI's analysis. This allows the childcare environment safety system to improve safety within the nursery. The childcare environment safety system analyzes voice data within the nursery, detects problematic behavior early, and suggests optimizing the layout and surveillance cameras, thereby improving the safety of the childcare environment. For example, this can provide parents with a sense of security and reduce the burden on childcare workers. Furthermore, childcare workers can quickly respond to problematic behavior detected by the generating AI, creating a smooth childcare environment.

[0029] A childcare environment safety system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a suggestion unit. The collection unit collects audio data from within the childcare center. The audio data from the childcare center includes, but is not limited to, sounds of children crying, screaming, and fighting. The collection unit collects the audio data using, for example, microphones installed within the childcare center. The collection unit can also prioritize collecting audio data from specific areas within the childcare center. For example, the collection unit can prioritize collecting audio data from play areas and dining areas. Furthermore, the collection unit can filter environmental and background sounds to remove noise when collecting the audio data. For example, the collection unit can filter out air conditioner noise and external traffic noise to clearly collect the children's voices. The analysis unit uses a generation AI to analyze the audio data collected by the collection unit. The analysis can be performed using, for example, voice recognition technology or frequency analysis technology, but is not limited to, examples. For example, the generation AI can analyze the frequency components of the audio data to identify patterns of crying and screaming. The analysis unit can also identify behavioral patterns by analyzing the duration and intervals of the audio data. For example, the analysis unit can identify a child's stress level by analyzing the duration of crying. The detection unit detects problematic behavior based on the data analyzed by the analysis unit. Problematic behaviors include, but are not limited to, crying, screaming, and fighting. The detection unit can identify problematic behavior by analyzing, for example, fluctuation patterns in the analyzed audio data. The detection unit can also evaluate the severity of problematic behavior by analyzing the intensity and frequency of the audio data. For example, the detection unit can evaluate the severity of problematic behavior by analyzing the intensity of the audio data. The suggestion unit makes optimization suggestions for layout and surveillance cameras based on the results detected by the detection unit. Suggestions include, but are not limited to, optimizing the placement of surveillance cameras within the nursery school or changing the layout within the nursery school. For example, the suggestion unit can analyze video data from surveillance cameras and suggest optimal placement of surveillance cameras. The suggestion unit can also optimize the layout by taking into account the movement and activity patterns of childcare workers. For example, the suggestion unit can analyze the movement and activity patterns of childcare workers to suggest an efficient layout.As a result, the childcare environment safety system of the embodiment can improve the safety of the childcare environment by analyzing audio data within the nursery, detecting problematic behavior early, and proposing optimization of the layout and surveillance cameras.

[0030] The proposal unit includes an action unit in which childcare workers act based on the AI's suggestions. The action unit is a department in which childcare workers act based on the AI's suggestions. The action unit, for example, supports childcare workers in responding quickly based on the AI's suggestions. For example, the action unit notifies childcare workers of the location and time of the problematic behavior and encourages them to respond appropriately. The action unit can also provide training for childcare workers to act based on the AI's suggestions. For example, the action unit provides childcare workers with a training program to learn how to respond based on the AI's suggestions. Furthermore, the action unit can collect feedback when childcare workers act based on the AI's suggestions and provide data to improve the accuracy of the suggestions. For example, the action unit records the results of childcare workers acting based on the AI's suggestions and feeds the data back to the AI. In this way, the action unit supports childcare workers in responding quickly based on the AI's suggestions, thereby improving the safety of the childcare environment.

[0031] The suggestion unit includes a notification unit that notifies information to parents. The notification unit is a department for notifying information to parents. The notification unit, for example, provides parents with information regarding safety within the nursery school. For example, the notification unit notifies parents of problematic behaviors that have occurred within the nursery school and the status of responses to them. The notification unit can also provide parents with regular reports regarding safety within the nursery school. For example, the notification unit creates a monthly report regarding safety within the nursery school and sends it to parents. Furthermore, the notification unit can collect feedback from parents and improve the content of notifications based on that feedback. For example, the notification unit reviews the content and method of notifications based on feedback from parents and provides more effective information. In this way, the notification unit can provide parents with information to give them a sense of security regarding safety within the nursery school.

[0032] The collection unit can collect audio data from within the nursery school. The collection unit collects the audio data using, for example, microphones installed within the nursery school. Audio data from the nursery school includes, but is not limited to, sounds of children crying, screaming, and fighting. The collection unit can also prioritize collecting audio data from specific areas within the nursery school. For example, audio data from the play area or the eating area can be prioritized. The collection unit can also filter environmental and background sounds to remove noise when collecting audio data. For example, the sound of air conditioners and external traffic sounds can be filtered to clearly collect children's voices. This enables early detection of problem behaviors by collecting audio data from the nursery school. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data acquired by microphones within the nursery school into a generation AI and cause the generation AI to collect audio data.

[0033] The analysis unit can analyze the collected audio data and identify patterns of crying or screaming. The analysis unit can analyze the collected audio data using, for example, a generation AI. The analysis can be performed using, for example, voice recognition technology or frequency analysis technology, but is not limited to these examples. For example, the generation AI can analyze the frequency components of the audio data to identify patterns of crying or screaming. The analysis unit can also analyze the duration and intervals of the audio data to identify behavioral patterns. For example, the duration of crying can be analyzed to identify a child's stress level. Furthermore, the analysis unit can analyze the intensity and frequency of the audio data to evaluate the severity of problem behavior. For example, the intensity of the audio data can be analyzed to evaluate the severity of problem behavior. This enables early detection of problem behavior by identifying patterns of crying or screaming. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the collected audio data to the generation AI and have the generation AI identify patterns of crying or screaming.

[0034] The detection unit can detect problem behavior based on the analyzed data. The detection unit, for example, analyzes the fluctuation pattern of the voice data analyzed by the analysis unit to identify problem behavior. Problem behavior includes, but is not limited to, crying, screaming, and fighting. The detection unit, for example, analyzes sudden fluctuations in the voice data to identify problem behavior. The detection unit can also analyze continuous fluctuations in the voice data to identify problem behavior. The detection unit can also analyze periodic fluctuations in the voice data to identify problem behavior. For example, periodic fluctuations in the voice data can be analyzed to identify problem behavior. This allows problem behavior to be detected based on the analyzed data, allowing childcare workers to respond quickly. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the analyzed voice data to a generation AI and have the generation AI detect problem behavior.

[0035] The proposal unit can analyze surveillance camera video data and propose an appropriate layout and surveillance camera placement. The proposal unit can analyze the surveillance camera video data using, for example, a generation AI. The analysis can be performed using, for example, video recognition technology or motion analysis technology, but is not limited to these examples. For example, the generation AI can analyze the surveillance camera video data and propose an optimal surveillance camera placement. The proposal unit can also optimize the layout by taking into account the movement and activity patterns of childcare workers. For example, the proposal unit can analyze the movement and activity patterns of childcare workers to propose an efficient layout. Furthermore, the proposal unit can optimize the placement of surveillance cameras in specific areas of the nursery school. For example, the proposal unit can optimize the placement of surveillance cameras in play areas and dining areas. This can improve the safety of the childcare environment by analyzing the surveillance camera video data. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the proposal unit can input surveillance camera video data to the generation AI and cause the generation AI to propose an optimal layout and surveillance camera placement.

[0036] The collection unit can prioritize collecting audio data from specific areas within the nursery school. The collection unit, for example, collects audio data using a microphone installed in a specific area within the nursery school. Specific areas include, but are not limited to, play areas, dining areas, and rest areas. The collection unit, for example, prioritizes collecting audio data from play areas to monitor children's play. The collection unit can also prioritize collecting audio data from dining areas to detect problematic behavior during meals. Furthermore, the collection unit can prioritize collecting audio data from rest areas to monitor children's behavior during breaks. By preferentially collecting audio data from specific areas, it becomes easier to identify the location where problematic behavior occurs. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input audio data from specific areas to a generation AI and cause the generation AI to prioritize the collection of audio data.

[0037] The collection unit can filter environmental sounds and background sounds to remove noise when collecting audio data. The collection unit can use, for example, noise reduction technology to filter environmental sounds and background sounds when collecting audio data. Examples of environmental sounds and background sounds include, but are not limited to, air conditioner sounds, external traffic sounds, and conversations between childcare workers. The collection unit can, for example, filter air conditioner sounds and external traffic sounds to clearly collect children's voices. The collection unit can also filter conversations between childcare workers and other children to collect specific children's voices. Furthermore, the collection unit can filter specific frequency bands to remove noise when collecting audio data. For example, the collection unit can filter specific frequency bands to remove unnecessary noise. This allows for the collection of clearer audio data by filtering environmental sounds and background sounds. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data to a generation AI and have the generation AI perform noise filtering.

[0038] The collection unit can improve collection accuracy by identifying the direction of a sound source when collecting audio data. The collection unit can identify the direction of a sound source, for example, using a microphone array. Methods for identifying the direction of a sound source include, but are not limited to, analyzing the phase difference of audio data and arranging multiple microphones. The collection unit can collect audio data from a specific direction, for example, using a microphone array. The collection unit can also improve collection accuracy by arranging multiple microphones to identify the direction of a sound source. Furthermore, the collection unit can analyze the phase difference of audio data to identify the direction of a sound source. For example, the collection unit can analyze the phase difference of audio data to identify the direction of a sound source. By identifying the direction of a sound source, collection accuracy is improved and more accurate data can be obtained. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input audio data to a generation AI and cause the generation AI to identify the direction of a sound source.

[0039] When collecting voice data, the collection unit can adjust the collection range taking into account the movements and location information of the childcare worker. The collection unit, for example, uses a motion sensor or a GPS to acquire the movements and location information of the childcare worker. Acquisition of the movements and location information of the childcare worker includes, but is not limited to, a motion sensor or a location information system. For example, when the childcare worker is in a specific area, the collection unit prioritizes collecting voice data from that area. Furthermore, when the childcare worker is moving, the collection unit can also collect voice data from the area to which the childcare worker moves. Furthermore, when the childcare worker is conversing with a specific child, the collection unit can collect voice data of the conversation. This enables more effective data collection by taking into account the movements and location information of the childcare worker. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the movements and location information of the childcare worker into the generation AI and cause the generation AI to adjust the collection range.

