System

Abstract

An object of a system according to an embodiment is to efficiently make a childcare plan suitable for the individuality and interest of a child and to cooperate with a protector.SOLUTION: A system includes an analysis part, a childcare plan creation part, a scheduling part, and a cooperation part. The analysis part analyzes the contents of the diary and the drawing. The childcare plan making unit makes a childcare plan based on the result analyzed by the analysis unit. The scheduling unit adjusts the schedule of the childcare giver based on the childcare plan drafted by the childcare plan drafting unit. The cooperation unit cooperates the nursing plan drafted by the nursing plan drafting unit with the protector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently create childcare plans tailored to each child's personality and interests and to collaborate with parents.

[0005] The system according to the embodiment aims to efficiently create a childcare plan that matches the personality and interests of each child and to cooperate with parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a childcare plan creation unit, a scheduling unit, and a linking unit. The analysis unit analyzes the contents of the diary and drawings. The childcare plan creation unit creates a childcare plan based on the results of the analysis by the analysis unit. The scheduling unit adjusts the schedule of the caregiver based on the childcare plan created by the childcare plan creation unit. The linking unit shares the childcare plan created by the childcare plan creation unit with the parents. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create childcare plans that are tailored to the individuality and interests of each child, and can work in cooperation with parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The childcare plan proposal system according to an embodiment of the present invention proposes childcare plans tailored to each child's personality and interests. This system uses a generation AI to analyze the contents of daily diaries and drawings, grasp changes in personality and interests, and create childcare plans based on this information. It then proposes action plans tailored to each child, with a schedule that is not too demanding for caregivers. It also supports children's development at home through collaboration with parents. This allows the childcare plan proposal system to propose childcare plans tailored to each child's personality and interests, supporting children's development while reducing the burden on caregivers.

[0029] A childcare plan proposal system according to an embodiment includes an analysis unit, a childcare plan creation unit, a scheduling unit, and a linking unit. The analysis unit analyzes the contents of the diary and drawings. For example, the diary records a child's behavior, comments, and activities of interest. The analysis unit analyzes this information to understand changes in the child's personality and interests. The childcare plan creation unit creates a childcare plan based on the results of the analysis by the analysis unit. For example, if a child is interested in animals, the scheduling unit proposes a childcare plan that incorporates many animal-related activities for that child. The scheduling unit adjusts the schedule of caregivers based on the childcare plan created by the childcare plan creation unit. For example, the scheduling unit proposes an efficient schedule taking into account the number of children and types of activities that the caregiver can handle per day. The linking unit shares the childcare plan created by the childcare plan creation unit with parents. For example, the linking unit reports to parents the activities at the nursery school and changes in the child's interests, and suggests support methods at home. As a result, the childcare plan proposal system according to an embodiment proposes a childcare plan tailored to the child's personality and interests, thereby supporting the child's growth while reducing the burden on caregivers.

[0030] When analyzing the contents of the diary or drawings, the analysis unit also uses audio and video data, allowing for a more multifaceted understanding of personality. For example, the analysis unit analyzes the child's audio data in addition to the contents of the diary or drawings. For example, it analyzes the tone of voice and speaking style when reading the diary to infer emotions and interests. It also uses video data to analyze the child's behavior and facial expressions. For example, it records a video of the child drawing or writing the diary, and the generation AI analyzes the movements and facial expressions. It integrates audio and video data with text data to understand personality from a more multifaceted perspective. For example, it combines information extracted from audio and video with the contents of the diary or drawings for analysis. In this way, by using audio and video data together, it is possible to understand personality from a more multifaceted perspective.

[0031] The analysis unit can track trends in a child's behavioral patterns and interests over the long term based on the data analyzed by the generative AI, and predict the development of their personality. For example, the analysis unit tracks trends in a child's behavioral patterns over the long term based on the data analyzed by the generative AI. For example, it regularly analyzes the contents of diaries and drawings to identify changes in behavioral patterns. It tracks trends in interests over the long term and predicts the development of their personality. For example, if interest in a particular topic continues to grow, it suggests activities related to that topic. It predicts the development of their personality based on trends in behavioral patterns and interests. For example, it predicts future changes in interests based on past data and creates a childcare plan accordingly. This makes it possible to track trends in behavioral patterns and interests over the long term and predict the development of their personality.

[0032] In addition to the contents of the diary and drawings, the analysis unit also analyzes records of crafts and play, allowing for a more diverse range of data to be used to understand a child's personality. For example, the analysis unit may include crafts made by a child in its analysis. For example, it may analyze the theme of the craft, the materials used, and the level of completion to understand a child's interests and creativity. It may also include play records in its analysis. For example, it may record the content, frequency, and method of play, and the generation AI may analyze these to understand a child's interests and behavioral patterns. By integrating the records of crafts and play with the contents of the diary and drawings, a more diverse range of data can be used to understand a child's personality. For example, it may analyze the records of crafts and play in combination with text data. In this way, by including the records of crafts and play in its analysis, a more diverse range of data can be used to understand a child's personality.

