system

Abstract

The system according to this embodiment aims to streamline the work of childcare workers and to appropriately manage children's developmental records. [Solution] The system according to the embodiment comprises a reception unit, a plan proposal unit, a record management unit, an analysis unit, and a communication unit. The reception unit receives information entered by childcare workers. The plan proposal unit proposes a childcare plan based on the information received by the reception unit. The record management unit manages the child's developmental records based on the childcare plan proposed by the plan proposal unit. The analysis unit analyzes the developmental records managed by the record management unit. The communication unit supports communication with parents based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the work of nursery teachers is complicated, and it is difficult to efficiently create a childcare plan and manage children's development records.

[0005] The system according to the embodiment aims to improve the work efficiency of nursery teachers and appropriately manage children's development records.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a plan proposal unit, a record management unit, an analysis unit, and a communication unit. The reception unit receives information entered by childcare workers. The plan proposal unit proposes a childcare plan based on the information received by the reception unit. The record management unit manages the child's developmental records based on the childcare plan proposed by the plan proposal unit. The analysis unit analyzes the developmental records managed by the record management unit. The communication unit supports communication with parents based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the work of childcare workers and appropriately manage children's developmental records. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

[0018] 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant system according to an embodiment of the present invention is a system for streamlining the work of childcare workers and providing better childcare to children. This AI assistant system supports tasks such as creating childcare plans, managing children's developmental records, and communicating with parents. Through AI-driven data analysis, it enables early detection of developmental problems in children and leads to appropriate support. This improves the quality of childcare and reforms the working style of childcare workers. For example, when a childcare worker inputs information such as the child's age, developmental stage, and interests, the AI ​​assistant system proposes an optimal childcare plan based on past childcare data and child development theories. Next, the AI ​​assistant system analyzes photos and videos of children and automatically records their developmental status. For example, the AI ​​can analyze a video of a child taking their first steps and add it to the developmental record. Furthermore, the AI ​​assistant system analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's eating habits," the AI ​​generates an answer based on past data and childcare theories. Finally, the AI ​​assistant system analyzes children's developmental data and detects developmental problems early. For example, the AI ​​can analyze a child's language development data and detect delays in language development. This allows childcare workers to provide appropriate support based on problems identified by the AI. As a result, the AI ​​assistant system can streamline childcare workers' tasks and provide better care for children.

[0029] The AI ​​assistant system according to this embodiment comprises a reception unit, a plan proposal unit, a record management unit, an analysis unit, and a communication unit. The reception unit receives information entered by childcare workers. The information entered by childcare workers includes, but is not limited to, the child's age, developmental stage, and interests. The reception unit receives, for example, information such as the child's age, developmental stage, and interests entered by childcare workers. The plan proposal unit proposes a childcare plan based on the information received by the reception unit. The plan proposal unit proposes an optimal childcare plan based, for example, past childcare data and child development theories. For example, when the plan proposal unit creates a childcare plan for a 3-year-old class, it analyzes past data and proposes activities and teaching materials suitable for 3-year-olds. The record management unit manages the child's developmental records based on the childcare plan proposed by the plan proposal unit. The record management unit analyzes, for example, photos and videos of children and automatically records their developmental status. For example, the record management unit can analyze a video of a child taking their first steps and add it to the developmental record. The Analysis Department analyzes developmental records managed by the Records Management Department. For example, the Analysis Department analyzes children's developmental data to detect developmental problems early. For example, the Analysis Department can analyze children's language development data to detect delays in language development. The Communication Department supports communication with parents based on the analysis results obtained by the Analysis Department. For example, the Communication Department analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the Communication Department generates an answer based on past data and childcare theories. In this way, the AI ​​assistant system according to the embodiment can streamline the work of childcare workers and provide better childcare to children.

[0030] The reception desk receives information entered by childcare workers. This information may include, but is not limited to, a child's age, developmental stage, and interests. Specifically, the reception desk provides a dedicated input interface for childcare workers, allowing them to easily enter information. This interface is accessible on devices such as tablets, smartphones, and personal computers, and is designed to enable childcare workers to quickly input information on-site. Furthermore, the reception desk features voice input, allowing childcare workers to input information by voice. Voice input is converted to text using natural language processing technology and incorporated into the system. This allows childcare workers to input information without using their hands, improving work efficiency. The reception desk also has a function to automatically categorize the entered information and save it to the necessary databases. For example, information regarding a child's age and developmental stage is saved in the developmental database, and information regarding interests is saved in the activity suggestion database. This ensures smoother subsequent processing. Furthermore, the reception desk is equipped with a checking function to verify the accuracy of the entered information, preventing childcare workers from making input errors. For example, if the age is set within an incorrect range or if required fields are left blank, a warning message is displayed prompting the childcare worker to make corrections. This allows the reception desk to receive information accurately and quickly, improving the overall reliability of the system.

[0031] The Planning Proposal Department proposes childcare plans based on information received by the Reception Department. For example, the Planning Proposal Department proposes optimal childcare plans based on past childcare data and child development theories. For instance, when creating a childcare plan for a 3-year-old class, it analyzes past data and proposes activities and materials suitable for 3-year-olds. Specifically, the Planning Proposal Department uses AI to analyze past childcare data and selects optimal activities and materials according to the child's age and developmental stage. For example, when creating a childcare plan for a 3-year-old class, it proposes activities and materials that children of the same age group are likely to be interested in, based on past data from 3-year-old classes. Furthermore, the Planning Proposal Department can also propose customized childcare plans tailored to each child's individual interests. For example, if a child is particularly interested in drawing, it will propose a childcare plan that includes many drawing activities. The Planning Proposal Department also provides an interface that allows childcare workers to easily modify and adjust the proposed childcare plans. Based on the proposed plan, childcare workers can create optimal childcare plans utilizing their own experience and knowledge. Additionally, the Planning Proposal Department has a feedback function that evaluates the effectiveness of the proposed childcare plan and incorporates the feedback into future proposals. This allows the planning and proposal department to always propose optimal childcare plans based on the latest information and historical data, thereby supporting the work of childcare workers.

[0032] The Records Management Department manages children's developmental records based on the childcare plans proposed by the Planning and Proposal Department. For example, the Records Management Department analyzes children's photos and videos and automatically records their developmental status. For instance, it can analyze a video of a child taking their first steps and add it to the developmental record. Specifically, the Records Management Department has a function to automatically classify photos and videos taken by childcare workers and save them as developmental records. For example, a video of a child taking their first steps would be classified as "First Steps" and added to the developmental record. The Records Management Department also uses AI to analyze the content of photos and videos and evaluate the child's developmental status. For example, it can analyze a photo of a child drawing and evaluate the child's creativity and manual dexterity based on the content and style of the drawing. Furthermore, the Records Management Department also manages developmental records manually entered by childcare workers. Childcare workers can input their daily observations and insights into the Records Management Department, allowing them to record the child's developmental status in detail. This enables the Records Management Department to manage comprehensive developmental records that include not only photos and videos but also the observation records of childcare workers. Furthermore, the records management department has a function to automatically generate developmental record reports for sharing with parents. This allows parents to regularly check their child's developmental status and facilitates smoother communication with childcare workers. As a result, the records management department can accurately and thoroughly record children's developmental status and provide useful information for both childcare workers and parents.

[0033] The Analysis Department analyzes developmental records managed by the Records Management Department. For example, the Analysis Department analyzes children's developmental data to detect developmental problems early. For instance, the Analysis Department can analyze children's language development data to detect language development delays. Specifically, the Analysis Department uses AI to analyze children's developmental data and detect abnormal patterns or developmental delays. For example, it analyzes children's language development data and issues a warning to caregivers if language development is delayed compared to the average for their age. The Analysis Department can also predict future development based on children's developmental data. For example, it can predict future developmental progress based on the current developmental status and propose necessary support and interventions. Furthermore, the Analysis Department can integrate data from multiple children to evaluate the developmental status of the entire class. This allows caregivers to understand the developmental trends of the entire class and formulate appropriate childcare plans. The Analysis Department can also perform trend analysis based on past data to understand long-term developmental trends. This allows caregivers to track changes in children's development over the long term and take appropriate action. Furthermore, the analytics department has the capability to provide childcare workers and parents with detailed reports on developmental issues and risks. This allows the analytics department to analyze children's developmental status in detail and support early problem detection and appropriate support.