[0040] The collection unit may also collect environmental information, such as temperature and humidity, within the nursery school when collecting the voice data. The collection unit may use, for example, a temperature sensor or a humidity sensor to collect environmental information within the nursery school. Environmental information may include, but is not limited to, temperature, humidity, and illuminance. For example, the collection unit may also collect data from a temperature sensor when collecting the voice data. The collection unit may also collect data from a humidity sensor when collecting the voice data. The collection unit may also collect data from an environmental sensor when collecting the voice data. For example, the collection unit may also collect data from an illuminance sensor when collecting the voice data. Collecting environmental information improves the accuracy of analyzing the voice data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data from an environmental sensor into the generation AI and cause the generation AI to collect environmental information.

[0041] When collecting voice data, the collection unit can focus on collecting voice data during a specific time period within the nursery school. The collection unit, for example, collects voice data during a specific time period within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The collection unit, for example, focuses on collecting voice data during the morning arrival time. The collection unit can also focus on collecting voice data during the lunch time. Furthermore, the collection unit can also focus on collecting voice data during the evening dismissal time. This makes it easier to identify the timing of occurrence of problem behavior by focusing on collecting voice data during specific time periods. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data during a specific time period into a generation AI and cause the generation AI to perform focused collection of voice data.

[0042] When analyzing the voice data, the analysis unit can identify problematic behavior by analyzing the frequency components of the voice in detail. The analysis unit, for example, uses a generation AI to analyze the frequency components of the voice data. The analysis is performed, for example, using spectral analysis technology, but is not limited to such an example. For example, the generation AI can analyze the high-frequency components of the voice data to identify screams and crying. The analysis unit can also analyze the low-frequency components of the voice data to identify sounds of fighting. Furthermore, the analysis unit can analyze the mid-frequency components of the voice data to identify talking and laughter. This improves the accuracy of identifying problematic behavior by analyzing the frequency components of the voice in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the voice data to the generation AI and have the generation AI analyze the frequency components.

[0043] When analyzing the voice data, the analysis unit can identify behavioral patterns by analyzing the duration and intervals of the voice. The analysis unit, for example, uses a generation AI to analyze the duration and intervals of the voice data. The analysis is performed, for example, using time domain analysis technology, but is not limited to such an example. For example, the generation AI can analyze the duration of crying to identify a child's stress level. The analysis unit can also analyze the intervals between cries to identify the frequency of problematic behavior. Furthermore, the analysis unit can analyze the duration of speaking to identify a child's communication pattern. Thus, behavioral patterns can be identified by analyzing the duration and intervals of voice. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input voice data to the generation AI and have the generation AI analyze the duration and intervals.

[0044] When analyzing voice data, the analysis unit can integrate multiple pieces of voice data to improve analysis accuracy. The analysis unit integrates multiple pieces of voice data using, for example, a generation AI. The integration is performed using, for example, data fusion technology, but is not limited to such an example. For example, the generation AI integrates and analyzes voice data collected from multiple microphones. The analysis unit can also integrate and analyze voice data collected over multiple time periods. Furthermore, the analysis unit can also integrate and analyze voice data collected from multiple areas. In this way, by integrating multiple pieces of voice data, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input multiple pieces of voice data to the generation AI and have the generation AI integrate the data.

[0045] When analyzing the audio data, the analysis unit can analyze the voice and instructions of the childcare worker to identify the child's reaction. The analysis unit, for example, uses a generation AI to analyze the voice and instructions of the childcare worker. The analysis is performed, for example, using voice recognition technology, but is not limited to such an example. For example, the generation AI can analyze the child's reaction to the childcare worker's instructions to identify the effect of the instructions. The analysis unit can also analyze the child's reaction to the tone of the childcare worker's voice to identify changes in emotion. Furthermore, the analysis unit can analyze the child's reaction to the intensity of the childcare worker's voice to identify changes in behavior. This makes it easier to identify the child's reaction by analyzing the childcare worker's voice and instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the voice and instructions of the childcare worker into the generation AI and have the generation AI identify the child's reaction.

[0046] When analyzing the voice data, the analysis unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The analysis unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, and illuminance, but is not limited to these examples. The analysis unit, for example, analyzes temperature data and voice data together to identify the impact of environmental changes on children's behavior. The analysis unit can also analyze humidity data and voice data together to identify the impact of humidity changes on children's behavior. Furthermore, the analysis unit can analyze illuminance data and voice data together to identify the impact of illuminance changes on children's behavior. This makes it easier to identify the impact of environmental changes on children's behavior by analyzing other sensor data together. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0047] When analyzing voice data, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, uses a generation AI to refer to past analysis results. The referencing can be performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI can adjust the parameters of the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis algorithm based on past analysis results. Furthermore, the analysis unit can also improve the processing speed of the analysis algorithm based on past analysis results. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI optimize the analysis algorithm.

[0048] Upon detection, the detection unit can analyze the fluctuation pattern of the voice data to identify problematic behavior. The detection unit, for example, uses a generation AI to analyze the fluctuation pattern of the voice data. The analysis is performed, for example, using time domain analysis technology, but is not limited to such an example. For example, the generation AI can analyze sudden fluctuations in the voice data to identify problematic behavior. The detection unit can also analyze continuous fluctuations in the voice data to identify problematic behavior. Furthermore, the detection unit can analyze periodic fluctuations in the voice data to identify problematic behavior. In this way, analyzing the fluctuation pattern of the voice data improves the accuracy of identifying problematic behavior. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input voice data to the generation AI and have the generation AI analyze the fluctuation pattern.

[0049] Upon detection, the detection unit can analyze the intensity and frequency of the audio data to evaluate the severity of the problematic behavior. The detection unit, for example, uses a generation AI to analyze the intensity and frequency of the audio data. The analysis can be performed using, for example, amplitude analysis technology or frequency analysis technology, but is not limited to these examples. For example, the generation AI can analyze the intensity of the audio data to evaluate the severity of the problematic behavior. The detection unit can also analyze the frequency of the audio data to evaluate the severity of the problematic behavior. Furthermore, the detection unit can analyze the duration of the audio data to evaluate the severity of the problematic behavior. This makes it easier to evaluate the severity of the problematic behavior by analyzing the intensity and frequency of the audio data. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input audio data to the generation AI and have the generation AI perform an analysis of the intensity and frequency.

[0050] Upon detection, the detection unit can compare multiple pieces of audio data to identify the location of the problematic behavior. The detection unit, for example, uses a generation AI to compare the multiple pieces of audio data. The comparison is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI can compare audio data collected from multiple microphones to identify the location of the problematic behavior. The detection unit can also compare audio data collected over multiple time periods to identify the location of the problematic behavior. Furthermore, the detection unit can compare audio data collected from multiple areas to identify the location of the problematic behavior. This makes it easier to identify the location of the problematic behavior by comparing multiple pieces of audio data. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input multiple pieces of audio data to the generation AI and have the generation AI compare the data.

[0051] The detection unit can identify the cause of problem behavior by taking into account the behavior and location information of the childcare worker during detection. The detection unit, for example, uses a motion sensor or GPS to acquire the behavior and location information of the childcare worker. Acquisition of the behavior and location information of the childcare worker includes, but is not limited to, a motion sensor or a location information system. For example, when the childcare worker is in a specific area, the detection unit can identify the cause of problem behavior in that area. Furthermore, when the childcare worker is interacting with a specific child, the detection unit can also identify whether the interaction is the cause of the problem behavior. Furthermore, the detection unit can analyze the movement pattern of the childcare worker to identify the cause of problem behavior. This makes it easier to identify the cause of problem behavior by taking into account the behavior and location information of the childcare worker. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the behavior and location information of the childcare worker into a generation AI and cause the generation AI to identify the cause of the problem behavior.

[0052] During detection, the detection unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The detection unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, and illuminance, but is not limited to these examples. The detection unit, for example, analyzes temperature data and audio data together to identify the impact of environmental changes on problem behavior. The detection unit can also analyze humidity data and audio data together to identify the impact of humidity changes on problem behavior. Furthermore, the detection unit can analyze illuminance data and audio data together to identify the impact of illuminance changes on problem behavior. This makes it easier to identify the impact of environmental changes on problem behavior by analyzing other sensor data together. Some or all of the above-described processing in the detection unit may be performed, for example, using AI or without AI. For example, the detection unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0053] During detection, the detection unit can optimize the detection algorithm by referring to past detection results. The detection unit, for example, uses a generation AI to refer to past detection results. The referencing is performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI adjusts the parameters of the detection algorithm based on past detection results. The detection unit can also improve the accuracy of the detection algorithm based on past detection results. Furthermore, the detection unit can also improve the processing speed of the detection algorithm based on past detection results. As a result, the accuracy of the detection algorithm is improved by referring to past detection results. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input past detection results to the generation AI and have the generation AI optimize the detection algorithm.

[0054] When making a proposal, the suggestion unit can integrate audio data and video data from within the nursery school to make an optimal proposal. The suggestion unit, for example, uses a generation AI to integrate audio data and video data from within the nursery school. The integration can be performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI can integrate the audio data and video data to identify locations where problematic behavior occurs and propose optimal surveillance camera placement. The suggestion unit can also integrate the audio data and video data to propose a layout that optimizes the movement of childcare workers. Furthermore, the suggestion unit can integrate the audio data and video data to make a proposal that optimizes the placement of children's play areas. In this way, integrating the audio data and video data enables more accurate proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the audio data and video data into the generation AI and have the generation AI integrate the data.

[0055] When making a proposal, the proposal unit can optimize the layout by taking into account the movement lines and activity patterns of the childcare workers. The proposal unit, for example, uses a generation AI to analyze the movement lines and activity patterns of the childcare workers. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generation AI analyzes the movement lines and activity patterns of the childcare workers to propose an efficient layout. The proposal unit can also analyze the activity patterns of the childcare workers to propose optimal surveillance camera placement. Furthermore, the proposal unit can make a proposal to optimize the layout by taking into account the rest areas of the childcare workers. This enables a more efficient layout by taking into account the movement lines and activity patterns of the childcare workers. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the movement lines and activity patterns of the childcare workers into the generation AI and cause the generation AI to optimize the layout.