[0033] The analysis unit can share data between different childcare facilities and analyze differences in personality due to region and culture. For example, the analysis unit can share diary and drawing data between different childcare facilities and analyze differences in personality due to region and culture. For example, it can compare data from childcare facilities in urban and rural areas. Based on the shared data, it can analyze differences in personality due to region and culture. For example, it can compare the interests and behavioral patterns of children in different regions and find similarities and differences. Data can be shared between different childcare facilities, and the generation AI can analyze differences in personality due to region and culture. For example, it can analyze data from children with different cultural backgrounds and understand cultural influences. This makes it possible to share data between different childcare facilities and analyze differences in personality due to region and culture.

[0034] When formulating a childcare plan, the childcare plan formulation unit creates a database of past successes and failures, and the generation AI can refer to them to generate the optimal plan. The childcare plan formulation unit, for example, creates a database of past successes and failures, and the generation AI can refer to them to generate the optimal childcare plan. For example, a plan is formulated based on data on successful and unsuccessful activities. The generation AI analyzes past successes and failures and generates the optimal childcare plan based on that. For example, a plan that incorporates elements of successful activities is proposed. The generation AI formulates the optimal childcare plan based on the database of successes and failures. For example, a plan that avoids unsuccessful activities and incorporates many successful activities is proposed. In this way, past successes and failures can be compiled into a database and the optimal plan can be generated.

[0035] The childcare plan creation unit can incorporate data on children of different age groups and developmental stages when creating a childcare plan, and generate plans that cater to a wide range of ages. The childcare plan creation unit, for example, incorporates data on children of different age groups and developmental stages to create a childcare plan that caters to a wide range of ages. For example, it creates a plan based on data from toddlers to elementary school children. The generation AI analyzes data on different age groups and developmental stages, and generates an optimal childcare plan based on that. For example, it suggests activity content according to age. A childcare plan that caters to a wide range of ages is created based on data on children of different age groups and developmental stages. For example, it suggests a plan that combines activities according to the developmental stage. This makes it possible to incorporate data on children of different age groups and developmental stages to create a plan that caters to a wide range of ages.

[0036] The childcare plan formulation unit can link the childcare plan with home activities and local community events, thereby creating an environment in which the individuality of children can be developed throughout the home and the community. The childcare plan formulation unit, for example, links the childcare plan with home activities and local community events. For example, it can incorporate home homework and local events into the childcare plan. The childcare plan is linked in order to create an environment in which the individuality of children can be developed throughout the home and the community. For example, it can incorporate activities at local libraries and parks into the plan. The childcare plan is linked with home and local community activities to create an environment in which the individuality of children can be developed. For example, it can incorporate reading time at home and local sporting events into the plan. In this way, it is possible to create an environment in which the individuality of children can be developed throughout the home and the community.

[0037] The scheduling unit analyzes the childcare worker's past schedule data, and the generation AI can learn and propose the optimal schedule pattern. The scheduling unit, for example, analyzes the childcare worker's past schedule data, and the generation AI can learn and propose the optimal schedule pattern. For example, from past data, it identifies the time periods when the childcare worker can work most efficiently. The generation AI proposes the optimal schedule pattern based on the past schedule data. For example, it creates a schedule that will prevent the childcare worker from getting tired. By analyzing past schedule data, the generation AI can learn and propose the optimal schedule pattern. For example, it proposes a schedule that properly allocates break times for childcare workers. In this way, it is possible to analyze the childcare worker's past schedule data and propose the optimal schedule pattern.

[0038] The scheduling unit can incorporate break times and refreshing activities into the childcare worker's schedule and propose a schedule that takes long-term health management into consideration. For example, the scheduling unit incorporates break times into the childcare worker's schedule and proposes a schedule that takes long-term health management into consideration. For example, it sets regular break times. It incorporates refreshing activities into the schedule and takes care of the childcare worker's health management. For example, it incorporates refreshing activities into the schedule. The generation AI proposes a schedule that takes care of the childcare worker's health management into consideration. For example, it appropriately allocates time for the childcare worker to refresh. This makes it possible to incorporate break times and refreshing activities into the childcare worker's schedule and propose a schedule that takes long-term health management into consideration.

[0039] The scheduling unit shares schedule data between different childcare facilities, and the generation AI can generate an optimal schedule model. The scheduling unit, for example, shares schedule data between different childcare facilities, and the generation AI generates an optimal schedule model. For example, it proposes an optimal schedule based on data from multiple facilities. The generation AI generates an optimal schedule model based on the shared schedule data. For example, it analyzes data from different facilities and proposes a common optimal schedule. Schedule data is shared between different childcare facilities, and the generation AI generates an optimal schedule model. For example, it proposes a schedule that takes into account the characteristics of each facility. This makes it possible to share schedule data between different childcare facilities, and generate an optimal schedule model.

[0040] The scheduling unit can link the childcare worker's schedule with home and local events, allowing the childcare worker to participate in local activities as well. The scheduling unit, for example, links the childcare worker's schedule with home and local events. For example, it adjusts the schedule so that the childcare worker can participate in local events. It proposes a schedule that links with home and local events, allowing the childcare worker to participate in local activities as well. For example, it sets aside time for the childcare worker to participate in local volunteer activities. The generation AI proposes a schedule that links with home and local events. For example, it creates a schedule that aligns with local events and festivities. This makes it possible to adjust the schedule so that the childcare worker can participate in local activities as well.

