System

Abstract

An object of the system according to the embodiment is to analyze data related to growth of a child and predict future growth.SOLUTION: A system includes a data reception unit, an information acquisition unit, a prediction unit, and a presentation unit. The data reception unit allows the parent to upload data on the growth of the child. The information acquisition unit acquires information on growth using the image or video data based on the data uploaded by the data reception unit. The prediction unit analyzes past data of the child on the basis of the information acquired by the information acquisition unit and predicts future growth of the child. The presentation unit visually presents the prediction result obtained by the prediction 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] With conventional technology, it has been difficult to effectively utilize data on a child's growth to predict future growth, and there has been a lack of means to alleviate parents' anxiety.

[0005] The system according to the embodiment aims to analyze data relating to a child's growth and predict future growth. [Means for solving the problem]

[0006] The system according to the embodiment includes a data accepting unit, an information acquiring unit, a prediction unit, and a presentation unit. The data accepting unit allows a parent to upload data related to the child's growth. The information acquiring unit acquires information related to growth using image or video data based on the data uploaded by the data accepting unit. The prediction unit analyzes the child's past data and predicts future growth based on the information acquired by the information acquiring unit. The presentation unit visually presents the prediction results obtained by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data relating to a child's growth and predict future growth. [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 growth prediction system according to an embodiment of the present invention allows parents to upload data related to their child's growth, and a generation AI performs growth prediction and visually presents the results. The growth prediction system allows parents to upload data related to their child's growth, and a generation AI performs growth prediction and visually presents the results. This helps resolve parents' concerns and help them prepare for future stages of development. For example, in a growth prediction system, parents upload data related to their child's growth. For example, the growth prediction system inputs data related to height, weight, and development. The growth prediction system then obtains important growth information through image and video data. For example, the growth prediction system uploads photos and videos of the child, and the generation AI analyzes facial features, body movements, and other factors to obtain growth information. The growth prediction system then uses the generation AI to make future predictions related to height, weight, and development based on the child's past data. For example, the generation AI predicts future height and weight trends based on past data. The growth prediction system also presents results in a visual format. For example, the growth prediction system visually displays future height and weight trends using graphs and charts. Furthermore, the growth prediction system provides alerts regarding growth at specific points in the future. For example, an alert will be issued if height or weight at a specific point in time exceeds a predicted value, or if developmental abnormalities are detected. Finally, the growth prediction system will help consult a specialist if necessary. For example, if a growth abnormality is detected, an alert will be issued recommending consultation with a specialist. This allows the growth prediction system to resolve parents' questions and concerns about their child's growth and help them prepare for future stages. This allows the growth prediction system to resolve parents' questions and concerns about their child's growth and help them prepare for future stages. For example, parents simply upload data about their child's growth, and the generative AI will predict growth and present the results visually, allowing parents to intuitively understand their child's growth. Furthermore, if a growth abnormality is detected, an alert will be issued recommending consultation with a specialist, allowing parents to take early action.

[0029] A growth prediction system according to an embodiment includes a data accepting unit, an information acquiring unit, a prediction unit, and a presentation unit. The data accepting unit allows a parent to upload data related to the growth of their child. The data related to the growth of their child includes, but is not limited to, data related to height, weight, and development. For example, the data accepting unit allows a parent to upload data such as a record of the child's height and weight, and developmental progress. The data accepting unit can also acquire important information related to growth through image or video data. For example, when a parent uploads photos or videos of their child, the information acquiring unit uses a generation AI to analyze facial features, body movements, and the like to acquire information related to growth. For example, the information acquiring unit uses the generation AI to analyze image or video data and acquire information related to growth. The information acquiring unit can also extract information related to growth from the image or video data using the generation AI. The prediction unit uses the generation AI to predict future growth based on past data of the child. For example, the prediction unit predicts future changes in height and weight based on past data. The prediction unit can also predict development using the generation AI. For example, the generation AI predicts future developmental stages based on past data. The presentation unit visually presents the prediction results obtained by the prediction unit. The presentation unit visually displays future changes in height and weight using, for example, graphs or charts. The presentation unit can also provide alerts regarding growth at specific points in time. For example, an alert is issued when height or weight at a specific point in time exceeds a predicted value or when a developmental abnormality is detected. Furthermore, the presentation unit can also issue an alert recommending consultation with a specialist when a growth abnormality is detected. In this way, the growth prediction system according to the embodiment can help parents resolve their questions and anxieties about their child's growth and help them prepare for future stages.

[0030] The prediction unit can predict future changes in height and weight based on past data. The prediction unit, for example, predicts future changes in height and weight based on past data. For example, the prediction unit predicts future changes in height and weight using a growth curve. The prediction unit can also predict future changes in height and weight using a statistical model. For example, the prediction unit performs regression analysis based on past data to predict future changes in height and weight. The prediction unit can also predict future changes in height and weight using a machine learning algorithm. For example, the prediction unit trains a machine learning model based on past data to predict future changes in height and weight. This allows parents to more accurately understand their child's growth by predicting future changes in height and weight based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data into a generation AI and cause the generation AI to predict future changes in height and weight.

[0031] The prediction unit can make predictions about development based on past data. The prediction unit, for example, makes predictions about development based on past data. For example, the prediction unit makes predictions about future development based on developmental stages. The prediction unit can also make predictions about future development based on developmental indicators. For example, the prediction unit calculates developmental indicators based on past data and makes predictions about future development. The prediction unit can also make predictions about future development using a machine learning algorithm. For example, the prediction unit trains a machine learning model based on past data and makes predictions about future development. This allows parents to more accurately understand their child's developmental status by making developmental predictions based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data into a generation AI and cause the generation AI to make predictions about future development.