[0034] The Communications Department supports communication with parents based on the analysis results obtained by the Analysis Department. For example, the Communications Department analyzes questions from parents and generates appropriate answers. For instance, if a parent asks, "Please tell me about my child's diet," the Communications Department generates an answer based on past data and childcare theories. Specifically, the Communications Department uses AI to analyze questions from parents and generate appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," it provides advice on appropriate meals and timing based on past meal data and nutritional knowledge. The Communications Department also provides information about the child's developmental status and childcare plans through dialogue with parents. For example, if a parent asks about the child's developmental status, it can provide a detailed explanation based on data obtained from the Records Management Department and the Analysis Department. Furthermore, the Communications Department has a function to collect feedback from parents and use it to improve the entire system. The feedback provided by parents is reflected in the Planning Proposal Department and the Records Management Department, leading to better childcare plans and management of developmental records. The Communications Department also provides various means to facilitate smooth communication with parents. For example, parents and childcare workers can share information in real time and respond quickly through email, chat, video calls, etc. This allows the communications department to support smooth communication with parents and optimally support the child's development.

[0035] The reception desk can receive information such as the child's age, developmental stage, and interests entered by the childcare worker. For example, the reception desk can receive information such as the child's age, developmental stage, and interests entered by the childcare worker. Based on this information, it can propose an appropriate childcare plan. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the information entered by the childcare worker into the AI, which can then analyze the information and extract the data necessary to propose a childcare plan.

[0036] The planning proposal unit can propose an optimal childcare plan based on past childcare data and child development theories. For example, when creating a childcare plan for a 3-year-old class, the planning proposal unit analyzes past data and proposes activities and teaching materials suitable for 3-year-olds. This allows it to propose an optimal childcare plan based on past data and theories. Some or all of the above-described processes in the planning proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the planning proposal unit can input past childcare data into a generative AI, which can then analyze the data and propose an optimal childcare plan.

[0037] The record management unit can analyze children's photos and videos and automatically record their developmental status. For example, the record management unit can analyze a video of a child taking their first steps and add it to the developmental record. This reduces the administrative work of childcare workers by automatically recording children's developmental status. Some or all of the above processing in the record management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the record management unit can input children's photos and videos into a generation AI, which can then analyze the images and videos to record their developmental status.

[0038] The analysis unit can analyze children's developmental data and detect developmental problems early. For example, the analysis unit can analyze children's developmental data and detect delays in language development. This allows for the early detection of developmental problems and the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input children's developmental data into a generative AI, which can then analyze the data and detect developmental problems.

[0039] The communication department can analyze questions from parents and generate appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the communication department will generate an answer based on past data and childcare theories. This allows for the generation of appropriate answers to parents' questions and facilitates smooth communication. Some or all of the above processing in the communication department may be performed using, for example, a generative AI, or without a generative AI. For example, the communication department can input a question from a parent into a generative AI, which can then analyze the question and generate an appropriate answer.

[0040] The reception desk can analyze the past input history of childcare workers and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that childcare workers have frequently used in the past (such as voice input or text input). The reception desk can also suggest input methods suitable for specific time slots based on the childcare worker's past input history. Furthermore, the reception desk can analyze patterns in the information that childcare workers have entered in the past and suggest efficient input methods. This allows the reception desk to suggest the optimal input method based on the childcare worker's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the childcare worker's past input history data into an AI, which can then analyze the data and select the optimal input method.

[0041] The reception desk can filter information input based on the childcare worker's current work status and areas of interest. For example, the reception desk may suggest prioritizing the input of information related to the childcare worker's current tasks. The reception desk can also filter and input relevant information based on the childcare worker's areas of interest. Furthermore, the reception desk can monitor the childcare worker's work status in real time and adjust the input of appropriate information. This ensures that appropriate information is input according to the childcare worker's work status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input childcare worker work status data into AI, which can analyze the data and filter the appropriate information.

[0042] The reception desk can prioritize inputting highly relevant information when entering data, taking into account the geographical location of the childcare worker. For example, if a childcare worker works in a specific area, the reception desk can prioritize inputting information related to that area. Furthermore, if a childcare worker is on the move, the reception desk can prioritize inputting information related to their current location. Additionally, if a childcare worker works at a specific facility, the reception desk can prioritize inputting information related to that facility. This allows for the priority input of highly relevant information based on the childcare worker's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the childcare worker's geographical location data into an AI, which can then analyze the data and prioritize inputting highly relevant information.

[0043] The reception desk can analyze the social media activities of childcare workers and input relevant information when entering data. For example, the reception desk can input relevant information based on information shared by childcare workers on social media. The reception desk can also analyze topics of interest from childcare workers' social media activities and input relevant information. Furthermore, the reception desk can input relevant information based on information about accounts that childcare workers follow on social media. This allows for the input of relevant information based on childcare workers' social media activities. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input childcare workers' social media activity data into an AI, which can then analyze the data and input relevant information.

[0044] The planning proposal unit can adjust the level of detail in a childcare plan based on the child's importance when proposing a plan. For example, the planning proposal unit can propose a detailed childcare plan according to the child's developmental stage. Furthermore, the planning proposal unit can propose an individually customized childcare plan based on the child's special needs. In addition, the planning proposal unit can propose a childcare plan that includes relevant activities based on the child's interests. This allows the planning proposal unit to propose a childcare plan with an appropriate level of detail according to the child's importance. Some or all of the above processes in the planning proposal unit may be performed using AI, for example, or not. For example, the planning proposal unit can input child developmental stage data into an AI, which can then analyze the data and propose a detailed childcare plan.

[0045] The planning proposal unit can apply different proposal algorithms depending on the child's category when proposing a childcare plan. For example, the planning proposal unit can apply an algorithm that proposes different childcare plans for each age group. It can also apply an algorithm that proposes different childcare plans for each developmental stage. Furthermore, it can apply an algorithm that proposes different childcare plans for each interest or concern. This allows the unit to propose the optimal childcare plan according to the child's category. Some or all of the above processing in the planning proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the planning proposal unit can input child category data into a generative AI, which can then analyze the data and propose the optimal childcare plan.

[0046] The planning proposal department can determine the priority of childcare plans based on the child's submission timing when proposing childcare plans. For example, the planning proposal department can propose childcare plans that should be prioritized according to the child's developmental stage. The planning proposal department can also determine priorities based on the child's special needs. Furthermore, the planning proposal department can propose childcare plans that include activities that should be prioritized based on the child's interests. This allows priorities to be determined based on the child's submission timing. Some or all of the above processes in the planning proposal department may be performed using AI, for example, or not using AI. For example, the planning proposal department can input child submission timing data into AI, and the AI ​​can analyze the data to determine priorities.

[0047] The planning proposal unit can adjust the order of childcare plans based on the children's relevance when proposing childcare plans. For example, the planning proposal unit can adjust the order of childcare plans according to the children's developmental stages. It can also adjust the order of childcare plans based on the children's special needs. Furthermore, it can adjust the order of childcare plans based on the children's interests and concerns. This allows the planning proposal unit to propose childcare plans in an appropriate order based on the children's relevance. Some or all of the above processing in the planning proposal unit may be performed using AI, for example, or without AI. For example, the planning proposal unit can input children's relevance data into AI, and the AI ​​can analyze the data to adjust the order of childcare plans.