[0056] When making a proposal, the proposal unit can optimize the proposal algorithm by referring to past proposal results. The proposal unit, for example, uses a generation AI to refer to past proposal results. The referencing is performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI adjusts parameters of the proposal algorithm based on past proposal results. The proposal unit can also improve the accuracy of the proposal algorithm based on past proposal results. Furthermore, the proposal unit can also improve the processing speed of the proposal algorithm based on past proposal results. As a result, the accuracy of the proposal algorithm is improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI. For example, the proposal unit can input past proposal results to the generation AI and cause the generation AI to optimize the proposal algorithm.

[0057] When making a proposal, the proposal unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The proposal unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The proposal unit, for example, analyzes temperature data and audio data together to propose an optimal layout. The proposal unit can also analyze humidity data and audio data together to propose an optimal surveillance camera placement. Furthermore, the proposal unit can analyze illuminance data and audio data together to propose an optimal childcare environment. This enables more accurate proposals by analyzing other sensor data together. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0058] When making a proposal, the suggestion unit can customize the proposal content by reflecting feedback from the childcare workers. The suggestion unit, for example, uses questionnaires or interviews to collect feedback from the childcare workers. Examples of collecting feedback include, but are not limited to, questionnaire surveys and interviews. The suggestion unit, for example, customizes the placement of surveillance cameras based on the feedback from the childcare workers. The suggestion unit can also customize the layout based on the feedback from the childcare workers. Furthermore, the suggestion unit can optimize the proposal content based on the feedback from the childcare workers. This enables more practical proposals by reflecting the feedback from the childcare workers. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the feedback from the childcare workers into a generation AI and have the generation AI customize the proposal content.

[0059] When making a proposal, the suggestion unit can focus on analyzing data from specific time periods within the nursery school and make the proposal. The suggestion unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The suggestion unit, for example, can focus on analyzing data from the morning arrival time period and propose an optimal layout. The suggestion unit can also focus on analyzing data from the lunch time period and propose optimal surveillance camera placement. Furthermore, the suggestion unit can focus on analyzing data from the evening dismissal time period and propose an optimal childcare environment. This enables more appropriate proposals by focusing on analyzing data from specific time periods. Some or all of the above-described processing by the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input data from specific time periods into the generation AI and cause the generation AI to perform a focused analysis of the data.

[0060] The response unit can select the optimal response method by referring to the childcare worker's past response history when responding. The response unit, for example, uses a generation AI to reference the childcare worker's past response history. The reference can be performed, for example, using database reference technology, but is not limited to such an example. For example, the generation AI selects the optimal response method based on the childcare worker's past response history. The response unit can also identify the cause of the problem behavior based on the childcare worker's past response history. Furthermore, the response unit can evaluate the effectiveness of the response method based on the childcare worker's past response history. This enables more effective response by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the childcare worker's past response history into the generation AI and have the generation AI select the optimal response method.

[0061] When responding, the response unit can integrate audio data and video data from the nursery school to optimize the response method. The response unit, for example, uses a generation AI to integrate audio data and video data from the nursery school. The integration is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI integrates the audio data and video data to select the optimal response method. The response unit can also integrate the audio data and video data to identify the cause of the problem behavior. Furthermore, the response unit can integrate the audio data and video data to evaluate the effectiveness of the response method. In this way, integrating the audio data and video data enables a more accurate response. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI or without AI. For example, the response unit can input the audio data and video data into the generation AI and have the generation AI integrate the data.

[0062] The response unit can improve the response method by reflecting the childcare worker's feedback when responding. The response unit, for example, uses questionnaires or interviews to collect the childcare worker's feedback. Examples of collecting feedback include, but are not limited to, questionnaires and interviews. The response unit, for example, improves the response method based on the childcare worker's feedback. The response unit can also identify the cause of the problem behavior based on the childcare worker's feedback. Furthermore, the response unit can evaluate the effectiveness of the response method based on the childcare worker's feedback. This enables a more effective response by reflecting the childcare worker's feedback. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the childcare worker's feedback into the generation AI and have the generation AI improve the response method.

[0063] When responding, the response unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The response unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The response unit, for example, analyzes temperature data and audio data together to select an optimal response method. The response unit can also analyze humidity data and audio data together to select an optimal response method. Furthermore, the response unit can analyze illuminance data and audio data together to select an optimal response method. This enables a more accurate response by analyzing other sensor data together. Some or all of the above-described processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0064] The response unit can optimize the response method by taking into account the movement lines and activity patterns of the childcare worker when responding. The response unit, for example, uses a generation AI to analyze the movement lines and activity patterns of the childcare worker. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generation AI analyzes the movement lines of the childcare worker to select an efficient response method. The response unit can also analyze the activity patterns of the childcare worker to select an optimal response method. Furthermore, the response unit can optimize the response method by taking into account the childcare worker's rest area. This enables a more efficient response by taking into account the movement lines and activity patterns of the childcare worker. Some or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the movement lines and activity patterns of the childcare worker into the generation AI and have the generation AI optimize the response method.

[0065] When responding, the response unit can focus on analyzing data from specific time periods within the nursery school and respond accordingly. The response unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The response unit, for example, focuses on analyzing data from the morning arrival time period and selects the optimal response method. The response unit can also focus on analyzing data from the lunch time period and select the optimal response method. Furthermore, the response unit can also focus on analyzing data from the evening dismissal time period and select the optimal response method. This enables a more appropriate response by focusing on analyzing data from specific time periods. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input data from specific time periods into the generation AI and have the generation AI perform a focused analysis of the data.

[0066] When providing information, the providing unit can select the optimal information provision method by referring to the parent's past feedback. The providing unit, for example, uses a generation AI to refer to the parent's past feedback. The referencing can be performed, for example, using a database reference technology, but is not limited to such an example. For example, the generation AI selects the optimal information provision method based on the parent's past feedback. The providing unit can also customize the content of the information provision based on the parent's past feedback. Furthermore, the providing unit can also adjust the frequency of information provision based on the parent's past feedback. This enables more effective information provision by referring to the parent's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the parent's past feedback into the generation AI and cause the generation AI to select the optimal information provision method.

[0067] When providing information, the providing unit can integrate audio data and video data from within the nursery school to optimize the information provision. The providing unit, for example, uses a generating AI to integrate audio data and video data from within the nursery school. The integration is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generating AI can integrate audio data and video data to provide detailed information to parents. The providing unit can also integrate audio data and video data to provide concise information to parents. Furthermore, the providing unit can integrate audio data and video data to provide standard information to parents. This makes it possible to provide more accurate information by integrating audio data and video data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input audio data and video data to the generating AI and cause the generating AI to integrate the data.

[0068] The providing unit can improve the information provision method by reflecting parental feedback when providing information. The providing unit, for example, uses questionnaires or interviews to collect parental feedback. Examples of collecting feedback include, but are not limited to, questionnaires and interviews. The providing unit, for example, improves the information provision method based on parental feedback. The providing unit can also customize the content of the information provided based on parental feedback. Furthermore, the providing unit can also adjust the frequency of information provision based on parental feedback. This enables more effective information provision by reflecting parental feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input parental feedback into a generating AI and cause the generating AI to improve the information provision method.

[0069] When providing information, the providing unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The providing unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The providing unit, for example, analyzes temperature data and audio data together to select an optimal information provision method. The providing unit can also analyze humidity data and audio data together to select an optimal information provision method. Furthermore, the providing unit can also analyze illuminance data and audio data together to select an optimal information provision method. This enables more accurate information provision by analyzing other sensor data together. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input other sensor data to the generation AI and have the generation AI perform a combined analysis of the data.

[0070] The providing unit can optimize the information provision method by taking into account the guardian's movement line and activity pattern when providing information. The providing unit, for example, uses a generating AI to analyze the guardian's movement line and activity pattern. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generating AI analyzes the guardian's movement line and selects an efficient information provision method. The providing unit can also analyze the guardian's activity pattern and select an optimal information provision method. Furthermore, the providing unit can optimize the information provision method by taking into account the guardian's rest area. This enables more effective information provision by taking into account the guardian's movement line and activity pattern. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the guardian's movement line and activity pattern into the generating AI and cause the generating AI to optimize the information provision method.

[0071] When providing information, the providing unit can provide information by focusing on analyzing data from specific time periods within the nursery school. The providing unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time period, the lunch time period, and the evening dismissal time period. The providing unit, for example, focuses on analyzing data from the morning arrival time period and selects the optimal information provision method. The providing unit can also focus on analyzing data from the lunch time period and select the optimal information provision method. Furthermore, the providing unit can also focus on analyzing data from the evening dismissal time period and select the optimal information provision method. This enables more appropriate information provision by focusing on analyzing data from specific time periods. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data from specific time periods into the generation AI and cause the generation AI to perform a focused analysis of the data.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The childcare environment safety system may further include a health monitoring unit that monitors the child's health condition. The health monitoring unit monitors the child's health condition in real time, for example, using sensors that measure the child's body temperature and heart rate. The health monitoring unit may also analyze the child's health data and notify the childcare worker if an abnormality is detected. For example, if the child's body temperature rises suddenly or the heart rate is abnormally high, an alert may be sent to the childcare worker. Furthermore, the health monitoring unit may record the child's health data over the long term and provide regular reports to the parents. In this way, the childcare environment safety system can provide a safer childcare environment by monitoring the child's health condition.

[0074] The childcare environment safety system can further include a behavior recording unit that records a child's behavior history. The behavior recording unit, for example, records a child's play and learning behavior and provides the information to parents. The behavior recording unit can also analyze a child's behavior patterns and propose a childcare plan suitable for each child. For example, if a particular child prefers a particular activity at a particular time, the behavior recording unit can propose a childcare plan that prioritizes that activity. Furthermore, the behavior recording unit can provide childcare workers with advice on how to stimulate the child's interests and curiosity based on the child's behavior data. In this way, the childcare environment safety system can record a child's behavior history and provide childcare that is suitable for each child.

[0075] The childcare environment safety system can further include a stress monitoring unit that monitors the stress level of childcare workers. The stress monitoring unit monitors the stress level of childcare workers in real time, for example, using sensors that measure the childcare worker's heart rate and electrodermal activity. The stress monitoring unit can also analyze the childcare worker's stress data and notify the administrator if an abnormality is detected. For example, if the heart rate is abnormally high or if there is a sudden change in electrodermal activity, an alert is sent to the administrator. Furthermore, the stress monitoring unit can provide advice for stress reduction based on the childcare worker's stress data. In this way, the childcare environment safety system can provide a healthy and comfortable working environment for childcare workers by monitoring the childcare worker's stress level.

