System

Abstract

An object of a system according to an embodiment is to propose appropriate play and stimulation according to the age of a child.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and a notification unit. The input unit receives an input of a diary. The analysis unit analyzes the diary received by the input unit. The suggestion unit suggests play and stimulation based on the result analyzed by the analysis unit. The notification unit notifies the content proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks the information needed to provide appropriate play and stimulation for children according to their age, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest appropriate games and stimuli according to the child's age. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a suggestion unit, and a notification unit. The input unit accepts diary input. The analysis unit analyzes the diary accepted by the input unit. The suggestion unit suggests activities and stimuli based on the results of the analysis by the analysis unit. The notification unit notifies the user of the content suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate games and stimuli according to the child's age. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention recommends games and stimuli tailored to a child's age based on a mother's diary-like history. This system allows a mother to record her child's growth and daily events in a diary-like format, and analyzes the records to suggest appropriate games and stimuli tailored to the child's age. For example, a mother records her child's growth and daily events in a diary-like format. She records details of the child's age, weight, height, diet, and play activities. For example, she might record content such as "Today, my child crawled for the first time" or "My child played with a new toy." This information is input into a generation AI, which then analyzes the input information. The generation AI then understands the contents of the mother's diary and suggests games and stimuli tailored to the child's age. For example, the system might suggest, "Since your child is six months old, we recommend hand-based play," or "Since your child is playing with a new toy, try a toy that makes noise to further stimulate their interest." The mother is then notified of these suggestions. The mother can then use the system's suggestions to provide appropriate games and stimuli for her child. For example, based on suggestions from the system, a mother can purchase new toys or incorporate specific play activities. This allows the system to provide appropriate play and stimulation for the mother in line with her child's development, promoting the child's development and reducing the burden of childcare on the mother. In addition, the contents of the mother's diary can be accumulated and used as a record of the child's growth.

[0029] A childcare support system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a notification unit. The input unit allows a mother to record her child's growth and daily events, similar to a diary. The information recorded by the mother includes, but is not limited to, the child's age in months, weight, height, diet, and play. The input unit includes, for example, a text input unit and a voice input unit, allowing the mother to input the diary entry in text or voice. The text input unit supports, for example, keyboard input and handwriting input. The voice input unit converts the mother's voice into text using, for example, voice recognition technology. The analysis unit uses a generation AI to analyze the diary entry received by the input unit. The analysis unit understands the contents of the mother's diary entry using, for example, natural language processing technology, and suggests appropriate activities and stimuli according to the child's age. The generation AI analyzes the diary entry using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests appropriate activities and stimuli according to the child's age based on the results of the analysis by the analysis unit. The suggestion unit, for example, uses a generation AI to suggest games and stimuli according to the child's age. The notification unit notifies the mother of the content suggested by the suggestion unit. The notification unit, for example, includes an in-app notification unit and an email notification unit, and the mother can receive the suggestions via in-app or email notification. The in-app notification unit, for example, provides in-app pop-ups or banner notifications. The email notification unit, for example, supports customization of the timing and content of email sending. This allows the childcare support system according to the embodiment to provide mothers with appropriate games and stimuli tailored to their children's development. For example, the system can automate the entire process from diary entry to analysis, suggestions, and notifications, thereby reducing the burden of childcare on mothers.

[0030] The input unit includes a text input unit and a voice input unit. The text input unit supports, for example, keyboard input and handwriting input. For example, the mother can input her diary entries using a keyboard. The text input unit also supports handwriting input, and the mother can also input her diary entries by hand. The voice input unit converts the mother's voice into text using, for example, voice recognition technology. For example, the mother can input her diary entries by voice using a microphone, and the voice recognition technology converts the voice into text. The voice input unit can also convert voice into text in real time. For example, text is displayed on the screen as the mother speaks. This allows the mother to input her diary entries by text or voice.

[0031] The notification unit includes an in-app notification unit and an email notification unit. The in-app notification unit provides, for example, in-app pop-ups or banner notifications. For example, when the mother opens the app, the suggestion content is displayed as a pop-up. The in-app notification unit can also provide banner notifications, and the suggestion content can be displayed as a banner while the mother is using the app. The email notification unit supports, for example, customization of the timing and content of email transmission. For example, the mother can receive the suggestion content by email. The email notification unit can also customize the content of the email according to the mother's preferences. This allows the mother to receive the suggestion by in-app notification or email notification.

[0032] The analysis unit can analyze the contents of the mother's diary and suggest games and stimuli according to the child's age. The analysis unit can analyze the contents of the mother's diary, for example, using natural language processing technology. For example, the analysis unit can analyze text data recorded by the mother and suggest appropriate games and stimuli according to the child's age. The analysis unit can also analyze the contents of the mother's diary using emotion analysis technology. For example, the analysis unit can analyze emotions from the text data recorded by the mother and make suggestions based on the results. This makes it possible to analyze the contents of the mother's diary and make appropriate suggestions according to the child's age.

[0033] The suggestion unit can suggest games and stimuli according to the child's age in months. For example, the suggestion unit uses a generation AI to suggest games and stimuli according to the child's age in months. For example, the suggestion unit uses the generation AI to suggest appropriate games and stimuli based on the child's age in months. The suggestion unit can also make suggestions based on the results of the generation AI analyzing the contents of the mother's diary. For example, the suggestion unit uses the generation AI to analyze the contents of the mother's diary and suggest games and stimuli according to the child's age in months. This allows the suggestion unit to suggest appropriate games and stimuli according to the child's age in months.

