system

Abstract

The system according to the embodiment aims to reduce the burden on nursery teachers and efficiently record and report the behavior and meal status of children. [Solution] The system according to the embodiment includes an analysis unit, a data provision unit, a generation unit, and a revision unit. The analysis unit analyzes the movements or facial expressions of the children. The data provision unit passes the data analyzed by the analysis unit to a generation AI. The generation unit generates sentences based on the data provided by the data provision unit. The revision unit allows a childcare worker to revise the sentences generated by the generation unit.

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] With conventional technology, childcare workers have to spend a lot of time and effort writing in communication books and keeping track of children's eating habits, so there is room for improvement in efficiency.

[0005] The system according to the embodiment aims to reduce the burden on nursery teachers and efficiently record and report the behavior and meal status of children. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a data provision unit, a generation unit, and a revision unit. The analysis unit analyzes the movements or facial expressions of the children. The data provision unit passes the data analyzed by the analysis unit to the generation AI. The generation unit generates sentences based on the data provided by the data provision unit. In the revision unit, a childcare worker revises the sentences generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment reduces the burden on nursery teachers and allows them to efficiently record and report the behavior and eating habits of children. [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 support system according to an embodiment of the present invention utilizes a generation AI and a camera image analysis AI to solve problems faced by daycare centers. To address the issue of daycare teachers having to write daily communication notebooks, particularly documenting the children's behaviors, this system uses a camera image analysis AI to analyze the children's behavior and a generation AI to create text based on the results. Since the childcare worker only needs to revise the generated text, labor costs are significantly reduced. To address the issue of parents not knowing about the children's meals (leftovers), a camera image analysis AI is used to check for leftovers, and based on the results, a generation AI suggests a food waste history and recipes to combat food waste. This information is automatically transcribed into the daycare center's communication notebook, providing valuable information to parents. For example, a camera image analysis AI is used to analyze the children's behaviors and eating habits in real time, and a generation AI writes up the children's behaviors and food waste history based on the analysis results. The childcare worker then revises the generated text and enters it in the communication notebook. Based on the food waste history, the generating AI suggests recipes to combat food waste and automatically posts them in the communication book. This system is expected to reduce the burden on childcare workers and provide useful information for parents. As a result, the childcare support system can reduce the burden on childcare workers and provide useful information to parents.

[0029] A childcare support system according to an embodiment includes an analysis unit, a data providing unit, a generation unit, and a revision unit. The analysis unit analyzes the movements or facial expressions of children. For example, the analysis unit analyzes the movements of children, such as walking, jumping, and hand movements. The analysis unit can also analyze the expressions of children, such as smiling, anger, and sadness. The analysis unit can also analyze the movements and facial expressions of children in detail using image analysis and motion analysis algorithms. The data providing unit passes the data analyzed by the analysis unit to the generation AI. For example, the data providing unit provides data in the form of image data or text data. The data providing unit can also provide data taking into account data transfer protocols and timing. The generation unit generates sentences based on the data provided by the data providing unit. For example, the generation unit generates sentences using natural language generation technology or template-based generation. The generation unit can also use the generation AI to create detailed sentences describing the behavior of children. The revision unit allows a childcare worker to revise the sentences generated by the generation unit. For example, the revision unit corrects grammar and adds content. The revision unit can also provide an interface that allows the childcare worker to easily revise the generated sentences. As a result, the childcare support system according to the embodiment analyzes the movements and expressions of the children, generates sentences based on the results, and the childcare worker can revise them, thereby reducing the burden on the childcare worker.

[0030] The childcare support system includes a meal analysis unit that analyzes the eating habits of the children. The meal analysis unit analyzes the eating habits of the children. For example, the meal analysis unit analyzes the speed at which the children eat and the amount of food they leave behind. The meal analysis unit can also use image analysis and motion analysis algorithms to analyze the eating habits of the children in detail. For example, the meal analysis unit can use image analysis technology to analyze images of the children eating taken with a camera and measure the amount of food they leave behind. The meal analysis unit can also analyze the movements of the children while they are eating and evaluate their food intake. Furthermore, the meal analysis unit can monitor the eating habits of the children in real time and understand their food intake status. This allows the meal analysis unit to understand the eating habits of the children in detail and provide useful information to childcare workers and parents.

[0031] The meal analysis unit includes a meal data provision unit that passes the data analyzed by the meal analysis unit to the generation AI. The meal data provision unit passes the data analyzed by the meal analysis unit to the generation AI. For example, the meal data provision unit provides data in the form of image data, text data, or other formats. The meal data provision unit can also provide data taking into account data transfer protocols and timing. For example, the meal data provision unit inputs image data of meals analyzed by the meal analysis unit to the generation AI, and the generation AI generates a food waste history and overcoming recipes based on that data. This allows the meal data provision unit to efficiently process information about meals and provide useful information to childcare workers and parents.

[0032] The meal data providing unit includes a meal generation unit that generates a food waste history and overcoming recipes based on the data provided by the meal data providing unit. The meal generation unit generates a food waste history and overcoming recipes based on the data provided by the meal data providing unit. For example, the meal generation unit generates a history of the type, amount, and frequency of food waste based on image data of meals provided by the meal data providing unit. The meal generation unit can also generate overcoming recipes, such as ingredient combinations and cooking methods, based on the data provided by the meal data providing unit. Furthermore, the meal generation unit can generate food waste history and overcoming recipes using an algorithm or database reference. This allows the meal generation unit to improve the eating habits of kindergarten children and provide useful information to childcare workers and parents.

[0033] The meal generation unit includes a transcription unit that automatically transcribes the leftovers history and overcoming recipes generated by the meal generation unit into a communication notebook. The transcription unit automatically transcribes the leftovers history and overcoming recipes generated by the meal generation unit into the communication notebook. For example, the transcription unit automatically transcribes the generated leftovers history and overcoming recipes according to a specific format of the communication notebook. The transcription unit can also transcribe into the communication notebook at an appropriate time, taking into account the timing and format of transcription. For example, the transcription unit automatically transcribes the leftovers history generated by the meal generation unit into the communication notebook to inform parents of the meal situation. The transcription unit can also transcribe the generated overcoming recipes into the communication notebook and suggest ways to improve meals to parents. In this way, the transcription unit can reduce the burden on childcare workers and provide useful information to parents.

[0034] The analysis unit can analyze the past movement history of the child and reflect it in the current movement analysis. The analysis unit can analyze the past movement history of the child and reflect it in the current movement analysis. For example, the analysis unit can analyze the current movement pattern based on the movements that the child frequently performed in the past. The analysis unit can also predict the movements that the child will perform at a specific time period from the past movement history and reflect it in the analysis. Furthermore, the analysis unit can analyze the past movement history of the child and set criteria for detecting abnormal movements. This allows the analysis unit to perform current movement analysis based on the past movement history of the child and obtain more accurate analysis results. The movement history analysis is performed using data such as the type, frequency, and timing of past movements. This allows the analysis unit to perform current movement analysis taking the past movement history of the child into consideration and provide useful information to childcare workers and parents.

[0035] When analyzing the movements and facial expressions of children, the analysis unit can correct the analysis results based on the children's health conditions. When analyzing the movements and facial expressions of children, the analysis unit corrects the analysis results based on the children's health conditions. For example, if a child has a cold, the analysis unit corrects slowness of movements and changes in facial expressions based on the child's health conditions. In addition, if a child is having an allergic reaction, the analysis unit can also reflect specific movements and changes in facial expressions in the analysis results. Furthermore, if a child is tired, the analysis unit can correct slowness of movements and changes in facial expressions based on the child's health conditions. In this way, the analysis unit can correct the analysis results taking the children's health conditions into account and obtain more accurate analysis results. The health conditions are evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed, for example, taking into account the correction algorithm and the timing of the correction. In this way, the analysis unit can correct the analysis results of the movements and facial expressions taking the children's health conditions into account and provide useful information to childcare workers and parents.

[0036] The analysis unit can perform the analysis by taking into consideration the child's home environment or lifestyle habits when analyzing the child's movements and facial expressions. The analysis unit performs the analysis by taking into consideration the child's home environment and lifestyle habits when analyzing the child's movements and facial expressions. For example, the analysis unit analyzes specific patterns of movements and facial expressions based on the child's home environment. The analysis unit can also analyze movements and facial expressions made at specific times based on the child's lifestyle habits. Furthermore, the analysis unit can set criteria for detecting abnormal movements and facial expressions by taking into consideration the child's home environment and lifestyle habits. This allows the analysis unit to perform the analysis by taking into consideration the child's home environment and lifestyle habits and obtain more accurate analysis results. The home environment is evaluated using data such as family composition and lifestyle habits. The lifestyle habits are evaluated using data such as meal times and sleep patterns. This allows the analysis unit to analyze the child's movements and facial expressions by taking into consideration the child's home environment and lifestyle habits, and provide useful information to childcare workers and parents.

[0037] When analyzing the movements and facial expressions of a child, the analysis unit can perform the analysis based on the child's friendships. When analyzing the movements and facial expressions of a child, the analysis unit performs the analysis based on the child's friendships. For example, the analysis unit analyzes changes in movements and facial expressions when the child is with a specific friend. The analysis unit can also prioritize analysis of positive movements and facial expressions when the child is playing with a friend. Furthermore, the analysis unit can prioritize analysis of negative movements and facial expressions when the child is fighting with a friend. This allows the analysis unit to perform analysis taking the child's friendships into account and obtain more accurate analysis results. The friend relationships are evaluated using data such as the number of friends and the depth of the relationships. This allows the analysis unit to analyze the movements and facial expressions taking the child's friendships into account and provide useful information to childcare workers and parents.

[0038] The data providing unit can select the optimal data providing method by referring to the child's past data when providing data. The data providing unit selects the optimal data providing method by referring to the child's past data when providing data. For example, the data providing unit preferentially selects a data providing method that the child has previously preferred. The data providing unit can also select the optimal data providing method for a specific time period from the child's past data. Furthermore, the data providing unit can analyze the child's past data and select the most effective data providing method. This allows the data providing unit to select the optimal data providing method based on the child's past data and provide effective data. The past data is referenced using data such as past behavior history and past emotion data. The optimal data providing method is selected by taking into consideration, for example, the format of the data and the timing of the data provision. This allows the data providing unit to select the optimal data providing method by taking into consideration the child's past data and provide useful information to childcare workers and parents.

[0039] The data providing unit can filter the data based on the current activity status of the children when providing the data. The data providing unit filters the data based on the current activity status of the children when providing the data. For example, if the children are playing, the data providing unit can preferentially provide data related to playing. Furthermore, if the children are eating, the data providing unit can preferentially provide data related to eating. Furthermore, if the children are taking a break, the data providing unit can preferentially provide data related to relaxation. In this way, the data providing unit can filter the data based on the current activity status of the children and provide highly relevant data. The current activity status is evaluated using data such as current movements and current emotions. The filtering method is performed, for example, taking into account a filtering algorithm and timing of filtering. In this way, the data providing unit can filter the data taking into account the current activity status of the children and provide useful information to childcare workers and parents.