[0032] The presentation unit can visually display the prediction results using a graph or a chart. The presentation unit visually displays the prediction results using, for example, a graph. For example, the presentation unit displays future changes in height and weight using a line graph. The presentation unit can also visually display the prediction results using a bar graph. For example, the presentation unit displays future developmental stages using a bar graph. The presentation unit can also visually display the prediction results using a pie chart. For example, the presentation unit displays future growth stages using a pie chart. By visually displaying the prediction results using a graph or chart, parents can intuitively understand the growth prediction of their child. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can convert the prediction results into a graph or chart using a generation AI and visually display it.

[0033] The presentation unit can provide an alert regarding growth at a specific time point. The presentation unit provides an alert regarding growth at a specific time point, for example. For example, the presentation unit issues an alert when height or weight at a specific time point exceeds a predicted value. The presentation unit can also issue an alert when a developmental abnormality is detected. For example, the presentation unit issues an alert when a developmental index deviates from a standard deviation. Furthermore, by providing an alert regarding growth at a specific time point, the presentation unit enables parents to take measures early. As a result, by providing an alert regarding growth at a specific time point, parents can take measures early. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can generate a growth alert using a generation AI and notify a parent.

[0034] The presentation unit can issue an alert recommending consultation with a specialist when a growth abnormality is detected. The presentation unit, for example, issues an alert recommending consultation with a specialist when a growth abnormality is detected. For example, the presentation unit issues an alert recommending consultation with a specialist when a growth index deviates from a standard deviation. The presentation unit can also issue an alert recommending consultation with a specialist when a developmental abnormality is detected. For example, the presentation unit issues an alert recommending consultation with a specialist when a developmental index indicates an abnormal value. In this way, by issuing an alert recommending consultation with a specialist when a growth abnormality is detected, parents can receive appropriate medical support. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can detect growth abnormalities using a generation AI and generate an alert recommending consultation with a specialist.

[0035] The data acceptance unit can analyze the parent's past data submission history and select an appropriate acceptance method. The data acceptance unit, for example, analyzes the parent's past data submission history and selects an appropriate acceptance method. For example, the data acceptance unit prioritizes and suggests data submission methods (text, image, video, etc.) that the parent has frequently used in the past. The data acceptance unit can also analyze the time period in which the parent previously submitted data and suggest the optimal submission time. For example, the data acceptance unit analyzes the format of data previously submitted by the parent and suggests the optimal data format. In this way, by analyzing the parent's past data submission history, the optimal acceptance method can be selected and the efficiency of data submission can be improved. Some or all of the above-described processing in the data acceptance unit may be performed, for example, using AI or without AI. For example, the data acceptance unit can input the parent's past data submission history into a generation AI and have the generation AI select the optimal acceptance method.

[0036] The data accepting unit can filter the data based on the parent's current living situation or areas of interest when accepting data. The data accepting unit, for example, filters the data based on the parent's current living situation when accepting data. For example, the data accepting unit accepts only data necessary for the parent based on the parent's current living situation. The data accepting unit can also accept only related data based on the parent's areas of interest. For example, the data accepting unit preferentially accepts related data based on the parent's areas of interest. The data accepting unit can also determine the priority of data based on the parent's living situation and areas of interest. For example, the data accepting unit determines the priority of data based on the parent's living situation and areas of interest. By filtering the data based on the parent's living situation and areas of interest, only the necessary data can be efficiently accepted. Some or all of the above-described processing in the data accepting unit may be performed using, or without, AI. For example, the data accepting unit can input data on the parent's living situation and areas of interest to a generation AI and cause the generation AI to perform filtering.

[0037] The data acceptance unit can select an appropriate acceptance means depending on the parent's input method when accepting data. For example, the data acceptance unit selects an appropriate acceptance means depending on the parent's input method when accepting data. For example, if the parent uses voice input, the data acceptance unit can preferentially accept voice data. Also, if the parent uses text input, the data acceptance unit can preferentially accept text data. For example, if the parent uses images or videos, the data acceptance unit can preferentially accept image or video data. Also, the data acceptance unit can select the optimal acceptance means depending on the parent's input method. For example, the data acceptance unit selects the optimal acceptance means depending on the parent's input method. This improves the convenience of data submission by selecting the optimal acceptance means depending on the parent's input method. Some or all of the above-described processing in the data acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acceptance unit can input data of the parent's input method to a generation AI and cause the generation AI to select the optimal acceptance means.

[0038] When receiving data, the data receiving unit can prioritize receiving highly relevant data by taking into account the geographical location information of the parent. For example, when receiving data, the data receiving unit prioritizes receiving highly relevant data by taking into account the geographical location information of the parent. For example, when the parent is in a specific area, the data receiving unit prioritizes receiving data related to that area. Furthermore, when the parent is traveling, the data receiving unit can also prioritize receiving data related to the travel destination. For example, when the parent is at home, the data receiving unit prioritizes receiving data related to the home. This allows for more appropriate data to be collected by preferentially receiving highly relevant data by taking into account the geographical location information of the parent. Some or all of the above-described processing in the data receiving unit may be performed using, or without, AI. For example, the data receiving unit may input the geographical location information of the parent to the generation AI and cause the generation AI to preferentially receive highly relevant data.

[0039] The data receiving unit can analyze the parent's social media activity and receive related data when receiving data. The data receiving unit, for example, analyzes the parent's social media activity and receives related data when receiving data. For example, the data receiving unit receives related data based on information shared by the parent on social media. The data receiving unit can also analyze the parent's social media activity and prioritize receiving related data. For example, the data receiving unit receives related data with reference to the activities of the parent's friends on social media. This allows the parent's social media activity to be analyzed and related data to be received efficiently. Some or all of the above-described processing in the data receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the data receiving unit can input data on the parent's social media activity to the generation AI and cause the generation AI to receive related data.