[0048] The records management department can analyze a child's past developmental records to select the optimal management method during record management. For example, the records management department can propose the optimal record management method based on the child's past developmental records. The records management department can also customize the record management method based on the child's special needs. Furthermore, the records management department can adjust the record management method according to the child's developmental stage. This allows it to propose the optimal record management method based on the child's past developmental records. Some or all of the above processes in the records management department may be performed using AI, for example, or not. For example, the records management department can input the child's past developmental record data into AI, which can then analyze the data and select the optimal record management method.

[0049] The records management unit can customize the means of recording based on the child's current developmental stage during record management. For example, the records management unit can customize the means of recording according to the child's developmental stage. The records management unit can also adjust the means of recording based on the child's special needs. Furthermore, the records management unit can customize the means of recording based on the child's interests and concerns. This allows for the provision of appropriate means of recording according to the child's current developmental stage. Some or all of the above processes in the records management unit may be performed using AI, for example, or not using AI. For example, the records management unit can input data on the child's current developmental stage into AI, and the AI ​​can analyze the data to customize the means of recording.

[0050] The Records Management Unit can select the optimal record management method when managing records, taking into account the child's geographical location information. For example, the Records Management Unit can propose the optimal record management method based on the child's geographical location information. The Records Management Unit can also adjust the record management method considering the child's movement history. Furthermore, the Records Management Unit can customize the record management method based on the child's current location. This allows the Records Management Unit to propose the optimal record management method based on the child's geographical location information. Some or all of the above processing in the Records Management Unit may be performed using AI, for example, or without AI. For example, the Records Management Unit can input the child's geographical location data into AI, which can then analyze the data and select the optimal record management method.

[0051] The records management department can analyze a child's social media activity and propose methods for record management during the record management process. For example, the records management department can propose the optimal record management method based on the child's social media activity. Furthermore, the records management department can analyze topics of interest from the child's social media activity and adjust the record management method accordingly. In addition, the records management department can customize the record management method considering the child's social media activity. This allows for the provision of optimal record management methods based on the child's social media activity. Some or all of the above processes in the records management department may be performed using AI, for example, or without AI. For example, the records management department can input the child's social media activity data into AI, which can then analyze the data and propose methods for record management.

[0052] The analysis unit can optimize its analysis algorithm by referring to past developmental data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past developmental data. The analysis unit can also adjust the analysis algorithm by referring to past developmental data. Furthermore, the analysis unit can optimize the analysis algorithm by utilizing past developmental data. This allows the optimal analysis algorithm to be selected based on past developmental data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past developmental data into AI, and the AI ​​can analyze the data to optimize the analysis algorithm.

[0053] The analysis unit can apply different analytical methods to each child category during analysis. For example, it can apply different analytical methods by age. It can also apply different analytical methods by developmental stage. Furthermore, it can apply different analytical methods by interests. This allows the optimal analytical method to be applied according to the child category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child category data into AI, which can analyze the data and apply the optimal analytical method.

[0054] The analysis unit can weight the analysis based on the child's submission timing. For example, the analysis unit can adjust the weighting according to the child's developmental stage. It can also weight the analysis based on the child's special needs. Furthermore, it can adjust the weighting based on the child's interests. This allows for appropriate weighting based on the child's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the child's submission timing data into an AI, which can then analyze the data and weight the analysis.

[0055] The analysis department can perform analysis by referring to child-related market data. For example, the analysis department can propose the optimal analysis method based on child-related market data. The analysis department can also adjust the analysis method by referring to child-related market data. Furthermore, the analysis department can optimize the analysis method by utilizing child-related market data. This allows it to propose the optimal analysis method based on child-related market data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input child-related market data into AI, and the AI ​​can analyze the data and propose the optimal analysis method.

[0056] The communication department can provide optimal answers by referring to the parent's past question history during communication. For example, the communication department can suggest the best answer based on the parent's past question history. The communication department can also adjust the answer method by referring to the parent's past question history. Furthermore, the communication department can optimize the answer method by utilizing the parent's past question history. This allows it to suggest the best answer based on the parent's past question history. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input the parent's past question history data into AI, and the AI ​​can analyze the data to provide the best answer.

[0057] The communication department can customize the means of responding to parental concerns based on their current interests during communication. For example, the communication department can suggest the most suitable response method based on the parental concerns. The communication department can also adjust the response method by referring to the parental concerns. Furthermore, the communication department can optimize the response method by utilizing the parental concerns. This allows it to suggest the most suitable response method based on the parental concerns. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input data on the parental concerns into AI, which can then analyze the data and customize the means of responding.

[0058] The communication department can provide optimal responses during communication, taking into account the geographical location information of the parent. For example, the communication department can suggest the optimal response method based on the parent's geographical location information. The communication department can also adjust the response method by referring to the parent's geographical location information. Furthermore, the communication department can optimize the response method by utilizing the parent's geographical location information. This allows the communication department to suggest the optimal response method based on the parent's geographical location information. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input the parent's geographical location data into AI, and the AI ​​can analyze the data to provide the optimal response.

[0059] The communications department can analyze parents' social media activity during communication and suggest appropriate response methods. For example, the communications department can suggest the optimal response method based on parents' social media activity. The communications department can also adjust the response method by referring to parents' social media activity. Furthermore, the communications department can optimize the response method by utilizing parents' social media activity. This allows the communications department to suggest the optimal response method based on parents' social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not. For example, the communications department can input parents' social media activity data into an AI, which can then analyze the data and suggest response methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the past input history of childcare workers and select the optimal input method. For example, it can prioritize suggesting input methods that childcare workers have frequently used in the past (such as voice input or text input). It can also suggest input methods suitable for specific time slots based on the childcare worker's past input history. Furthermore, it can analyze patterns in the information that childcare workers have entered in the past and suggest efficient input methods. This allows the system to suggest the optimal input method based on the childcare worker's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the childcare worker's past input history data into an AI, which can then analyze the data and select the optimal input method.

[0062] The reception desk can filter information input based on the childcare worker's current work status and areas of interest. For example, it can suggest prioritizing the input of information related to the childcare worker's current tasks. It can also filter and input relevant information based on the childcare worker's areas of interest. Furthermore, it can monitor the childcare worker's work status in real time and adjust the input of appropriate information. This ensures that appropriate information is entered according to the childcare worker's work status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input childcare worker work status data into AI, which can analyze the data and filter the appropriate information.

[0063] The reception desk can prioritize inputting highly relevant information when entering data, taking into account the geographical location of the childcare worker. For example, if a childcare worker works in a specific area, information related to that area can be prioritized. Similarly, if a childcare worker is on the move, information related to their current location can be prioritized. Furthermore, if a childcare worker works at a specific facility, information related to that facility can be prioritized. This allows for the priority input of highly relevant information based on the childcare worker's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk could input the childcare worker's geographical location data into an AI, which could then analyze the data and prioritize inputting highly relevant information.

[0064] The planning proposal unit can adjust the level of detail in a childcare plan based on the child's importance when proposing a plan. For example, it can propose a detailed childcare plan according to the child's developmental stage. It can also propose an individually customized childcare plan based on the child's special needs. Furthermore, it can propose a childcare plan that includes relevant activities based on the child's interests. This allows for the proposal of a childcare plan with an appropriate level of detail according to the child's importance. Some or all of the above processes in the planning proposal unit may be performed using AI, for example, or not. For example, the planning proposal unit can input child developmental stage data into an AI, which can then analyze the data and propose a detailed childcare plan.

[0065] The planning proposal unit can apply different proposal algorithms depending on the child's category when proposing a childcare plan. For example, it can apply an algorithm that proposes different childcare plans for each age group. It can also apply an algorithm that proposes different childcare plans for each developmental stage. Furthermore, it can apply an algorithm that proposes different childcare plans for each child's interests. This allows the unit to propose the optimal childcare plan according to the child's category. Some or all of the above processing in the planning proposal unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the planning proposal unit can input child category data into a generative AI, which can then analyze the data and propose the optimal childcare plan.