[0076] The childcare environment safety system can further include an air quality monitoring unit that monitors the air quality within the childcare center. The air quality monitoring unit monitors the air quality within the childcare center in real time using sensors that measure, for example, carbon dioxide concentration and volatile organic compound (VOC) concentrations. The air quality monitoring unit can also analyze air quality data and notify childcare workers if an abnormality is detected. For example, if the carbon dioxide concentration or VOC concentration increases, an alert is sent to the childcare worker. Furthermore, the air quality monitoring unit can suggest ventilation or the use of an air purifier based on the air quality data. In this way, the childcare environment safety system can provide a healthier childcare environment by monitoring the air quality within the childcare center.

[0077] The childcare environment safety system can further include a schedule management unit that supports the schedule management of childcare workers. The schedule management unit, for example, manages the working hours and break times of childcare workers and proposes efficient schedules. The schedule management unit can also analyze the schedule data of childcare workers and notify the manager if signs of overwork or stress are detected. For example, if a childcare worker has been working long hours consecutively or if there are insufficient breaks, an alert can be sent to the manager. Furthermore, the schedule management unit can provide advice on appropriate breaks and refreshment based on the childcare worker's schedule. In this way, the childcare environment safety system can provide a healthy and comfortable working environment for childcare workers by supporting their schedule management.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The collection unit collects audio data within the nursery school. The audio data within the nursery school includes, for example, sounds of children crying, screaming, and fighting. The collection unit collects the audio data using microphones installed within the nursery school. It is also possible to preferentially collect audio data from specific areas. Furthermore, the collection unit filters out environmental and background sounds to remove noise when collecting the audio data. Step 2: The analysis unit uses the generation AI to analyze the voice data collected by the collection unit. The analysis is performed using voice recognition technology and frequency analysis technology. For example, the generation AI analyzes the frequency components of the voice data to identify crying or screaming patterns. It can also analyze the duration and intervals of the voice data to identify behavioral patterns. Step 3: The detection unit detects problematic behaviors based on the data analyzed by the analysis unit. Problematic behaviors include crying, screaming, and fighting. The detection unit identifies problematic behaviors by analyzing the fluctuation patterns of the analyzed audio data. It can also evaluate the severity of problematic behaviors by analyzing the intensity and frequency of the audio data. Step 4: The proposal unit makes optimization proposals for the layout and surveillance cameras based on the results detected by the detection unit. Proposals include optimizing the placement of surveillance cameras within the nursery school and changing the layout within the nursery school. For example, it analyzes video data from surveillance cameras and proposes optimal placement of surveillance cameras. It can also optimize the layout by taking into account the movement and activity patterns of nursery staff.

[0080] (Example 2) A childcare environment safety system according to an embodiment of the present invention collects audio data from within a nursery school, analyzes it using a generation AI, detects problematic behavior early, and proposes optimization of the layout and surveillance cameras. The childcare environment safety system collects audio data from within a nursery school, and analyzes the audio data using a generation AI to detect problematic behavior among children early. For example, the childcare environment safety system analyzes children's crying, screaming, and fighting sounds using a generation AI to identify problematic behavior. Furthermore, the childcare environment safety system makes proposals to optimize the layout and surveillance camera placement within the nursery school. The generation AI analyzes the audio data and video data from surveillance cameras within the nursery school and proposes optimal layouts and surveillance camera placements, thereby improving the safety of the childcare environment. For example, the childcare environment safety system collects audio data from within the nursery school. For example, the childcare environment safety system collects audio data using microphones within the nursery school. Next, the childcare environment safety system uses a generation AI to analyze the collected audio data and detect problematic behavior. The input to the generation AI is the collected audio data itself, and the generation AI identifies problematic behavior based on the content of the audio data. For example, the generating AI receives a prompt saying, "Please detect problematic behavior from this voice data," and analyzes the patterns of the voice data to identify problematic behavior. The childcare environment safety system then makes suggestions to optimize the layout and placement of surveillance cameras within the nursery based on the results of the generating AI's analysis. This allows the childcare environment safety system to improve safety within the nursery. The childcare environment safety system analyzes voice data within the nursery, detects problematic behavior early, and suggests optimizing the layout and surveillance cameras, thereby improving the safety of the childcare environment. For example, this can provide parents with a sense of security and reduce the burden on childcare workers. Furthermore, childcare workers can quickly respond to problematic behavior detected by the generating AI, creating a smooth childcare environment.

[0081] A childcare environment safety system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a suggestion unit. The collection unit collects audio data from within the childcare center. The audio data from the childcare center includes, but is not limited to, sounds of children crying, screaming, and fighting. The collection unit collects the audio data using, for example, microphones installed within the childcare center. The collection unit can also prioritize collecting audio data from specific areas within the childcare center. For example, the collection unit can prioritize collecting audio data from play areas and dining areas. Furthermore, the collection unit can filter environmental and background sounds to remove noise when collecting the audio data. For example, the collection unit can filter out air conditioner noise and external traffic noise to clearly collect the children's voices. The analysis unit uses a generation AI to analyze the audio data collected by the collection unit. The analysis can be performed using, for example, voice recognition technology or frequency analysis technology, but is not limited to, examples. For example, the generation AI can analyze the frequency components of the audio data to identify patterns of crying and screaming. The analysis unit can also identify behavioral patterns by analyzing the duration and intervals of the audio data. For example, the analysis unit can identify a child's stress level by analyzing the duration of crying. The detection unit detects problematic behavior based on the data analyzed by the analysis unit. Problematic behaviors include, but are not limited to, crying, screaming, and fighting. The detection unit can identify problematic behavior by analyzing, for example, fluctuation patterns in the analyzed audio data. The detection unit can also evaluate the severity of problematic behavior by analyzing the intensity and frequency of the audio data. For example, the detection unit can evaluate the severity of problematic behavior by analyzing the intensity of the audio data. The suggestion unit makes optimization suggestions for layout and surveillance cameras based on the results detected by the detection unit. Suggestions include, but are not limited to, optimizing the placement of surveillance cameras within the nursery school or changing the layout within the nursery school. For example, the suggestion unit can analyze video data from surveillance cameras and suggest optimal placement of surveillance cameras. The suggestion unit can also optimize the layout by taking into account the movement and activity patterns of childcare workers. For example, the suggestion unit can analyze the movement and activity patterns of childcare workers to suggest an efficient layout.As a result, the childcare environment safety system of the embodiment can improve the safety of the childcare environment by analyzing audio data within the nursery, detecting problematic behavior early, and proposing optimization of the layout and surveillance cameras.

[0082] The proposal unit includes an action unit in which childcare workers act based on the AI's suggestions. The action unit is a department in which childcare workers act based on the AI's suggestions. The action unit, for example, supports childcare workers in responding quickly based on the AI's suggestions. For example, the action unit notifies childcare workers of the location and time of the problematic behavior and encourages them to respond appropriately. The action unit can also provide training for childcare workers to act based on the AI's suggestions. For example, the action unit provides childcare workers with a training program to learn how to respond based on the AI's suggestions. Furthermore, the action unit can collect feedback when childcare workers act based on the AI's suggestions and provide data to improve the accuracy of the suggestions. For example, the action unit records the results of childcare workers acting based on the AI's suggestions and feeds the data back to the AI. In this way, the action unit supports childcare workers in responding quickly based on the AI's suggestions, thereby improving the safety of the childcare environment.

[0083] The suggestion unit includes a notification unit that notifies information to parents. The notification unit is a department for notifying information to parents. The notification unit, for example, provides parents with information regarding safety within the nursery school. For example, the notification unit notifies parents of problematic behaviors that have occurred within the nursery school and the status of responses to them. The notification unit can also provide parents with regular reports regarding safety within the nursery school. For example, the notification unit creates a monthly report regarding safety within the nursery school and sends it to parents. Furthermore, the notification unit can collect feedback from parents and improve the content of notifications based on that feedback. For example, the notification unit reviews the content and method of notifications based on feedback from parents and provides more effective information. In this way, the notification unit can provide parents with information to give them a sense of security regarding safety within the nursery school.

[0084] The collection unit can collect audio data from within the nursery school. The collection unit collects the audio data using, for example, microphones installed within the nursery school. Audio data from the nursery school includes, but is not limited to, sounds of children crying, screaming, and fighting. The collection unit can also prioritize collecting audio data from specific areas within the nursery school. For example, audio data from the play area or the eating area can be prioritized. The collection unit can also filter environmental and background sounds to remove noise when collecting audio data. For example, the sound of air conditioners and external traffic sounds can be filtered to clearly collect children's voices. This enables early detection of problem behaviors by collecting audio data from the nursery school. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data acquired by microphones within the nursery school into a generation AI and cause the generation AI to collect audio data.

[0085] The analysis unit can analyze the collected audio data and identify patterns of crying or screaming. The analysis unit can analyze the collected audio data using, for example, a generation AI. The analysis can be performed using, for example, voice recognition technology or frequency analysis technology, but is not limited to these examples. For example, the generation AI can analyze the frequency components of the audio data to identify patterns of crying or screaming. The analysis unit can also analyze the duration and intervals of the audio data to identify behavioral patterns. For example, the duration of crying can be analyzed to identify a child's stress level. Furthermore, the analysis unit can analyze the intensity and frequency of the audio data to evaluate the severity of problem behavior. For example, the intensity of the audio data can be analyzed to evaluate the severity of problem behavior. This enables early detection of problem behavior by identifying patterns of crying or screaming. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the collected audio data to the generation AI and have the generation AI identify patterns of crying or screaming.

[0086] The detection unit can detect problem behavior based on the analyzed data. The detection unit, for example, analyzes the fluctuation pattern of the voice data analyzed by the analysis unit to identify problem behavior. Problem behavior includes, but is not limited to, crying, screaming, and fighting. The detection unit, for example, analyzes sudden fluctuations in the voice data to identify problem behavior. The detection unit can also analyze continuous fluctuations in the voice data to identify problem behavior. The detection unit can also analyze periodic fluctuations in the voice data to identify problem behavior. For example, periodic fluctuations in the voice data can be analyzed to identify problem behavior. This allows problem behavior to be detected based on the analyzed data, allowing childcare workers to respond quickly. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the analyzed voice data to a generation AI and have the generation AI detect problem behavior.