[0034] The input unit can analyze the mother's past diary entry history and select an entry method. The input unit, for example, analyzes the mother's past diary entry history. For example, the input unit preferentially suggests an entry method (voice, text, etc.) that the mother has frequently used in the past. The input unit can also analyze the time period in which the mother entered data in the past and send a notification prompting the mother to enter data during that time period. Furthermore, the input unit can analyze the trends in the content that the mother has entered in the past and automatically suggest related entry items. In this way, the optimal entry method can be suggested by analyzing the mother's past entry history.

[0035] The input unit can perform filtering based on the mother's current living situation and areas of interest when inputting the diary. The input unit performs filtering based on the mother's current living situation and areas of interest, for example. For example, the input unit preferentially displays input items related to the mother's current living situation. The input unit can also customize the input items based on the mother's areas of interest. Furthermore, the input unit can automatically complete the input content based on the mother's living situation and areas of interest. This allows the input items to be customized based on the mother's living situation and areas of interest.

[0036] When inputting a diary entry, the input unit can select an input means according to the mother's input method. The input unit selects the optimum input means according to the mother's input method (voice, text, image, etc.), for example. For example, if the mother desires voice input, voice input can be provided preferentially. Also, if the mother desires text input, text input can be provided preferentially. Furthermore, if the mother desires image input, image input can be provided preferentially. In this way, the optimum input means can be provided according to the mother's desired input method.

[0037] When inputting a diary entry, the input unit can prioritize inputting relevant content in consideration of the mother's geographical location information. The input unit, for example, prioritizes inputting highly relevant content in consideration of the mother's geographical location information. For example, if the mother is in a specific location, the input unit can prioritize inputting content related to that location. Also, if the mother is traveling, the input unit can prioritize inputting content related to the travel destination. Furthermore, if the mother is at home, the input unit can prioritize inputting content related to home. In this way, highly relevant content can be prioritized input based on the mother's geographical location information.

[0038] The input unit can analyze the mother's social media activity and input related content when inputting the diary entry. The input unit, for example, analyzes the mother's social media activity and inputs related content. For example, the input unit can automatically reflect content posted by the mother on social media in the diary entry. The input unit can also analyze the mother's social media activity and suggest related content. Furthermore, the input unit can also suggest related content by referring to the activity of the mother's friends on social media. This allows related content to be input based on the mother's social media activity.

[0039] The input unit can adjust the input method by reflecting the mother's past feedback when inputting the diary. The input unit adjusts the input method by reflecting the mother's past feedback, for example. For example, the input method is customized based on feedback provided by the mother in the past. The input unit can also analyze the mother's past feedback and suggest an optimal input method. Furthermore, the input unit can improve the input interface by reflecting the mother's past feedback. This allows the input method to be customized based on the mother's past feedback.

[0040] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the diary. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the diary. For example, the analysis unit performs a detailed analysis of important diary content. It can also perform a concise analysis of general diary content. It can also perform a special analysis of diary content related to a specific event. In this way, by adjusting the level of detail of the analysis based on the importance of the diary, it is possible to perform a detailed analysis of important content.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the diary category. For example, the analysis unit applies different analysis algorithms depending on the diary category. For example, the analysis unit applies a health-related analysis algorithm to diary content related to health. Also, the analysis unit can apply an education-related analysis algorithm to diary content related to education. Furthermore, the analysis unit can apply a play-related analysis algorithm to diary content related to play. In this way, by applying an appropriate analysis algorithm depending on the diary category, more accurate analysis can be performed.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the mother's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the mother's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the mother's past analysis results. The analysis unit can also analyze the mother's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the analysis parameters by referring to the mother's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the mother's past analysis results.

[0043] During analysis, the analysis unit can determine the order of analysis based on the submission time of the diary. The analysis unit determines the priority of analysis based on, for example, the submission time of the diary. For example, the analysis unit prioritizes analysis of diary content that has been submitted most recently. The analysis unit can also prioritize analysis of diary content related to a specific event. Furthermore, the analysis unit can also prioritize analysis of diary content that is of particular interest to the mother. In this way, by determining the priority of analysis based on the submission time of the diary, the most recent content can be analyzed preferentially.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the diaries. The analysis unit adjusts the order of analysis based on, for example, the relevance of the diaries. For example, the analysis unit prioritizes analysis of diary content with high relevance. The analysis unit can also postpone analysis of diary content with low relevance. Furthermore, the analysis unit can also analyze diary content related to a specific theme all at once. In this way, by adjusting the order of analysis based on the relevance of the diaries, highly relevant content can be analyzed with priority.

[0045] During analysis, the analysis unit can adjust the use of analytical terminology according to the mother's level of expertise. The analysis unit adjusts the use of technical terminology according to the mother's level of expertise, for example. For example, if the mother has technical knowledge, the analysis result can be provided using technical terminology. Also, if the mother does not have technical knowledge, the analysis result can be provided in simple language. Furthermore, the way in which the analysis result is expressed can be adjusted according to the mother's level of expertise. In this way, by adjusting the way in which the analysis result is expressed according to the mother's level of expertise, it is possible to provide analysis results that are easier to understand.

[0046] When making a suggestion, the suggestion unit can adjust the accuracy of the suggestion based on the importance of the activity or stimulus. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the activity or stimulus. For example, the suggestion unit makes detailed suggestions for important activities or stimuli. It can also make brief suggestions for general activities or stimuli. It can also make special suggestions for activities or stimuli related to a specific event. In this way, by adjusting the level of detail of the suggestion based on the importance of the activity or stimulus, it is possible to make detailed suggestions for important content.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the game or stimulus. The suggestion unit applies different suggestion algorithms depending on, for example, the category of the game or stimulus. For example, the suggestion unit applies a suggestion algorithm for games that use hands to a game that uses hands. Furthermore, the suggestion unit can apply a music-related suggestion algorithm to a music-related stimulus. Furthermore, the suggestion unit can apply an exercise-related suggestion algorithm to a game that involves exercise. In this way, by applying an appropriate suggestion algorithm depending on the category of the game or stimulus, more accurate suggestions can be made.