[0040] When providing data, the data providing unit can prioritize providing highly relevant data taking into consideration the child's home environment. When providing data, the data providing unit prioritizes providing highly relevant data taking into consideration the child's home environment. For example, the data providing unit prioritizes providing specific data based on the child's home environment. The data providing unit can also provide highly relevant data for a specific time period taking into consideration the child's home environment. Furthermore, the data providing unit can analyze the child's home environment and provide the most relevant data. This allows the data providing unit to provide highly relevant data based on the child's home environment and provide more appropriate data. The home environment is evaluated using data such as family composition and lifestyle habits. The highly relevant data is provided based on, for example, data related to current activities and data related to past history. This allows the data providing unit to provide highly relevant data taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0041] The data providing unit can analyze the social media activities of the children when providing data and provide related data. The data providing unit can analyze the social media activities of the children when providing data and provide related data. For example, the data providing unit can prioritize providing specific data based on the children's social media activities. The data providing unit can also analyze the children's social media activities and provide the most relevant data. Furthermore, the data providing unit can provide data related to a specific time period in consideration of the children's social media activities. This allows the data providing unit to provide related data based on the children's social media activities and provide more appropriate data. The social media activities are evaluated using data such as the content of posts and the number of likes. The related data is provided based on, for example, data related to social media activities and data related to past history. This allows the data providing unit to provide related data in consideration of the children's social media activities and provide useful information to childcare workers and parents.

[0042] The generation unit can adjust the level of detail of the sentence based on important events of the child when generating the sentence. The generation unit adjusts the level of detail of the sentence based on important events of the child when generating the sentence. For example, if a child participates in a specific event, the generation unit generates a sentence including details of the event. Furthermore, if a child achieves a specific result, the generation unit can generate a sentence including details of the event. Furthermore, if a child faces a specific problem, the generation unit can generate a sentence including details of the problem. In this way, the generation unit can adjust the level of detail of the sentence based on important events of the child and generate more appropriate sentences. Important events are evaluated using data such as birthdays and special events. The level of detail is adjusted by taking into account, for example, detailed descriptions or concise descriptions. In this way, the generation unit can adjust the level of detail of the sentence by taking into account important events of the child and provide useful information to childcare workers and parents.

[0043] The generation unit can apply different generation algorithms depending on the activity category of the child when generating a sentence. The generation unit applies different generation algorithms depending on the activity category of the child when generating a sentence. For example, when a child is playing, the generation unit applies a generation algorithm related to play. Furthermore, when a child is learning, the generation unit can apply a generation algorithm related to learning. Furthermore, when a child is taking a break, the generation unit can apply a generation algorithm related to relaxation. In this way, the generation unit can apply different generation algorithms depending on the activity category of the child and generate more appropriate sentences. The activity category is evaluated using data such as exercise, learning, and play. The generation algorithm is applied using techniques such as a natural language generation algorithm or template-based generation. In this way, the generation unit can apply different generation algorithms taking into account the activity category of the child and provide useful information to childcare workers and parents.

[0044] The generation unit, when generating sentences, can determine the priority of sentences based on the period of the children's activities. The generation unit, when generating sentences, can determine the priority of sentences based on the period of the children's activities. For example, if a child participates in a specific event, the generation unit can prioritize generating sentences related to the event. Furthermore, if a child achieves a specific result, the generation unit can prioritize generating sentences related to the result. Furthermore, if a child faces a specific problem, the generation unit can prioritize generating sentences related to the problem. In this way, the generation unit can determine the priority of sentences based on the period of the children's activities and provide important information preferentially. The evaluation of the period of the activities is performed using data such as the semester, season, or specific event period. The priority is determined based on, for example, an importance score or urgency. In this way, the generation unit can determine the priority of sentences taking into account the period of the children's activities and provide useful information to childcare workers and parents.

[0045] The generation unit can adjust the order of sentences based on the relevance of the child when generating sentences. The generation unit adjusts the order of sentences based on the relevance of the child when generating sentences. For example, if a child participates in a specific event, the generation unit can first generate sentences related to the event. Furthermore, if a child achieves a specific result, the generation unit can first generate sentences related to the result. Furthermore, if a child faces a specific problem, the generation unit can first generate sentences related to the problem. In this way, the generation unit can adjust the order of sentences based on the relevance of the child and generate more appropriate sentences. The evaluation of relevance is performed using data such as content relevance and temporal relevance. The adjustment of the order is performed, for example, taking into account order of importance or chronological order. In this way, the generation unit can adjust the order of sentences taking into account the relevance of the child and provide useful information to childcare workers and parents.

[0046] The revision unit can suggest the optimal revision method by referring to the childcare worker's past revision history when making revisions. The revision unit can suggest the optimal revision method by referring to the childcare worker's past revision history when making revisions. For example, the revision unit suggests the optimal revision method based on revision methods used by the childcare worker in the past. The revision unit can also suggest the optimal revision method for a specific time period based on the childcare worker's past revision history. Furthermore, the revision unit can analyze the childcare worker's past revision history and suggest the most effective revision method. This allows the revision unit to suggest the optimal revision method based on the childcare worker's past revision history and perform efficient revisions. The revision history is referenced using data such as the content of past revisions and the frequency of revisions. The optimal revision method is suggested by taking into account, for example, the revision procedure and the tools used. This allows the revision unit to propose an optimal revision method in consideration of the childcare worker's past revision history, and provide efficient revisions for the childcare worker.

[0047] The repair unit can simplify the repair procedure during repairs by taking into account the childcare worker's current workload. The repair unit simplifies the repair procedure during repairs by taking into account the childcare worker's current workload. For example, the repair unit minimizes the repair procedure when the childcare worker is busy. The repair unit can also provide detailed repair procedures when the childcare worker has time to spare. Furthermore, the repair unit can monitor the childcare worker's workload in real time and dynamically adjust the repair procedure. This allows the repair unit to simplify the repair procedure by taking into account the childcare worker's current workload and perform efficient repairs. The workload is evaluated using data such as work time and work volume. The procedure is simplified by taking into account, for example, reducing steps and automating tools. This allows the repair unit to simplify the repair procedure by taking into account the childcare worker's current workload and provide efficient repairs for the childcare worker.

[0048] The revision unit can provide revision advice taking into account the specialized knowledge of the childcare worker when revising. The revision unit can provide revision advice taking into account the specialized knowledge of the childcare worker when revising. For example, the revision unit suggests an optimal revision method based on the specialized knowledge of the childcare worker. The revision unit can also provide detailed revision advice taking into account the specialized knowledge of the childcare worker. Furthermore, the revision unit can analyze the specialized knowledge of the childcare worker and provide the most effective revision advice. This allows the revision unit to provide revision advice based on the specialized knowledge of the childcare worker and perform more effective revisions. The evaluation of the specialized knowledge is performed using data such as the childcare worker's qualifications and years of experience. The provision of advice is performed, for example, by taking into account the presentation of specific revision suggestions and reference materials. This allows the revision unit to provide revision advice taking into account the specialized knowledge of the childcare worker and provide effective revisions for the childcare worker.

[0049] The revision unit can adjust the timing of revisions taking into account the working hours of the childcare workers when making revisions. The revision unit adjusts the timing of revisions taking into account the working hours of the childcare workers when making revisions. For example, the revision unit optimizes the timing of revisions based on the working hours of the childcare workers. The revision unit can also determine the priority of revisions taking into account the working hours of the childcare workers. Furthermore, the revision unit can monitor the working hours of the childcare workers in real time and dynamically adjust the timing of revisions. This allows the revision unit to adjust the timing of revisions taking into account the working hours of the childcare workers and perform efficient revisions. The evaluation of working hours is performed using data such as shift times and overtime hours. The timing adjustment is performed taking into account, for example, the priority of work and the time period. This allows the revision unit to adjust the timing of revisions taking into account the working hours of the childcare workers and provide efficient revisions for the childcare workers.

[0050] The dietary analysis unit can analyze the child's past dietary history and reflect the results in the current dietary analysis. The dietary analysis unit can analyze the child's past dietary history and reflect the results in the current dietary analysis. For example, the dietary analysis unit can analyze the child's current dietary intake based on the child's past preferred meals. The dietary analysis unit can also analyze the child's optimal meals for a specific time period based on the child's past dietary history. Furthermore, the dietary analysis unit can analyze the child's past dietary history and set criteria for detecting abnormal dietary intake. This allows the dietary analysis unit to perform a current dietary analysis based on the child's past dietary history and obtain more accurate analysis results. The dietary history analysis is performed using data such as the type, amount, and time of food eaten. This allows the dietary analysis unit to perform a current dietary analysis taking the child's past dietary history into consideration and provide useful information to childcare workers and parents.

[0051] The dietary analysis unit can correct the analysis results by taking into account the health condition of the child when analyzing the diet. The dietary analysis unit corrects the analysis results by taking into account the health condition of the child when analyzing the diet. For example, if a child has a cold, the dietary analysis unit corrects the amount of food intake based on the health condition. Furthermore, if a child is showing an allergic reaction, the dietary analysis unit can also reflect the amount of food intake of a specific meal in the analysis results. Furthermore, if a child is tired, the dietary analysis unit can correct the amount of food intake based on the health condition. In this way, the dietary analysis unit can correct the analysis results by taking into account the health condition of the child and obtain more accurate analysis results. The health condition is evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed by taking into account, for example, a correction algorithm and the timing of the correction. In this way, the dietary analysis unit can correct the analysis results by taking into account the health condition of the child and provide useful information to childcare workers and parents.

[0052] The dietary analysis unit can perform an analysis of meals taking into consideration the child's home environment and eating habits. The dietary analysis unit performs an analysis of meals taking into consideration the child's home environment and eating habits. For example, the dietary analysis unit analyzes a specific dietary intake based on the child's home environment. The dietary analysis unit can also analyze an optimal dietary intake for a specific time period based on the child's eating habits. Furthermore, the dietary analysis unit can set criteria for detecting abnormal dietary intakes taking into consideration the child's home environment and eating habits. This allows the dietary analysis unit to perform an analysis taking into consideration the child's home environment and eating habits, thereby obtaining more accurate analysis results. The home environment is evaluated using data such as family composition and lifestyle habits. The eating habits are evaluated using data such as meal times and amount eaten. This allows the dietary analysis unit to analyze meals taking into consideration the child's home environment and eating habits, and provide useful information to childcare workers and parents.

[0053] The dietary analysis unit can take into consideration the child's friendships when analyzing meals. The dietary analysis unit can take into consideration the child's friendships when analyzing meals. For example, the dietary analysis unit analyzes the child's dietary intake when the child is eating with a specific friend. The dietary analysis unit can also prioritize analysis of positive dietary intake when the child is eating with a friend. Furthermore, the dietary analysis unit can also prioritize analysis of negative dietary intake when the child is fighting with a friend. This allows the dietary analysis unit to take into consideration the child's friendships and obtain more accurate analysis results. The friendships are evaluated using data such as the number of friends and the depth of the relationships. This allows the dietary analysis unit to analyze meals while taking into consideration the child's friendships and provide useful information to childcare workers and parents.

[0054] When providing meal data, the meal data providing unit can select the optimal data providing method by referring to the child's past meal data. When providing meal data, the meal data providing unit selects the optimal data providing method by referring to the child's past meal data. For example, the meal data providing unit preferentially selects a meal data providing method that the child has previously preferred. The meal data providing unit can also select the optimal data providing method for a specific time period based on the child's past meal data. Furthermore, the meal data providing unit can analyze the child's past meal data and select the most effective data providing method. This allows the meal data providing unit to select the optimal data providing method based on the child's past meal data and provide effective data. The past meal data is referenced using data such as the type, amount, and time of food eaten. The optimal data providing method is selected by taking into consideration, for example, the format of the data and the timing of provision. This allows the meal data providing unit to select the optimal data providing method by taking into consideration the child's past meal data and provide useful information to childcare workers and parents.