[0040] The data acceptance unit can customize the acceptance method by reflecting the parent's past feedback when accepting data. The data acceptance unit, for example, customizes the acceptance method by reflecting the parent's past feedback when accepting data. For example, the data acceptance unit suggests an optimal acceptance method based on feedback provided by the parent in the past. The data acceptance unit can also analyze the parent's past feedback and improve the acceptance method. For example, the data acceptance unit customizes the acceptance method by reflecting the parent's feedback. In this way, the acceptance method is customized by reflecting the parent's past feedback, improving the efficiency of data submission. Some or all of the above-mentioned processing in the data acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acceptance unit can input data of the parent's past feedback into the generation AI and cause the generation AI to customize the acceptance method.

[0041] The information acquisition unit can adjust the level of detail of the information acquisition based on the child's important growth data when acquiring information. For example, the information acquisition unit adjusts the level of detail of the information acquisition based on the child's important growth data when acquiring information. For example, the information acquisition unit prioritizes acquiring important growth data such as the child's height and weight. The information acquisition unit can also acquire detailed data related to the child's development. For example, the information acquisition unit acquires detailed data related to the child's health condition. This allows necessary information to be acquired efficiently by adjusting the level of detail of the information acquisition based on the child's important growth data. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input the child's important growth data to the generation AI and cause the generation AI to adjust the level of detail of the information acquisition.

[0042] The information acquisition unit can apply different acquisition algorithms depending on the child's growth category when acquiring information. For example, the information acquisition unit applies different acquisition algorithms depending on the child's growth category when acquiring information. For example, the information acquisition unit applies an algorithm for acquiring data regarding the child's height and weight. The information acquisition unit can also apply an algorithm for acquiring data regarding the child's development. For example, the information acquisition unit applies an algorithm for acquiring data regarding the child's health condition. By applying different acquisition algorithms depending on the child's growth category, the accuracy of information acquisition is improved. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data of the child's growth category to the generation AI and cause the generation AI to apply different acquisition algorithms.

[0043] The information acquisition unit can improve the accuracy of information acquisition by referring to the parent's past information acquisition results when acquiring information. For example, the information acquisition unit improves the accuracy of information acquisition by referring to the parent's past information acquisition results when acquiring information. For example, the information acquisition unit improves the accuracy of information acquisition based on information acquired by the parent in the past. The information acquisition unit can also analyze the parent's past information acquisition results to improve the accuracy of information acquisition. For example, the information acquisition unit improves the accuracy of information acquisition by referring to the parent's past information acquisition results. As a result, the accuracy of information acquisition is improved by referring to the parent's past information acquisition results. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data on the parent's past information acquisition results into the generation AI and cause the generation AI to improve the accuracy of information acquisition.

[0044] The information acquisition unit can determine the priority of acquisition based on the time of submission of the child's growth data when acquiring information. The information acquisition unit, for example, determines the priority of acquisition based on the time of submission of the child's growth data when acquiring information. For example, if the child's growth data has been recently submitted, the information acquisition unit prioritizes acquisition of that data. The information acquisition unit can also prioritize acquisition of if the child's growth data has been submitted in the past. For example, the information acquisition unit determines the priority of acquisition based on the time of submission of the child's growth data. In this way, important data can be prioritized for acquisition by determining the priority of acquisition based on the time of submission of the child's growth data. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input data on the time of submission of the child's growth data to the generation AI and cause the generation AI to determine the priority of acquisition.

[0045] The information acquisition unit can adjust the order of acquisition based on the relevance of the child's growth data when acquiring information. The information acquisition unit, for example, adjusts the order of acquisition based on the relevance of the child's growth data when acquiring information. For example, if the relevance of the child's growth data is high, the information acquisition unit prioritizes acquisition of that data. Furthermore, if the relevance of the child's growth data is low, the information acquisition unit can postpone acquisition of that data. For example, the information acquisition unit adjusts the order of acquisition based on the relevance of the child's growth data. In this way, by adjusting the order of acquisition based on the relevance of the child's growth data, important data can be prioritized for acquisition. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input data on the relevance of the child's growth data to the generation AI and cause the generation AI to adjust the order of acquisition.

[0046] The information acquisition unit can adjust the use of technical terms in the acquisition according to the parent's level of expertise when acquiring information. For example, the information acquisition unit adjusts the use of technical terms in the acquisition according to the parent's level of expertise when acquiring information. For example, if the parent has technical knowledge, the information acquisition unit acquires information using technical terms. Furthermore, if the parent does not have technical knowledge, the information acquisition unit can acquire information using simple terms. For example, the information acquisition unit adjusts the use of technical terms in the acquisition according to the parent's level of expertise. This makes it easier for the parent to understand the information. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data on the parent's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of the child's growth data when making a prediction. The prediction unit, for example, improves the accuracy of the prediction by taking into account the interrelationships of the child's growth data when making a prediction. For example, the prediction unit makes a prediction by taking into account the interrelationships between the child's height and weight. The prediction unit can also make a prediction by taking into account the interrelationships between the child's development and health condition. For example, the prediction unit improves the accuracy of the prediction by taking into account the interrelationships of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the interrelationships of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the interrelationships of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0048] The prediction unit can make a prediction taking into account attribute information of the submitter of the child's growth data. For example, the prediction unit makes a prediction taking into account attribute information of the submitter of the child's growth data. For example, if the submitter of the child's growth data is a parent, the prediction unit makes a prediction taking into account the attribute information of the parent. Furthermore, if the submitter of the child's growth data is a doctor, the prediction unit can also make a prediction taking into account the attribute information of the doctor. For example, the prediction unit makes a prediction taking into account attribute information of the submitter of the child's growth data. By taking into account the attribute information of the submitter of the child's growth data, the accuracy of the prediction is improved. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input attribute information data of the submitter of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0049] The prediction unit can weight the prediction based on the frequency of submission of the child's growth data when making a prediction. The prediction unit, for example, weights the prediction based on the frequency of submission of the child's growth data when making a prediction. For example, if the child's growth data is frequently submitted, the prediction unit weights the data when making a prediction. Furthermore, if the child's growth data is rarely submitted, the prediction unit can weight the data when making a prediction. For example, the prediction unit weights the prediction based on the frequency of submission of the child's growth data. As a result, weighting the prediction based on the frequency of submission of the child's growth data improves the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the frequency of submission of the child's growth data to the generation AI and cause the generation AI to weight the prediction.