[0048] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the mother's past proposal results. The proposal unit improves the accuracy of the proposal by referring to, for example, the mother's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the mother's past proposal results. The proposal unit can also analyze the mother's past proposal results and improve the accuracy of the proposal. Furthermore, the proposal unit can optimize the proposal parameters by referring to the mother's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the mother's past proposal results.

[0049] When making suggestions, the suggestion unit can determine the order of suggestions based on the time when the games and stimuli were submitted. The suggestion unit determines the priority of suggestions based on, for example, the time when the games and stimuli were submitted. For example, the suggestion unit can preferentially suggest games and stimuli that have been submitted recently. The suggestion unit can also preferentially suggest games and stimuli related to a specific event. Furthermore, the suggestion unit can also preferentially suggest games and stimuli that the mother is particularly interested in. In this way, by determining the priority of suggestions based on the time when the games and stimuli were submitted, the latest content can be preferentially suggested.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the activities and stimuli when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the activities and stimuli. For example, the suggestion unit prioritizes suggesting highly relevant activities and stimuli. The suggestion unit can also postpone less relevant activities and stimuli. Furthermore, the suggestion unit can collectively suggest activities and stimuli related to a specific theme. In this way, by adjusting the order of suggestions based on the relevance of the activities and stimuli, highly relevant content can be prioritized.

[0051] The suggestion unit can adjust the use of terminology in the suggestion depending on the mother's level of expertise when making a suggestion. The suggestion unit adjusts the use of technical terminology in the suggestion depending on the mother's level of expertise, for example. For example, if the mother has technical knowledge, the suggestion unit can provide the suggestion using technical terminology. Also, if the mother does not have technical knowledge, the suggestion unit can provide the suggestion in simple language. Furthermore, the way the suggestion is expressed can be adjusted depending on the mother's level of expertise. In this way, by adjusting the way the suggestion is expressed depending on the mother's level of expertise, it is possible to provide a suggestion that is easier to understand.

[0052] The notification unit can select the notification method by referring to the mother's past notification history when making a notification. The notification unit, for example, selects the optimal notification method by referring to the mother's past notification history. For example, the notification unit can preferentially provide the notification method that the mother has preferred in the past. The notification unit can also analyze the mother's past notification history and suggest the optimal notification method. Furthermore, the notification unit can also adjust the timing of notification by referring to the mother's past notification history. In this way, the optimal notification method can be provided by referring to the mother's past notification history.

[0053] The notification unit can customize the notification content according to the mother's current task when notifying. The notification unit customizes the notification content according to, for example, the mother's current task. For example, the notification unit can provide a brief notification when the mother is concentrating on her current task. Also, the notification unit can provide a detailed notification when the mother is relaxed. Furthermore, the notification unit can provide only important notifications when the mother is busy. In this way, by customizing the notification content according to the mother's current task, more appropriate notifications can be provided.

[0054] The notification unit can select the optimal notification method in consideration of the mother's device information when providing notification. The notification unit selects the optimal notification method in consideration of the mother's device information, for example. For example, if the mother is using a smartphone, a notification method suited to the screen size can be provided. Also, if the mother is using a tablet, a notification method optimized for a large screen can be provided. Furthermore, if the mother is using a smartwatch, a simple and highly visible notification method can be provided. In this way, the optimal notification method can be provided based on the mother's device information.

[0055] The notification unit can provide multilingual notification content according to the mother's language setting when providing a notification. The notification unit automatically sets the notification language based on the language setting of the mother's device, for example. For example, if the mother sets the device's language setting to English, the notification content is also provided in English. In addition, if the mother uses multiple languages, the notification unit can also provide a language switching function. Furthermore, if the mother selects a specific language, the notification can be provided in that language. This makes it possible to provide multilingual notifications based on the mother's language setting.

[0056] The notification unit may provide relevant information by analyzing the mother's social media activity at the time of notification. For example, the notification unit may provide relevant information by analyzing the mother's social media activity. For example, the notification unit may provide information about places where the mother has checked in on social media. The notification unit may also analyze the content of the mother's social media posts and provide relevant information. Furthermore, the notification unit may provide relevant information by referring to the activities of the mother's friends on social media. This allows the provision of relevant information based on the mother's social media activity.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The input unit can analyze the mother's past diary entry history and select the entry method. For example, it can preferentially suggest the entry method (voice, text, etc.) that the mother has frequently used in the past. The input unit can also analyze the time period in which the mother entered entries in the past and send a notification prompting her to enter entries during that time period. Furthermore, the input unit can analyze the trends in the content that the mother has entered in the past and automatically suggest related entry items. In this way, the optimal entry method can be suggested by analyzing the mother's past entry history.

[0059] The input unit can filter entries based on the mother's current living situation and areas of interest when entering diary entries. For example, the input unit preferentially displays relevant entry items depending on the mother's current living situation. The input unit can also customize the entry items based on the mother's areas of interest. Furthermore, the input unit can automatically complete the entry based on the mother's living situation and areas of interest. This allows the entry items to be customized based on the mother's living situation and areas of interest.

[0060] When entering diary entries, the input unit can select an input means according to the mother's input method. For example, if the mother desires voice input, the input unit can provide voice input with priority. Also, if the mother desires text input, the input unit can provide text input with priority. Furthermore, if the mother desires image input, the input unit can provide image input with priority. This makes it possible to provide the optimal input means according to the mother's desired input method.

[0061] When inputting the diary, the input unit can prioritize inputting related content in consideration of the mother's geographical location information. For example, if the mother is in a specific location, content related to that location can be prioritized. Also, if the mother is traveling, content related to the travel destination can be prioritized. Furthermore, if the mother is at home, content related to the home can be prioritized. In this way, content that is highly relevant based on the mother's geographical location information can be prioritized and input.