[0041] When communicating with parents, the communication unit analyzes past communication data, and the generation AI can propose the optimal method of communication. The communication unit, for example, analyzes past communication data, and the generation AI proposes the optimal method of communication. For example, it identifies the parent's preferred means of contact and timing. The generation AI proposes the optimal method of communication based on past communication data. For example, it provides information in a format that is easy for parents to understand. The communication data is analyzed, and the generation AI proposes the optimal method of communication. For example, it identifies the content and frequency that parents are likely to respond to. This makes it possible to analyze past communication data and propose the optimal method of communication.

[0042] The liaison department can collaborate with parents through an online platform, enabling them to support their children's development even from a distance. The liaison department can, for example, collaborate with parents through an online platform, enabling them to support their children's development even from a distance. For example, it can use video calls or chat functions. It can utilize an online platform to build a system that allows parents to support their children's development even from a distance. For example, it can share growth reports and activity records online. It can collaborate through an online platform so that parents can support their children's development even from a distance. For example, it can provide a dedicated portal site that parents can access. This makes it possible to support children's development even from a distance through the online platform.

[0043] The Liaison Department can share its collaboration with parents with other parents in the community, and support children's development throughout the community. The Liaison Department can, for example, share its collaboration with parents with other parents in the community, and support children's development throughout the community. For example, it can provide a platform where parents can exchange information. It can collaborate with other parents in the community and build a system to support children's development throughout the community. For example, it can share local events and activities. It can share its collaboration with parents with other parents in the community, and support children's development throughout the community. For example, it can create a system where parents can work together to support children's development. This will allow it to collaborate with other parents in the community and support children's development throughout the community.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] In addition to the contents of the diary and drawings, the analysis unit can also include a child's sleep patterns and dietary records in its analysis. For example, a child's sleep duration and quality can be recorded, and the generation AI can analyze them to understand the child's health and stress level. By including dietary records in the analysis, it is possible to understand a child's nutritional status and food preferences. This allows for a more comprehensive understanding of a child's personality and health by analyzing sleep and dietary data.

[0046] When analyzing the contents of the diary or drawings, the analysis unit also uses audio and video data, allowing for a more multifaceted understanding of personality. For example, in addition to the contents of the diary or drawings, the analysis unit analyzes the child's audio data. For example, the tone of voice and speaking style when reading the diary can be analyzed to infer emotions and interests. Video data can also be used to analyze the child's behavior and facial expressions. For example, a video of the child drawing or writing the diary can be recorded, and the generation AI can analyze the child's movements and facial expressions. Audio and video data can be integrated with text data to understand personality from a more multifaceted perspective. For example, information extracted from audio and video can be combined with the contents of the diary or drawings for analysis. In this way, by using audio and video data together, personality can be understood from a more multifaceted perspective.

[0047] The analysis unit can track a child's behavioral patterns and interest trends over the long term based on the data analyzed by the generative AI, and predict personality development. For example, it tracks a child's behavioral patterns over the long term based on the data analyzed by the generative AI. For example, it regularly analyzes the contents of diaries and drawings to identify changes in behavioral patterns. It tracks interest trends over the long term and predicts personality development. For example, if interest in a particular topic continues to grow, it suggests activities related to that topic. It predicts personality development based on behavioral patterns and interest trends. For example, it predicts future changes in interests based on past data and creates a childcare plan accordingly. This makes it possible to track behavioral patterns and interest trends over the long term and predict personality development.

[0048] In addition to the contents of the diary and drawings, the analysis unit also analyzes records of crafts and play, allowing for a more diverse range of data to be used to understand a child's personality. For example, crafts made by a child are included in the analysis. For example, the theme of the craft, the materials used, and the level of completion are analyzed to understand a child's interests and creativity. Play records are also included in the analysis. For example, the content, frequency, and method of play are recorded, and the generation AI analyzes these to understand a child's interests and behavioral patterns. Crafts and play records are integrated with the contents of the diary and drawings to understand a child's personality from a more diverse range of data. For example, crafts and play records are combined with text data for analysis. In this way, by including crafts and play records in the analysis, a more diverse range of data can be used to understand a child's personality.

[0049] The analysis unit can share data between different childcare facilities and analyze differences in personality due to region and culture. For example, diary and drawing data can be shared between different childcare facilities to analyze differences in personality due to region and culture. For example, data from childcare facilities in urban and rural areas can be compared. Based on the shared data, differences in personality due to region and culture can be analyzed. For example, the interests and behavioral patterns of children in different regions can be compared to find similarities and differences. Data can be shared between different childcare facilities, and the generation AI can analyze differences in personality due to region and culture. For example, data from children with different cultural backgrounds can be analyzed to understand cultural influences. This makes it possible to share data between different childcare facilities and analyze differences in personality due to region and culture.

[0050] When formulating a childcare plan, the childcare plan planning unit creates a database of past successes and failures, and the generation AI can refer to these to generate the optimal plan. For example, a plan is created based on data on successful and unsuccessful activities. The generation AI analyzes past successes and failures and generates the optimal childcare plan based on that. For example, it proposes a plan that incorporates elements of successful activities. The generation AI creates the optimal childcare plan based on the database of successes and failures. For example, it proposes a plan that avoids unsuccessful activities and incorporates many successful activities. In this way, past successes and failures can be created in a database, and the optimal plan can be generated.