[0050] The prediction unit can make predictions taking into account the geographical distribution of the child's growth data. For example, the prediction unit makes predictions taking into account the geographical distribution of the child's growth data. For example, if the child's growth data is concentrated in a specific region, the prediction unit makes predictions taking into account the characteristics of that region. Furthermore, if the child's growth data is distributed across multiple regions, the prediction unit can also make predictions taking into account the characteristics of each region. For example, the prediction unit improves the accuracy of the prediction by taking into account the geographical distribution of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the geographical distribution of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the geographical distribution of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0051] The prediction unit can improve the accuracy of the prediction by referring to literature related to the child's growth data when making a prediction. The prediction unit, for example, improves the accuracy of the prediction by referring to literature related to the child's growth data when making a prediction. For example, the prediction unit makes a prediction by referring to the latest research paper related to the child's growth data. The prediction unit can also make a prediction by referring to past research paper related to the child's growth data. For example, the prediction unit improves the accuracy of the prediction by referring to literature related to the child's growth data. In this way, the accuracy of the prediction is improved by referring to literature related to the child's growth data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input data from literature related to the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0052] The prediction unit can make a prediction taking into account the market value of the child's growth data. The prediction unit, for example, makes a prediction taking into account the market value of the child's growth data. For example, if the market value of the child's growth data is high, the prediction unit makes a prediction by prioritizing that data. Furthermore, if the market value of the child's growth data is low, the prediction unit can make a prediction by disregarding that data. For example, the prediction unit improves the accuracy of the prediction by taking into account the market value of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the market value of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the market value of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0053] The presentation unit can display the current growth prediction by referring to past trends in the child's growth data when presenting the data. The presentation unit, for example, displays the current growth prediction by referring to past trends in the child's growth data when presenting the data. For example, the presentation unit displays the child's past growth data in a graph and overlays the current growth prediction. The presentation unit can also display the child's past growth data in a chart and overlay the current growth prediction. For example, the presentation unit displays the child's past growth data in a table format and overlays the current growth prediction. This allows the current growth prediction to be displayed more accurately by referring to past trends in the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may input data on past trends in the child's growth data into the generation AI and cause the generation AI to display the current growth prediction.

[0054] The presentation unit can apply different display methods to each category of the child's growth data when presenting the data. For example, the presentation unit applies different display methods to each category of the child's growth data when presenting the data. For example, the presentation unit displays the child's height data in a graph and the weight data in a chart. The presentation unit can also display the child's development data in a table format and the health data in a graph. For example, the presentation unit applies an optimal display method to each category of the child's growth data. By applying different display methods to each category of the child's growth data, information can be presented in a more understandable manner. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data of the child's growth data category to the generation AI and cause the generation AI to apply different display methods.

[0055] The presentation unit can perform display taking into consideration attribute information of the submitter of the child's growth data when presenting the data. The presentation unit, for example, performs display taking into consideration attribute information of the submitter of the child's growth data when presenting the data. For example, if the submitter of the child's growth data is a parent, the presentation unit performs display taking into consideration the attribute information of the parent. Furthermore, if the submitter of the child's growth data is a doctor, the presentation unit can also perform display taking into consideration the attribute information of the doctor. For example, the presentation unit selects an optimal display method taking into consideration the attribute information of the submitter of the child's growth data. This allows information to be displayed more appropriately by taking into consideration the attribute information of the submitter of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data of attribute information of the submitter of the child's growth data to the generation AI and cause the generation AI to select a display method.

[0056] The presentation unit can display a change in presentation based on the time of submission of the child's growth data at the time of presentation. The presentation unit, for example, displays a change in presentation based on the time of submission of the child's growth data at the time of presentation. For example, if the child's growth data has been recently submitted, the presentation unit highlights the data. Furthermore, if the child's growth data has been submitted in the past, the presentation unit can dim the data. For example, the presentation unit displays a change in presentation based on the time of submission of the child's growth data. This makes it possible to emphasize the newness of the information by displaying a change in presentation based on the time of submission of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data on the time of submission of the child's growth data to the generation AI and cause the generation AI to display a change in presentation.

[0057] The presentation unit can refer to market data related to the child's growth data when presenting the information. The presentation unit, for example, refers to market data related to the child's growth data when presenting the information. For example, the presentation unit displays market data related to the child's growth data in a graph. The presentation unit can also display market data related to the child's growth data in a chart. For example, the presentation unit displays market data related to the child's growth data in a table format. This increases the relevance of the information by referring to the market data related to the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit inputs market data related to the child's growth data into the generation AI and causes the generation AI to display the presentation.

[0058] The presentation unit may present the child's growth data while taking into consideration the technical maturity of the data. For example, the presentation unit may present the child's growth data while taking into consideration the technical maturity of the data. For example, if the technical maturity of the child's growth data is high, the presentation unit may display the data in detail. Furthermore, if the technical maturity of the child's growth data is low, the presentation unit may display the data in a concise manner. For example, the presentation unit may select an optimal display method while taking into consideration the technical maturity of the child's growth data. This enables appropriate display of information by taking into consideration the technical maturity of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input data on the technical maturity of the child's growth data to a generation AI and cause the generation AI to select a display method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The growth prediction system can also collect data on parents' lifestyle habits and reflect this in growth predictions. For example, it can collect data on parents' diet, exercise habits, sleep patterns, etc., and use this data to predict the impact on a child's growth. Growth predictions can also be made more accurately by taking into account the parents' stress levels and work situations. Furthermore, it can provide advice recommending healthy lifestyle habits based on the parents' lifestyle data. This supports parents in managing their own health and has a positive impact on their child's growth.