[0055] The meal data providing unit can filter the data based on the child's current eating status when providing the meal data. The meal data providing unit filters the data based on the child's current eating status when providing the meal data. For example, if the child is eating, the meal data providing unit can prioritize data related to eating. Furthermore, if the child is playing, the meal data providing unit can prioritize data related to playing. Furthermore, if the child is taking a break, the meal data providing unit can prioritize data related to relaxation. In this way, the meal data providing unit can filter data based on the child's current eating status and provide highly relevant data. The current eating status is evaluated using data such as what the child is eating and the speed at which it is eating. The filtering method is performed, for example, taking into account the filtering algorithm and the timing of filtering. In this way, the meal data providing unit can filter the data taking into account the child's current eating status and provide useful information to childcare workers and parents.

[0056] When providing meal data, the meal data providing unit can prioritize providing highly relevant data taking into consideration the child's home environment. When providing meal data, the meal data providing unit prioritizes providing highly relevant data taking into consideration the child's home environment. For example, the meal data providing unit prioritizes providing specific meal data based on the child's home environment. The meal data providing unit can also provide highly relevant meal data for a specific time period taking into consideration the child's home environment. Furthermore, the meal data providing unit can analyze the child's home environment and provide the most relevant meal data. This allows the meal data providing unit to provide highly relevant data based on the child's home environment and provide more appropriate data. The home environment is evaluated using data such as family composition and lifestyle habits. The highly relevant data is provided based on, for example, data related to current activities and data related to past history. This allows the meal data providing unit to provide highly relevant data taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0057] The meal data providing unit can analyze the social media activities of the children when providing the meal data and provide related data. The meal data providing unit can analyze the social media activities of the children when providing the meal data and provide related data. For example, the meal data providing unit can prioritize providing specific meal data based on the children's social media activities. The meal data providing unit can also analyze the children's social media activities and provide the most relevant meal data. Furthermore, the meal data providing unit can provide meal data related to a specific time period in consideration of the children's social media activities. This allows the meal data providing unit to provide related data based on the children's social media activities and provide more appropriate data. Social media activities are evaluated using data such as the content of posts and the number of likes. Related data is provided based on, for example, data related to social media activities and data related to past history. This allows the meal data providing unit to provide related data in consideration of the children's social media activities and provide useful information to childcare workers and parents.

[0058] The meal generation unit can select an optimal generation method by referring to the child's past meal history when generating a leftovers history and a recipe for overcoming the problem. The meal generation unit can select an optimal generation method by referring to the child's past meal history when generating a leftovers history and a recipe for overcoming the problem. For example, the meal generation unit generates an optimal leftovers history and a recipe for overcoming the problem based on the child's past favorite meals. The meal generation unit can also generate an optimal leftovers history and a recipe for overcoming the problem for a specific time period from the child's past meal history. Furthermore, the meal generation unit can analyze the child's past meal history and generate the most effective leftovers history and a recipe for overcoming the problem. This allows the meal generation unit to select an optimal generation method based on the child's past meal history and provide effective information. The meal history is referenced using data such as the type, amount, and time of food eaten. The optimal generation method is selected by taking into consideration, for example, an algorithm or database reference. This allows the meal generation unit to select an optimal generation method by taking into consideration the child's past meal history and provide useful information to childcare workers and parents.

[0059] The meal generation unit can correct the generated results by taking into account the child's health condition when generating the leftover history and overcoming recipes. The meal generation unit corrects the generated results by taking into account the child's health condition when generating the leftover history and overcoming recipes. For example, if a child has a cold, the meal generation unit corrects the leftover history and overcoming recipes based on the child's health condition. In addition, if a child is having an allergic reaction, the meal generation unit can generate an overcoming recipe that avoids specific ingredients. Furthermore, if a child is tired, the meal generation unit can correct the leftover history and overcoming recipes based on the child's health condition. In this way, the meal generation unit can correct the generated results by taking into account the child's health condition and provide more appropriate information. The health condition is evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed by taking into account, for example, a correction algorithm and the timing of the correction. In this way, the meal generation unit can correct the generated results of the leftover history and overcoming recipes by taking into account the child's health condition and provide useful information to childcare workers and parents.

[0060] The meal generation unit can select the optimal generation method by taking into consideration the child's home environment when generating the child's leftover history and overcoming recipes. The meal generation unit selects the optimal generation method by taking into consideration the child's home environment when generating the child's leftover history and overcoming recipes. For example, the meal generation unit generates an overcoming recipe using specific ingredients based on the child's home environment. The meal generation unit can also generate the child's leftover history and overcoming recipes that are optimal for a specific time period by taking into consideration the child's home environment. Furthermore, the meal generation unit can analyze the child's home environment and generate the most effective leftover history and overcoming recipes. This allows the meal generation unit to select the optimal generation method based on the child's home environment and provide more appropriate information. The home environment is evaluated using data such as family composition and lifestyle habits. The optimal generation method is selected by taking into consideration, for example, an algorithm or database reference. This allows the meal generation unit to select the optimal generation method by taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0061] The meal generation unit can analyze the child's social media activity when generating the child's leftover history and overcoming recipes, and provide related generated results. The meal generation unit can analyze the child's social media activity when generating the child's leftover history and overcoming recipes, and provide related generated results. For example, the meal generation unit generates overcoming recipes using specific ingredients based on the child's social media activity. The meal generation unit can also analyze the child's social media activity to generate the most relevant leftover history and overcoming recipes. Furthermore, the meal generation unit can generate leftover history and overcoming recipes related to a specific time period, taking the child's social media activity into consideration. This allows the meal generation unit to provide related generated results based on the child's social media activity and provide more appropriate information. Social media activity is evaluated using data such as the content of posts and the number of likes. Related generated results are provided based on, for example, data related to social media activity and data related to past history. This allows the meal generation unit to provide related generated results taking the child's social media activity into consideration and provide useful information to childcare workers and parents.

[0062] The transcription unit can suggest an optimal transcription method by referring to the childcare worker's past transcription history when transcribing. The transcription unit can suggest an optimal transcription method by referring to the childcare worker's past transcription history when transcribing. For example, the transcription unit suggests an optimal transcription method based on transcription methods used by the childcare worker in the past. The transcription unit can also suggest an optimal transcription method for a specific time period based on the childcare worker's past transcription history. Furthermore, the transcription unit can analyze the childcare worker's past transcription history and suggest the most effective transcription method. This allows the transcription unit to suggest an optimal transcription method based on the childcare worker's past transcription history and perform efficient transcription. The transcription history is referenced using data such as the content of past transcriptions and the frequency of transcription. The optimal transcription method is suggested by taking into account, for example, the transcription procedure and the tools used. This allows the transcription unit to suggest an optimal transcription method by taking into account the childcare worker's past transcription history and provide efficient transcription for the childcare worker.

[0063] The transcription unit can simplify the transcription procedure by taking into account the childcare worker's current workload when transcribing. The transcription unit simplifies the transcription procedure by taking into account the childcare worker's current workload when transcribing. For example, the transcription unit minimizes the transcription procedure when the childcare worker is busy. The transcription unit can also provide detailed transcription procedures when the childcare worker has time to spare. Furthermore, the transcription unit can monitor the childcare worker's workload in real time and dynamically adjust the transcription procedure. This allows the transcription unit to simplify the transcription procedure by taking into account the childcare worker's current workload and provide efficient transcription. The workload is evaluated using data such as work time and workload. The procedure is simplified by taking into account, for example, reducing steps or automating tools. This allows the transcription unit to simplify the transcription procedure by taking into account the childcare worker's current workload and provide efficient transcription for the childcare worker.

[0064] The transcription unit can provide transcription advice taking into account the specialized knowledge of the childcare worker when transcribing. The transcription unit can provide transcription advice taking into account the specialized knowledge of the childcare worker when transcribing. For example, the transcription unit suggests an optimal transcription method based on the specialized knowledge of the childcare worker. The transcription unit can also provide detailed transcription advice taking into account the specialized knowledge of the childcare worker. Furthermore, the transcription unit can analyze the specialized knowledge of the childcare worker and provide the most effective transcription advice. This allows the transcription unit to provide transcription advice based on the specialized knowledge of the childcare worker and perform more effective transcription. The evaluation of the specialized knowledge is performed using data such as the childcare worker's qualifications and years of experience. The provision of advice is performed, for example, taking into account the presentation of specific revision suggestions and reference materials. This allows the transcription unit to provide transcription advice taking into account the specialized knowledge of the childcare worker and provide effective transcription for the childcare worker.

[0065] The transcription unit can adjust the timing of transcription during transcription, taking into account the working hours of the childcare worker. The transcription unit adjusts the timing of transcription during transcription, taking into account the working hours of the childcare worker. For example, the transcription unit optimizes the timing of transcription based on the working hours of the childcare worker. The transcription unit can also determine the priority of transcription, taking into account the working hours of the childcare worker. Furthermore, the transcription unit can monitor the working hours of the childcare worker in real time and dynamically adjust the timing of transcription. This allows the transcription unit to adjust the timing of transcription, taking into account the working hours of the childcare worker, and perform efficient transcription. The evaluation of working hours is performed using data such as shift times and overtime hours. The timing adjustment is performed, for example, taking into account the priority of work and the time period. This allows the transcription unit to adjust the timing of transcription, taking into account the working hours of the childcare worker, and provide efficient transcription for the childcare worker.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The childcare support system may further include a health monitoring unit that monitors the health status of the children. The health monitoring unit collects health data such as the children's body temperature, heart rate, and activity level in real time and provides it to the analysis unit. For example, if a child has a fever, the health monitoring unit transmits the body temperature data to the analysis unit, which can then correct the analysis results of the child's movements and facial expressions based on that data. In addition, if a child is tired, the health monitoring unit may provide data on the child's heart rate and activity level, which can then be used by the analysis unit to improve the accuracy of movement analysis. Furthermore, the health monitoring unit may regularly record the children's health status and provide health reports to childcare workers and parents. This allows the childcare support system to perform analysis that takes the children's health status into account and provide more accurate information.

[0068] The childcare support system can further include a learning monitoring unit that monitors the learning status of the children. The learning monitoring unit records the children's learning activities in real time and provides the data to the analysis unit. For example, if a child is reading a picture book, the learning monitoring unit records the time and content of the reading, and the analysis unit can analyze the child's learning status based on that data. Also, if a child is solving a puzzle, the learning monitoring unit records the progress and solution time, and the analysis unit can evaluate the child's learning progress based on that data. Furthermore, the learning monitoring unit can regularly report the children's learning results and provide them to childcare workers and parents. This allows the childcare support system to grasp the children's learning status in detail and provide appropriate support.

[0069] The childcare support system may further include a sleep monitoring unit that monitors the children's sleep status. The sleep monitoring unit records the children's sleep patterns in real time and provides the data to the analysis unit. For example, if a child is taking a nap, the sleep monitoring unit records the start and end times of the nap, and the analysis unit can analyze the children's sleep status based on that data. The sleep monitoring unit may also record the children's movements while they are sleeping, and the analysis unit can evaluate the quality of their sleep based on that data. Furthermore, the sleep monitoring unit may periodically report the children's sleep data and provide it to childcare workers and parents. This allows the childcare support system to have a detailed understanding of the children's sleep status and provide appropriate support.