[0062] The input unit can analyze the mother's social media activity and input relevant content when inputting diary entries. For example, the content posted by the mother on social media is automatically reflected in the diary. The input unit can also analyze the mother's social media activity and suggest relevant content. Furthermore, the input unit can also suggest relevant content by taking into account the activity of the mother's friends on social media. In this way, relevant content can be input based on the mother's social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The input unit allows the mother to record her child's growth and daily events, like a diary. The information recorded by the mother includes, for example, the child's age in months, weight, height, diet, and play activities. The input unit is equipped with a text input unit and a voice input unit, allowing the mother to enter her diary entries using text or voice. The text input unit supports keyboard input and handwriting input, while the voice input unit converts the mother's voice into text using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the diary received by the input unit. The analysis unit uses natural language processing technology to understand the contents of the mother's diary and suggest appropriate activities and stimuli according to the child's age. The generation AI analyzes the diary contents using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The suggestion unit uses the generation AI to suggest appropriate play and stimuli according to the child's age based on the results of the analysis by the analysis unit. Step 4: The notification unit notifies the mother of the content suggested by the suggestion unit. The notification unit includes an in-app notification unit and an email notification unit, allowing the mother to receive the suggestions via in-app or email notification. The in-app notification unit provides in-app pop-up and banner notifications, while the email notification unit supports customization of the timing and content of email transmissions.

[0065] (Example 2) A system according to an embodiment of the present invention recommends games and stimuli tailored to a child's age based on a mother's diary-like history. This system allows a mother to record her child's growth and daily events in a diary-like format, and analyzes the records to suggest appropriate games and stimuli tailored to the child's age. For example, a mother records her child's growth and daily events in a diary-like format. She records details of the child's age, weight, height, diet, and play activities. For example, she might record content such as "Today, my child crawled for the first time" or "My child played with a new toy." This information is input into a generation AI, which then analyzes the input information. The generation AI then understands the contents of the mother's diary and suggests games and stimuli tailored to the child's age. For example, the system might suggest, "Since your child is six months old, we recommend hand-based play," or "Since your child is playing with a new toy, try a toy that makes noise to further stimulate their interest." The mother is then notified of these suggestions. The mother can then use the system's suggestions to provide appropriate games and stimuli for her child. For example, based on suggestions from the system, a mother can purchase new toys or incorporate specific play activities. This allows the system to provide appropriate play and stimulation for the mother in line with her child's development, promoting the child's development and reducing the burden of childcare on the mother. In addition, the contents of the mother's diary can be accumulated and used as a record of the child's growth.

[0066] A childcare support system according to an embodiment includes an input unit, an analysis unit, a suggestion unit, and a notification unit. The input unit allows a mother to record her child's growth and daily events, similar to a diary. The information recorded by the mother includes, but is not limited to, the child's age in months, weight, height, diet, and play. The input unit includes, for example, a text input unit and a voice input unit, allowing the mother to input the diary entry in text or voice. The text input unit supports, for example, keyboard input and handwriting input. The voice input unit converts the mother's voice into text using, for example, voice recognition technology. The analysis unit uses a generation AI to analyze the diary entry received by the input unit. The analysis unit understands the contents of the mother's diary entry using, for example, natural language processing technology, and suggests appropriate activities and stimuli according to the child's age. The generation AI analyzes the diary entry using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests appropriate activities and stimuli according to the child's age based on the results of the analysis by the analysis unit. The suggestion unit, for example, uses a generation AI to suggest games and stimuli according to the child's age. The notification unit notifies the mother of the content suggested by the suggestion unit. The notification unit, for example, includes an in-app notification unit and an email notification unit, and the mother can receive the suggestions via in-app or email notification. The in-app notification unit, for example, provides in-app pop-ups or banner notifications. The email notification unit, for example, supports customization of the timing and content of email sending. This allows the childcare support system according to the embodiment to provide mothers with appropriate games and stimuli tailored to their children's development. For example, the system can automate the entire process from diary entry to analysis, suggestions, and notifications, thereby reducing the burden of childcare on mothers.

[0067] The input unit includes a text input unit and a voice input unit. The text input unit supports, for example, keyboard input and handwriting input. For example, the mother can input her diary entries using a keyboard. The text input unit also supports handwriting input, and the mother can also input her diary entries by hand. The voice input unit converts the mother's voice into text using, for example, voice recognition technology. For example, the mother can input her diary entries by voice using a microphone, and the voice recognition technology converts the voice into text. The voice input unit can also convert voice into text in real time. For example, text is displayed on the screen as the mother speaks. This allows the mother to input her diary entries by text or voice.

[0068] The notification unit includes an in-app notification unit and an email notification unit. The in-app notification unit provides, for example, in-app pop-ups or banner notifications. For example, when the mother opens the app, the suggestion content is displayed as a pop-up. The in-app notification unit can also provide banner notifications, and the suggestion content can be displayed as a banner while the mother is using the app. The email notification unit supports, for example, customization of the timing and content of email transmission. For example, the mother can receive the suggestion content by email. The email notification unit can also customize the content of the email according to the mother's preferences. This allows the mother to receive the suggestion by in-app notification or email notification.

[0069] The analysis unit can analyze the contents of the mother's diary and suggest games and stimuli according to the child's age. The analysis unit can analyze the contents of the mother's diary, for example, using natural language processing technology. For example, the analysis unit can analyze text data recorded by the mother and suggest appropriate games and stimuli according to the child's age. The analysis unit can also analyze the contents of the mother's diary using emotion analysis technology. For example, the analysis unit can analyze emotions from the text data recorded by the mother and make suggestions based on the results. This makes it possible to analyze the contents of the mother's diary and make appropriate suggestions according to the child's age.