[0066] The records management department can analyze a child's past developmental records to select the optimal management method during record management. For example, it can propose the optimal record management method based on the child's past developmental records. It can also customize the record management method based on the child's special needs. Furthermore, it can adjust the record management method according to the child's developmental stage. This allows the department to propose the optimal record management method based on the child's past developmental records. Some or all of the above processes in the records management department may be performed using AI, for example, or not. For example, the records management department can input the child's past developmental record data into an AI, which can then analyze the data and select the optimal record management method.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk receives the information entered by the childcare worker. This information includes, for example, the child's age, developmental stage, and interests. By receiving this information, the reception desk provides the data necessary for the next processing step. Step 2: The planning proposal department proposes a childcare plan based on the information received by the reception department. The planning proposal department proposes the optimal childcare plan based on past childcare data and child development theories. For example, when creating a childcare plan for a 3-year-old class, they analyze past data and propose activities and teaching materials suitable for 3-year-olds. Step 3: The Records Management Department manages the child's developmental records based on the childcare plan proposed by the Planning Proposal Department. The Records Management Department analyzes the child's photos and videos and automatically records their developmental status. For example, it can analyze a video of the moment a child takes their first steps and add it to the developmental record. Step 4: The Analysis Department analyzes the developmental records managed by the Records Management Department. The Analysis Department analyzes the child's developmental data to detect developmental problems early. For example, it can analyze the child's language development data to detect delays in language development. Step 5: The Communications Department supports communication with parents based on the analysis results obtained by the Analysis Department. The Communications Department analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the Communications Department will generate an answer based on past data and childcare theories.

[0069] (Example of form 2) The AI ​​assistant system according to an embodiment of the present invention is a system for streamlining the work of childcare workers and providing better childcare to children. This AI assistant system supports tasks such as creating childcare plans, managing children's developmental records, and communicating with parents. Through AI-driven data analysis, it enables early detection of developmental problems in children and leads to appropriate support. This improves the quality of childcare and reforms the working style of childcare workers. For example, when a childcare worker inputs information such as the child's age, developmental stage, and interests, the AI ​​assistant system proposes an optimal childcare plan based on past childcare data and child development theories. Next, the AI ​​assistant system analyzes photos and videos of children and automatically records their developmental status. For example, the AI ​​can analyze a video of a child taking their first steps and add it to the developmental record. Furthermore, the AI ​​assistant system analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's eating habits," the AI ​​generates an answer based on past data and childcare theories. Finally, the AI ​​assistant system analyzes children's developmental data and detects developmental problems early. For example, the AI ​​can analyze a child's language development data and detect delays in language development. This allows childcare workers to provide appropriate support based on problems identified by the AI. As a result, the AI ​​assistant system can streamline childcare workers' tasks and provide better care for children.

[0070] The AI ​​assistant system according to this embodiment comprises a reception unit, a plan proposal unit, a record management unit, an analysis unit, and a communication unit. The reception unit receives information entered by childcare workers. The information entered by childcare workers includes, but is not limited to, the child's age, developmental stage, and interests. The reception unit receives, for example, information such as the child's age, developmental stage, and interests entered by childcare workers. The plan proposal unit proposes a childcare plan based on the information received by the reception unit. The plan proposal unit proposes an optimal childcare plan based, for example, past childcare data and child development theories. For example, when the plan proposal unit creates a childcare plan for a 3-year-old class, it analyzes past data and proposes activities and teaching materials suitable for 3-year-olds. The record management unit manages the child's developmental records based on the childcare plan proposed by the plan proposal unit. The record management unit analyzes, for example, photos and videos of children and automatically records their developmental status. For example, the record management unit can analyze a video of a child taking their first steps and add it to the developmental record. The Analysis Department analyzes developmental records managed by the Records Management Department. For example, the Analysis Department analyzes children's developmental data to detect developmental problems early. For example, the Analysis Department can analyze children's language development data to detect delays in language development. The Communication Department supports communication with parents based on the analysis results obtained by the Analysis Department. For example, the Communication Department analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the Communication Department generates an answer based on past data and childcare theories. In this way, the AI ​​assistant system according to the embodiment can streamline the work of childcare workers and provide better childcare to children.

[0071] The reception desk receives information entered by childcare workers. This information may include, but is not limited to, a child's age, developmental stage, and interests. Specifically, the reception desk provides a dedicated input interface for childcare workers, allowing them to easily enter information. This interface is accessible on devices such as tablets, smartphones, and personal computers, and is designed to enable childcare workers to quickly input information on-site. Furthermore, the reception desk features voice input, allowing childcare workers to input information by voice. Voice input is converted to text using natural language processing technology and incorporated into the system. This allows childcare workers to input information without using their hands, improving work efficiency. The reception desk also has a function to automatically categorize the entered information and save it to the necessary databases. For example, information regarding a child's age and developmental stage is saved in the developmental database, and information regarding interests is saved in the activity suggestion database. This ensures smoother subsequent processing. Furthermore, the reception desk is equipped with a checking function to verify the accuracy of the entered information, preventing childcare workers from making input errors. For example, if the age is set within an incorrect range or if required fields are left blank, a warning message is displayed prompting the childcare worker to make corrections. This allows the reception desk to receive information accurately and quickly, improving the overall reliability of the system.

[0072] The Planning Proposal Department proposes childcare plans based on information received by the Reception Department. For example, the Planning Proposal Department proposes optimal childcare plans based on past childcare data and child development theories. For instance, when creating a childcare plan for a 3-year-old class, it analyzes past data and proposes activities and materials suitable for 3-year-olds. Specifically, the Planning Proposal Department uses AI to analyze past childcare data and selects optimal activities and materials according to the child's age and developmental stage. For example, when creating a childcare plan for a 3-year-old class, it proposes activities and materials that children of the same age group are likely to be interested in, based on past data from 3-year-old classes. Furthermore, the Planning Proposal Department can also propose customized childcare plans tailored to each child's individual interests. For example, if a child is particularly interested in drawing, it will propose a childcare plan that includes many drawing activities. The Planning Proposal Department also provides an interface that allows childcare workers to easily modify and adjust the proposed childcare plans. Based on the proposed plan, childcare workers can create optimal childcare plans utilizing their own experience and knowledge. Additionally, the Planning Proposal Department has a feedback function that evaluates the effectiveness of the proposed childcare plan and incorporates the feedback into future proposals. This allows the planning and proposal department to always propose optimal childcare plans based on the latest information and historical data, thereby supporting the work of childcare workers.

[0073] The Records Management Department manages children's developmental records based on the childcare plans proposed by the Planning and Proposal Department. For example, the Records Management Department analyzes children's photos and videos and automatically records their developmental status. For instance, it can analyze a video of a child taking their first steps and add it to the developmental record. Specifically, the Records Management Department has a function to automatically classify photos and videos taken by childcare workers and save them as developmental records. For example, a video of a child taking their first steps would be classified as "First Steps" and added to the developmental record. The Records Management Department also uses AI to analyze the content of photos and videos and evaluate the child's developmental status. For example, it can analyze a photo of a child drawing and evaluate the child's creativity and manual dexterity based on the content and style of the drawing. Furthermore, the Records Management Department also manages developmental records manually entered by childcare workers. Childcare workers can input their daily observations and insights into the Records Management Department, allowing them to record the child's developmental status in detail. This enables the Records Management Department to manage comprehensive developmental records that include not only photos and videos but also the observation records of childcare workers. Furthermore, the records management department has a function to automatically generate developmental record reports for sharing with parents. This allows parents to regularly check their child's developmental status and facilitates smoother communication with childcare workers. As a result, the records management department can accurately and thoroughly record children's developmental status and provide useful information for both childcare workers and parents.