[0051] The childcare plan creation unit incorporates data on children of different age groups and developmental stages when creating childcare plans, and can generate plans that cater to a wide range of ages. For example, it creates plans based on data from toddlers to elementary school children. The generation AI analyzes data on different age groups and developmental stages, and generates the optimal childcare plan based on that data. For example, it suggests activities according to age. Childcare plans that cater to a wide range of ages are created based on data on children of different age groups and developmental stages. For example, it suggests plans that combine activities according to developmental stages. This makes it possible to incorporate data on children of different age groups and developmental stages, and generate plans that cater to a wide range of ages.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The analysis unit analyzes the contents of the diary and drawings. For example, the diary records the child's behavior, comments, and activities that interest them, and the analysis unit analyzes this information to understand changes in the child's personality and interests. Step 2: The childcare plan creation unit creates a childcare plan based on the results of the analysis by the analysis unit. For example, if a child is interested in animals, the system will propose a childcare plan that incorporates many animal-related activities for that child. Step 3: The scheduling unit adjusts the schedule of the childcare worker based on the childcare plan created by the childcare plan creation unit. For example, it proposes an efficient schedule taking into consideration the number of children and types of activities that the childcare worker can handle in a day. Step 4: The Liaison Department communicates the childcare plan developed by the Childcare Plan Development Department to parents. For example, they report to parents on the activities at the nursery school and changes in their child's interests, and suggest ways to support them at home.

[0054] (Example 2) The childcare plan proposal system according to an embodiment of the present invention proposes childcare plans tailored to each child's personality and interests. This system uses a generation AI to analyze the contents of daily diaries and drawings, grasp changes in personality and interests, and create childcare plans based on this information. It then proposes action plans tailored to each child, with a schedule that is not too demanding for caregivers. It also supports children's development at home through collaboration with parents. This allows the childcare plan proposal system to propose childcare plans tailored to each child's personality and interests, supporting children's development while reducing the burden on caregivers.

[0055] A childcare plan proposal system according to an embodiment includes an analysis unit, a childcare plan creation unit, a scheduling unit, and a linking unit. The analysis unit analyzes the contents of the diary and drawings. For example, the diary records a child's behavior, comments, and activities of interest. The analysis unit analyzes this information to understand changes in the child's personality and interests. The childcare plan creation unit creates a childcare plan based on the results of the analysis by the analysis unit. For example, if a child is interested in animals, the scheduling unit proposes a childcare plan that incorporates many animal-related activities for that child. The scheduling unit adjusts the schedule of caregivers based on the childcare plan created by the childcare plan creation unit. For example, the scheduling unit proposes an efficient schedule taking into account the number of children and types of activities that the caregiver can handle per day. The linking unit shares the childcare plan created by the childcare plan creation unit with parents. For example, the linking unit reports to parents the activities at the nursery school and changes in the child's interests, and suggests support methods at home. As a result, the childcare plan proposal system according to an embodiment proposes a childcare plan tailored to the child's personality and interests, thereby supporting the child's growth while reducing the burden on caregivers.

[0056] The analysis unit can infer emotions from the contents of the diary and drawings, and analyze changes in personality and interests based on changes in emotions. The analysis unit, for example, uses generative AI to infer emotions from the contents of a child's diary and drawings. For example, it analyzes the text written in the diary, the colors used in the drawings, and the motifs drawn to infer emotions such as joy, sadness, and surprise. It analyzes changes in a child's personality and interests based on the emotion inference results. For example, if the contents written in the diary indicate positive emotions, it can determine that the child has a strong interest in that activity. It tracks changes in emotions over the long term and predicts the development of personality and interests. For example, it can regularly analyze the contents of the diary and drawings to understand patterns of emotional change and predict trends in a child's interests. This makes it possible to analyze changes in a child's personality and interests based on changes in their emotions.

[0057] When analyzing the contents of the diary or drawings, the analysis unit also uses audio and video data, allowing for a more multifaceted understanding of personality. For example, the analysis unit analyzes the child's audio data in addition to the contents of the diary or drawings. For example, it analyzes the tone of voice and speaking style when reading the diary to infer emotions and interests. It also uses video data to analyze the child's behavior and facial expressions. For example, it records a video of the child drawing or writing the diary, and the generation AI analyzes the movements and facial expressions. It integrates audio and video data with text data to understand personality from a more multifaceted perspective. For example, it combines information extracted from audio and video with the contents of the diary or drawings for analysis. In this way, by using audio and video data together, it is possible to understand personality from a more multifaceted perspective.

[0058] The analysis unit can track trends in a child's behavioral patterns and interests over the long term based on the data analyzed by the generative AI, and predict the development of their personality. For example, the analysis unit tracks trends in a child's behavioral patterns over the long term based on the data analyzed by the generative AI. For example, it regularly analyzes the contents of diaries and drawings to identify changes in behavioral patterns. It tracks trends in interests over the long term and predicts the development of their personality. For example, if interest in a particular topic continues to grow, it suggests activities related to that topic. It predicts the development of their personality based on trends in behavioral patterns and interests. For example, it predicts future changes in interests based on past data and creates a childcare plan accordingly. This makes it possible to track trends in behavioral patterns and interests over the long term and predict the development of their personality.