[0061] Growth prediction systems can also take into account a child's genetic information. For example, based on the parents' genetic information, genetic factors that may affect a child's growth can be identified and reflected in growth predictions. Genetic information can also be used to predict the risk of certain diseases and disabilities and take early measures. Genetic information can also be used to predict the development of a child's specific talents and abilities and provide appropriate education and training. This allows for more comprehensive support for a child's development.

[0062] The growth prediction system can also collect data on a child's social environment and reflect this in its growth predictions. For example, it can collect data on the child's school, local environment, friendships, and other factors, and use this data to predict the impact on a child's growth. Based on this data, it can also predict social problems and stress that a child may face, allowing for early intervention. Furthermore, based on this data, it can provide advice to support the development of a child's social skills and communication abilities. This allows for more comprehensive support for a child's growth.

[0063] The growth prediction system can also collect psychological data about children and incorporate it into growth predictions. For example, it can collect data on children's emotions, moods, stress levels, and other factors and use this data to predict the impact on a child's growth. It can also use psychological data to predict psychological problems and stress a child may face and take early action. Furthermore, it can provide advice to support a child's psychological health based on the psychological data. This allows for more comprehensive support for a child's growth.

[0064] The growth prediction system can also collect children's learning data and reflect this in its growth predictions. For example, it can collect data on children's learning outcomes, learning styles, learning environments, etc., and use this data to predict the impact on children's growth. It can also use learning data to predict learning problems and stress that children may face, allowing for early countermeasures. It can also use learning data to provide advice to support children's learning abilities and motivation. This allows for more comprehensive support for children's growth.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The data receiving unit allows parents to upload data about their child's growth. The data uploaded by parents includes data about height, weight, and development. For example, parents can upload data such as records of their child's height and weight, and developmental progress. Step 2: The information acquisition unit acquires growth-related information using image or video data based on the data uploaded by the data acceptance unit. For example, when a parent uploads a photo or video of their child, the generative AI analyzes facial features and body movements to acquire growth-related information. Step 3: The prediction unit analyzes the child's past data based on the information acquired by the information acquisition unit and predicts future growth. For example, it uses generation AI to predict future changes in height and weight based on past data. It can also make predictions about development. Step 4: The presentation unit visually presents the prediction results obtained by the prediction unit. For example, it may visually display future changes in height and weight using graphs or charts. It may also provide alerts regarding growth at specific points in time.

[0067] (Example 2) A growth prediction system according to an embodiment of the present invention allows parents to upload data related to their child's growth, and a generation AI performs growth prediction and visually presents the results. The growth prediction system allows parents to upload data related to their child's growth, and a generation AI performs growth prediction and visually presents the results. This helps resolve parents' concerns and help them prepare for future stages of development. For example, in a growth prediction system, parents upload data related to their child's growth. For example, the growth prediction system inputs data related to height, weight, and development. The growth prediction system then obtains important growth information through image and video data. For example, the growth prediction system uploads photos and videos of the child, and the generation AI analyzes facial features, body movements, and other factors to obtain growth information. The growth prediction system then uses the generation AI to make future predictions related to height, weight, and development based on the child's past data. For example, the generation AI predicts future height and weight trends based on past data. The growth prediction system also presents results in a visual format. For example, the growth prediction system visually displays future height and weight trends using graphs and charts. Furthermore, the growth prediction system provides alerts regarding growth at specific points in the future. For example, an alert will be issued if height or weight at a specific point in time exceeds a predicted value, or if developmental abnormalities are detected. Finally, the growth prediction system will help consult a specialist if necessary. For example, if a growth abnormality is detected, an alert will be issued recommending consultation with a specialist. This allows the growth prediction system to resolve parents' questions and concerns about their child's growth and help them prepare for future stages. This allows the growth prediction system to resolve parents' questions and concerns about their child's growth and help them prepare for future stages. For example, parents simply upload data about their child's growth, and the generative AI will predict growth and present the results visually, allowing parents to intuitively understand their child's growth. Furthermore, if a growth abnormality is detected, an alert will be issued recommending consultation with a specialist, allowing parents to take early action.

[0068] A growth prediction system according to an embodiment includes a data accepting unit, an information acquiring unit, a prediction unit, and a presentation unit. The data accepting unit allows a parent to upload data related to the growth of their child. The data related to the growth of their child includes, but is not limited to, data related to height, weight, and development. For example, the data accepting unit allows a parent to upload data such as a record of the child's height and weight, and developmental progress. The data accepting unit can also acquire important information related to growth through image or video data. For example, when a parent uploads photos or videos of their child, the information acquiring unit uses a generation AI to analyze facial features, body movements, and the like to acquire information related to growth. For example, the information acquiring unit uses the generation AI to analyze image or video data and acquire information related to growth. The information acquiring unit can also extract information related to growth from the image or video data using the generation AI. The prediction unit uses the generation AI to predict future growth based on past data of the child. For example, the prediction unit predicts future changes in height and weight based on past data. The prediction unit can also predict development using the generation AI. For example, the generation AI predicts future developmental stages based on past data. The presentation unit visually presents the prediction results obtained by the prediction unit. The presentation unit visually displays future changes in height and weight using, for example, graphs or charts. The presentation unit can also provide alerts regarding growth at specific points in time. For example, an alert is issued when height or weight at a specific point in time exceeds a predicted value or when a developmental abnormality is detected. Furthermore, the presentation unit can also issue an alert recommending consultation with a specialist when a growth abnormality is detected. In this way, the growth prediction system according to the embodiment can help parents resolve their questions and anxieties about their child's growth and help them prepare for future stages.