[0074] The Analysis Department analyzes developmental records managed by the Records Management Department. For example, the Analysis Department analyzes children's developmental data to detect developmental problems early. For instance, the Analysis Department can analyze children's language development data to detect language development delays. Specifically, the Analysis Department uses AI to analyze children's developmental data and detect abnormal patterns or developmental delays. For example, it analyzes children's language development data and issues a warning to caregivers if language development is delayed compared to the average for their age. The Analysis Department can also predict future development based on children's developmental data. For example, it can predict future developmental progress based on the current developmental status and propose necessary support and interventions. Furthermore, the Analysis Department can integrate data from multiple children to evaluate the developmental status of the entire class. This allows caregivers to understand the developmental trends of the entire class and formulate appropriate childcare plans. The Analysis Department can also perform trend analysis based on past data to understand long-term developmental trends. This allows caregivers to track changes in children's development over the long term and take appropriate action. Furthermore, the analytics department has the capability to provide childcare workers and parents with detailed reports on developmental issues and risks. This allows the analytics department to analyze children's developmental status in detail and support early problem detection and appropriate support.

[0075] The Communications Department supports communication with parents based on the analysis results obtained by the Analysis Department. For example, the Communications Department analyzes questions from parents and generates appropriate answers. For instance, if a parent asks, "Please tell me about my child's diet," the Communications Department generates an answer based on past data and childcare theories. Specifically, the Communications Department uses AI to analyze questions from parents and generate appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," it provides advice on appropriate meals and timing based on past meal data and nutritional knowledge. The Communications Department also provides information about the child's developmental status and childcare plans through dialogue with parents. For example, if a parent asks about the child's developmental status, it can provide a detailed explanation based on data obtained from the Records Management Department and the Analysis Department. Furthermore, the Communications Department has a function to collect feedback from parents and use it to improve the entire system. The feedback provided by parents is reflected in the Planning Proposal Department and the Records Management Department, leading to better childcare plans and management of developmental records. The Communications Department also provides various means to facilitate smooth communication with parents. For example, parents and childcare workers can share information in real time and respond quickly through email, chat, video calls, etc. This allows the communications department to support smooth communication with parents and optimally support the child's development.

[0076] The reception desk can receive information such as the child's age, developmental stage, and interests entered by the childcare worker. For example, the reception desk can receive information such as the child's age, developmental stage, and interests entered by the childcare worker. Based on this information, it can propose an appropriate childcare plan. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the information entered by the childcare worker into the AI, which can then analyze the information and extract the data necessary to propose a childcare plan.

[0077] The planning proposal unit can propose an optimal childcare plan based on past childcare data and child development theories. For example, when creating a childcare plan for a 3-year-old class, the planning proposal unit analyzes past data and proposes activities and teaching materials suitable for 3-year-olds. This allows it to propose an optimal childcare plan based on past data and theories. Some or all of the above-described processes in the planning proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the planning proposal unit can input past childcare data into a generative AI, which can then analyze the data and propose an optimal childcare plan.

[0078] The record management unit can analyze children's photos and videos and automatically record their developmental status. For example, the record management unit can analyze a video of a child taking their first steps and add it to the developmental record. This reduces the administrative work of childcare workers by automatically recording children's developmental status. Some or all of the above processing in the record management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the record management unit can input children's photos and videos into a generation AI, which can then analyze the images and videos to record their developmental status.

[0079] The analysis unit can analyze children's developmental data and detect developmental problems early. For example, the analysis unit can analyze children's developmental data and detect delays in language development. This allows for the early detection of developmental problems and the provision of appropriate support. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input children's developmental data into a generative AI, which can then analyze the data and detect developmental problems.

[0080] The communication department can analyze questions from parents and generate appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the communication department will generate an answer based on past data and childcare theories. This allows for the generation of appropriate answers to parents' questions and facilitates smooth communication. Some or all of the above processing in the communication department may be performed using, for example, a generative AI, or without a generative AI. For example, the communication department can input a question from a parent into a generative AI, which can then analyze the question and generate an appropriate answer.

[0081] The reception desk can estimate the emotions of childcare workers and adjust the timing of information input based on the estimated emotions. For example, if a childcare worker is stressed, the reception desk can use AI to delay the input timing so that the worker can input information in a relaxed state. The reception desk can also use AI to adjust the input timing if the childcare worker is busy, so that the worker can input information efficiently in between tasks. Furthermore, if the childcare worker is focused, the reception desk can use AI to immediately prompt input and receive information quickly. This allows for efficient information input by adjusting the timing of information input according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the input timing.

[0082] The reception desk can analyze the past input history of childcare workers and select the optimal input method. For example, the reception desk can prioritize suggesting input methods that childcare workers have frequently used in the past (such as voice input or text input). The reception desk can also suggest input methods suitable for specific time slots based on the childcare worker's past input history. Furthermore, the reception desk can analyze patterns in the information that childcare workers have entered in the past and suggest efficient input methods. This allows the reception desk to suggest the optimal input method based on the childcare worker's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the childcare worker's past input history data into an AI, which can then analyze the data and select the optimal input method.

[0083] The reception desk can filter information input based on the childcare worker's current work status and areas of interest. For example, the reception desk may suggest prioritizing the input of information related to the childcare worker's current tasks. The reception desk can also filter and input relevant information based on the childcare worker's areas of interest. Furthermore, the reception desk can monitor the childcare worker's work status in real time and adjust the input of appropriate information. This ensures that appropriate information is input according to the childcare worker's work status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input childcare worker work status data into AI, which can analyze the data and filter the appropriate information.

[0084] The reception desk can estimate the emotions of childcare workers and determine the priority of information to be entered based on the estimated emotions. For example, if a childcare worker is tired, the reception desk may suggest prioritizing the input of high-priority information. If the childcare worker is relaxed, the reception desk may also suggest inputting detailed information. Furthermore, if the childcare worker is in a hurry, the reception desk may suggest inputting only the most important information. This enables efficient information input by prioritizing information according to the emotions of the childcare worker. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the childcare worker's facial expression data into a generative AI, which can estimate emotions and determine the priority of information.

[0085] The reception desk can prioritize inputting highly relevant information when entering data, taking into account the geographical location of the childcare worker. For example, if a childcare worker works in a specific area, the reception desk can prioritize inputting information related to that area. Furthermore, if a childcare worker is on the move, the reception desk can prioritize inputting information related to their current location. Additionally, if a childcare worker works at a specific facility, the reception desk can prioritize inputting information related to that facility. This allows for the priority input of highly relevant information based on the childcare worker's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the childcare worker's geographical location data into an AI, which can then analyze the data and prioritize inputting highly relevant information.

[0086] The reception desk can analyze the social media activities of childcare workers and input relevant information when entering data. For example, the reception desk can input relevant information based on information shared by childcare workers on social media. The reception desk can also analyze topics of interest from childcare workers' social media activities and input relevant information. Furthermore, the reception desk can input relevant information based on information about accounts that childcare workers follow on social media. This allows for the input of relevant information based on childcare workers' social media activities. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input childcare workers' social media activity data into an AI, which can then analyze the data and input relevant information.

[0087] The planning proposal unit can estimate the emotions of childcare workers and adjust the way the childcare plan is presented based on the estimated emotions. For example, if a childcare worker is relaxed, the planning proposal unit can propose a detailed childcare plan. If a childcare worker is tired, the planning proposal unit can propose a concise and to-the-point childcare plan. Furthermore, if a childcare worker is excited, the planning proposal unit can propose a visually appealing childcare plan. This allows for the proposal of efficient childcare plans by adjusting the presentation of the childcare plan according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning proposal unit may be performed using AI, or not using AI. For example, the planning proposal unit can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the presentation of the childcare plan.