[0059] In addition to the contents of the diary and drawings, the analysis unit also analyzes records of crafts and play, allowing for a more diverse range of data to be used to understand a child's personality. For example, the analysis unit may include crafts made by a child in its analysis. For example, it may analyze the theme of the craft, the materials used, and the level of completion to understand a child's interests and creativity. It may also include play records in its analysis. For example, it may record the content, frequency, and method of play, and the generation AI may analyze these to understand a child's interests and behavioral patterns. By integrating the records of crafts and play with the contents of the diary and drawings, a more diverse range of data can be used to understand a child's personality. For example, it may analyze the records of crafts and play in combination with text data. In this way, by including the records of crafts and play in its analysis, a more diverse range of data can be used to understand a child's personality.

[0060] The analysis unit can share data between different childcare facilities and analyze differences in personality due to region and culture. For example, the analysis unit can share diary and drawing data between different childcare facilities and analyze differences in personality due to region and culture. For example, it can compare data from childcare facilities in urban and rural areas. Based on the shared data, it can analyze differences in personality due to region and culture. For example, it can compare the interests and behavioral patterns of children in different regions and find similarities and differences. Data can be shared between different childcare facilities, and the generation AI can analyze differences in personality due to region and culture. For example, it can analyze data from children with different cultural backgrounds and understand cultural influences. This makes it possible to share data between different childcare facilities and analyze differences in personality due to region and culture.

[0061] The analysis unit uses the emotion estimation function to analyze the emotions of the child when writing a diary or drawing a picture in real time, and is able to grasp changes in personality on the spot. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of the child when writing a diary or drawing a picture in real time. For example, it uses a camera or microphone to analyze the child's facial expressions and voice and infer emotions. Based on the emotion data analyzed in real time, changes in personality are grasped on the spot. For example, it analyzes changes in emotions while the child is writing a diary and grasps changes in interests. The emotion estimation function is used to analyze the emotions of the child when writing a diary or drawing a picture in real time, and grasp changes in personality based on that data. For example, it analyzes patterns of emotional change and predicts the development of personality. In this way, the emotion estimation function can be used to analyze the emotions of the child when writing a diary or drawing a picture in real time, and grasp changes in personality on the spot.

[0062] The childcare plan creation unit can use the generation AI to create a childcare plan that elicits a positive emotional response based on the child's emotion estimation results. The childcare plan creation unit, for example, uses the generation AI to create a childcare plan that elicits a positive emotional response based on the child's emotion estimation results. For example, by incorporating many activities that bring joy to the child. A childcare plan that elicits a positive response is created based on the emotion estimation results. For example, by proposing a plan to continuously carry out activities that the child enjoys. The generation AI analyzes the emotion estimation results and creates a childcare plan that elicits a positive response. For example, by incorporating many activities related to topics that the child is interested in. In this way, a childcare plan that elicits a positive emotional response can be created.

[0063] When formulating a childcare plan, the childcare plan formulation unit creates a database of past successes and failures, and the generation AI can refer to them to generate the optimal plan. The childcare plan formulation unit, for example, creates a database of past successes and failures, and the generation AI can refer to them to generate the optimal childcare plan. For example, a plan is formulated based on data on successful and unsuccessful activities. The generation AI analyzes past successes and failures and generates the optimal childcare plan based on that. For example, a plan that incorporates elements of successful activities is proposed. The generation AI formulates the optimal childcare plan based on the database of successes and failures. For example, a plan that avoids unsuccessful activities and incorporates many successful activities is proposed. In this way, past successes and failures can be compiled into a database and the optimal plan can be generated.

[0064] The childcare plan creation unit can incorporate data on children of different age groups and developmental stages when creating a childcare plan, and generate plans that cater to a wide range of ages. The childcare plan creation unit, for example, incorporates data on children of different age groups and developmental stages to create a childcare plan that caters to a wide range of ages. For example, it creates a plan based on data from toddlers to elementary school children. The generation AI analyzes data on different age groups and developmental stages, and generates an optimal childcare plan based on that. For example, it suggests activity content according to age. A childcare plan that caters to a wide range of ages is created based on data on children of different age groups and developmental stages. For example, it suggests a plan that combines activities according to the developmental stage. This makes it possible to incorporate data on children of different age groups and developmental stages to create a plan that caters to a wide range of ages.

[0065] The childcare plan formulation unit can link the childcare plan with home activities and local community events, thereby creating an environment in which the individuality of children can be developed throughout the home and the community. The childcare plan formulation unit, for example, links the childcare plan with home activities and local community events. For example, it can incorporate home homework and local events into the childcare plan. The childcare plan is linked in order to create an environment in which the individuality of children can be developed throughout the home and the community. For example, it can incorporate activities at local libraries and parks into the plan. The childcare plan is linked with home and local community activities to create an environment in which the individuality of children can be developed. For example, it can incorporate reading time at home and local sporting events into the plan. In this way, it is possible to create an environment in which the individuality of children can be developed throughout the home and the community.