[0070] The childcare support system may further include a play monitoring unit that monitors the play status of the children. The play monitoring unit records the children's play activities in real time and provides the data to the analysis unit. For example, if a child is playing with blocks, the play monitoring unit records the play time and the type of blocks used, and the analysis unit can analyze the child's creativity and concentration based on that data. In addition, if a child is playing with friends, the play monitoring unit records the friends and the content of the play, and the analysis unit can evaluate the child's social development based on that data. Furthermore, the play monitoring unit can regularly report the children's play data and provide it to childcare workers and parents. This allows the childcare support system to grasp the children's play status in detail and provide appropriate support.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The analysis unit analyzes the movements or facial expressions of the children. For example, the analysis unit can analyze the movements of the children, such as walking, jumping, and hand movements, as well as the facial expressions of the children, such as smiling, angry, and sad. Furthermore, the analysis unit uses image analysis and motion analysis algorithms to analyze the movements and facial expressions of the children in detail. Step 2: The data provider passes the data analyzed by the analysis unit to the generation AI. For example, the data provider may provide data in the form of image data or text data, and may provide the data taking into account the data transfer protocol and timing. Step 3: The generation unit generates sentences based on the data provided by the data provider. For example, the generation unit may generate sentences using natural language generation technology or template-based generation, and may also use generation AI to create detailed sentences describing the children's behavior. Step 4: In the revising unit, the childcare worker revises the sentences generated by the generating unit. For example, the revising unit can correct grammar, add content, etc., and can also provide an interface that allows the childcare worker to easily revise the generated sentences.

[0073] (Example 2) A childcare support system according to an embodiment of the present invention utilizes a generation AI and a camera image analysis AI to solve problems faced by daycare centers. To address the issue of daycare teachers having to write daily communication notebooks, particularly documenting the children's behaviors, this system uses a camera image analysis AI to analyze the children's behavior and a generation AI to create text based on the results. Since the childcare worker only needs to revise the generated text, labor costs are significantly reduced. To address the issue of parents not knowing about the children's meals (leftovers), a camera image analysis AI is used to check for leftovers, and based on the results, a generation AI suggests a food waste history and recipes to combat food waste. This information is automatically transcribed into the daycare center's communication notebook, providing valuable information to parents. For example, a camera image analysis AI is used to analyze the children's behaviors and eating habits in real time, and a generation AI writes up the children's behaviors and food waste history based on the analysis results. The childcare worker then revises the generated text and enters it in the communication notebook. Based on the food waste history, the generating AI suggests recipes to combat food waste and automatically posts them in the communication book. This system is expected to reduce the burden on childcare workers and provide useful information for parents. As a result, the childcare support system can reduce the burden on childcare workers and provide useful information to parents.

[0074] A childcare support system according to an embodiment includes an analysis unit, a data providing unit, a generation unit, and a revision unit. The analysis unit analyzes the movements or facial expressions of children. For example, the analysis unit analyzes the movements of children, such as walking, jumping, and hand movements. The analysis unit can also analyze the expressions of children, such as smiling, anger, and sadness. The analysis unit can also analyze the movements and facial expressions of children in detail using image analysis and motion analysis algorithms. The data providing unit passes the data analyzed by the analysis unit to the generation AI. For example, the data providing unit provides data in the form of image data or text data. The data providing unit can also provide data taking into account data transfer protocols and timing. The generation unit generates sentences based on the data provided by the data providing unit. For example, the generation unit generates sentences using natural language generation technology or template-based generation. The generation unit can also use the generation AI to create detailed sentences describing the behavior of children. The revision unit allows a childcare worker to revise the sentences generated by the generation unit. For example, the revision unit corrects grammar and adds content. The revision unit can also provide an interface that allows the childcare worker to easily revise the generated sentences. As a result, the childcare support system according to the embodiment analyzes the movements and expressions of the children, generates sentences based on the results, and the childcare worker can revise them, thereby reducing the burden on the childcare worker.

[0075] The childcare support system includes a meal analysis unit that analyzes the eating habits of the children. The meal analysis unit analyzes the eating habits of the children. For example, the meal analysis unit analyzes the speed at which the children eat and the amount of food they leave behind. The meal analysis unit can also use image analysis and motion analysis algorithms to analyze the eating habits of the children in detail. For example, the meal analysis unit can use image analysis technology to analyze images of the children eating taken with a camera and measure the amount of food they leave behind. The meal analysis unit can also analyze the movements of the children while they are eating and evaluate their food intake. Furthermore, the meal analysis unit can monitor the eating habits of the children in real time and understand their food intake status. This allows the meal analysis unit to understand the eating habits of the children in detail and provide useful information to childcare workers and parents.

[0076] The meal analysis unit includes a meal data provision unit that passes the data analyzed by the meal analysis unit to the generation AI. The meal data provision unit passes the data analyzed by the meal analysis unit to the generation AI. For example, the meal data provision unit provides data in the form of image data, text data, or other formats. The meal data provision unit can also provide data taking into account data transfer protocols and timing. For example, the meal data provision unit inputs image data of meals analyzed by the meal analysis unit to the generation AI, and the generation AI generates a food waste history and overcoming recipes based on that data. This allows the meal data provision unit to efficiently process information about meals and provide useful information to childcare workers and parents.

[0077] The meal data providing unit includes a meal generation unit that generates a food waste history and overcoming recipes based on the data provided by the meal data providing unit. The meal generation unit generates a food waste history and overcoming recipes based on the data provided by the meal data providing unit. For example, the meal generation unit generates a history of the type, amount, and frequency of food waste based on image data of meals provided by the meal data providing unit. The meal generation unit can also generate overcoming recipes, such as ingredient combinations and cooking methods, based on the data provided by the meal data providing unit. Furthermore, the meal generation unit can generate food waste history and overcoming recipes using an algorithm or database reference. This allows the meal generation unit to improve the eating habits of kindergarten children and provide useful information to childcare workers and parents.

[0078] The meal generation unit includes a transcription unit that automatically transcribes the leftovers history and overcoming recipes generated by the meal generation unit into a communication notebook. The transcription unit automatically transcribes the leftovers history and overcoming recipes generated by the meal generation unit into the communication notebook. For example, the transcription unit automatically transcribes the generated leftovers history and overcoming recipes according to a specific format of the communication notebook. The transcription unit can also transcribe into the communication notebook at an appropriate time, taking into account the timing and format of transcription. For example, the transcription unit automatically transcribes the leftovers history generated by the meal generation unit into the communication notebook to inform parents of the meal situation. The transcription unit can also transcribe the generated overcoming recipes into the communication notebook and suggest ways to improve meals to parents. In this way, the transcription unit can reduce the burden on childcare workers and provide useful information to parents.

[0079] The analysis unit can estimate the emotions of the children and improve the analysis accuracy of their movements and facial expressions based on the estimated emotions. The analysis unit can estimate the emotions of the children and improve the analysis accuracy of their movements and facial expressions based on the estimated emotions. For example, if the children are having fun, the analysis unit can emphasize smiling and active movements. If the children are tired, the analysis unit can also analyze in detail the slowness of their movements and changes in their facial expressions. Furthermore, if the children are feeling anxious, the analysis unit can focus on analyzing subtle changes in their facial expressions and unnatural movements. This allows the analysis unit to improve the analysis accuracy of their movements and facial expressions based on the children's emotions and obtain more accurate analysis results. Emotion estimation is performed using technologies such as facial expression analysis and voice analysis. The improvement in analysis accuracy is achieved, for example, using algorithms and datasets for accuracy improvement. This allows the analysis unit to improve the analysis accuracy of their movements and facial expressions by taking the children's emotions into consideration and provide useful information to childcare workers and parents.

[0080] The analysis unit can analyze the past movement history of the child and reflect it in the current movement analysis. The analysis unit can analyze the past movement history of the child and reflect it in the current movement analysis. For example, the analysis unit can analyze the current movement pattern based on the movements that the child frequently performed in the past. The analysis unit can also predict the movements that the child will perform at a specific time period from the past movement history and reflect it in the analysis. Furthermore, the analysis unit can analyze the past movement history of the child and set criteria for detecting abnormal movements. This allows the analysis unit to perform current movement analysis based on the past movement history of the child and obtain more accurate analysis results. The movement history analysis is performed using data such as the type, frequency, and timing of past movements. This allows the analysis unit to perform current movement analysis taking the past movement history of the child into consideration and provide useful information to childcare workers and parents.

[0081] When analyzing the movements and facial expressions of children, the analysis unit can correct the analysis results based on the children's health conditions. When analyzing the movements and facial expressions of children, the analysis unit corrects the analysis results based on the children's health conditions. For example, if a child has a cold, the analysis unit corrects slowness of movements and changes in facial expressions based on the child's health conditions. In addition, if a child is having an allergic reaction, the analysis unit can also reflect specific movements and changes in facial expressions in the analysis results. Furthermore, if a child is tired, the analysis unit can correct slowness of movements and changes in facial expressions based on the child's health conditions. In this way, the analysis unit can correct the analysis results taking the children's health conditions into account and obtain more accurate analysis results. The health conditions are evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed, for example, taking into account the correction algorithm and the timing of the correction. In this way, the analysis unit can correct the analysis results of the movements and facial expressions taking the children's health conditions into account and provide useful information to childcare workers and parents.

[0082] The analysis unit can estimate the emotions of the children and prioritize the analysis results based on the estimated emotions of the children. The analysis unit can estimate the emotions of the children and prioritize the analysis results based on the estimated emotions of the children. For example, if the children are having fun, the analysis unit can prioritize analyzing positive movements and facial expressions. Also, if the children are feeling anxious, the analysis unit can prioritize analyzing negative movements and facial expressions. Furthermore, if the children are tired, the analysis unit can prioritize analyzing slowness of movements and changes in facial expressions. In this way, the analysis unit can prioritize the analysis results based on the emotions of the children and analyze important information preferentially. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. Priority is determined based on, for example, importance scores and urgency. In this way, the analysis unit can prioritize the analysis results taking the emotions of the children into consideration and provide useful information to childcare workers and parents.

[0083] The analysis unit can perform the analysis by taking into consideration the child's home environment or lifestyle habits when analyzing the child's movements and facial expressions. The analysis unit performs the analysis by taking into consideration the child's home environment and lifestyle habits when analyzing the child's movements and facial expressions. For example, the analysis unit analyzes specific patterns of movements and facial expressions based on the child's home environment. The analysis unit can also analyze movements and facial expressions made at specific times based on the child's lifestyle habits. Furthermore, the analysis unit can set criteria for detecting abnormal movements and facial expressions by taking into consideration the child's home environment and lifestyle habits. This allows the analysis unit to perform the analysis by taking into consideration the child's home environment and lifestyle habits and obtain more accurate analysis results. The home environment is evaluated using data such as family composition and lifestyle habits. The lifestyle habits are evaluated using data such as meal times and sleep patterns. This allows the analysis unit to analyze the child's movements and facial expressions by taking into consideration the child's home environment and lifestyle habits, and provide useful information to childcare workers and parents.

[0084] When analyzing the movements and facial expressions of a child, the analysis unit can perform the analysis based on the child's friendships. When analyzing the movements and facial expressions of a child, the analysis unit performs the analysis based on the child's friendships. For example, the analysis unit analyzes changes in movements and facial expressions when the child is with a specific friend. The analysis unit can also prioritize analysis of positive movements and facial expressions when the child is playing with a friend. Furthermore, the analysis unit can prioritize analysis of negative movements and facial expressions when the child is fighting with a friend. This allows the analysis unit to perform analysis taking the child's friendships into account and obtain more accurate analysis results. The friend relationships are evaluated using data such as the number of friends and the depth of the relationships. This allows the analysis unit to analyze the movements and facial expressions taking the child's friendships into account and provide useful information to childcare workers and parents.