[0088] The planning proposal unit can adjust the level of detail in a childcare plan based on the child's importance when proposing a plan. For example, the planning proposal unit can propose a detailed childcare plan according to the child's developmental stage. Furthermore, the planning proposal unit can propose an individually customized childcare plan based on the child's special needs. In addition, the planning proposal unit can propose a childcare plan that includes relevant activities based on the child's interests. This allows the planning proposal unit to propose a childcare plan with an appropriate level of detail according to the child's importance. Some or all of the above processes in the planning proposal unit may be performed using AI, for example, or not. For example, the planning proposal unit can input child developmental stage data into an AI, which can then analyze the data and propose a detailed childcare plan.

[0089] The planning proposal unit can apply different proposal algorithms depending on the child's category when proposing a childcare plan. For example, the planning proposal unit can apply an algorithm that proposes different childcare plans for each age group. It can also apply an algorithm that proposes different childcare plans for each developmental stage. Furthermore, it can apply an algorithm that proposes different childcare plans for each interest or concern. This allows the unit to propose the optimal childcare plan according to the child's category. Some or all of the above processing in the planning proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the planning proposal unit can input child category data into a generative AI, which can then analyze the data and propose the optimal childcare plan.

[0090] The planning proposal unit can estimate the emotions of childcare workers and adjust the length of the childcare plan based on the estimated emotions. For example, if a childcare worker is relaxed, the planning proposal unit can propose a detailed and longer childcare plan. If a childcare worker is tired, the planning proposal unit can propose a concise and shorter childcare plan. Furthermore, if a childcare worker is in a hurry, the planning proposal unit can propose a short, to-the-point childcare plan. This allows for the proposal of efficient childcare plans by adjusting the length of the childcare plan according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning proposal unit may be performed using AI or not using AI. For example, the planning proposal unit can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the length of the childcare plan.

[0091] The planning proposal department can determine the priority of childcare plans based on the child's submission timing when proposing childcare plans. For example, the planning proposal department can propose childcare plans that should be prioritized according to the child's developmental stage. The planning proposal department can also determine priorities based on the child's special needs. Furthermore, the planning proposal department can propose childcare plans that include activities that should be prioritized based on the child's interests. This allows priorities to be determined based on the child's submission timing. Some or all of the above processes in the planning proposal department may be performed using AI, for example, or not using AI. For example, the planning proposal department can input child submission timing data into AI, and the AI ​​can analyze the data to determine priorities.

[0092] The planning proposal unit can adjust the order of childcare plans based on the children's relevance when proposing childcare plans. For example, the planning proposal unit can adjust the order of childcare plans according to the children's developmental stages. It can also adjust the order of childcare plans based on the children's special needs. Furthermore, it can adjust the order of childcare plans based on the children's interests and concerns. This allows the planning proposal unit to propose childcare plans in an appropriate order based on the children's relevance. Some or all of the above processing in the planning proposal unit may be performed using AI, for example, or without AI. For example, the planning proposal unit can input children's relevance data into AI, and the AI ​​can analyze the data to adjust the order of childcare plans.

[0093] The record management department can estimate the emotions of childcare workers and adjust the record management method based on the estimated emotions. For example, if a childcare worker is relaxed, the record management department can suggest a detailed record management method. If a childcare worker is tired, the record management department can also suggest a concise and efficient record management method. Furthermore, if a childcare worker is in a hurry, the record management department can suggest a concise record management method. This allows for efficient record management by adjusting the record management method according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the record management department may be performed using AI, for example, or not using AI. For example, the record management department can input the childcare worker's facial expression data into a generative AI, which can estimate emotions and adjust the record management method.

[0094] The records management department can analyze a child's past developmental records to select the optimal management method during record management. For example, the records management department can propose the optimal record management method based on the child's past developmental records. The records management department can also customize the record management method based on the child's special needs. Furthermore, the records management department can adjust the record management method according to the child's developmental stage. This allows it to propose the optimal record management method based on the child's past developmental records. Some or all of the above processes in the records management department may be performed using AI, for example, or not. For example, the records management department can input the child's past developmental record data into AI, which can then analyze the data and select the optimal record management method.

[0095] The records management unit can customize the means of recording based on the child's current developmental stage during record management. For example, the records management unit can customize the means of recording according to the child's developmental stage. The records management unit can also adjust the means of recording based on the child's special needs. Furthermore, the records management unit can customize the means of recording based on the child's interests and concerns. This allows for the provision of appropriate means of recording according to the child's current developmental stage. Some or all of the above processes in the records management unit may be performed using AI, for example, or not using AI. For example, the records management unit can input data on the child's current developmental stage into AI, and the AI ​​can analyze the data to customize the means of recording.

[0096] The records management department can estimate the emotions of childcare workers and determine the priority of records based on the estimated emotions. For example, if a childcare worker is relaxed, the records management department will prioritize detailed records. If a childcare worker is tired, the records management department can also prioritize high-priority records. Furthermore, if a childcare worker is in a hurry, the records management department can also prioritize the most important records. This enables efficient records management by determining the priority of records according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the records management department may be performed using AI, or not using AI. For example, the records management department can input childcare worker facial expression data into a generative AI, which can estimate emotions and determine the priority of records.

[0097] The Records Management Unit can select the optimal record management method when managing records, taking into account the child's geographical location information. For example, the Records Management Unit can propose the optimal record management method based on the child's geographical location information. The Records Management Unit can also adjust the record management method considering the child's movement history. Furthermore, the Records Management Unit can customize the record management method based on the child's current location. This allows the Records Management Unit to propose the optimal record management method based on the child's geographical location information. Some or all of the above processing in the Records Management Unit may be performed using AI, for example, or without AI. For example, the Records Management Unit can input the child's geographical location data into AI, which can then analyze the data and select the optimal record management method.

[0098] The records management department can analyze a child's social media activity and propose methods for record management during the record management process. For example, the records management department can propose the optimal record management method based on the child's social media activity. Furthermore, the records management department can analyze topics of interest from the child's social media activity and adjust the record management method accordingly. In addition, the records management department can customize the record management method considering the child's social media activity. This allows for the provision of optimal record management methods based on the child's social media activity. Some or all of the above processes in the records management department may be performed using AI, for example, or without AI. For example, the records management department can input the child's social media activity data into AI, which can then analyze the data and propose methods for record management.

[0099] The analysis unit can estimate the emotions of childcare workers and adjust the analysis method based on the estimated emotions. For example, if a childcare worker is relaxed, the analysis unit can suggest a detailed analysis method. If a childcare worker is tired, the analysis unit can suggest a concise and efficient analysis method. Furthermore, if a childcare worker is in a hurry, the analysis unit can suggest a concise analysis method. This allows for efficient analysis by adjusting the analysis method according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the childcare worker's facial expression data into a generative AI, which can then estimate emotions and adjust the analysis method.

[0100] The analysis unit can optimize its analysis algorithm by referring to past developmental data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past developmental data. The analysis unit can also adjust the analysis algorithm by referring to past developmental data. Furthermore, the analysis unit can optimize the analysis algorithm by utilizing past developmental data. This allows the optimal analysis algorithm to be selected based on past developmental data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past developmental data into AI, and the AI ​​can analyze the data to optimize the analysis algorithm.

[0101] The analysis unit can apply different analytical methods to each child category during analysis. For example, it can apply different analytical methods by age. It can also apply different analytical methods by developmental stage. Furthermore, it can apply different analytical methods by interests. This allows the optimal analytical method to be applied according to the child category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child category data into AI, which can analyze the data and apply the optimal analytical method.