[0066] The childcare plan creation unit can use the emotion estimation function to monitor the child's emotions while the childcare plan is being implemented and adjust the plan in real time. The childcare plan creation unit, for example, uses the emotion estimation function to monitor the child's emotions while the childcare plan is being implemented. For example, a camera or microphone is used to analyze the child's facial expressions and voice and estimate their emotions. The childcare plan is adjusted based on the emotion data monitored in real time. For example, activities that the child enjoys are extended and activities that the child is not interested in are shortened. The emotion estimation function can be used to monitor the child's emotions while the childcare plan is being implemented and adjust the plan based on the data. For example, the content of activities is changed in accordance with changes in emotions. This makes it possible to monitor the child's emotions while the childcare plan is being implemented and adjust the plan in real time.

[0067] The scheduling unit can use the generation AI to propose a schedule that allows childcare workers to work most efficiently based on the childcare worker's emotion estimation results. The scheduling unit, for example, uses the generation AI to propose a schedule that allows childcare workers to work most efficiently based on the childcare worker's emotion estimation results. For example, important tasks are allocated to times when childcare workers are least likely to feel stressed. A schedule that maximizes the childcare worker's efficiency is planned based on the emotion estimation results. For example, time is appropriately allocated for childcare workers to refresh themselves. The generation AI analyzes the childcare worker's emotion data and proposes a schedule that allows childcare workers to work most efficiently. For example, the order of tasks is adjusted according to the childcare worker's emotional state. This makes it possible to propose a schedule that allows childcare workers to work most efficiently.

[0068] The scheduling unit analyzes the childcare worker's past schedule data, and the generation AI can learn and propose the optimal schedule pattern. The scheduling unit, for example, analyzes the childcare worker's past schedule data, and the generation AI can learn and propose the optimal schedule pattern. For example, from past data, it identifies the time periods when the childcare worker can work most efficiently. The generation AI proposes the optimal schedule pattern based on the past schedule data. For example, it creates a schedule that will prevent the childcare worker from getting tired. By analyzing past schedule data, the generation AI can learn and propose the optimal schedule pattern. For example, it proposes a schedule that properly allocates break times for childcare workers. In this way, it is possible to analyze the childcare worker's past schedule data and propose the optimal schedule pattern.

[0069] The scheduling unit can incorporate break times and refreshing activities into the childcare worker's schedule and propose a schedule that takes long-term health management into consideration. For example, the scheduling unit incorporates break times into the childcare worker's schedule and proposes a schedule that takes long-term health management into consideration. For example, it sets regular break times. It incorporates refreshing activities into the schedule and takes care of the childcare worker's health management. For example, it incorporates refreshing activities into the schedule. The generation AI proposes a schedule that takes care of the childcare worker's health management into consideration. For example, it appropriately allocates time for the childcare worker to refresh. This makes it possible to incorporate break times and refreshing activities into the childcare worker's schedule and propose a schedule that takes long-term health management into consideration.

[0070] The scheduling unit shares schedule data between different childcare facilities, and the generation AI can generate an optimal schedule model. The scheduling unit, for example, shares schedule data between different childcare facilities, and the generation AI generates an optimal schedule model. For example, it proposes an optimal schedule based on data from multiple facilities. The generation AI generates an optimal schedule model based on the shared schedule data. For example, it analyzes data from different facilities and proposes a common optimal schedule. Schedule data is shared between different childcare facilities, and the generation AI generates an optimal schedule model. For example, it proposes a schedule that takes into account the characteristics of each facility. This makes it possible to share schedule data between different childcare facilities, and generate an optimal schedule model.

[0071] The scheduling unit can link the childcare worker's schedule with home and local events, allowing the childcare worker to participate in local activities as well. The scheduling unit, for example, links the childcare worker's schedule with home and local events. For example, it adjusts the schedule so that the childcare worker can participate in local events. It proposes a schedule that links with home and local events, allowing the childcare worker to participate in local activities as well. For example, it sets aside time for the childcare worker to participate in local volunteer activities. The generation AI proposes a schedule that links with home and local events. For example, it creates a schedule that aligns with local events and festivities. This makes it possible to adjust the schedule so that the childcare worker can participate in local activities as well.

[0072] The scheduling unit can use the emotion estimation function to monitor the emotional state of the caregiver in real time and adjust the schedule as necessary. The scheduling unit, for example, uses the emotion estimation function to monitor the emotional state of the caregiver in real time. For example, a camera or microphone is used to analyze the facial expressions and voice of the caregiver to estimate their emotions. The schedule is adjusted based on the emotional data monitored in real time. For example, if a caregiver is feeling stressed, the break time can be increased. The emotion estimation function can be used to monitor the emotional state of the caregiver in real time and adjust the schedule based on that data. For example, the order of tasks can be changed according to changes in emotions. In this way, the emotional state of the caregiver can be monitored in real time and the schedule can be adjusted as necessary.

[0073] The collaboration unit can use the generation AI to provide a child's growth report to parents in a format that is easiest for parents to understand, based on the parent's emotion estimation results. The collaboration unit, for example, uses the generation AI to provide a child's growth report to parents in a format that is easiest for parents to understand, based on the parent's emotion estimation results. For example, it creates a report in a format that evokes positive emotions in the parent. Based on the emotion estimation results, it provides a growth report in a format that is easy for parents to understand. For example, it uses language and expressions that the parent prefers. The generation AI analyzes the parent's emotion data and provides a growth report in a format that is easiest for parents to understand. For example, it emphasizes content that the parent is interested in. This allows a child's growth report to be provided to parents in a format that is easiest for parents to understand.