[0069] The prediction unit can predict future changes in height and weight based on past data. The prediction unit, for example, predicts future changes in height and weight based on past data. For example, the prediction unit predicts future changes in height and weight using a growth curve. The prediction unit can also predict future changes in height and weight using a statistical model. For example, the prediction unit performs regression analysis based on past data to predict future changes in height and weight. The prediction unit can also predict future changes in height and weight using a machine learning algorithm. For example, the prediction unit trains a machine learning model based on past data to predict future changes in height and weight. This allows parents to more accurately understand their child's growth by predicting future changes in height and weight based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data into a generation AI and cause the generation AI to predict future changes in height and weight.

[0070] The prediction unit can make predictions about development based on past data. The prediction unit, for example, makes predictions about development based on past data. For example, the prediction unit makes predictions about future development based on developmental stages. The prediction unit can also make predictions about future development based on developmental indicators. For example, the prediction unit calculates developmental indicators based on past data and makes predictions about future development. The prediction unit can also make predictions about future development using a machine learning algorithm. For example, the prediction unit trains a machine learning model based on past data and makes predictions about future development. This allows parents to more accurately understand their child's developmental status by making developmental predictions based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data into a generation AI and cause the generation AI to make predictions about future development.

[0071] The presentation unit can visually display the prediction results using a graph or a chart. The presentation unit visually displays the prediction results using, for example, a graph. For example, the presentation unit displays future changes in height and weight using a line graph. The presentation unit can also visually display the prediction results using a bar graph. For example, the presentation unit displays future developmental stages using a bar graph. The presentation unit can also visually display the prediction results using a pie chart. For example, the presentation unit displays future growth stages using a pie chart. By visually displaying the prediction results using a graph or chart, parents can intuitively understand the growth prediction of their child. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can convert the prediction results into a graph or chart using a generation AI and visually display it.

[0072] The presentation unit can provide an alert regarding growth at a specific time point. The presentation unit provides an alert regarding growth at a specific time point, for example. For example, the presentation unit issues an alert when height or weight at a specific time point exceeds a predicted value. The presentation unit can also issue an alert when a developmental abnormality is detected. For example, the presentation unit issues an alert when a developmental index deviates from a standard deviation. Furthermore, by providing an alert regarding growth at a specific time point, the presentation unit enables parents to take measures early. As a result, by providing an alert regarding growth at a specific time point, parents can take measures early. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can generate a growth alert using a generation AI and notify a parent.

[0073] The presentation unit can issue an alert recommending consultation with a specialist when a growth abnormality is detected. The presentation unit, for example, issues an alert recommending consultation with a specialist when a growth abnormality is detected. For example, the presentation unit issues an alert recommending consultation with a specialist when a growth index deviates from a standard deviation. The presentation unit can also issue an alert recommending consultation with a specialist when a developmental abnormality is detected. For example, the presentation unit issues an alert recommending consultation with a specialist when a developmental index indicates an abnormal value. In this way, by issuing an alert recommending consultation with a specialist when a growth abnormality is detected, parents can receive appropriate medical support. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can detect growth abnormalities using a generation AI and generate an alert recommending consultation with a specialist.

[0074] The data accepting unit can estimate the parent's emotions and adjust the timing of data acceptance based on the estimated parent's emotions. The data accepting unit, for example, estimates the parent's emotions and adjusts the timing of data acceptance based on the estimated parent's emotions. For example, if the parent is feeling stressed, the data accepting unit delays accepting data and sends a notification again when the parent is relaxed. Furthermore, if the parent is busy, the data accepting unit can temporarily stop accepting data and send a notification again later. For example, if the parent is relaxed, the data accepting unit immediately accepts data and smoothly proceeds with processing. By adjusting the timing of data acceptance according to the parent's emotions, the parent can submit data in a relaxed state. Emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the data accepting unit may be performed using AI, for example, or without AI. For example, the data accepting unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The data acceptance unit can analyze the parent's past data submission history and select an appropriate acceptance method. The data acceptance unit, for example, analyzes the parent's past data submission history and selects an appropriate acceptance method. For example, the data acceptance unit prioritizes and suggests data submission methods (text, image, video, etc.) that the parent has frequently used in the past. The data acceptance unit can also analyze the time period in which the parent previously submitted data and suggest the optimal submission time. For example, the data acceptance unit analyzes the format of data previously submitted by the parent and suggests the optimal data format. In this way, by analyzing the parent's past data submission history, the optimal acceptance method can be selected and the efficiency of data submission can be improved. Some or all of the above-described processing in the data acceptance unit may be performed, for example, using AI or without AI. For example, the data acceptance unit can input the parent's past data submission history into a generation AI and have the generation AI select the optimal acceptance method.

[0076] The data accepting unit can filter the data based on the parent's current living situation or areas of interest when accepting data. The data accepting unit, for example, filters the data based on the parent's current living situation when accepting data. For example, the data accepting unit accepts only data necessary for the parent based on the parent's current living situation. The data accepting unit can also accept only related data based on the parent's areas of interest. For example, the data accepting unit preferentially accepts related data based on the parent's areas of interest. The data accepting unit can also determine the priority of data based on the parent's living situation and areas of interest. For example, the data accepting unit determines the priority of data based on the parent's living situation and areas of interest. By filtering the data based on the parent's living situation and areas of interest, only the necessary data can be efficiently accepted. Some or all of the above-described processing in the data accepting unit may be performed using, or without, AI. For example, the data accepting unit can input data on the parent's living situation and areas of interest to a generation AI and cause the generation AI to perform filtering.