[0102] The analysis unit can estimate the emotions of childcare workers and determine the priority of analysis based on the estimated emotions. For example, if a childcare worker is relaxed, the analysis unit may prioritize detailed analysis. If a childcare worker is tired, the analysis unit may also prioritize high-priority analysis. Furthermore, if a childcare worker is in a hurry, the analysis unit may prioritize the most important analysis. This allows for efficient analysis by prioritizing analysis according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the childcare worker's facial expression data into a generative AI, which can then estimate emotions and determine the priority of analysis.

[0103] The analysis unit can weight the analysis based on the child's submission timing. For example, the analysis unit can adjust the weighting according to the child's developmental stage. It can also weight the analysis based on the child's special needs. Furthermore, it can adjust the weighting based on the child's interests. This allows for appropriate weighting based on the child's submission timing. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the child's submission timing data into an AI, which can then analyze the data and weight the analysis.

[0104] The analysis department can perform analysis by referring to child-related market data. For example, the analysis department can propose the optimal analysis method based on child-related market data. The analysis department can also adjust the analysis method by referring to child-related market data. Furthermore, the analysis department can optimize the analysis method by utilizing child-related market data. This allows it to propose the optimal analysis method based on child-related market data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input child-related market data into AI, and the AI ​​can analyze the data and propose the optimal analysis method.

[0105] The communication department can estimate the emotions of childcare workers and adjust its communication methods based on those estimated emotions. For example, if a childcare worker is relaxed, the communication department can suggest detailed communication methods. If a childcare worker is tired, the communication department can also suggest concise and efficient communication methods. Furthermore, if a childcare worker is in a hurry, the communication department can suggest concise and to-the-point communication methods. This allows for efficient communication by adjusting the communication method according to the childcare worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication department may be performed using AI, or not using AI. For example, the communication department can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the communication method.

[0106] The communication department can provide optimal answers by referring to the parent's past question history during communication. For example, the communication department can suggest the best answer based on the parent's past question history. The communication department can also adjust the answer method by referring to the parent's past question history. Furthermore, the communication department can optimize the answer method by utilizing the parent's past question history. This allows it to suggest the best answer based on the parent's past question history. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input the parent's past question history data into AI, and the AI ​​can analyze the data to provide the best answer.

[0107] The communication department can customize the means of responding to parental concerns based on their current interests during communication. For example, the communication department can suggest the most suitable response method based on the parental concerns. The communication department can also adjust the response method by referring to the parental concerns. Furthermore, the communication department can optimize the response method by utilizing the parental concerns. This allows it to suggest the most suitable response method based on the parental concerns. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input data on the parental concerns into AI, which can then analyze the data and customize the means of responding.

[0108] The communication department can estimate the emotions of childcare workers and determine communication priorities based on the estimated emotions. For example, if a childcare worker is relaxed, the communication department will prioritize detailed communication. If a childcare worker is tired, the communication department can also prioritize high-priority communication. Furthermore, if a childcare worker is in a hurry, the communication department can also prioritize the most important communication. This enables efficient communication by determining communication priorities according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication department may be performed using AI or not using AI. For example, the communication department can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and determine communication priorities.

[0109] The communication department can provide optimal responses during communication, taking into account the geographical location information of the parent. For example, the communication department can suggest the optimal response method based on the parent's geographical location information. The communication department can also adjust the response method by referring to the parent's geographical location information. Furthermore, the communication department can optimize the response method by utilizing the parent's geographical location information. This allows the communication department to suggest the optimal response method based on the parent's geographical location information. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department can input the parent's geographical location data into AI, and the AI ​​can analyze the data to provide the optimal response.

[0110] The communications department can analyze parents' social media activity during communication and suggest appropriate response methods. For example, the communications department can suggest the optimal response method based on parents' social media activity. The communications department can also adjust the response method by referring to parents' social media activity. Furthermore, the communications department can optimize the response method by utilizing parents' social media activity. This allows the communications department to suggest the optimal response method based on parents' social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not. For example, the communications department can input parents' social media activity data into an AI, which can then analyze the data and suggest response methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can estimate the emotions of childcare workers and adjust the timing of information input based on the estimated emotions. For example, if a childcare worker is stressed, the AI ​​can delay the input timing to allow them to input in a relaxed state. Also, if a childcare worker is busy, the AI ​​can adjust the input timing to allow them to input efficiently in between tasks. Furthermore, if a childcare worker is focused, the AI ​​can immediately prompt input to receive information quickly. In this way, efficient information input is possible by adjusting the timing of information input according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the input timing.

[0113] The reception desk can analyze the past input history of childcare workers and select the optimal input method. For example, it can prioritize suggesting input methods that childcare workers have frequently used in the past (such as voice input or text input). It can also suggest input methods suitable for specific time slots based on the childcare worker's past input history. Furthermore, it can analyze patterns in the information that childcare workers have entered in the past and suggest efficient input methods. This allows the system to suggest the optimal input method based on the childcare worker's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the childcare worker's past input history data into an AI, which can then analyze the data and select the optimal input method.

[0114] The reception desk can filter information input based on the childcare worker's current work status and areas of interest. For example, it can suggest prioritizing the input of information related to the childcare worker's current tasks. It can also filter and input relevant information based on the childcare worker's areas of interest. Furthermore, it can monitor the childcare worker's work status in real time and adjust the input of appropriate information. This ensures that appropriate information is entered according to the childcare worker's work status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input childcare worker work status data into AI, which can analyze the data and filter the appropriate information.

[0115] The reception desk can estimate the emotions of childcare workers and determine the priority of information to be entered based on the estimated emotions. For example, if a childcare worker is tired, it may suggest prioritizing the input of highly important information. If the childcare worker is relaxed, it may also suggest inputting detailed information. Furthermore, if the childcare worker is in a hurry, it may suggest inputting only the most important information. This enables efficient information input by prioritizing information according to the emotions of the childcare worker. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the childcare worker's facial expression data into a generative AI, which can estimate emotions and determine the priority of information.

[0116] The reception desk can prioritize inputting highly relevant information when entering data, taking into account the geographical location of the childcare worker. For example, if a childcare worker works in a specific area, information related to that area can be prioritized. Similarly, if a childcare worker is on the move, information related to their current location can be prioritized. Furthermore, if a childcare worker works at a specific facility, information related to that facility can be prioritized. This allows for the priority input of highly relevant information based on the childcare worker's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or without AI. For example, the reception desk could input the childcare worker's geographical location data into an AI, which could then analyze the data and prioritize inputting highly relevant information.

[0117] The planning proposal unit can estimate the emotions of childcare workers and adjust the way the childcare plan is presented based on the estimated emotions. For example, if a childcare worker is relaxed, a detailed childcare plan can be proposed. If the childcare worker is tired, a concise and to-the-point plan can be proposed. Furthermore, if the childcare worker is excited, a visually appealing childcare plan can be proposed. This allows for the proposal of efficient childcare plans by adjusting the presentation of the plan according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning proposal unit may be performed using AI, or not using AI. For example, the planning proposal unit can input the childcare worker's facial expression data into the generative AI, which can estimate emotions and adjust the presentation of the childcare plan.

[0118] The planning proposal unit can adjust the level of detail in a childcare plan based on the child's importance when proposing a plan. For example, it can propose a detailed childcare plan according to the child's developmental stage. It can also propose an individually customized childcare plan based on the child's special needs. Furthermore, it can propose a childcare plan that includes relevant activities based on the child's interests. This allows for the proposal of a childcare plan with an appropriate level of detail according to the child's importance. Some or all of the above processes in the planning proposal unit may be performed using AI, for example, or not. For example, the planning proposal unit can input child developmental stage data into an AI, which can then analyze the data and propose a detailed childcare plan.

[0119] The planning proposal unit can apply different proposal algorithms depending on the child's category when proposing a childcare plan. For example, it can apply an algorithm that proposes different childcare plans for each age group. It can also apply an algorithm that proposes different childcare plans for each developmental stage. Furthermore, it can apply an algorithm that proposes different childcare plans for each child's interests. This allows the unit to propose the optimal childcare plan according to the child's category. Some or all of the above processing in the planning proposal unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the planning proposal unit can input child category data into a generative AI, which can then analyze the data and propose the optimal childcare plan.