[0087] The proposal unit can analyze surveillance camera video data and propose an appropriate layout and surveillance camera placement. The proposal unit can analyze the surveillance camera video data using, for example, a generation AI. The analysis can be performed using, for example, video recognition technology or motion analysis technology, but is not limited to these examples. For example, the generation AI can analyze the surveillance camera video data and propose an optimal surveillance camera placement. The proposal unit can also optimize the layout by taking into account the movement and activity patterns of childcare workers. For example, the proposal unit can analyze the movement and activity patterns of childcare workers to propose an efficient layout. Furthermore, the proposal unit can optimize the placement of surveillance cameras in specific areas of the nursery school. For example, the proposal unit can optimize the placement of surveillance cameras in play areas and dining areas. This can improve the safety of the childcare environment by analyzing the surveillance camera video data. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the proposal unit can input surveillance camera video data to the generation AI and cause the generation AI to propose an optimal layout and surveillance camera placement.

[0088] The collection unit can estimate the child's emotions and adjust the timing of collecting voice data based on the estimated child's emotions. The collection unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expressions to estimate the child's emotions. The collection unit also adjusts the timing of collecting voice data based on the estimated child's emotions. For example, if the child is excited, the collection frequency can be increased to collect detailed voice data. On the other hand, if the child is calm, the collection frequency can be reduced to collect the minimum amount of voice data necessary. Furthermore, if the child is crying, the collection frequency can be set to a medium level to track changes in the crying. This enables more accurate data collection by adjusting the timing of collecting voice data according to the child's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the timing of collecting voice data.

[0089] The collection unit can prioritize collecting audio data from specific areas within the nursery school. The collection unit, for example, collects audio data using a microphone installed in a specific area within the nursery school. Specific areas include, but are not limited to, play areas, dining areas, and rest areas. The collection unit, for example, prioritizes collecting audio data from play areas to monitor children's play. The collection unit can also prioritize collecting audio data from dining areas to detect problematic behavior during meals. Furthermore, the collection unit can prioritize collecting audio data from rest areas to monitor children's behavior during breaks. By preferentially collecting audio data from specific areas, it becomes easier to identify the location where problematic behavior occurs. Some or all of the above-described processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input audio data from specific areas to a generation AI and cause the generation AI to prioritize the collection of audio data.

[0090] The collection unit can filter environmental sounds and background sounds to remove noise when collecting audio data. The collection unit can use, for example, noise reduction technology to filter environmental sounds and background sounds when collecting audio data. Examples of environmental sounds and background sounds include, but are not limited to, air conditioner sounds, external traffic sounds, and conversations between childcare workers. The collection unit can, for example, filter air conditioner sounds and external traffic sounds to clearly collect children's voices. The collection unit can also filter conversations between childcare workers and other children to collect specific children's voices. Furthermore, the collection unit can filter specific frequency bands to remove noise when collecting audio data. For example, the collection unit can filter specific frequency bands to remove unnecessary noise. This allows for the collection of clearer audio data by filtering environmental sounds and background sounds. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input audio data to a generation AI and have the generation AI perform noise filtering.

[0091] The collection unit can improve collection accuracy by identifying the direction of a sound source when collecting audio data. The collection unit can identify the direction of a sound source, for example, using a microphone array. Methods for identifying the direction of a sound source include, but are not limited to, analyzing the phase difference of audio data and arranging multiple microphones. The collection unit can collect audio data from a specific direction, for example, using a microphone array. The collection unit can also improve collection accuracy by arranging multiple microphones to identify the direction of a sound source. Furthermore, the collection unit can analyze the phase difference of audio data to identify the direction of a sound source. For example, the collection unit can analyze the phase difference of audio data to identify the direction of a sound source. By identifying the direction of a sound source, collection accuracy is improved and more accurate data can be obtained. Some or all of the above-described processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input audio data to a generation AI and cause the generation AI to identify the direction of a sound source.

[0092] The collection unit can estimate the child's emotions and determine the priority of the voice data to be collected based on the estimated child's emotions. The collection unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The collection unit also determines the priority of the voice data to be collected based on the estimated child's emotions. For example, if the child is excited, the collection unit prioritizes the collection of that voice data. Also, if the child is crying, the collection unit can prioritize the collection of that voice data. Furthermore, if the child is quiet, the collection unit can lower the priority of that voice data. In this way, by prioritizing the voice data based on the child's emotions, important data 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 the child's emotion data to the generation AI and have the generation AI determine the priority of the voice data.

[0093] When collecting voice data, the collection unit can adjust the collection range taking into account the movements and location information of the childcare worker. The collection unit, for example, uses a motion sensor or a GPS to acquire the movements and location information of the childcare worker. Acquisition of the movements and location information of the childcare worker includes, but is not limited to, a motion sensor or a location information system. For example, when the childcare worker is in a specific area, the collection unit prioritizes collecting voice data from that area. Furthermore, when the childcare worker is moving, the collection unit can also collect voice data from the area to which the childcare worker moves. Furthermore, when the childcare worker is conversing with a specific child, the collection unit can collect voice data of the conversation. This enables more effective data collection by taking into account the movements and location information of the childcare worker. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the movements and location information of the childcare worker into the generation AI and cause the generation AI to adjust the collection range.

[0094] The collection unit may also collect environmental information, such as temperature and humidity, within the nursery school when collecting the voice data. The collection unit may use, for example, a temperature sensor or a humidity sensor to collect environmental information within the nursery school. Environmental information may include, but is not limited to, temperature, humidity, and illuminance. For example, the collection unit may also collect data from a temperature sensor when collecting the voice data. The collection unit may also collect data from a humidity sensor when collecting the voice data. The collection unit may also collect data from an environmental sensor when collecting the voice data. For example, the collection unit may also collect data from an illuminance sensor when collecting the voice data. Collecting environmental information improves the accuracy of analyzing the voice data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data from an environmental sensor into the generation AI and cause the generation AI to collect environmental information.

[0095] When collecting voice data, the collection unit can focus on collecting voice data during a specific time period within the nursery school. The collection unit, for example, collects voice data during a specific time period within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The collection unit, for example, focuses on collecting voice data during the morning arrival time. The collection unit can also focus on collecting voice data during the lunch time. Furthermore, the collection unit can also focus on collecting voice data during the evening dismissal time. This makes it easier to identify the timing of occurrence of problem behavior by focusing on collecting voice data during specific time periods. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data during a specific time period into a generation AI and cause the generation AI to perform focused collection of voice data.

[0096] The analysis unit can estimate the child's emotions and adjust the analysis method of the voice data based on the estimated child's emotions. The analysis unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to these examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The analysis unit also adjusts the analysis method of the voice data based on the estimated child's emotions. For example, if the child is excited, the frequency components of the voice data can be analyzed in detail. If the child is crying, the duration and interval of the voice data can be analyzed. Furthermore, if the child is quiet, the intensity and frequency of the voice data can be analyzed. This allows for more accurate analysis by adjusting the analysis method based on the child's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the analysis method of the voice data.

[0097] When analyzing the voice data, the analysis unit can identify problematic behavior by analyzing the frequency components of the voice in detail. The analysis unit, for example, uses a generation AI to analyze the frequency components of the voice data. The analysis is performed, for example, using spectral analysis technology, but is not limited to such an example. For example, the generation AI can analyze the high-frequency components of the voice data to identify screams and crying. The analysis unit can also analyze the low-frequency components of the voice data to identify sounds of fighting. Furthermore, the analysis unit can analyze the mid-frequency components of the voice data to identify talking and laughter. This improves the accuracy of identifying problematic behavior by analyzing the frequency components of the voice in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the voice data to the generation AI and have the generation AI analyze the frequency components.

[0098] When analyzing the voice data, the analysis unit can identify behavioral patterns by analyzing the duration and intervals of the voice. The analysis unit, for example, uses a generation AI to analyze the duration and intervals of the voice data. The analysis is performed, for example, using time domain analysis technology, but is not limited to such an example. For example, the generation AI can analyze the duration of crying to identify a child's stress level. The analysis unit can also analyze the intervals between cries to identify the frequency of problematic behavior. Furthermore, the analysis unit can analyze the duration of speaking to identify a child's communication pattern. Thus, behavioral patterns can be identified by analyzing the duration and intervals of voice. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input voice data to the generation AI and have the generation AI analyze the duration and intervals.

[0099] When analyzing voice data, the analysis unit can integrate multiple pieces of voice data to improve analysis accuracy. The analysis unit integrates multiple pieces of voice data using, for example, a generation AI. The integration is performed using, for example, data fusion technology, but is not limited to such an example. For example, the generation AI integrates and analyzes voice data collected from multiple microphones. The analysis unit can also integrate and analyze voice data collected over multiple time periods. Furthermore, the analysis unit can also integrate and analyze voice data collected from multiple areas. In this way, by integrating multiple pieces of voice data, analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input multiple pieces of voice data to the generation AI and have the generation AI integrate the data.

[0100] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. The analysis unit estimates the child's emotions using, for example, a generation AI. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to these examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The analysis unit also adjusts the display method of the analysis results based on the estimated child's emotions. For example, if the child is excited, a detailed analysis result can be displayed. If the child is crying, a concise analysis result can be displayed. Furthermore, if the child is quiet, a standard analysis result can be displayed. This allows for more appropriate information provision by adjusting the display method of the analysis results based on the child's emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0101] When analyzing the audio data, the analysis unit can analyze the voice and instructions of the childcare worker to identify the child's reaction. The analysis unit, for example, uses a generation AI to analyze the voice and instructions of the childcare worker. The analysis is performed, for example, using voice recognition technology, but is not limited to such an example. For example, the generation AI can analyze the child's reaction to the childcare worker's instructions to identify the effect of the instructions. The analysis unit can also analyze the child's reaction to the tone of the childcare worker's voice to identify changes in emotion. Furthermore, the analysis unit can analyze the child's reaction to the intensity of the childcare worker's voice to identify changes in behavior. This makes it easier to identify the child's reaction by analyzing the childcare worker's voice and instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the voice and instructions of the childcare worker into the generation AI and have the generation AI identify the child's reaction.