[0070] The suggestion unit can suggest games and stimuli according to the child's age in months. For example, the suggestion unit uses a generation AI to suggest games and stimuli according to the child's age in months. For example, the suggestion unit uses the generation AI to suggest appropriate games and stimuli based on the child's age in months. The suggestion unit can also make suggestions based on the results of the generation AI analyzing the contents of the mother's diary. For example, the suggestion unit uses the generation AI to analyze the contents of the mother's diary and suggest games and stimuli according to the child's age in months. This allows the suggestion unit to suggest appropriate games and stimuli according to the child's age in months.

[0071] The input unit can estimate the mother's emotions and adjust the timing of diary entry based on the estimated emotions. The input unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the input unit analyzes the mother's facial expressions and voice when entering her diary entry to estimate her emotions. The input unit can also adjust the timing of diary entry based on the mother's emotions. For example, if the mother is feeling stressed, the input unit can prompt her to enter her diary entry at a time when she is relaxed. Also, if the mother is relaxed, the input unit can send a notification prompting her to enter her diary entry. Furthermore, if the mother is busy, a simplified input form can be provided so that entry can be completed in a short amount of time. This allows the diary entry to be more appropriately timed by adjusting the timing of diary entry according to the mother's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The input unit can analyze the mother's past diary entry history and select an entry method. The input unit, for example, analyzes the mother's past diary entry history. For example, the input unit preferentially suggests an entry method (voice, text, etc.) that the mother has frequently used in the past. The input unit can also analyze the time period in which the mother entered data in the past and send a notification prompting the mother to enter data during that time period. Furthermore, the input unit can analyze the trends in the content that the mother has entered in the past and automatically suggest related entry items. In this way, the optimal entry method can be suggested by analyzing the mother's past entry history.

[0073] The input unit can perform filtering based on the mother's current living situation and areas of interest when inputting the diary. The input unit performs filtering based on the mother's current living situation and areas of interest, for example. For example, the input unit preferentially displays input items related to the mother's current living situation. The input unit can also customize the input items based on the mother's areas of interest. Furthermore, the input unit can automatically complete the input content based on the mother's living situation and areas of interest. This allows the input items to be customized based on the mother's living situation and areas of interest.

[0074] When inputting a diary entry, the input unit can select an input means according to the mother's input method. The input unit selects the optimum input means according to the mother's input method (voice, text, image, etc.), for example. For example, if the mother desires voice input, voice input can be provided preferentially. Also, if the mother desires text input, text input can be provided preferentially. Furthermore, if the mother desires image input, image input can be provided preferentially. In this way, the optimum input means can be provided according to the mother's desired input method.

[0075] The input unit can estimate the mother's emotions and determine the priority of diary entries to be entered based on the estimated emotions of the mother. The input unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the input unit analyzes the mother's facial expressions and voice when entering her diary entries to estimate her emotions. The input unit can also determine the priority of diary entries to be entered based on the mother's emotions. For example, if the mother is stressed, the input of relaxing content can be prioritized. Also, if the mother is relaxed, the input of detailed content can be prioritized. Furthermore, if the mother is busy, the input of important content can be prioritized. In this way, by determining the priority of diary entries according to the mother's emotions, important content can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When inputting a diary entry, the input unit can prioritize inputting relevant content in consideration of the mother's geographical location information. The input unit, for example, prioritizes inputting highly relevant content in consideration of the mother's geographical location information. For example, if the mother is in a specific location, the input unit can prioritize inputting content related to that location. Also, if the mother is traveling, the input unit can prioritize inputting content related to the travel destination. Furthermore, if the mother is at home, the input unit can prioritize inputting content related to home. In this way, highly relevant content can be prioritized input based on the mother's geographical location information.

[0077] The input unit can analyze the mother's social media activity and input related content when inputting the diary entry. The input unit, for example, analyzes the mother's social media activity and inputs related content. For example, the input unit can automatically reflect content posted by the mother on social media in the diary entry. The input unit can also analyze the mother's social media activity and suggest related content. Furthermore, the input unit can also suggest related content by referring to the activity of the mother's friends on social media. This allows related content to be input based on the mother's social media activity.

[0078] The input unit can adjust the input method by reflecting the mother's past feedback when inputting the diary. The input unit adjusts the input method by reflecting the mother's past feedback, for example. For example, the input method is customized based on feedback provided by the mother in the past. The input unit can also analyze the mother's past feedback and suggest an optimal input method. Furthermore, the input unit can improve the input interface by reflecting the mother's past feedback. This allows the input method to be customized based on the mother's past feedback.

[0079] The analysis unit can estimate the mother's emotions and adjust the way the analysis is presented based on the estimated emotions of the mother. The analysis unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the analysis unit analyzes the mother's facial expressions and voice when she is entering her diary and estimates her emotions. The analysis unit can also adjust the way the analysis is presented based on the mother's emotions. For example, if the mother is relaxed, a detailed analysis result can be provided. If the mother is stressed, a concise analysis result can be provided. Furthermore, if the mother is excited, a visually appealing analysis result can be provided. In this way, by adjusting the way the analysis is presented according to the mother's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the diary. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the diary. For example, the analysis unit performs a detailed analysis of important diary content. It can also perform a concise analysis of general diary content. It can also perform a special analysis of diary content related to a specific event. In this way, by adjusting the level of detail of the analysis based on the importance of the diary, it is possible to perform a detailed analysis of important content.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the diary category. For example, the analysis unit applies different analysis algorithms depending on the diary category. For example, the analysis unit applies a health-related analysis algorithm to diary content related to health. Also, the analysis unit can apply an education-related analysis algorithm to diary content related to education. Furthermore, the analysis unit can apply a play-related analysis algorithm to diary content related to play. In this way, by applying an appropriate analysis algorithm depending on the diary category, more accurate analysis can be performed.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the mother's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the mother's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the mother's past analysis results. The analysis unit can also analyze the mother's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimize the analysis parameters by referring to the mother's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the mother's past analysis results.