[0120] The record management department can estimate the emotions of childcare workers and adjust the record management method based on the estimated emotions. For example, if a childcare worker is relaxed, it can suggest a detailed record management method. If a childcare worker is tired, it can suggest a concise and efficient record management method. Furthermore, if a childcare worker is in a hurry, it can suggest a record management method that gets straight to the point. This allows for efficient record management by adjusting the record management method according to the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the record management department may be performed using AI, or not using AI. For example, the record management department can input the childcare worker's facial expression data into a generative AI, which can estimate emotions and adjust the record management method.

[0121] The records management department can analyze a child's past developmental records to select the optimal management method during record management. For example, it can propose the optimal record management method based on the child's past developmental records. It can also customize the record management method based on the child's special needs. Furthermore, it can adjust the record management method according to the child's developmental stage. This allows the department to propose the optimal record management method based on the child's past developmental records. Some or all of the above processes in the records management department may be performed using AI, for example, or not. For example, the records management department can input the child's past developmental record data into an AI, which can then analyze the data and select the optimal record management method.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives the information entered by the childcare worker. This information includes, for example, the child's age, developmental stage, and interests. By receiving this information, the reception desk provides the data necessary for the next processing step. Step 2: The planning proposal department proposes a childcare plan based on the information received by the reception department. The planning proposal department proposes the optimal childcare plan based on past childcare data and child development theories. For example, when creating a childcare plan for a 3-year-old class, they analyze past data and propose activities and teaching materials suitable for 3-year-olds. Step 3: The Records Management Department manages the child's developmental records based on the childcare plan proposed by the Planning Proposal Department. The Records Management Department analyzes the child's photos and videos and automatically records their developmental status. For example, it can analyze a video of the moment a child takes their first steps and add it to the developmental record. Step 4: The Analysis Department analyzes the developmental records managed by the Records Management Department. The Analysis Department analyzes the child's developmental data to detect developmental problems early. For example, it can analyze the child's language development data to detect delays in language development. Step 5: The Communications Department supports communication with parents based on the analysis results obtained by the Analysis Department. The Communications Department analyzes questions from parents and generates appropriate answers. For example, if a parent asks, "Please tell me about my child's diet," the Communications Department will generate an answer based on past data and childcare theories.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the reception unit, plan proposal unit, record management unit, analysis unit, and communication unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information entered by the childcare worker. The plan proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a childcare plan based on past childcare data and child development theory. The record management unit analyzes photos and videos of children using the camera 42 of the smart device 14 and records their developmental status. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes developmental data to detect problems early. The communication unit is implemented by, for example, the control unit 46A of the smart device 14 and generates appropriate answers to questions from parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, plan proposal unit, record management unit, analysis unit, and communication unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information entered by the childcare worker. The plan proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a childcare plan based on past childcare data and child development theory. The record management unit analyzes photos and videos of children using the camera 42 of the smart glasses 214 and records their developmental status. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes developmental data to detect problems early. The communication unit is implemented by, for example, the control unit 46A of the smart glasses 214 and generates appropriate answers to questions from parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, plan proposal unit, record management unit, analysis unit, and communication unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information entered by the childcare worker. The plan proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a childcare plan based on past childcare data and child development theory. The record management unit analyzes photos and videos of children using the camera 42 of the headset terminal 314 and records their developmental status. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes developmental data to detect problems early. The communication unit is implemented by, for example, the control unit 46A of the headset terminal 314 and generates appropriate answers to questions from parents. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, plan proposal unit, record management unit, analysis unit, and communication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information entered by the childcare worker. The plan proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a childcare plan based on past childcare data and child development theory. The record management unit analyzes photos and videos of children using the camera 42 of the robot 414 and records their developmental status. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes developmental data to detect problems early. The communication unit is implemented by, for example, the control unit 46A of the robot 414 and generates appropriate answers to questions from parents. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The reception desk receives the information entered by the childcare worker, A planning proposal department proposes a childcare plan based on the information received by the aforementioned reception department, A record management department manages the child development records based on the childcare plan proposed by the aforementioned planning proposal department, An analysis unit that analyzes developmental records managed by the aforementioned record management unit, The system includes a communication unit that supports communication with parents based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept information entered by childcare workers, such as the child's age, developmental stage, interests, and other relevant details. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned planning proposal department, We propose the optimal childcare plan based on past childcare data and child development theories. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned record management unit, It analyzes photos and videos of children and automatically records their developmental progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyzing children's developmental data to detect developmental problems early. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned communications department, Analyze questions from parents and generate appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the emotions of childcare workers and adjusts the timing of information input based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the past input history of childcare workers and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, filtering is performed based on the childcare worker's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the emotions of childcare workers and determines the priority of information to input based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the geographical location of childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the social media activity of childcare workers is analyzed and relevant information is entered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning proposal department, The system estimates the emotions of childcare workers and adjusts the way childcare plans are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned planning proposal department, When proposing a childcare plan, adjust the level of detail in the plan based on the importance of each child. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned planning proposal department, When proposing childcare plans, different proposal algorithms are applied depending on the child's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned planning proposal department, The system estimates the emotions of childcare workers and adjusts the length of the childcare plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning proposal department, When proposing a childcare plan, prioritize the plan based on when the child submits their information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning proposal department, When proposing a childcare plan, adjust the order of the plan based on the children's relationships. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned record management unit, Estimate the emotions of childcare workers and adjust the record management method based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned record management unit, When managing records, analyze the child's past developmental records to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned record management unit, When managing records, customize the recording methods based on the child's current developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned record management unit, The system estimates the emotions of childcare workers and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned record management unit, When managing records, select the optimal record management method while considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned record management unit, When managing records, we analyze children's social media activities and propose methods for record management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate the emotions of the childcare workers and adjust the analysis method based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past developmental data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each category of child. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is We estimate the emotions of childcare workers and determine the priority of analysis based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, the analysis will be weighted based on when the children submitted their work. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, we will refer to market data related to children. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications department, The system estimates the emotions of childcare workers and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned communications department, During communication, we refer to the parent's past question history to provide the most appropriate answer. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned communications department, When communicating, customize the means of response based on the parent's current concerns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned communications department, The system estimates the emotions of childcare workers and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned communications department, When communicating, we take into account the geographical location of the parent to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned communications department, During communication, we analyze parents' social media activity and suggest ways to respond. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives the information entered by the childcare worker, A planning proposal department proposes a childcare plan based on the information received by the aforementioned reception department, A record management department manages the child development records based on the childcare plan proposed by the aforementioned planning proposal department, An analysis unit that analyzes developmental records managed by the aforementioned record management unit, The system includes a communication unit that supports communication with parents based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts information entered by childcare workers, such as the child's age, developmental stage, interests, and other relevant details. The system according to feature 1.

3. The aforementioned planning proposal department, We propose the optimal childcare plan based on past childcare data and child development theories. The system according to feature 1.

4. The aforementioned record management unit, It analyzes photos and videos of children and automatically records their developmental progress. The system according to feature 1.

5. The aforementioned analysis unit is Analyzing children's developmental data to detect developmental problems early. The system according to feature 1.

6. The aforementioned communications department, Analyze questions from parents and generate appropriate answers. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the emotions of childcare workers and adjusts the timing of information input based on the estimated emotions of the childcare workers. The system according to feature 1.

8. The aforementioned reception unit is Analyze the past input history of childcare workers and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering information, filtering is performed based on the childcare worker's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the emotions of childcare workers and determines the priority of information to input based on the estimated emotions of the childcare workers. The system according to feature 1.

Images