[0102] When analyzing the voice data, the analysis unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The analysis unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, and illuminance, but is not limited to these examples. The analysis unit, for example, analyzes temperature data and voice data together to identify the impact of environmental changes on children's behavior. The analysis unit can also analyze humidity data and voice data together to identify the impact of humidity changes on children's behavior. Furthermore, the analysis unit can analyze illuminance data and voice data together to identify the impact of illuminance changes on children's behavior. This makes it easier to identify the impact of environmental changes on children's behavior by analyzing other sensor data together. Some or all of the above-described processing by the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0103] When analyzing voice data, the analysis unit can optimize the analysis algorithm by referring to past analysis results. The analysis unit, for example, uses a generation AI to refer to past analysis results. The referencing can be performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI can adjust the parameters of the analysis algorithm based on past analysis results. The analysis unit can also improve the accuracy of the analysis algorithm based on past analysis results. Furthermore, the analysis unit can also improve the processing speed of the analysis algorithm based on past analysis results. As a result, the accuracy of the analysis algorithm is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI optimize the analysis algorithm.

[0104] The detection unit can estimate the child's emotions and adjust the problem behavior detection criteria based on the estimated child's emotions. The detection unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The detection unit also adjusts the problem behavior detection criteria based on the estimated child's emotions. For example, if the child is excited, the problem behavior detection criteria can be tightened. Also, if the child is crying, the problem behavior detection criteria can be loosened. Furthermore, if the child is quiet, standard detection criteria can be applied. This allows for more accurate detection of problem behavior by adjusting the detection criteria based on the child's emotions. Some or all of the above-described processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the problem behavior detection criteria.

[0105] Upon detection, the detection unit can analyze the fluctuation pattern of the voice data to identify problematic behavior. The detection unit, for example, uses a generation AI to analyze the fluctuation pattern of the voice data. The analysis is performed, for example, using time domain analysis technology, but is not limited to such an example. For example, the generation AI can analyze sudden fluctuations in the voice data to identify problematic behavior. The detection unit can also analyze continuous fluctuations in the voice data to identify problematic behavior. Furthermore, the detection unit can analyze periodic fluctuations in the voice data to identify problematic behavior. In this way, analyzing the fluctuation pattern of the voice data improves the accuracy of identifying problematic behavior. Some or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input voice data to the generation AI and have the generation AI analyze the fluctuation pattern.

[0106] Upon detection, the detection unit can analyze the intensity and frequency of the audio data to evaluate the severity of the problematic behavior. The detection unit, for example, uses a generation AI to analyze the intensity and frequency of the audio data. The analysis can be performed using, for example, amplitude analysis technology or frequency analysis technology, but is not limited to these examples. For example, the generation AI can analyze the intensity of the audio data to evaluate the severity of the problematic behavior. The detection unit can also analyze the frequency of the audio data to evaluate the severity of the problematic behavior. Furthermore, the detection unit can analyze the duration of the audio data to evaluate the severity of the problematic behavior. This makes it easier to evaluate the severity of the problematic behavior by analyzing the intensity and frequency of the audio data. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input audio data to the generation AI and have the generation AI perform an analysis of the intensity and frequency.

[0107] Upon detection, the detection unit can compare multiple pieces of audio data to identify the location of the problematic behavior. The detection unit, for example, uses a generation AI to compare the multiple pieces of audio data. The comparison is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI can compare audio data collected from multiple microphones to identify the location of the problematic behavior. The detection unit can also compare audio data collected over multiple time periods to identify the location of the problematic behavior. Furthermore, the detection unit can compare audio data collected from multiple areas to identify the location of the problematic behavior. This makes it easier to identify the location of the problematic behavior by comparing multiple pieces of audio data. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input multiple pieces of audio data to the generation AI and have the generation AI compare the data.

[0108] The detection unit can estimate the child's emotion and adjust the display method of the detection result based on the estimated emotion. The detection unit estimates the child's emotion using, for example, a generation AI. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotion. The detection unit also adjusts the display method of the detection result based on the estimated emotion of the child. For example, if the child is excited, a detailed detection result can be displayed. If the child is crying, a concise detection result can be displayed. Furthermore, if the child is quiet, a standard detection result can be displayed. This allows for more appropriate information provision by adjusting the display method of the detection result based on the child's emotion. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the display method of the detection result.

[0109] The detection unit can identify the cause of problem behavior by taking into account the behavior and location information of the childcare worker during detection. The detection unit, for example, uses a motion sensor or GPS to acquire the behavior and location information of the childcare worker. Acquisition of the behavior and location information of the childcare worker includes, but is not limited to, a motion sensor or a location information system. For example, when the childcare worker is in a specific area, the detection unit can identify the cause of problem behavior in that area. Furthermore, when the childcare worker is interacting with a specific child, the detection unit can also identify whether the interaction is the cause of the problem behavior. Furthermore, the detection unit can analyze the movement pattern of the childcare worker to identify the cause of problem behavior. This makes it easier to identify the cause of problem behavior by taking into account the behavior and location information of the childcare worker. Some or all of the above-described processing by the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the behavior and location information of the childcare worker into a generation AI and cause the generation AI to identify the cause of the problem behavior.

[0110] During detection, the detection unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The detection unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, and illuminance, but is not limited to these examples. The detection unit, for example, analyzes temperature data and audio data together to identify the impact of environmental changes on problem behavior. The detection unit can also analyze humidity data and audio data together to identify the impact of humidity changes on problem behavior. Furthermore, the detection unit can analyze illuminance data and audio data together to identify the impact of illuminance changes on problem behavior. This makes it easier to identify the impact of environmental changes on problem behavior by analyzing other sensor data together. Some or all of the above-described processing in the detection unit may be performed, for example, using AI or without AI. For example, the detection unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0111] During detection, the detection unit can optimize the detection algorithm by referring to past detection results. The detection unit, for example, uses a generation AI to refer to past detection results. The referencing is performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI adjusts the parameters of the detection algorithm based on past detection results. The detection unit can also improve the accuracy of the detection algorithm based on past detection results. Furthermore, the detection unit can also improve the processing speed of the detection algorithm based on past detection results. As a result, the accuracy of the detection algorithm is improved by referring to past detection results. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input past detection results to the generation AI and have the generation AI optimize the detection algorithm.

[0112] The suggestion unit can estimate the child's emotions and adjust the proposed layout and surveillance camera placement based on the estimated child's emotions. The suggestion unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI can analyze the child's tone of voice and facial expression to estimate the child's emotions. The suggestion unit also adjusts the proposed layout and surveillance camera placement based on the estimated child's emotions. For example, if the child is excited, the system can increase the number of surveillance cameras to expand the monitoring area. If the child is crying, the system can change the layout to improve the visibility of the childcare worker. Furthermore, if the child is quiet, the system can suggest a standard layout and surveillance camera placement. This allows for more appropriate layout and surveillance camera placement by adjusting the proposal based on the child's emotions. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the proposed layout and surveillance camera placement.

[0113] When making a proposal, the suggestion unit can integrate audio data and video data from within the nursery school to make an optimal proposal. The suggestion unit, for example, uses a generation AI to integrate audio data and video data from within the nursery school. The integration can be performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI can integrate the audio data and video data to identify locations where problematic behavior occurs and propose optimal surveillance camera placement. The suggestion unit can also integrate the audio data and video data to propose a layout that optimizes the movement of childcare workers. Furthermore, the suggestion unit can integrate the audio data and video data to make a proposal that optimizes the placement of children's play areas. In this way, integrating the audio data and video data enables more accurate proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the audio data and video data into the generation AI and have the generation AI integrate the data.

[0114] When making a proposal, the proposal unit can optimize the layout by taking into account the movement lines and activity patterns of the childcare workers. The proposal unit, for example, uses a generation AI to analyze the movement lines and activity patterns of the childcare workers. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generation AI analyzes the movement lines and activity patterns of the childcare workers to propose an efficient layout. The proposal unit can also analyze the activity patterns of the childcare workers to propose optimal surveillance camera placement. Furthermore, the proposal unit can make a proposal to optimize the layout by taking into account the rest areas of the childcare workers. This enables a more efficient layout by taking into account the movement lines and activity patterns of the childcare workers. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit can input the movement lines and activity patterns of the childcare workers into the generation AI and cause the generation AI to optimize the layout.

[0115] When making a proposal, the proposal unit can optimize the proposal algorithm by referring to past proposal results. The proposal unit, for example, uses a generation AI to refer to past proposal results. The referencing is performed, for example, using a database reference technique, but is not limited to such an example. For example, the generation AI adjusts parameters of the proposal algorithm based on past proposal results. The proposal unit can also improve the accuracy of the proposal algorithm based on past proposal results. Furthermore, the proposal unit can also improve the processing speed of the proposal algorithm based on past proposal results. As a result, the accuracy of the proposal algorithm is improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI or without using AI. For example, the proposal unit can input past proposal results to the generation AI and cause the generation AI to optimize the proposal algorithm.

[0116] The suggestion unit can estimate the child's emotions and prioritize the suggestions based on the estimated emotions. The suggestion unit estimates the child's emotions using, for example, a generation AI. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to these examples. For example, the generation AI can analyze the child's tone of voice and facial expression to estimate the child's emotions. The suggestion unit also prioritizes the suggestions based on the estimated emotions of the child. For example, if the child is excited, a proposal for the placement of a surveillance camera can be prioritized. If the child is crying, a proposal for a layout change can be prioritized. Furthermore, if the child is quiet, standard suggestions can be prioritized. In this way, by prioritizing the suggestions based on the child's emotions, important suggestions can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the child's emotion data into the generation AI and cause the generation AI to prioritize the suggestions.

[0117] When making a proposal, the proposal unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The proposal unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The proposal unit, for example, analyzes temperature data and audio data together to propose an optimal layout. The proposal unit can also analyze humidity data and audio data together to propose an optimal surveillance camera placement. Furthermore, the proposal unit can analyze illuminance data and audio data together to propose an optimal childcare environment. This enables more accurate proposals by analyzing other sensor data together. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0118] When making a proposal, the suggestion unit can customize the proposal content by reflecting feedback from the childcare workers. The suggestion unit, for example, uses questionnaires or interviews to collect feedback from the childcare workers. Examples of collecting feedback include, but are not limited to, questionnaire surveys and interviews. The suggestion unit, for example, customizes the placement of surveillance cameras based on the feedback from the childcare workers. The suggestion unit can also customize the layout based on the feedback from the childcare workers. Furthermore, the suggestion unit can optimize the proposal content based on the feedback from the childcare workers. This enables more practical proposals by reflecting the feedback from the childcare workers. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the feedback from the childcare workers into a generation AI and have the generation AI customize the proposal content.