[0083] The analysis unit can estimate the mother's emotions and adjust the length of the analysis based on the estimated emotions of the mother. The analysis unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the analysis unit analyzes the mother's facial expressions and voice when she is entering her diary and estimates her emotions. The analysis unit can also adjust the length of the analysis based on the mother's emotions. For example, if the mother is relaxed, a detailed analysis result can be provided. If the mother is stressed, a concise analysis result can be provided. Furthermore, if the mother is excited, a visually appealing analysis result can be provided. Thus, by adjusting the length of the analysis according to the mother's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] During analysis, the analysis unit can determine the order of analysis based on the submission time of the diary. The analysis unit determines the priority of analysis based on, for example, the submission time of the diary. For example, the analysis unit prioritizes analysis of diary content that has been submitted most recently. The analysis unit can also prioritize analysis of diary content related to a specific event. Furthermore, the analysis unit can also prioritize analysis of diary content that is of particular interest to the mother. In this way, by determining the priority of analysis based on the submission time of the diary, the most recent content can be analyzed preferentially.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the diaries. The analysis unit adjusts the order of analysis based on, for example, the relevance of the diaries. For example, the analysis unit prioritizes analysis of diary content with high relevance. The analysis unit can also postpone analysis of diary content with low relevance. Furthermore, the analysis unit can also analyze diary content related to a specific theme all at once. In this way, by adjusting the order of analysis based on the relevance of the diaries, highly relevant content can be analyzed with priority.

[0086] During analysis, the analysis unit can adjust the use of analytical terminology according to the mother's level of expertise. The analysis unit adjusts the use of technical terminology according to the mother's level of expertise, for example. For example, if the mother has technical knowledge, the analysis result can be provided using technical terminology. Also, if the mother does not have technical knowledge, the analysis result can be provided in simple language. Furthermore, the way in which the analysis result is expressed can be adjusted according to the mother's level of expertise. In this way, by adjusting the way in which the analysis result is expressed according to the mother's level of expertise, it is possible to provide analysis results that are easier to understand.

[0087] The suggestion unit can estimate the mother's emotions and adjust the way the suggestions are expressed based on the estimated emotions of the mother. The suggestion unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the suggestion unit analyzes the mother's facial expressions and voice when she is entering her diary and estimates her emotions. The suggestion unit can also adjust the way the suggestions are expressed based on the mother's emotions. For example, if the mother is relaxed, detailed suggestions can be provided. If the mother is stressed, concise suggestions can be provided. Furthermore, if the mother is excited, visually appealing suggestions can be provided. In this way, by adjusting the way the suggestions are expressed according to the mother's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] When making a suggestion, the suggestion unit can adjust the accuracy of the suggestion based on the importance of the activity or stimulus. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the activity or stimulus. For example, the suggestion unit makes detailed suggestions for important activities or stimuli. It can also make brief suggestions for general activities or stimuli. It can also make special suggestions for activities or stimuli related to a specific event. In this way, by adjusting the level of detail of the suggestion based on the importance of the activity or stimulus, it is possible to make detailed suggestions for important content.

[0089] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the game or stimulus. The suggestion unit applies different suggestion algorithms depending on, for example, the category of the game or stimulus. For example, the suggestion unit applies a suggestion algorithm for games that use hands to a game that uses hands. Furthermore, the suggestion unit can apply a music-related suggestion algorithm to a music-related stimulus. Furthermore, the suggestion unit can apply an exercise-related suggestion algorithm to a game that involves exercise. In this way, by applying an appropriate suggestion algorithm depending on the category of the game or stimulus, more accurate suggestions can be made.

[0090] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to the mother's past proposal results. The proposal unit improves the accuracy of the proposal by referring to, for example, the mother's past proposal results. For example, the proposal unit adjusts the proposal algorithm based on the mother's past proposal results. The proposal unit can also analyze the mother's past proposal results and improve the accuracy of the proposal. Furthermore, the proposal unit can optimize the proposal parameters by referring to the mother's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the mother's past proposal results.

[0091] The suggestion unit can estimate the mother's emotions and adjust the length of the suggestions based on the estimated emotions of the mother. The suggestion unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the suggestion unit analyzes the mother's facial expressions and voice when she is entering her diary and estimates her emotions. The suggestion unit can also adjust the length of the suggestions based on the mother's emotions. For example, if the mother is relaxed, detailed suggestions can be provided. If the mother is stressed, concise suggestions can be provided. Furthermore, if the mother is excited, visually appealing suggestions can be provided. In this way, by adjusting the length of the suggestions according to the mother's emotions, more appropriate suggestions can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] When making suggestions, the suggestion unit can determine the order of suggestions based on the time when the games and stimuli were submitted. The suggestion unit determines the priority of suggestions based on, for example, the time when the games and stimuli were submitted. For example, the suggestion unit can preferentially suggest games and stimuli that have been submitted recently. The suggestion unit can also preferentially suggest games and stimuli related to a specific event. Furthermore, the suggestion unit can also preferentially suggest games and stimuli that the mother is particularly interested in. In this way, by determining the priority of suggestions based on the time when the games and stimuli were submitted, the latest content can be preferentially suggested.

[0093] The suggestion unit can adjust the order of suggestions based on the relevance of the activities and stimuli when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the activities and stimuli. For example, the suggestion unit prioritizes suggesting highly relevant activities and stimuli. The suggestion unit can also postpone less relevant activities and stimuli. Furthermore, the suggestion unit can collectively suggest activities and stimuli related to a specific theme. In this way, by adjusting the order of suggestions based on the relevance of the activities and stimuli, highly relevant content can be prioritized.