[0077] The data acceptance unit can select an appropriate acceptance means depending on the parent's input method when accepting data. For example, the data acceptance unit selects an appropriate acceptance means depending on the parent's input method when accepting data. For example, if the parent uses voice input, the data acceptance unit can preferentially accept voice data. Also, if the parent uses text input, the data acceptance unit can preferentially accept text data. For example, if the parent uses images or videos, the data acceptance unit can preferentially accept image or video data. Also, the data acceptance unit can select the optimal acceptance means depending on the parent's input method. For example, the data acceptance unit selects the optimal acceptance means depending on the parent's input method. This improves the convenience of data submission by selecting the optimal acceptance means depending on the parent's input method. Some or all of the above-described processing in the data acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acceptance unit can input data of the parent's input method to a generation AI and cause the generation AI to select the optimal acceptance means.

[0078] The data accepting unit can estimate the parent's emotions and determine the priority of data to be accepted based on the estimated parent's emotions. The data accepting unit, for example, estimates the parent's emotions and determines the priority of data to be accepted based on the estimated parent's emotions. For example, if the parent is stressed, the data accepting unit prioritizes accepting only important data. Furthermore, if the parent is relaxed, the data accepting unit can equally accept all data. For example, if the parent is in a hurry, the data accepting unit prioritizes accepting data that requires rapid processing. Thus, by determining the priority of data according to the parent's emotions, important data can be accepted preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data accepting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data accepting unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0079] When receiving data, the data receiving unit can prioritize receiving highly relevant data by taking into account the geographical location information of the parent. For example, when receiving data, the data receiving unit prioritizes receiving highly relevant data by taking into account the geographical location information of the parent. For example, when the parent is in a specific area, the data receiving unit prioritizes receiving data related to that area. Furthermore, when the parent is traveling, the data receiving unit can also prioritize receiving data related to the travel destination. For example, when the parent is at home, the data receiving unit prioritizes receiving data related to the home. This allows for more appropriate data to be collected by preferentially receiving highly relevant data by taking into account the geographical location information of the parent. Some or all of the above-described processing in the data receiving unit may be performed using, or without, AI. For example, the data receiving unit may input the geographical location information of the parent to the generation AI and cause the generation AI to preferentially receive highly relevant data.

[0080] The data receiving unit can analyze the parent's social media activity and receive related data when receiving data. The data receiving unit, for example, analyzes the parent's social media activity and receives related data when receiving data. For example, the data receiving unit receives related data based on information shared by the parent on social media. The data receiving unit can also analyze the parent's social media activity and prioritize receiving related data. For example, the data receiving unit receives related data with reference to the activities of the parent's friends on social media. This allows the parent's social media activity to be analyzed and related data to be received efficiently. Some or all of the above-described processing in the data receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the data receiving unit can input data on the parent's social media activity to the generation AI and cause the generation AI to receive related data.

[0081] The data acceptance unit can customize the acceptance method by reflecting the parent's past feedback when accepting data. The data acceptance unit, for example, customizes the acceptance method by reflecting the parent's past feedback when accepting data. For example, the data acceptance unit suggests an optimal acceptance method based on feedback provided by the parent in the past. The data acceptance unit can also analyze the parent's past feedback and improve the acceptance method. For example, the data acceptance unit customizes the acceptance method by reflecting the parent's feedback. In this way, the acceptance method is customized by reflecting the parent's past feedback, improving the efficiency of data submission. Some or all of the above-mentioned processing in the data acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the data acceptance unit can input data of the parent's past feedback into the generation AI and cause the generation AI to customize the acceptance method.

[0082] The information acquisition unit can estimate the parent's emotion and adjust the expression method of information acquisition based on the estimated parent's emotion. The information acquisition unit, for example, estimates the parent's emotion and adjusts the expression method of information acquisition based on the estimated parent's emotion. For example, if the parent is stressed, the information acquisition unit acquires information using a simple expression method. Furthermore, if the parent is relaxed, the information acquisition unit can acquire information using a detailed expression method. For example, if the parent is in a hurry, the information acquisition unit acquires information quickly. This makes it easier for the parent to understand the information by adjusting the expression method of information acquisition according to the parent's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information acquisition unit may be performed using an AI, for example, or without an AI. For example, the information acquisition unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The information acquisition unit can adjust the level of detail of the information acquisition based on the child's important growth data when acquiring information. For example, the information acquisition unit adjusts the level of detail of the information acquisition based on the child's important growth data when acquiring information. For example, the information acquisition unit prioritizes acquiring important growth data such as the child's height and weight. The information acquisition unit can also acquire detailed data related to the child's development. For example, the information acquisition unit acquires detailed data related to the child's health condition. This allows necessary information to be acquired efficiently by adjusting the level of detail of the information acquisition based on the child's important growth data. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input the child's important growth data to the generation AI and cause the generation AI to adjust the level of detail of the information acquisition.

[0084] The information acquisition unit can apply different acquisition algorithms depending on the child's growth category when acquiring information. For example, the information acquisition unit applies different acquisition algorithms depending on the child's growth category when acquiring information. For example, the information acquisition unit applies an algorithm for acquiring data regarding the child's height and weight. The information acquisition unit can also apply an algorithm for acquiring data regarding the child's development. For example, the information acquisition unit applies an algorithm for acquiring data regarding the child's health condition. By applying different acquisition algorithms depending on the child's growth category, the accuracy of information acquisition is improved. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data of the child's growth category to the generation AI and cause the generation AI to apply different acquisition algorithms.