[0074] When communicating with parents, the communication unit analyzes past communication data, and the generation AI can propose the optimal method of communication. The communication unit, for example, analyzes past communication data, and the generation AI proposes the optimal method of communication. For example, it identifies the parent's preferred means of contact and timing. The generation AI proposes the optimal method of communication based on past communication data. For example, it provides information in a format that is easy for parents to understand. The communication data is analyzed, and the generation AI proposes the optimal method of communication. For example, it identifies the content and frequency that parents are likely to respond to. This makes it possible to analyze past communication data and propose the optimal method of communication.

[0075] The liaison department can collaborate with parents through an online platform, enabling them to support their children's development even from a distance. The liaison department can, for example, collaborate with parents through an online platform, enabling them to support their children's development even from a distance. For example, it can use video calls or chat functions. It can utilize an online platform to build a system that allows parents to support their children's development even from a distance. For example, it can share growth reports and activity records online. It can collaborate through an online platform so that parents can support their children's development even from a distance. For example, it can provide a dedicated portal site that parents can access. This makes it possible to support children's development even from a distance through the online platform.

[0076] The Liaison Department can share its collaboration with parents with other parents in the community, and support children's development throughout the community. The Liaison Department can, for example, share its collaboration with parents with other parents in the community, and support children's development throughout the community. For example, it can provide a platform where parents can exchange information. It can collaborate with other parents in the community and build a system to support children's development throughout the community. For example, it can share local events and activities. It can share its collaboration with parents with other parents in the community, and support children's development throughout the community. For example, it can create a system where parents can work together to support children's development. This will allow it to collaborate with other parents in the community and support children's development throughout the community.

[0077] The collaboration unit uses the emotion estimation function to monitor the emotional state of the guardian in real time and can perform collaboration at the optimal timing. The collaboration unit, for example, uses the emotion estimation function to monitor the emotional state of the guardian in real time. For example, it uses a camera or microphone to analyze the guardian's facial expressions and voice and estimate their emotions. Collaboration is performed at the optimal timing based on the emotional data monitored in real time. For example, contacting the guardian during a time when the guardian is relaxed. The emotion estimation function is used to monitor the emotional state of the guardian in real time and collaborate at the optimal timing based on that data. For example, the timing of contact is adjusted according to changes in emotions. This makes it possible to monitor the emotional state of the guardian in real time and collaborate at the optimal timing.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] In addition to the contents of the diary and drawings, the analysis unit can also include a child's sleep patterns and dietary records in its analysis. For example, a child's sleep duration and quality can be recorded, and the generation AI can analyze them to understand the child's health and stress level. By including dietary records in the analysis, it is possible to understand a child's nutritional status and food preferences. This allows for a more comprehensive understanding of a child's personality and health by analyzing sleep and dietary data.

[0080] The analysis unit can infer emotions from the contents of the diary and drawings, and analyze changes in personality and interests based on changes in emotions. For example, it can analyze the text written in the diary, the colors used in the drawings, and the motifs drawn to infer emotions such as joy, sadness, and surprise. It can analyze changes in a child's personality and interests based on the emotion inference results. For example, if the contents written in the diary indicate positive emotions, it can determine that the child has a strong interest in that activity. It can track changes in emotions over the long term and predict the development of personality and interests. For example, it can regularly analyze the contents of the diary and drawings to understand patterns of emotional change and predict trends in a child's interests. This makes it possible to analyze changes in a child's personality and interests based on changes in their emotions.

[0081] When analyzing the contents of the diary or drawings, the analysis unit also uses audio and video data, allowing for a more multifaceted understanding of personality. For example, in addition to the contents of the diary or drawings, the analysis unit analyzes the child's audio data. For example, the tone of voice and speaking style when reading the diary can be analyzed to infer emotions and interests. Video data can also be used to analyze the child's behavior and facial expressions. For example, a video of the child drawing or writing the diary can be recorded, and the generation AI can analyze the child's movements and facial expressions. Audio and video data can be integrated with text data to understand personality from a more multifaceted perspective. For example, information extracted from audio and video can be combined with the contents of the diary or drawings for analysis. In this way, by using audio and video data together, personality can be understood from a more multifaceted perspective.

[0082] The analysis unit can track a child's behavioral patterns and interest trends over the long term based on the data analyzed by the generative AI, and predict personality development. For example, it tracks a child's behavioral patterns over the long term based on the data analyzed by the generative AI. For example, it regularly analyzes the contents of diaries and drawings to identify changes in behavioral patterns. It tracks interest trends over the long term and predicts personality development. For example, if interest in a particular topic continues to grow, it suggests activities related to that topic. It predicts personality development based on behavioral patterns and interest trends. For example, it predicts future changes in interests based on past data and creates a childcare plan accordingly. This makes it possible to track behavioral patterns and interest trends over the long term and predict personality development.

[0083] In addition to the contents of the diary and drawings, the analysis unit also analyzes records of crafts and play, allowing for a more diverse range of data to be used to understand a child's personality. For example, crafts made by a child are included in the analysis. For example, the theme of the craft, the materials used, and the level of completion are analyzed to understand a child's interests and creativity. Play records are also included in the analysis. For example, the content, frequency, and method of play are recorded, and the generation AI analyzes these to understand a child's interests and behavioral patterns. Crafts and play records are integrated with the contents of the diary and drawings to understand a child's personality from a more diverse range of data. For example, crafts and play records are combined with text data for analysis. In this way, by including crafts and play records in the analysis, a more diverse range of data can be used to understand a child's personality.