[0085] The data providing unit can estimate the emotions of the children and adjust the timing of providing data based on the estimated emotions of the children. The data providing unit can estimate the emotions of the children and adjust the timing of providing data based on the estimated emotions of the children. For example, the data providing unit can provide data in real time if the children are having fun. The data providing unit can also delay the provision of data if the children are feeling anxious. Furthermore, the data providing unit can refrain from providing data if the children are tired. This allows the data providing unit to adjust the timing of providing data based on the emotions of the children and provide data at an appropriate time. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. The adjustment of the timing of providing data is performed using methods such as real-time provision and batch provision. This allows the data providing unit to adjust the timing of providing data in consideration of the emotions of the children and provide useful information to childcare workers and parents.

[0086] The data providing unit can select the optimal data providing method by referring to the child's past data when providing data. The data providing unit selects the optimal data providing method by referring to the child's past data when providing data. For example, the data providing unit preferentially selects a data providing method that the child has previously preferred. The data providing unit can also select the optimal data providing method for a specific time period from the child's past data. Furthermore, the data providing unit can analyze the child's past data and select the most effective data providing method. This allows the data providing unit to select the optimal data providing method based on the child's past data and provide effective data. The past data is referenced using data such as past behavior history and past emotion data. The optimal data providing method is selected by taking into consideration, for example, the format of the data and the timing of the data provision. This allows the data providing unit to select the optimal data providing method by taking into consideration the child's past data and provide useful information to childcare workers and parents.

[0087] The data providing unit can filter the data based on the current activity status of the children when providing the data. The data providing unit filters the data based on the current activity status of the children when providing the data. For example, if the children are playing, the data providing unit can preferentially provide data related to playing. Furthermore, if the children are eating, the data providing unit can preferentially provide data related to eating. Furthermore, if the children are taking a break, the data providing unit can preferentially provide data related to relaxation. In this way, the data providing unit can filter the data based on the current activity status of the children and provide highly relevant data. The current activity status is evaluated using data such as current movements and current emotions. The filtering method is performed, for example, taking into account a filtering algorithm and timing of filtering. In this way, the data providing unit can filter the data taking into account the current activity status of the children and provide useful information to childcare workers and parents.

[0088] The data providing unit can estimate the emotions of the children and determine the priority of data to be provided based on the estimated emotions of the children. The data providing unit can estimate the emotions of the children and determine the priority of data to be provided based on the estimated emotions of the children. For example, if the children are having fun, the data providing unit can prioritize providing positive data. Furthermore, if the children are feeling anxious, the data providing unit can prioritize providing data that gives a sense of security. Furthermore, if the children are tired, the data providing unit can prioritize providing data related to relaxation. In this way, the data providing unit can prioritize the priority of data to be provided based on the emotions of the children and provide important data preferentially. Emotions are estimated using techniques such as facial expression analysis and voice analysis. The priority is determined based on, for example, an importance score or urgency. In this way, the data providing unit can prioritize the data to be provided taking into account the emotions of the children and provide useful information to childcare workers and parents.

[0089] When providing data, the data providing unit can prioritize providing highly relevant data taking into consideration the child's home environment. When providing data, the data providing unit prioritizes providing highly relevant data taking into consideration the child's home environment. For example, the data providing unit prioritizes providing specific data based on the child's home environment. The data providing unit can also provide highly relevant data for a specific time period taking into consideration the child's home environment. Furthermore, the data providing unit can analyze the child's home environment and provide the most relevant data. This allows the data providing unit to provide highly relevant data based on the child's home environment and provide more appropriate data. The home environment is evaluated using data such as family composition and lifestyle habits. The highly relevant data is provided based on, for example, data related to current activities and data related to past history. This allows the data providing unit to provide highly relevant data taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0090] The data providing unit can analyze the social media activities of the children when providing data and provide related data. The data providing unit can analyze the social media activities of the children when providing data and provide related data. For example, the data providing unit can prioritize providing specific data based on the children's social media activities. The data providing unit can also analyze the children's social media activities and provide the most relevant data. Furthermore, the data providing unit can provide data related to a specific time period in consideration of the children's social media activities. This allows the data providing unit to provide related data based on the children's social media activities and provide more appropriate data. The social media activities are evaluated using data such as the content of posts and the number of likes. The related data is provided based on, for example, data related to social media activities and data related to past history. This allows the data providing unit to provide related data in consideration of the children's social media activities and provide useful information to childcare workers and parents.

[0091] The generation unit can estimate the emotions of the children and adjust the expression style of the generated sentences based on the estimated emotions of the children. The generation unit can estimate the emotions of the children and adjust the expression style of the generated sentences based on the estimated emotions of the children. For example, if the children are having fun, the generation unit can generate sentences that make use of positive expressions. Furthermore, if the children are feeling anxious, the generation unit can generate sentences that make use of expressions that give a sense of security. Furthermore, if the children are tired, the generation unit can generate sentences that make use of expressions related to relaxation. In this way, the generation unit can adjust the expression style of the sentences based on the emotions of the children and generate more appropriate sentences. The estimation of emotions is performed using techniques such as facial expression analysis and voice analysis. The adjustment of the expression style is performed by taking into account the tone and wording of the sentences, for example. In this way, the generation unit can adjust the expression style of the sentences in consideration of the emotions of the children and provide useful information to childcare workers and parents.

[0092] The generation unit can adjust the level of detail of the sentence based on important events of the child when generating the sentence. The generation unit adjusts the level of detail of the sentence based on important events of the child when generating the sentence. For example, if a child participates in a specific event, the generation unit generates a sentence including details of the event. Furthermore, if a child achieves a specific result, the generation unit can generate a sentence including details of the event. Furthermore, if a child faces a specific problem, the generation unit can generate a sentence including details of the problem. In this way, the generation unit can adjust the level of detail of the sentence based on important events of the child and generate more appropriate sentences. Important events are evaluated using data such as birthdays and special events. The level of detail is adjusted by taking into account, for example, detailed descriptions or concise descriptions. In this way, the generation unit can adjust the level of detail of the sentence by taking into account important events of the child and provide useful information to childcare workers and parents.

[0093] The generation unit can apply different generation algorithms depending on the activity category of the child when generating a sentence. The generation unit applies different generation algorithms depending on the activity category of the child when generating a sentence. For example, when a child is playing, the generation unit applies a generation algorithm related to play. Furthermore, when a child is learning, the generation unit can apply a generation algorithm related to learning. Furthermore, when a child is taking a break, the generation unit can apply a generation algorithm related to relaxation. In this way, the generation unit can apply different generation algorithms depending on the activity category of the child and generate more appropriate sentences. The activity category is evaluated using data such as exercise, learning, and play. The generation algorithm is applied using techniques such as a natural language generation algorithm or template-based generation. In this way, the generation unit can apply different generation algorithms taking into account the activity category of the child and provide useful information to childcare workers and parents.

[0094] The generation unit can estimate the emotions of the children and adjust the length of the sentences to be generated based on the estimated emotions of the children. The generation unit can estimate the emotions of the children and adjust the length of the sentences to be generated based on the estimated emotions of the children. For example, the generation unit can generate detailed sentences when the children are having fun. The generation unit can also generate concise sentences when the children are feeling anxious. Furthermore, the generation unit can generate short sentences when the children are tired. This allows the generation unit to adjust the length of the sentences based on the emotions of the children and generate more appropriate sentences. Emotions are estimated using techniques such as facial expression analysis and voice analysis. The length of the sentences is adjusted by taking into account, for example, the number of characters or the number of paragraphs. This allows the generation unit to adjust the length of the sentences taking into account the emotions of the children and provide useful information to childcare workers and parents.

[0095] The generation unit, when generating sentences, can determine the priority of sentences based on the period of the children's activities. The generation unit, when generating sentences, can determine the priority of sentences based on the period of the children's activities. For example, if a child participates in a specific event, the generation unit can prioritize generating sentences related to the event. Furthermore, if a child achieves a specific result, the generation unit can prioritize generating sentences related to the result. Furthermore, if a child faces a specific problem, the generation unit can prioritize generating sentences related to the problem. In this way, the generation unit can determine the priority of sentences based on the period of the children's activities and provide important information preferentially. The evaluation of the period of the activities is performed using data such as the semester, season, or specific event period. The priority is determined based on, for example, an importance score or urgency. In this way, the generation unit can determine the priority of sentences taking into account the period of the children's activities and provide useful information to childcare workers and parents.

[0096] The generation unit can adjust the order of sentences based on the relevance of the child when generating sentences. The generation unit adjusts the order of sentences based on the relevance of the child when generating sentences. For example, if a child participates in a specific event, the generation unit can first generate sentences related to the event. Furthermore, if a child achieves a specific result, the generation unit can first generate sentences related to the result. Furthermore, if a child faces a specific problem, the generation unit can first generate sentences related to the problem. In this way, the generation unit can adjust the order of sentences based on the relevance of the child and generate more appropriate sentences. The evaluation of relevance is performed using data such as content relevance and temporal relevance. The adjustment of the order is performed, for example, taking into account order of importance or chronological order. In this way, the generation unit can adjust the order of sentences taking into account the relevance of the child and provide useful information to childcare workers and parents.

[0097] The adjustment unit can estimate the childcare worker's emotions and adjust the adjustment interface based on the estimated childcare worker's emotions. The adjustment unit can estimate the childcare worker's emotions and adjust the adjustment interface based on the estimated childcare worker's emotions. For example, if the childcare worker is tired, the adjustment unit can provide a simple, highly visible interface. Also, if the childcare worker is relaxed, the adjustment unit can provide an interface including detailed options. Furthermore, if the childcare worker is in a hurry, the adjustment unit can provide an interface that allows quick adjustments. In this way, the adjustment unit can adjust the adjustment interface based on the childcare worker's emotions and provide a more user-friendly interface. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. The interface adjustment is performed, for example, taking into account the design and operation method of the user interface. In this way, the adjustment unit can adjust the adjustment interface in consideration of the childcare worker's emotions and provide an easy-to-use environment for childcare workers.

[0098] The revision unit can suggest the optimal revision method by referring to the childcare worker's past revision history when making revisions. The revision unit can suggest the optimal revision method by referring to the childcare worker's past revision history when making revisions. For example, the revision unit suggests the optimal revision method based on revision methods used by the childcare worker in the past. The revision unit can also suggest the optimal revision method for a specific time period based on the childcare worker's past revision history. Furthermore, the revision unit can analyze the childcare worker's past revision history and suggest the most effective revision method. This allows the revision unit to suggest the optimal revision method based on the childcare worker's past revision history and perform efficient revisions. The revision history is referenced using data such as the content of past revisions and the frequency of revisions. The optimal revision method is suggested by taking into account, for example, the revision procedure and the tools used. This allows the revision unit to propose an optimal revision method in consideration of the childcare worker's past revision history, and provide efficient revisions for the childcare worker.

[0099] The repair unit can simplify the repair procedure during repairs by taking into account the childcare worker's current workload. The repair unit simplifies the repair procedure during repairs by taking into account the childcare worker's current workload. For example, the repair unit minimizes the repair procedure when the childcare worker is busy. The repair unit can also provide detailed repair procedures when the childcare worker has time to spare. Furthermore, the repair unit can monitor the childcare worker's workload in real time and dynamically adjust the repair procedure. This allows the repair unit to simplify the repair procedure by taking into account the childcare worker's current workload and perform efficient repairs. The workload is evaluated using data such as work time and work volume. The procedure is simplified by taking into account, for example, reducing steps and automating tools. This allows the repair unit to simplify the repair procedure by taking into account the childcare worker's current workload and provide efficient repairs for the childcare worker.