[0094] The suggestion unit can adjust the use of terminology in the suggestion depending on the mother's level of expertise when making a suggestion. The suggestion unit adjusts the use of technical terminology in the suggestion depending on the mother's level of expertise, for example. For example, if the mother has technical knowledge, the suggestion unit can provide the suggestion using technical terminology. Also, if the mother does not have technical knowledge, the suggestion unit can provide the suggestion in simple language. Furthermore, the way the suggestion is expressed can be adjusted depending on the mother's level of expertise. In this way, by adjusting the way the suggestion is expressed depending on the mother's level of expertise, it is possible to provide a suggestion that is easier to understand.

[0095] The notification unit can estimate the mother's emotions and adjust the notification method based on the estimated mother's emotions. The notification unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the notification unit analyzes the mother's facial expressions and voice when entering her diary entries to estimate her emotions. The notification unit can also adjust the notification method based on the mother's emotions. For example, if the mother is relaxed, a detailed notification can be provided. If the mother is stressed, a concise notification can be provided. Furthermore, if the mother is excited, a visually appealing notification can be provided. In this way, by adjusting the notification method according to the mother's emotions, more appropriate notifications can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The notification unit can select the notification method by referring to the mother's past notification history when making a notification. The notification unit, for example, selects the optimal notification method by referring to the mother's past notification history. For example, the notification unit can preferentially provide the notification method that the mother has preferred in the past. The notification unit can also analyze the mother's past notification history and suggest the optimal notification method. Furthermore, the notification unit can also adjust the timing of notification by referring to the mother's past notification history. In this way, the optimal notification method can be provided by referring to the mother's past notification history.

[0097] The notification unit can customize the notification content according to the mother's current task when notifying. The notification unit customizes the notification content according to, for example, the mother's current task. For example, the notification unit can provide a brief notification when the mother is concentrating on her current task. Also, the notification unit can provide a detailed notification when the mother is relaxed. Furthermore, the notification unit can provide only important notifications when the mother is busy. In this way, by customizing the notification content according to the mother's current task, more appropriate notifications can be provided.

[0098] The notification unit can estimate the mother's emotions and determine the order of notifications based on the estimated emotions of the mother. The notification unit estimates the mother's emotions using, for example, emotion analysis technology. For example, the notification unit analyzes the mother's facial expressions and voice when entering her diary and estimates her emotions. The notification unit can also determine the order of notifications based on the mother's emotions. For example, if the mother is relaxed, detailed notifications can be provided preferentially. If the mother is stressed, brief notifications can be provided preferentially. Furthermore, if the mother is excited, visually appealing notifications can be provided preferentially. In this way, by determining the priority of notifications according to the mother's emotions, more appropriate notifications can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The notification unit can select the optimal notification method in consideration of the mother's device information when providing notification. The notification unit selects the optimal notification method in consideration of the mother's device information, for example. For example, if the mother is using a smartphone, a notification method suited to the screen size can be provided. Also, if the mother is using a tablet, a notification method optimized for a large screen can be provided. Furthermore, if the mother is using a smartwatch, a simple and highly visible notification method can be provided. In this way, the optimal notification method can be provided based on the mother's device information.

[0100] The notification unit can provide multilingual notification content according to the mother's language setting when providing a notification. The notification unit automatically sets the notification language based on the language setting of the mother's device, for example. For example, if the mother sets the device's language setting to English, the notification content is also provided in English. In addition, if the mother uses multiple languages, the notification unit can also provide a language switching function. Furthermore, if the mother selects a specific language, the notification can be provided in that language. This makes it possible to provide multilingual notifications based on the mother's language setting.

[0101] The notification unit may provide relevant information by analyzing the mother's social media activity at the time of notification. For example, the notification unit may provide relevant information by analyzing the mother's social media activity. For example, the notification unit may provide information about places where the mother has checked in on social media. The notification unit may also analyze the content of the mother's social media posts and provide relevant information. Furthermore, the notification unit may provide relevant information by referring to the activities of the mother's friends on social media. This allows the provision of relevant information based on the mother's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, suggestion unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by a text input unit and a voice input unit of the smart device 14, allowing the mother to input a diary entry. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the diary entry using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate games and stimuli based on the analysis results. The notification unit is realized, for example, by an in-app notification unit and an email notification unit of the smart device 14, and notifies the mother of the suggestions. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, analysis unit, suggestion unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by a text input unit and a voice input unit of the smart glasses 214, allowing the mother to input a diary entry. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the diary entry using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate activities and stimulations based on the analysis results. The notification unit is realized, for example, by an in-app notification unit and an email notification unit of the smart glasses 214, and notifies the mother of the suggestions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by a text input unit and a voice input unit of the headset-type terminal 314, allowing the mother to input a diary entry. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the diary entry using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate games and stimuli based on the analysis results. The notification unit is realized, for example, by an in-app notification unit and an email notification unit of the headset-type terminal 314, and notifies the mother of the suggestions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, suggestion unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by a text input unit and a voice input unit of the robot 414, allowing the mother to input diary entries. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the diary using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate games and stimulations based on the analysis results. The notification unit is realized, for example, by an in-app notification unit and an email notification unit of the robot 414, and notifies the mother of the suggestions.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When analyzing the contents of the mother's diary, the analysis unit can estimate the mother's emotions and determine the analysis priority based on the estimated emotions. For example, if the mother is feeling stressed, it can prioritize suggestions for play or stimulation that will help relieve stress. Also, if the mother is relaxed, it can provide more detailed analysis results. Furthermore, if the mother is excited, it can provide visually appealing analysis results. This allows the analysis priority to be adjusted according to the mother's emotions, making it possible to make more appropriate suggestions.