[0084] The analysis unit can share data between different childcare facilities and analyze differences in personality due to region and culture. For example, diary and drawing data can be shared between different childcare facilities to analyze differences in personality due to region and culture. For example, data from childcare facilities in urban and rural areas can be compared. Based on the shared data, differences in personality due to region and culture can be analyzed. For example, the interests and behavioral patterns of children in different regions can be compared to find similarities and differences. Data can be shared between different childcare facilities, and the generation AI can analyze differences in personality due to region and culture. For example, data from children with different cultural backgrounds can be analyzed to understand cultural influences. This makes it possible to share data between different childcare facilities and analyze differences in personality due to region and culture.

[0085] The analysis unit uses the emotion estimation function to analyze the emotions a child feels when writing a diary or drawing a picture in real time, making it possible to grasp changes in their personality on the spot. For example, a camera or microphone is used to analyze the child's facial expressions and voice and estimate their emotions. Based on the emotion data analyzed in real time, changes in their personality are grasped on the spot. For example, changes in emotions while writing a diary are analyzed to grasp changes in interests. The emotion estimation function is used to analyze the emotions a child feels when writing a diary or drawing a picture in real time, making it possible to grasp changes in their personality on the spot. For example, patterns of emotional change are analyzed to predict the development of their personality. In this way, the emotion estimation function can be used to analyze the emotions a child feels when writing a diary or drawing a picture in real time, making it possible to grasp changes in their personality on the spot.

[0086] The childcare plan planning unit can use the generative AI to create a childcare plan that elicits a positive emotional response based on the child's emotion estimation results. For example, by incorporating many activities that bring joy to the child. A childcare plan that elicits a positive response is created based on the emotion estimation results. For example, a plan may be proposed to continue activities that the child enjoys. The generative AI analyzes the emotion estimation results and creates a childcare plan that elicits a positive response. For example, by incorporating many activities related to topics that the child is interested in. This makes it possible to create a childcare plan that elicits a positive emotional response.

[0087] When formulating a childcare plan, the childcare plan planning unit creates a database of past successes and failures, and the generation AI can refer to these to generate the optimal plan. For example, a plan is created based on data on successful and unsuccessful activities. The generation AI analyzes past successes and failures and generates the optimal childcare plan based on that. For example, it proposes a plan that incorporates elements of successful activities. The generation AI creates the optimal childcare plan based on the database of successes and failures. For example, it proposes a plan that avoids unsuccessful activities and incorporates many successful activities. In this way, past successes and failures can be created in a database, and the optimal plan can be generated.

[0088] The childcare plan creation unit incorporates data on children of different age groups and developmental stages when creating childcare plans, and can generate plans that cater to a wide range of ages. For example, it creates plans based on data from toddlers to elementary school children. The generation AI analyzes data on different age groups and developmental stages, and generates the optimal childcare plan based on that data. For example, it suggests activities according to age. Childcare plans that cater to a wide range of ages are created based on data on children of different age groups and developmental stages. For example, it suggests plans that combine activities according to developmental stages. This makes it possible to incorporate data on children of different age groups and developmental stages, and generate plans that cater to a wide range of ages.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The analysis unit analyzes the contents of the diary and drawings. For example, the diary records the child's behavior, comments, and activities that interest them, and the analysis unit analyzes this information to understand changes in the child's personality and interests. Step 2: The childcare plan creation unit creates a childcare plan based on the results of the analysis by the analysis unit. For example, if a child is interested in animals, the system will propose a childcare plan that incorporates many animal-related activities for that child. Step 3: The scheduling unit adjusts the schedule of the childcare worker based on the childcare plan created by the childcare plan creation unit. For example, it proposes an efficient schedule taking into consideration the number of children and types of activities that the childcare worker can handle in a day. Step 4: The Liaison Department communicates the childcare plan developed by the Childcare Plan Development Department to parents. For example, they report to parents on the activities at the nursery school and changes in their child's interests, and suggest ways to support them at home.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis section that analyzes the contents of the journals and drawings, a childcare plan drafting unit that drafts a childcare plan based on the results of the analysis by the analysis unit; a scheduling unit that adjusts the schedule of a childcare worker based on the childcare plan formulated by the childcare plan formulation unit; a linking unit that links the childcare plan drawn up by the childcare plan drawing up unit to parents; A system characterized by:

2. The analysis unit Emotions are estimated from the contents of the diary and drawings, and changes in personality and interests are analyzed based on changes in emotions.

2. The system of claim 1.

3. The analysis unit When analyzing the contents of the diary and drawings, audio and video data will also be used to understand the individual's personality from a more multifaceted perspective.

2. The system of claim 1.

4. The analysis unit Based on the data analyzed by generative AI, it tracks trends in behavioral patterns and interests over time and predicts the development of personality.

2. The system of claim 1.

5. The analysis unit In addition to the contents of the diary and drawings, records of crafts and play activities will also be included in the analysis to understand individuality from a wider variety of data.

2. The system of claim 1.

Images