[0100] The revision unit can estimate the emotions of the childcare worker and determine the priority of revisions based on the estimated emotions of the childcare worker. The revision unit can estimate the emotions of the childcare worker and determine the priority of revisions based on the estimated emotions of the childcare worker. For example, when the childcare worker is tired, the revision unit can prioritize important revisions. Furthermore, when the childcare worker is relaxed, the revision unit can prioritize detailed revisions. Furthermore, when the childcare worker is in a hurry, the revision unit can prioritize items that can be revised quickly. In this way, the revision unit can determine the priority of revisions based on the emotions of the childcare worker and prioritize important revisions. The estimation of emotions is performed using techniques such as facial expression analysis and voice analysis. The priority is determined based on, for example, an importance score or urgency. In this way, the revision unit can determine the priority of revisions taking into account the emotions of the childcare worker and provide efficient revisions for the childcare worker.

[0101] The revision unit can provide revision advice taking into account the specialized knowledge of the childcare worker when revising. The revision unit can provide revision advice taking into account the specialized knowledge of the childcare worker when revising. For example, the revision unit suggests an optimal revision method based on the specialized knowledge of the childcare worker. The revision unit can also provide detailed revision advice taking into account the specialized knowledge of the childcare worker. Furthermore, the revision unit can analyze the specialized knowledge of the childcare worker and provide the most effective revision advice. This allows the revision unit to provide revision advice based on the specialized knowledge of the childcare worker and perform more effective revisions. The evaluation of the specialized knowledge is performed using data such as the childcare worker's qualifications and years of experience. The provision of advice is performed, for example, by taking into account the presentation of specific revision suggestions and reference materials. This allows the revision unit to provide revision advice taking into account the specialized knowledge of the childcare worker and provide effective revisions for the childcare worker.

[0102] The revision unit can adjust the timing of revisions taking into account the working hours of the childcare workers when making revisions. The revision unit adjusts the timing of revisions taking into account the working hours of the childcare workers when making revisions. For example, the revision unit optimizes the timing of revisions based on the working hours of the childcare workers. The revision unit can also determine the priority of revisions taking into account the working hours of the childcare workers. Furthermore, the revision unit can monitor the working hours of the childcare workers in real time and dynamically adjust the timing of revisions. This allows the revision unit to adjust the timing of revisions taking into account the working hours of the childcare workers and perform efficient revisions. The evaluation of working hours is performed using data such as shift times and overtime hours. The timing adjustment is performed taking into account, for example, the priority of work and the time period. This allows the revision unit to adjust the timing of revisions taking into account the working hours of the childcare workers and provide efficient revisions for the childcare workers.

[0103] The dietary analysis unit can estimate the emotions of the children and improve the accuracy of dietary analysis based on the estimated emotions of the children. The dietary analysis unit can estimate the emotions of the children and improve the accuracy of dietary analysis based on the estimated emotions of the children. For example, if a child is having fun, the dietary analysis unit can analyze the amount of food intake as positive. Also, if a child is feeling anxious, the dietary analysis unit can analyze the amount of food intake as negative. Furthermore, if a child is tired, the dietary analysis unit can correct the amount of food intake based on the child's health condition. This allows the dietary analysis unit to improve the accuracy of dietary analysis based on the child's emotions and obtain more accurate analysis results. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. The improvement in analysis accuracy is achieved, for example, using algorithms and datasets for accuracy improvement. This allows the dietary analysis unit to improve the accuracy of dietary analysis by taking the emotions of the children into consideration and provide useful information to childcare workers and parents.

[0104] The dietary analysis unit can analyze the child's past dietary history and reflect the results in the current dietary analysis. The dietary analysis unit can analyze the child's past dietary history and reflect the results in the current dietary analysis. For example, the dietary analysis unit can analyze the child's current dietary intake based on the child's past preferred meals. The dietary analysis unit can also analyze the child's optimal meals for a specific time period based on the child's past dietary history. Furthermore, the dietary analysis unit can analyze the child's past dietary history and set criteria for detecting abnormal dietary intake. This allows the dietary analysis unit to perform a current dietary analysis based on the child's past dietary history and obtain more accurate analysis results. The dietary history analysis is performed using data such as the type, amount, and time of food eaten. This allows the dietary analysis unit to perform a current dietary analysis taking the child's past dietary history into consideration and provide useful information to childcare workers and parents.

[0105] The dietary analysis unit can correct the analysis results by taking into account the health condition of the child when analyzing the diet. The dietary analysis unit corrects the analysis results by taking into account the health condition of the child when analyzing the diet. For example, if a child has a cold, the dietary analysis unit corrects the amount of food intake based on the health condition. Furthermore, if a child is showing an allergic reaction, the dietary analysis unit can also reflect the amount of food intake of a specific meal in the analysis results. Furthermore, if a child is tired, the dietary analysis unit can correct the amount of food intake based on the health condition. In this way, the dietary analysis unit can correct the analysis results by taking into account the health condition of the child and obtain more accurate analysis results. The health condition is evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed by taking into account, for example, a correction algorithm and the timing of the correction. In this way, the dietary analysis unit can correct the analysis results by taking into account the health condition of the child and provide useful information to childcare workers and parents.

[0106] The dietary analysis unit can estimate the emotions of the children and prioritize the analysis results based on the estimated emotions of the children. The dietary analysis unit can estimate the emotions of the children and prioritize the analysis results based on the estimated emotions of the children. For example, if the children are having fun, the dietary analysis unit can prioritize analyzing positive dietary intakes. Also, if the children are feeling anxious, the dietary analysis unit can prioritize analyzing negative dietary intakes. Furthermore, if the children are tired, the dietary analysis unit can prioritize analyzing dietary intakes based on their health status. This allows the dietary analysis unit to prioritize the analysis results based on the children's emotions and prioritize analyzing important information. Emotions are estimated using techniques such as facial expression analysis and voice analysis. Priority is determined based on, for example, importance scores and urgency. This allows the dietary analysis unit to prioritize the analysis results taking the children's emotions into consideration and provide useful information to childcare workers and parents.

[0107] The dietary analysis unit can perform an analysis of meals taking into consideration the child's home environment and eating habits. The dietary analysis unit performs an analysis of meals taking into consideration the child's home environment and eating habits. For example, the dietary analysis unit analyzes a specific dietary intake based on the child's home environment. The dietary analysis unit can also analyze an optimal dietary intake for a specific time period based on the child's eating habits. Furthermore, the dietary analysis unit can set criteria for detecting abnormal dietary intakes taking into consideration the child's home environment and eating habits. This allows the dietary analysis unit to perform an analysis taking into consideration the child's home environment and eating habits, thereby obtaining more accurate analysis results. The home environment is evaluated using data such as family composition and lifestyle habits. The eating habits are evaluated using data such as meal times and amount eaten. This allows the dietary analysis unit to analyze meals taking into consideration the child's home environment and eating habits, and provide useful information to childcare workers and parents.

[0108] The dietary analysis unit can take into consideration the child's friendships when analyzing meals. The dietary analysis unit can take into consideration the child's friendships when analyzing meals. For example, the dietary analysis unit analyzes the child's dietary intake when the child is eating with a specific friend. The dietary analysis unit can also prioritize analysis of positive dietary intake when the child is eating with a friend. Furthermore, the dietary analysis unit can also prioritize analysis of negative dietary intake when the child is fighting with a friend. This allows the dietary analysis unit to take into consideration the child's friendships and obtain more accurate analysis results. The friendships are evaluated using data such as the number of friends and the depth of the relationships. This allows the dietary analysis unit to analyze meals while taking into consideration the child's friendships and provide useful information to childcare workers and parents.

[0109] The meal data providing unit can estimate the emotions of the children and adjust the timing of providing the meal data based on the estimated emotions of the children. The meal data providing unit can estimate the emotions of the children and adjust the timing of providing the meal data based on the estimated emotions of the children. For example, if the children are having fun, the meal data providing unit can provide the meal data in real time. Furthermore, if the children are feeling anxious, the meal data providing unit can delay providing the meal data. Furthermore, if the children are tired, the meal data providing unit can adjust the timing of providing the meal data based on the children's emotions and provide the data at an appropriate time. Emotions can be estimated using techniques such as facial expression analysis and voice analysis. The timing of providing the data can be adjusted using methods such as real-time provision or batch provision. Thus, the meal data providing unit can adjust the timing of providing the meal data in consideration of the children's emotions and provide useful information to childcare workers and parents.

[0110] When providing meal data, the meal data providing unit can select the optimal data providing method by referring to the child's past meal data. When providing meal data, the meal data providing unit selects the optimal data providing method by referring to the child's past meal data. For example, the meal data providing unit preferentially selects a meal data providing method that the child has previously preferred. The meal data providing unit can also select the optimal data providing method for a specific time period based on the child's past meal data. Furthermore, the meal data providing unit can analyze the child's past meal data and select the most effective data providing method. This allows the meal data providing unit to select the optimal data providing method based on the child's past meal data and provide effective data. The past meal data is referenced using data such as the type, amount, and time of food eaten. The optimal data providing method is selected by taking into consideration, for example, the format of the data and the timing of provision. This allows the meal data providing unit to select the optimal data providing method by taking into consideration the child's past meal data and provide useful information to childcare workers and parents.

[0111] The meal data providing unit can filter the data based on the child's current eating status when providing the meal data. The meal data providing unit filters the data based on the child's current eating status when providing the meal data. For example, if the child is eating, the meal data providing unit can prioritize data related to eating. Furthermore, if the child is playing, the meal data providing unit can prioritize data related to playing. Furthermore, if the child is taking a break, the meal data providing unit can prioritize data related to relaxation. In this way, the meal data providing unit can filter data based on the child's current eating status and provide highly relevant data. The current eating status is evaluated using data such as what the child is eating and the speed at which it is eating. The filtering method is performed, for example, taking into account the filtering algorithm and the timing of filtering. In this way, the meal data providing unit can filter the data taking into account the child's current eating status and provide useful information to childcare workers and parents.

[0112] The meal data providing unit can estimate the emotions of the children and determine the priority of the meal data to be provided based on the estimated emotions of the children. The meal data providing unit can estimate the emotions of the children and determine the priority of the meal data to be provided based on the estimated emotions of the children. For example, if the children are having fun, the meal data providing unit can prioritize providing positive meal data. Furthermore, if the children are feeling anxious, the meal data providing unit can prioritize providing meal data that gives a sense of security. Furthermore, if the children are tired, the meal data providing unit can prioritize providing meal data related to relaxation. In this way, the meal data providing unit can prioritize the meal data to be provided based on the emotions of the children and provide important data preferentially. Emotions are estimated using techniques such as facial expression analysis and voice analysis. The priority is determined based on, for example, an importance score or urgency. In this way, the meal data providing unit can prioritize the meal data to be provided taking into account the emotions of the children and provide useful information to childcare workers and parents.