[0119] When making a proposal, the suggestion unit can focus on analyzing data from specific time periods within the nursery school and make the proposal. The suggestion unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The suggestion unit, for example, can focus on analyzing data from the morning arrival time period and propose an optimal layout. The suggestion unit can also focus on analyzing data from the lunch time period and propose optimal surveillance camera placement. Furthermore, the suggestion unit can focus on analyzing data from the evening dismissal time period and propose an optimal childcare environment. This enables more appropriate proposals by focusing on analyzing data from specific time periods. Some or all of the above-described processing by the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input data from specific time periods into the generation AI and cause the generation AI to perform a focused analysis of the data.

[0120] The response unit can estimate the child's emotions and adjust a response method based on the estimated child's emotions. The response unit estimates the child's emotions using, for example, a generation AI. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The response unit also adjusts a response method based on the estimated child's emotions. For example, if the child is excited, the response unit can suggest a response method to calm the child. If the child is crying, the response unit can suggest a response method to comfort the child. Furthermore, if the child is quiet, the response unit can suggest a standard response method. This allows for a more appropriate response by adjusting the response method based on the child's emotions. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the child's emotion data into the generation AI and have the generation AI adjust the response method.

[0121] The response unit can select the optimal response method by referring to the childcare worker's past response history when responding. The response unit, for example, uses a generation AI to reference the childcare worker's past response history. The reference can be performed, for example, using database reference technology, but is not limited to such an example. For example, the generation AI selects the optimal response method based on the childcare worker's past response history. The response unit can also identify the cause of the problem behavior based on the childcare worker's past response history. Furthermore, the response unit can evaluate the effectiveness of the response method based on the childcare worker's past response history. This enables more effective response by referring to the past response history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the childcare worker's past response history into the generation AI and have the generation AI select the optimal response method.

[0122] When responding, the response unit can integrate audio data and video data from the nursery school to optimize the response method. The response unit, for example, uses a generation AI to integrate audio data and video data from the nursery school. The integration is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generation AI integrates the audio data and video data to select the optimal response method. The response unit can also integrate the audio data and video data to identify the cause of the problem behavior. Furthermore, the response unit can integrate the audio data and video data to evaluate the effectiveness of the response method. In this way, integrating the audio data and video data enables a more accurate response. Some or all of the above-mentioned processing in the response unit may be performed, for example, using AI or without AI. For example, the response unit can input the audio data and video data into the generation AI and have the generation AI integrate the data.

[0123] The response unit can improve the response method by reflecting the childcare worker's feedback when responding. The response unit, for example, uses questionnaires or interviews to collect the childcare worker's feedback. Examples of collecting feedback include, but are not limited to, questionnaires and interviews. The response unit, for example, improves the response method based on the childcare worker's feedback. The response unit can also identify the cause of the problem behavior based on the childcare worker's feedback. Furthermore, the response unit can evaluate the effectiveness of the response method based on the childcare worker's feedback. This enables a more effective response by reflecting the childcare worker's feedback. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the childcare worker's feedback into the generation AI and have the generation AI improve the response method.

[0124] The response unit can estimate the child's emotions and determine a priority of responses based on the estimated child's emotions. The response unit estimates the child's emotions using, for example, a generation AI. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The response unit also determines a priority of responses based on the estimated child's emotions. For example, if the child is excited, a response to that excitement can be prioritized. Also, if the child is crying, a response to that excitement can be prioritized. Furthermore, if the child is quiet, a standard response can be prioritized. In this way, by determining the priority of responses based on the child's emotions, important responses can be prioritized. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of responses.

[0125] When responding, the response unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The response unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The response unit, for example, analyzes temperature data and audio data together to select an optimal response method. The response unit can also analyze humidity data and audio data together to select an optimal response method. Furthermore, the response unit can analyze illuminance data and audio data together to select an optimal response method. This enables a more accurate response by analyzing other sensor data together. Some or all of the above-described processing in the response unit may be performed, for example, using AI, or may be performed without using AI. For example, the response unit can input other sensor data into the generation AI and have the generation AI perform a combined analysis of the data.

[0126] The response unit can optimize the response method by taking into account the movement lines and activity patterns of the childcare worker when responding. The response unit, for example, uses a generation AI to analyze the movement lines and activity patterns of the childcare worker. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generation AI analyzes the movement lines of the childcare worker to select an efficient response method. The response unit can also analyze the activity patterns of the childcare worker to select an optimal response method. Furthermore, the response unit can optimize the response method by taking into account the childcare worker's rest area. This enables a more efficient response by taking into account the movement lines and activity patterns of the childcare worker. Some or all of the above-mentioned processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input the movement lines and activity patterns of the childcare worker into the generation AI and have the generation AI optimize the response method.

[0127] When responding, the response unit can focus on analyzing data from specific time periods within the nursery school and respond accordingly. The response unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time, lunch time, and evening dismissal time. The response unit, for example, focuses on analyzing data from the morning arrival time period and selects the optimal response method. The response unit can also focus on analyzing data from the lunch time period and select the optimal response method. Furthermore, the response unit can also focus on analyzing data from the evening dismissal time period and select the optimal response method. This enables a more appropriate response by focusing on analyzing data from specific time periods. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit can input data from specific time periods into the generation AI and have the generation AI perform a focused analysis of the data.

[0128] The providing unit can estimate the child's emotions and adjust the method of providing information based on the estimated child's emotions. The providing unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The providing unit also adjusts the method of providing information based on the estimated child's emotions. For example, if the child is excited, detailed information can be provided. If the child is crying, brief information can be provided. Furthermore, if the child is quiet, standard information can be provided. This allows for more appropriate information to be provided by adjusting the method of providing information based on the child's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the method of providing information.

[0129] When providing information, the providing unit can select the optimal information provision method by referring to the parent's past feedback. The providing unit, for example, uses a generation AI to refer to the parent's past feedback. The referencing can be performed, for example, using a database reference technology, but is not limited to such an example. For example, the generation AI selects the optimal information provision method based on the parent's past feedback. The providing unit can also customize the content of the information provision based on the parent's past feedback. Furthermore, the providing unit can also adjust the frequency of information provision based on the parent's past feedback. This enables more effective information provision by referring to the parent's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the parent's past feedback into the generation AI and cause the generation AI to select the optimal information provision method.

[0130] When providing information, the providing unit can integrate audio data and video data from within the nursery school to optimize the information provision. The providing unit, for example, uses a generating AI to integrate audio data and video data from within the nursery school. The integration is performed, for example, using data fusion technology, but is not limited to such an example. For example, the generating AI can integrate audio data and video data to provide detailed information to parents. The providing unit can also integrate audio data and video data to provide concise information to parents. Furthermore, the providing unit can integrate audio data and video data to provide standard information to parents. This makes it possible to provide more accurate information by integrating audio data and video data. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input audio data and video data to the generating AI and cause the generating AI to integrate the data.

[0131] The providing unit can improve the information provision method by reflecting parental feedback when providing information. The providing unit, for example, uses questionnaires or interviews to collect parental feedback. Examples of collecting feedback include, but are not limited to, questionnaires and interviews. The providing unit, for example, improves the information provision method based on parental feedback. The providing unit can also customize the content of the information provided based on parental feedback. Furthermore, the providing unit can also adjust the frequency of information provision based on parental feedback. This enables more effective information provision by reflecting parental feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input parental feedback into a generating AI and cause the generating AI to improve the information provision method.

[0132] The providing unit can estimate the child's emotions and determine the priority of information provision based on the estimated child's emotions. The providing unit, for example, uses a generation AI to estimate the child's emotions. The estimation can be performed using, for example, voice analysis technology or facial expression recognition technology, but is not limited to such examples. For example, the generation AI analyzes the child's tone of voice and facial expression to estimate the child's emotions. The providing unit also determines the priority of information provision based on the estimated child's emotions. For example, if the child is excited, the provision of that information can be prioritized. Also, if the child is crying, the provision of that information can be prioritized. Furthermore, if the child is quiet, the provision of standard information can be prioritized. In this way, by determining the priority of information provision based on the child's emotions, important information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of information provision.

[0133] When providing information, the providing unit can also analyze other sensor data (e.g., temperature, humidity) within the nursery school. The providing unit, for example, uses a generation AI to also analyze other sensor data within the nursery school. Sensor data includes, for example, temperature, humidity, illuminance, etc., but is not limited to these examples. The providing unit, for example, analyzes temperature data and audio data together to select an optimal information provision method. The providing unit can also analyze humidity data and audio data together to select an optimal information provision method. Furthermore, the providing unit can also analyze illuminance data and audio data together to select an optimal information provision method. This enables more accurate information provision by analyzing other sensor data together. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input other sensor data to the generation AI and have the generation AI perform a combined analysis of the data.

[0134] The providing unit can optimize the information provision method by taking into account the guardian's movement line and activity pattern when providing information. The providing unit, for example, uses a generating AI to analyze the guardian's movement line and activity pattern. The analysis can be performed using, for example, a motion sensor or a location information system, but is not limited to these examples. For example, the generating AI analyzes the guardian's movement line and selects an efficient information provision method. The providing unit can also analyze the guardian's activity pattern and select an optimal information provision method. Furthermore, the providing unit can optimize the information provision method by taking into account the guardian's rest area. This enables more effective information provision by taking into account the guardian's movement line and activity pattern. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the guardian's movement line and activity pattern into the generating AI and cause the generating AI to optimize the information provision method.