[0104] The suggestion unit can estimate the mother's emotions and customize the content of the suggestions based on the estimated emotions. For example, if the mother is feeling stressed, the suggestion unit can suggest relaxing activities and stimulation. Also, if the mother is relaxed, the suggestion unit can provide detailed suggestions that will be useful for the child's development. Furthermore, if the mother is excited, the suggestion unit can provide visually appealing suggestions. In this way, by customizing the content of the suggestions according to the mother's emotions, more appropriate suggestions can be made.

[0105] The notification unit can estimate the mother's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the mother is feeling stressed, notifications can be sent at times when the mother is able to relax. Also, if the mother is relaxed, detailed notifications can be sent. Furthermore, if the mother is busy, only important notifications can be sent. In this way, more appropriate notifications can be provided by adjusting the timing of notifications according to the mother's emotions.

[0106] The input unit can estimate the mother's emotions and customize the input interface based on the estimated emotions. For example, if the mother is stressed, a simple input form can be provided. If the mother is relaxed, a detailed input form can be provided. Furthermore, if the mother is excited, a visually appealing input form can be provided. In this way, a more appropriate input environment can be provided by customizing the input interface according to the mother's emotions.

[0107] When analyzing the contents of the mother's diary, the analysis unit can estimate the mother's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the mother is relaxed, a detailed analysis result can be provided. If the mother is stressed, a concise analysis result can be provided. Furthermore, if the mother is excited, a visually appealing analysis result can be provided. In this way, by adjusting the way the analysis is presented according to the mother's emotions, more appropriate analysis results can be provided.

[0108] The input unit can analyze the mother's past diary entry history and select the entry method. For example, it can preferentially suggest the entry method (voice, text, etc.) that the mother has frequently used in the past. The input unit can also analyze the time period in which the mother entered entries in the past and send a notification prompting her to enter entries during that time period. Furthermore, the input unit can analyze the trends in the content that the mother has entered in the past and automatically suggest related entry items. In this way, the optimal entry method can be suggested by analyzing the mother's past entry history.

[0109] The input unit can filter entries based on the mother's current living situation and areas of interest when entering diary entries. For example, the input unit preferentially displays relevant entry items depending on the mother's current living situation. The input unit can also customize the entry items based on the mother's areas of interest. Furthermore, the input unit can automatically complete the entry based on the mother's living situation and areas of interest. This allows the entry items to be customized based on the mother's living situation and areas of interest.

[0110] When entering diary entries, the input unit can select an input means according to the mother's input method. For example, if the mother desires voice input, the input unit can provide voice input with priority. Also, if the mother desires text input, the input unit can provide text input with priority. Furthermore, if the mother desires image input, the input unit can provide image input with priority. This makes it possible to provide the optimal input means according to the mother's desired input method.

[0111] When inputting the diary, the input unit can prioritize inputting related content in consideration of the mother's geographical location information. For example, if the mother is in a specific location, content related to that location can be prioritized. Also, if the mother is traveling, content related to the travel destination can be prioritized. Furthermore, if the mother is at home, content related to the home can be prioritized. In this way, content that is highly relevant based on the mother's geographical location information can be prioritized and input.

[0112] The input unit can analyze the mother's social media activity and input relevant content when inputting diary entries. For example, the content posted by the mother on social media is automatically reflected in the diary. The input unit can also analyze the mother's social media activity and suggest relevant content. Furthermore, the input unit can also suggest relevant content by taking into account the activity of the mother's friends on social media. In this way, relevant content can be input based on the mother's social media activity.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The input unit allows the mother to record her child's growth and daily events, like a diary. The information recorded by the mother includes, for example, the child's age in months, weight, height, diet, and play activities. The input unit is equipped with a text input unit and a voice input unit, allowing the mother to enter her diary entries using text or voice. The text input unit supports keyboard input and handwriting input, while the voice input unit converts the mother's voice into text using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the diary received by the input unit. The analysis unit uses natural language processing technology to understand the contents of the mother's diary and suggest appropriate activities and stimuli according to the child's age. The generation AI analyzes the diary contents using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The suggestion unit uses the generation AI to suggest appropriate play and stimuli according to the child's age based on the results of the analysis by the analysis unit. Step 4: The notification unit notifies the mother of the content suggested by the suggestion unit. The notification unit includes an in-app notification unit and an email notification unit, allowing the mother to receive the suggestions via in-app or email notification. The in-app notification unit provides in-app pop-up and banner notifications, while the email notification unit supports customization of the timing and content of email transmissions.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 input unit for accepting diary entries; an analysis unit that analyzes the diary accepted by the input unit; a suggestion unit that suggests games and stimuli based on the results of the analysis by the analysis unit; a notification unit that notifies the content proposed by the proposal unit. A system characterized by:

2. The input unit Equipped with a text input section and a voice input section 2. The system of claim 1.

3. The notification unit Equipped with in-app and email notification functions 2. The system of claim 1.

4. The analysis unit Analyzing the contents of mothers' diaries and suggesting activities and stimuli appropriate for the child's age 2. The system of claim 1.

5. The proposal unit Suggesting play and stimulation according to the child's age 2. The system of claim 1.

6. The input unit The system estimates the mother's emotions and adjusts the timing of diary entries based on the estimated emotions.

2. The system of claim 1.

7. The input unit Analyze the mother's past diary entry history and select the entry method 2. The system of claim 1.

8. The input unit Filter diary entries based on the mother's current living situation and areas of interest 2. The system of claim 1.

9. The input unit When entering diary entries, select the input method according to the mother's input method.

2. The system of claim 1.

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