[0113] When providing meal data, the meal data providing unit can prioritize providing highly relevant data taking into consideration the child's home environment. When providing meal data, the meal data providing unit prioritizes providing highly relevant data taking into consideration the child's home environment. For example, the meal data providing unit prioritizes providing specific meal data based on the child's home environment. The meal data providing unit can also provide highly relevant meal data for a specific time period taking into consideration the child's home environment. Furthermore, the meal data providing unit can analyze the child's home environment and provide the most relevant meal data. This allows the meal data providing unit to provide highly relevant data based on the child's home environment and provide more appropriate data. The home environment is evaluated using data such as family composition and lifestyle habits. The highly relevant data is provided based on, for example, data related to current activities and data related to past history. This allows the meal data providing unit to provide highly relevant data taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0114] The meal data providing unit can analyze the social media activities of the children when providing the meal data and provide related data. The meal data providing unit can analyze the social media activities of the children when providing the meal data and provide related data. For example, the meal data providing unit can prioritize providing specific meal data based on the children's social media activities. The meal data providing unit can also analyze the children's social media activities and provide the most relevant meal data. Furthermore, the meal data providing unit can provide meal data related to a specific time period in consideration of the children's social media activities. This allows the meal data providing unit to provide related data based on the children's social media activities and provide more appropriate data. Social media activities are evaluated using data such as the content of posts and the number of likes. Related data is provided based on, for example, data related to social media activities and data related to past history. This allows the meal data providing unit to provide related data in consideration of the children's social media activities and provide useful information to childcare workers and parents.

[0115] The meal generation unit can estimate the emotions of the children and adjust the way in which the leftover history and overcoming recipes are presented based on the estimated emotions of the children. The meal generation unit can estimate the emotions of the children and adjust the way in which the leftover history and overcoming recipes are presented based on the estimated emotions of the children. For example, if a child is having fun, the meal generation unit can generate a leftover history and overcoming recipes that make frequent use of positive expressions. If a child is feeling anxious, the meal generation unit can also generate a leftover history and overcoming recipes that make frequent use of expressions that give a sense of security. Furthermore, if a child is tired, the meal generation unit can generate a leftover history and overcoming recipes that make frequent use of expressions related to relaxation. In this way, the meal generation unit can adjust the way in which the leftover history and overcoming recipes are presented based on the emotions of the children and provide more appropriate information. Emotions are estimated using technologies such as facial expression analysis and voice analysis. The way in which the presentation is adjusted is performed, for example, taking into account the tone of the text and the choice of words. This allows the meal generation unit to take into account the children's emotions and adjust the way in which the food waste history and overcoming recipes are presented, thereby providing useful information to nursery teachers and parents.

[0116] The meal generation unit can select an optimal generation method by referring to the child's past meal history when generating a leftovers history and a recipe for overcoming the problem. The meal generation unit can select an optimal generation method by referring to the child's past meal history when generating a leftovers history and a recipe for overcoming the problem. For example, the meal generation unit generates an optimal leftovers history and a recipe for overcoming the problem based on the child's past favorite meals. The meal generation unit can also generate an optimal leftovers history and a recipe for overcoming the problem for a specific time period from the child's past meal history. Furthermore, the meal generation unit can analyze the child's past meal history and generate the most effective leftovers history and a recipe for overcoming the problem. This allows the meal generation unit to select an optimal generation method based on the child's past meal history and provide effective information. The meal history is referenced using data such as the type, amount, and time of food eaten. The optimal generation method is selected by taking into consideration, for example, an algorithm or database reference. This allows the meal generation unit to select an optimal generation method by taking into consideration the child's past meal history and provide useful information to childcare workers and parents.

[0117] The meal generation unit can correct the generated results by taking into account the child's health condition when generating the leftover history and overcoming recipes. The meal generation unit corrects the generated results by taking into account the child's health condition when generating the leftover history and overcoming recipes. For example, if a child has a cold, the meal generation unit corrects the leftover history and overcoming recipes based on the child's health condition. In addition, if a child is having an allergic reaction, the meal generation unit can generate an overcoming recipe that avoids specific ingredients. Furthermore, if a child is tired, the meal generation unit can correct the leftover history and overcoming recipes based on the child's health condition. In this way, the meal generation unit can correct the generated results by taking into account the child's health condition and provide more appropriate information. The health condition is evaluated using data such as body temperature, heart rate, and activity level. The correction method is performed by taking into account, for example, a correction algorithm and the timing of the correction. In this way, the meal generation unit can correct the generated results of the leftover history and overcoming recipes by taking into account the child's health condition and provide useful information to childcare workers and parents.

[0118] The meal generation unit can estimate the emotions of the children and determine the priority of the generated leftover histories and overcoming recipes based on the estimated emotions of the children. The meal generation unit can estimate the emotions of the children and determine the priority of the generated leftover histories and overcoming recipes based on the estimated emotions of the children. For example, if a child is having fun, the meal generation unit can prioritize generating positive leftover histories and overcoming recipes. Also, if a child is feeling anxious, the meal generation unit can prioritize generating leftover histories and overcoming recipes that give a sense of security. Furthermore, if a child is tired, the meal generation unit can prioritize generating leftover histories and overcoming recipes related to relaxation. In this way, the meal generation unit can prioritize generating leftover histories and overcoming recipes based on the emotions of the children and provide important information preferentially. Emotions are estimated using technologies such as facial expression analysis and voice analysis. The priority is determined based on, for example, an importance score or urgency. This allows the meal generation unit to determine the priority of the food waste history and overcoming recipes generated taking into account the children's emotions, and provide useful information to nursery teachers and parents.

[0119] The meal generation unit can select the optimal generation method by taking into consideration the child's home environment when generating the child's leftover history and overcoming recipes. The meal generation unit selects the optimal generation method by taking into consideration the child's home environment when generating the child's leftover history and overcoming recipes. For example, the meal generation unit generates an overcoming recipe using specific ingredients based on the child's home environment. The meal generation unit can also generate the child's leftover history and overcoming recipes that are optimal for a specific time period by taking into consideration the child's home environment. Furthermore, the meal generation unit can analyze the child's home environment and generate the most effective leftover history and overcoming recipes. This allows the meal generation unit to select the optimal generation method based on the child's home environment and provide more appropriate information. The home environment is evaluated using data such as family composition and lifestyle habits. The optimal generation method is selected by taking into consideration, for example, an algorithm or database reference. This allows the meal generation unit to select the optimal generation method by taking into consideration the child's home environment and provide useful information to childcare workers and parents.

[0120] The meal generation unit can analyze the child's social media activity when generating the child's leftover history and overcoming recipes, and provide related generated results. The meal generation unit can analyze the child's social media activity when generating the child's leftover history and overcoming recipes, and provide related generated results. For example, the meal generation unit generates overcoming recipes using specific ingredients based on the child's social media activity. The meal generation unit can also analyze the child's social media activity to generate the most relevant leftover history and overcoming recipes. Furthermore, the meal generation unit can generate leftover history and overcoming recipes related to a specific time period, taking the child's social media activity into consideration. This allows the meal generation unit to provide related generated results based on the child's social media activity and provide more appropriate information. Social media activity is evaluated using data such as the content of posts and the number of likes. Related generated results are provided based on, for example, data related to social media activity and data related to past history. This allows the meal generation unit to provide related generated results taking the child's social media activity into consideration and provide useful information to childcare workers and parents.

[0121] The transcription unit can estimate the emotions of the childcare worker and adjust the transcription interface based on the estimated emotions of the childcare worker. The transcription unit can estimate the emotions of the childcare worker and adjust the transcription interface based on the estimated emotions of the childcare worker. For example, if the childcare worker is tired, the transcription unit can provide a simple, highly visible interface. Also, if the childcare worker is relaxed, the transcription unit can provide an interface including detailed options. Furthermore, if the childcare worker is in a hurry, the transcription unit can provide an interface that allows for quick transcription. In this way, the transcription unit can adjust the transcription interface based on the emotions of the childcare worker and provide a more user-friendly interface. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. The interface adjustment is performed, for example, taking into account the design and operation method of the user interface. In this way, the transcription unit can adjust the transcription interface in consideration of the emotions of the childcare worker and provide an easy-to-use environment for the childcare worker.

[0122] The transcription unit can suggest an optimal transcription method by referring to the childcare worker's past transcription history when transcribing. The transcription unit can suggest an optimal transcription method by referring to the childcare worker's past transcription history when transcribing. For example, the transcription unit suggests an optimal transcription method based on transcription methods used by the childcare worker in the past. The transcription unit can also suggest an optimal transcription method for a specific time period based on the childcare worker's past transcription history. Furthermore, the transcription unit can analyze the childcare worker's past transcription history and suggest the most effective transcription method. This allows the transcription unit to suggest an optimal transcription method based on the childcare worker's past transcription history and perform efficient transcription. The transcription history is referenced using data such as the content of past transcriptions and the frequency of transcription. The optimal transcription method is suggested by taking into account, for example, the transcription procedure and the tools used. This allows the transcription unit to suggest an optimal transcription method by taking into account the childcare worker's past transcription history and provide efficient transcription for the childcare worker.

[0123] The transcription unit can simplify the transcription procedure by taking into account the childcare worker's current workload when transcribing. The transcription unit simplifies the transcription procedure by taking into account the childcare worker's current workload when transcribing. For example, the transcription unit minimizes the transcription procedure when the childcare worker is busy. The transcription unit can also provide detailed transcription procedures when the childcare worker has time to spare. Furthermore, the transcription unit can monitor the childcare worker's workload in real time and dynamically adjust the transcription procedure. This allows the transcription unit to simplify the transcription procedure by taking into account the childcare worker's current workload and provide efficient transcription. The workload is evaluated using data such as work time and workload. The procedure is simplified by taking into account, for example, reducing steps or automating tools. This allows the transcription unit to simplify the transcription procedure by taking into account the childcare worker's current workload and provide efficient transcription for the childcare worker.

[0124] The transcription unit can estimate the emotions of the childcare worker and determine the transcription priority based on the estimated emotions of the childcare worker. The transcription unit can estimate the emotions of the childcare worker and determine the transcription priority based on the estimated emotions of the childcare worker. For example, if the childcare worker is tired, the transcription unit can prioritize important transcriptions. Also, if the childcare worker is relaxed, the transcription unit can prioritize detailed transcriptions. Furthermore, if the childcare worker is in a hurry, the transcription unit can prioritize items that can be transcribed quickly. In this way, the transcription unit can determine the transcription priority based on the emotions of the childcare worker and prioritize important transcriptions. Emotion estimation is performed using techniques such as facial expression analysis and voice analysis. The priority is determined based on, for example, an importance score or urgency. In this way, the transcription unit can determine the transcription priority taking into account the emotions of the childcare worker and provide efficient transcription for the childcare worker.

[0125] The transcription unit can provide transcription advice taking into account the specialized knowledge of the childcare worker when transcribing. The transcription unit can provide transcription advice taking into account the specialized knowledge of the childcare worker when transcribing. For example, the transcription unit suggests an optimal transcription method based on the specialized knowledge of the childcare worker. The transcription unit can also provide detailed transcription advice taking into account the specialized knowledge of the childcare worker. Furthermore, the transcription unit can analyze the specialized knowledge of the childcare worker and provide the most effective transcription advice. This allows the transcription unit to provide transcription advice based on the specialized knowledge of the childcare worker and perform more effective transcription. The evaluation of the specialized knowledge is performed using data such as the childcare worker's qualifications and years of experience. The provision of advice is performed, for example, taking into account the presentation of specific revision suggestions and reference materials. This allows the transcription unit to provide transcription advice taking into account the specialized knowledge of the childcare worker and provide effective transcription for the childcare worker.