[0135] When providing information, the providing unit can provide information by focusing on analyzing data from specific time periods within the nursery school. The providing unit, for example, uses a generation AI to analyze data from specific time periods within the nursery school. Specific time periods include, but are not limited to, the morning arrival time period, the lunch time period, and the evening dismissal time period. The providing unit, for example, focuses on analyzing data from the morning arrival time period and selects the optimal information provision method. The providing unit can also focus on analyzing data from the lunch time period and select the optimal information provision method. Furthermore, the providing unit can also focus on analyzing data from the evening dismissal time period and select the optimal information provision method. This enables more appropriate information provision by focusing on analyzing data from specific time periods. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data from specific time periods into the generation AI and cause the generation AI to perform a focused analysis of the data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, suggestion unit, behavior unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect voice data within the nursery school using the microphone of the smart device 14. The analysis unit can analyze the voice data using a generation AI by the specific processing unit 290 of the data processing device 12. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects problem behavior based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that proposes optimization of layout and surveillance cameras based on the detection results. The behavior unit is a unit that supports childcare workers in acting based on AI suggestions and is realized, for example, by the control unit 46A of the smart device 14. The notification unit is a unit that notifies parents of information and is realized, for example, by the control unit 46A of the smart device 14. The collection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the child's emotion and adjusts the timing of collecting the voice data based on the estimated child's emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, suggestion unit, action unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect voice data within the nursery school using the microphone of the smart glasses 214. The analysis unit can analyze the voice data using a generation AI by the specific processing unit 290 of the data processing device 12. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects problem behavior based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that proposes optimization of layouts and surveillance cameras based on the detection results. The action unit is a unit that supports childcare workers in acting based on AI suggestions and is realized, for example, by the control unit 46A of the smart glasses 214. The notification unit is a unit that notifies parents of information and is realized, for example, by the control unit 46A of the smart glasses 214. The collection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the child's emotion and adjusts the timing of collecting the voice data based on the estimated child's emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, suggestion unit, behavior unit, and notification unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect voice data within the nursery school using the microphone of the headset-type terminal 314. The analysis unit can analyze the voice data using a generation AI by the specific processing unit 290 of the data processing device 12. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects problem behavior based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that proposes optimization of layouts and surveillance cameras based on the detection results. The behavior unit is a unit that supports childcare workers in acting based on AI suggestions and is realized, for example, by the control unit 46A of the headset-type terminal 314. The notification unit is a unit that notifies parents of information and is realized, for example, by the control unit 46A of the headset-type terminal 314. The collection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the child's emotion and adjusts the timing of collecting the voice data based on the estimated child's emotion. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, suggestion unit, behavior unit, and notification 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 audio data within the nursery school using the microphone of the robot 414. The analysis unit can analyze the audio data using a generation AI by the specific processing unit 290 of the data processing device 12. The detection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that detects problematic behavior based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that proposes optimization of layouts and surveillance cameras based on the detection results. The behavior unit is a unit that supports childcare workers in acting based on AI suggestions and is realized, for example, by the control unit 46A of the robot 414. The notification unit is a unit that notifies parents of information and is realized, for example, by the control unit 46A of the robot 414. The collection unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the child's emotion and adjusts the timing of collecting the voice data based on the estimated child's emotion.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The childcare environment safety system may further include a health monitoring unit that monitors the child's health condition. The health monitoring unit monitors the child's health condition in real time, for example, using sensors that measure the child's body temperature and heart rate. The health monitoring unit may also analyze the child's health data and notify the childcare worker if an abnormality is detected. For example, if the child's body temperature rises suddenly or the heart rate is abnormally high, an alert may be sent to the childcare worker. Furthermore, the health monitoring unit may record the child's health data over the long term and provide regular reports to the parents. In this way, the childcare environment safety system can provide a safer childcare environment by monitoring the child's health condition.

[0138] The childcare environment safety system can further include a behavior recording unit that records a child's behavior history. The behavior recording unit, for example, records a child's play and learning behavior and provides the information to parents. The behavior recording unit can also analyze a child's behavior patterns and propose a childcare plan suitable for each child. For example, if a particular child prefers a particular activity at a particular time, the behavior recording unit can propose a childcare plan that prioritizes that activity. Furthermore, the behavior recording unit can provide childcare workers with advice on how to stimulate the child's interests and curiosity based on the child's behavior data. In this way, the childcare environment safety system can record a child's behavior history and provide childcare that is suitable for each child.

[0139] The childcare environment safety system can further include a stress monitoring unit that monitors the stress level of childcare workers. The stress monitoring unit monitors the stress level of childcare workers in real time, for example, using sensors that measure the childcare worker's heart rate and electrodermal activity. The stress monitoring unit can also analyze the childcare worker's stress data and notify the administrator if an abnormality is detected. For example, if the heart rate is abnormally high or if there is a sudden change in electrodermal activity, an alert is sent to the administrator. Furthermore, the stress monitoring unit can provide advice for stress reduction based on the childcare worker's stress data. In this way, the childcare environment safety system can provide a healthy and comfortable working environment for childcare workers by monitoring the childcare worker's stress level.

[0140] The childcare environment safety system can further include an air quality monitoring unit that monitors the air quality within the childcare center. The air quality monitoring unit monitors the air quality within the childcare center in real time using sensors that measure, for example, carbon dioxide concentration and volatile organic compound (VOC) concentrations. The air quality monitoring unit can also analyze air quality data and notify childcare workers if an abnormality is detected. For example, if the carbon dioxide concentration or VOC concentration increases, an alert is sent to the childcare worker. Furthermore, the air quality monitoring unit can suggest ventilation or the use of an air purifier based on the air quality data. In this way, the childcare environment safety system can provide a healthier childcare environment by monitoring the air quality within the childcare center.

[0141] The childcare environment safety system can further include a schedule management unit that supports the schedule management of childcare workers. The schedule management unit, for example, manages the working hours and break times of childcare workers and proposes efficient schedules. The schedule management unit can also analyze the schedule data of childcare workers and notify the manager if signs of overwork or stress are detected. For example, if a childcare worker has been working long hours consecutively or if there are insufficient breaks, an alert can be sent to the manager. Furthermore, the schedule management unit can provide advice on appropriate breaks and refreshment based on the childcare worker's schedule. In this way, the childcare environment safety system can provide a healthy and comfortable working environment for childcare workers by supporting their schedule management.

[0142] The childcare environment safety system can further estimate a child's emotions and provide advice to the childcare worker based on the estimated child's emotions. For example, if a child is excited, the system can suggest specific ways to calm the childcare worker. Also, if a child is crying, the system can suggest approaches to comfort the childcare worker. Furthermore, if a child is quiet, the system can provide advice to the childcare worker to maintain that state. In this way, the childcare environment safety system can provide advice to childcare workers based on the child's emotions, thereby achieving more appropriate childcare.

[0143] The childcare environment safety system can further estimate the child's emotions and provide information to the parents based on the estimated child's emotions. For example, if the child is excited, the system can explain to the parents the reason and how to deal with the situation. If the child is crying, the system can also provide the parents with the cause and how to deal with it. Furthermore, if the child is quiet, the system can provide the parents with advice on how to maintain that state. In this way, the childcare environment safety system can increase the parents' sense of security by providing information to the parents based on the child's emotions.

[0144] The childcare environment safety system can further estimate the child's emotions and adjust the childcare plan based on the estimated child's emotions. For example, if the child is excited, active play can be incorporated. If the child is crying, quiet time can be provided to help the child relax. Furthermore, if the child is quiet, learning activities can be promoted. In this way, the childcare environment safety system can provide more effective childcare by adjusting the childcare plan based on the child's emotions.

[0145] The childcare environment safety system can further estimate the child's emotions and adjust the allocation of childcare workers based on the estimated child's emotions. For example, if a child is excited, an experienced childcare worker can be assigned to that child. Also, if a child is crying, a specific childcare worker can be assigned to that child. Furthermore, if a child is quiet, an appropriate childcare worker can be assigned to that child. In this way, the childcare environment safety system can provide more appropriate childcare by adjusting the allocation of childcare workers based on the child's emotions.

[0146] The childcare environment safety system can further estimate a child's emotions and provide a training program for childcare workers based on the estimated child's emotions. For example, training can be provided to learn how to respond when a child is excited. Training can also be provided to learn how to respond when a child is crying. Furthermore, training can be provided to learn how to respond when a child is quiet. In this way, the childcare environment safety system can support the improvement of the skills of childcare workers by providing training programs for childcare workers based on children's emotions.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection unit collects audio data within the nursery school. The audio data within the nursery school includes, for example, sounds of children crying, screaming, and fighting. The collection unit collects the audio data using microphones installed within the nursery school. It is also possible to preferentially collect audio data from specific areas. Furthermore, the collection unit filters out environmental and background sounds to remove noise when collecting the audio data. Step 2: The analysis unit uses the generation AI to analyze the voice data collected by the collection unit. The analysis is performed using voice recognition technology and frequency analysis technology. For example, the generation AI analyzes the frequency components of the voice data to identify crying or screaming patterns. It can also analyze the duration and intervals of the voice data to identify behavioral patterns. Step 3: The detection unit detects problematic behaviors based on the data analyzed by the analysis unit. Problematic behaviors include crying, screaming, and fighting. The detection unit identifies problematic behaviors by analyzing the fluctuation patterns of the analyzed audio data. It can also evaluate the severity of problematic behaviors by analyzing the intensity and frequency of the audio data. Step 4: The proposal unit makes optimization proposals for the layout and surveillance cameras based on the results detected by the detection unit. Proposals include optimizing the placement of surveillance cameras within the nursery school and changing the layout within the nursery school. For example, it analyzes video data from surveillance cameras and proposes optimal placement of surveillance cameras. It can also optimize the layout by taking into account the movement and activity patterns of nursery staff.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 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 voice data; an analysis unit that analyzes the voice data collected by the collection unit; a detection unit that detects problem behavior based on the data analyzed by the analysis unit; a proposal unit that proposes a layout and a placement of surveillance cameras based on the results of detection by the detection unit. A system characterized by:

2. The proposal unit Equipping childcare workers with an action department that acts based on AI suggestions 2. The system of claim 1.

3. The proposal unit Equipped with a notification section that notifies parents of information 2. The system of claim 1.

4. The collecting unit Collecting voice data from within the nursery school 2. The system of claim 1.

5. The analysis unit Analyzing collected audio data to identify crying and screaming patterns 2. The system of claim 1.

6. The detection unit Detect problematic behavior based on analyzed data 2. The system of claim 1.

7. The proposal unit Analyzes surveillance camera video data and proposes appropriate layouts and placement of surveillance cameras 2. The system of claim 1.

8. The collecting unit The system estimates the child's emotions and adjusts the timing of voice data collection based on the estimated emotions.

2. The system of claim 1.

9. The collecting unit Prioritize collection of voice data from specific areas within the nursery school 2. The system of claim 1.

Images