[0126] The transcription unit can adjust the timing of transcription during transcription, taking into account the working hours of the childcare worker. The transcription unit adjusts the timing of transcription during transcription, taking into account the working hours of the childcare worker. For example, the transcription unit optimizes the timing of transcription based on the working hours of the childcare worker. The transcription unit can also determine the priority of transcription, taking into account the working hours of the childcare worker. Furthermore, the transcription unit can monitor the working hours of the childcare worker in real time and dynamically adjust the timing of transcription. This allows the transcription unit to adjust the timing of transcription, taking into account the working hours of the childcare worker, and perform efficient transcription. The evaluation of working hours is performed using data such as shift times and overtime hours. The timing adjustment is performed, for example, taking into account the priority of work and the time period. This allows the transcription unit to adjust the timing of transcription, taking into account the working hours of the childcare worker, and provide efficient transcription for the childcare worker. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, data providing unit, generation unit, revision unit, and meal analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes the movements and facial expressions of the children using the camera 42 of the smart device 14, and generates analysis results using the control unit 46A. The data providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and passes the analysis results to the generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sentences based on the provided data. The revision unit is realized, for example, by the control unit 46A of the smart device 14, and allows the childcare worker to revise the generated sentences. The meal analysis unit, for example, analyzes the children's eating habits using the camera 42 of the smart device 14, and measures the amount of food left over using the control unit 46A. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, data providing unit, generation unit, revision unit, and diet analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes the movements and facial expressions of the children using the camera 42 of the smart glasses 214, and generates analysis results using the control unit 46A. The data providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and passes the analysis results to the generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sentences based on the provided data. The revision unit is realized, for example, by the control unit 46A of the smart glasses 214, and a childcare worker revisions the generated sentences. The diet analysis unit analyzes the children's eating habits using the camera 42 of the smart glasses 214, and measures the amount of food left over using the control unit 46A. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned analysis unit, data provision unit, generation unit, revision unit, and meal analysis unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit analyzes the movements and facial expressions of the children using the camera 42 of the headset-type terminal 314, and generates analysis results using the control unit 46A. The data provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and passes the analysis results to the generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sentences based on the provided data. The revision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and the childcare worker revisions the generated sentences. The meal analysis unit, for example, analyzes the children's eating habits using the camera 42 of the headset-type terminal 314, and measures the amount of food left over using the control unit 46A. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned analysis unit, data provision unit, generation unit, revision unit, and meal analysis unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes the movements and facial expressions of the children using the camera 42 of the robot 414, and generates analysis results using the control unit 46A. The data provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and passes the analysis results to the generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sentences based on the provided data. The revision unit is realized, for example, by the control unit 46A of the robot 414, and the childcare worker revisions the generated sentences. The meal analysis unit, for example, analyzes the children's eating habits using the camera 42 of the robot 414, and measures the amount of food left over using the control unit 46A.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The childcare support system may further include a health monitoring unit that monitors the health status of the children. The health monitoring unit collects health data such as the children's body temperature, heart rate, and activity level in real time and provides it to the analysis unit. For example, if a child has a fever, the health monitoring unit transmits the body temperature data to the analysis unit, which can then correct the analysis results of the child's movements and facial expressions based on that data. In addition, if a child is tired, the health monitoring unit may provide data on the child's heart rate and activity level, which can then be used by the analysis unit to improve the accuracy of movement analysis. Furthermore, the health monitoring unit may regularly record the children's health status and provide health reports to childcare workers and parents. This allows the childcare support system to perform analysis that takes the children's health status into account and provide more accurate information.

[0129] The childcare support system can further include a learning monitoring unit that monitors the learning status of the children. The learning monitoring unit records the children's learning activities in real time and provides the data to the analysis unit. For example, if a child is reading a picture book, the learning monitoring unit records the time and content of the reading, and the analysis unit can analyze the child's learning status based on that data. Also, if a child is solving a puzzle, the learning monitoring unit records the progress and solution time, and the analysis unit can evaluate the child's learning progress based on that data. Furthermore, the learning monitoring unit can regularly report the children's learning results and provide them to childcare workers and parents. This allows the childcare support system to grasp the children's learning status in detail and provide appropriate support.

[0130] The childcare support system may further include a sleep monitoring unit that monitors the children's sleep status. The sleep monitoring unit records the children's sleep patterns in real time and provides the data to the analysis unit. For example, if a child is taking a nap, the sleep monitoring unit records the start and end times of the nap, and the analysis unit can analyze the children's sleep status based on that data. The sleep monitoring unit may also record the children's movements while they are sleeping, and the analysis unit can evaluate the quality of their sleep based on that data. Furthermore, the sleep monitoring unit may periodically report the children's sleep data and provide it to childcare workers and parents. This allows the childcare support system to have a detailed understanding of the children's sleep status and provide appropriate support.

[0131] The childcare support system may further include a play monitoring unit that monitors the play status of the children. The play monitoring unit records the children's play activities in real time and provides the data to the analysis unit. For example, if a child is playing with blocks, the play monitoring unit records the play time and the type of blocks used, and the analysis unit can analyze the child's creativity and concentration based on that data. In addition, if a child is playing with friends, the play monitoring unit records the friends and the content of the play, and the analysis unit can evaluate the child's social development based on that data. Furthermore, the play monitoring unit can regularly report the children's play data and provide it to childcare workers and parents. This allows the childcare support system to grasp the children's play status in detail and provide appropriate support.

[0132] The childcare support system can further include a feedback unit that estimates the emotions of the children and provides feedback to the childcare worker based on the estimated emotions. The feedback unit suggests appropriate ways to respond to the childcare worker based on the emotion data provided by the analysis unit. For example, if a child is feeling anxious, the feedback unit can suggest ways to make the childcare worker feel at ease. Also, if a child is having fun, the feedback unit can suggest games or activities to further bring out that emotion. Furthermore, the feedback unit can periodically analyze the emotion data of the children and provide the childcare worker with ways to respond in accordance with changes in emotion. This allows the childcare support system to provide appropriate support based on the emotions of the children and reduce the burden on the childcare worker.

[0133] The childcare support system may further include a feedback unit that estimates the emotions of the children and provides feedback to the parents based on the estimated emotions. The feedback unit reports the emotional state of the children to the parents based on the emotional data provided by the analysis unit. For example, if a child is having fun, the feedback unit may send the child's state to the parents along with photos or videos. If a child is feeling anxious, the feedback unit may also suggest the cause and countermeasures to the parents. Furthermore, the feedback unit may periodically analyze the emotional data of the children and provide advice to the parents based on changes in their emotions. This allows the childcare support system to provide appropriate information to parents based on the emotions of the children and strengthen support at home.

[0134] The childcare support system may further include an interaction promotion unit that estimates the emotions of the children and promotes interactions between the children based on the estimated emotions. The interaction promotion unit suggests games and activities for the children to play with each other based on the emotion data provided by the analysis unit. For example, if a child is having fun, the interaction promotion unit may suggest group activities that allow the child to share their emotions. If a child is feeling anxious, the interaction promotion unit may also suggest playing with friends that will make the child feel safe. Furthermore, the interaction promotion unit may periodically analyze the emotion data of the children and provide activities to promote interactions between the children. This allows the childcare support system to promote appropriate interactions based on the emotions of the children and support the development of social skills.

[0135] The childcare support system may further include a stress reduction unit that estimates the emotions of the children and reduces their stress based on the estimated emotions. The stress reduction unit suggests ways to reduce the children's stress based on the emotion data provided by the analysis unit. For example, if a child is feeling anxious, the stress reduction unit may suggest relaxing music or aromas. If a child is tired, the stress reduction unit may also suggest breaks or relaxing activities. Furthermore, the stress reduction unit may periodically analyze the children's emotion data, identify the causes of stress, and provide appropriate countermeasures. In this way, the childcare support system can provide appropriate stress reduction methods based on the children's emotions and support the children's physical and mental health.

[0136] The childcare support system may further include a learning motivation improvement unit that estimates the emotions of the children and improves their motivation to learn based on the estimated emotions. The learning motivation improvement unit suggests a method for improving the children's motivation to learn based on the emotion data provided by the analysis unit. For example, if a child is having fun, the learning motivation improvement unit may suggest learning activities that make use of that emotion. Also, if a child is feeling anxious, the learning motivation improvement unit may suggest a learning environment that gives a sense of security. Furthermore, the learning motivation improvement unit may periodically analyze the emotion data of the children and provide activities to improve their motivation to learn. In this way, the childcare support system can provide an appropriate method for improving learning motivation based on the emotions of the children, thereby improving the effectiveness of learning.

[0137] The childcare support system may further include a creativity promotion unit that estimates the emotions of the children and draws out their creativity based on the estimated emotions. The creativity promotion unit suggests a method for drawing out the children's creativity based on the emotion data provided by the analysis unit. For example, if a child is having fun, the creativity promotion unit may suggest a creative activity that makes use of that emotion. Also, if a child is feeling anxious, the creativity promotion unit may suggest a creative game that will give a child a sense of security. Furthermore, the creativity promotion unit may periodically analyze the emotion data of the children and provide activities to draw out their creativity. In this way, the childcare support system can provide an appropriate creativity promotion method based on the emotions of the children and foster their creativity.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The analysis unit analyzes the movements or facial expressions of the children. For example, the analysis unit can analyze the movements of the children, such as walking, jumping, and hand movements, as well as the facial expressions of the children, such as smiling, angry, and sad. Furthermore, the analysis unit uses image analysis and motion analysis algorithms to analyze the movements and facial expressions of the children in detail. Step 2: The data provider passes the data analyzed by the analysis unit to the generation AI. For example, the data provider may provide data in the form of image data or text data, and may provide the data taking into account the data transfer protocol and timing. Step 3: The generation unit generates sentences based on the data provided by the data provider. For example, the generation unit may generate sentences using natural language generation technology or template-based generation, and may also use generation AI to create detailed sentences describing the children's behavior. Step 4: In the revising unit, the childcare worker revises the sentences generated by the generating unit. For example, the revising unit can correct grammar, add content, etc., and can also provide an interface that allows the childcare worker to easily revise the generated sentences.

[0140] 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.

[0141] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Explanation of symbols]

[0212] 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. an analysis unit that analyzes the movements or facial expressions of the children; a data providing unit that passes the data analyzed by the analysis unit to a generation AI; a generation unit that generates a sentence based on the data provided by the data providing unit; a revision unit in which a childcare worker revises the sentences generated by the generation unit. A system characterized by:

2. Equipped with a meal analysis unit that analyzes meal patterns The system of claim 1 .

3. A meal data providing unit that passes the data analyzed by the meal analysis unit to the generation AI is provided.

3. The system of claim 2.

4. A meal generation unit generates a leftovers history and recipes for overcoming food waste based on the data provided by the meal data providing unit.

4. The system of claim 3.

5. A transcription unit is provided that automatically transcribes the leftovers history and overcoming recipes generated by the meal generation unit into a contact book.

5. The system of claim 4.

6. The analysis unit Estimate the emotions of kindergarten children and improve the accuracy of analyzing their movements and facial expressions based on the estimated emotions. The system of claim 1 .

7. The analysis unit Analyzing the past movement history of the children and reflecting it in the current movement analysis The system of claim 1 .

8. The analysis unit When analyzing children's movements and facial expressions, the analysis results are corrected based on the children's health status. The system of claim 1 .

9. The analysis unit Estimate the emotions of the children and prioritize the analysis results based on the estimated emotions of the children. The system of claim 1 .

10. The analysis unit When analyzing the movements and facial expressions of children, the analysis takes into account the child's home environment or lifestyle. The system of claim 1 .

11. The analysis unit When analyzing the movements and facial expressions of children, the analysis is based on the friendships of the children. The system of claim 1 .

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