[0085] The information acquisition unit can improve the accuracy of information acquisition by referring to the parent's past information acquisition results when acquiring information. For example, the information acquisition unit improves the accuracy of information acquisition by referring to the parent's past information acquisition results when acquiring information. For example, the information acquisition unit improves the accuracy of information acquisition based on information acquired by the parent in the past. The information acquisition unit can also analyze the parent's past information acquisition results to improve the accuracy of information acquisition. For example, the information acquisition unit improves the accuracy of information acquisition by referring to the parent's past information acquisition results. As a result, the accuracy of information acquisition is improved by referring to the parent's past information acquisition results. Some or all of the above-mentioned processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data on the parent's past information acquisition results into the generation AI and cause the generation AI to improve the accuracy of information acquisition.

[0086] The information acquisition unit can estimate the parent's emotion and adjust the length of information acquisition based on the estimated parent's emotion. The information acquisition unit, for example, estimates the parent's emotion and adjusts the length of information acquisition based on the estimated parent's emotion. For example, if the parent is stressed, the information acquisition unit acquires information in a short time. Also, if the parent is relaxed, the information acquisition unit can acquire detailed information over a long period of time. For example, if the parent is in a hurry, the information acquisition unit acquires information quickly. This adjusts the length of information acquisition according to the parent's emotion, making it easier for the parent to understand the information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the information acquisition unit may be performed using an AI, for example, or without an AI. For example, the information acquisition unit can input parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The information acquisition unit can determine the priority of acquisition based on the time of submission of the child's growth data when acquiring information. The information acquisition unit, for example, determines the priority of acquisition based on the time of submission of the child's growth data when acquiring information. For example, if the child's growth data has been recently submitted, the information acquisition unit prioritizes acquisition of that data. The information acquisition unit can also prioritize acquisition of if the child's growth data has been submitted in the past. For example, the information acquisition unit determines the priority of acquisition based on the time of submission of the child's growth data. In this way, important data can be prioritized for acquisition by determining the priority of acquisition based on the time of submission of the child's growth data. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input data on the time of submission of the child's growth data to the generation AI and cause the generation AI to determine the priority of acquisition.

[0088] The information acquisition unit can adjust the order of acquisition based on the relevance of the child's growth data when acquiring information. The information acquisition unit, for example, adjusts the order of acquisition based on the relevance of the child's growth data when acquiring information. For example, if the relevance of the child's growth data is high, the information acquisition unit prioritizes acquisition of that data. Furthermore, if the relevance of the child's growth data is low, the information acquisition unit can postpone acquisition of that data. For example, the information acquisition unit adjusts the order of acquisition based on the relevance of the child's growth data. In this way, by adjusting the order of acquisition based on the relevance of the child's growth data, important data can be prioritized for acquisition. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input data on the relevance of the child's growth data to the generation AI and cause the generation AI to adjust the order of acquisition.

[0089] The information acquisition unit can adjust the use of technical terms in the acquisition according to the parent's level of expertise when acquiring information. For example, the information acquisition unit adjusts the use of technical terms in the acquisition according to the parent's level of expertise when acquiring information. For example, if the parent has technical knowledge, the information acquisition unit acquires information using technical terms. Furthermore, if the parent does not have technical knowledge, the information acquisition unit can acquire information using simple terms. For example, the information acquisition unit adjusts the use of technical terms in the acquisition according to the parent's level of expertise. This makes it easier for the parent to understand the information. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input data on the parent's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0090] The prediction unit can estimate the parent's emotions and adjust the prediction criteria based on the estimated parent's emotions. For example, the prediction unit estimates the parent's emotions and adjusts the prediction criteria based on the estimated parent's emotions. For example, if the parent is stressed, the prediction unit makes a prediction using simple criteria. The prediction unit can also make a prediction using detailed criteria if the parent is relaxed. For example, if the parent is in a hurry, the prediction unit makes a quick prediction. This allows the parent to easily understand the prediction results by adjusting the prediction criteria according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit can be performed using an AI, for example, or without an AI. For example, the prediction unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationships of the child's growth data when making a prediction. The prediction unit, for example, improves the accuracy of the prediction by taking into account the interrelationships of the child's growth data when making a prediction. For example, the prediction unit makes a prediction by taking into account the interrelationships between the child's height and weight. The prediction unit can also make a prediction by taking into account the interrelationships between the child's development and health condition. For example, the prediction unit improves the accuracy of the prediction by taking into account the interrelationships of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the interrelationships of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the interrelationships of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0092] The prediction unit can make a prediction taking into account attribute information of the submitter of the child's growth data. For example, the prediction unit makes a prediction taking into account attribute information of the submitter of the child's growth data. For example, if the submitter of the child's growth data is a parent, the prediction unit makes a prediction taking into account the attribute information of the parent. Furthermore, if the submitter of the child's growth data is a doctor, the prediction unit can also make a prediction taking into account the attribute information of the doctor. For example, the prediction unit makes a prediction taking into account attribute information of the submitter of the child's growth data. By taking into account the attribute information of the submitter of the child's growth data, the accuracy of the prediction is improved. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input attribute information data of the submitter of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0093] The prediction unit can weight the prediction based on the frequency of submission of the child's growth data when making a prediction. The prediction unit, for example, weights the prediction based on the frequency of submission of the child's growth data when making a prediction. For example, if the child's growth data is frequently submitted, the prediction unit weights the data when making a prediction. Furthermore, if the child's growth data is rarely submitted, the prediction unit can weight the data when making a prediction. For example, the prediction unit weights the prediction based on the frequency of submission of the child's growth data. As a result, weighting the prediction based on the frequency of submission of the child's growth data improves the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the frequency of submission of the child's growth data to the generation AI and cause the generation AI to weight the prediction.

[0094] The prediction unit can estimate the parent's emotions and adjust the order in which the prediction results are displayed based on the estimated parent's emotions. The prediction unit, for example, estimates the parent's emotions and adjusts the order in which the prediction results are displayed based on the estimated parent's emotions. For example, if the parent is stressed, the prediction unit prioritizes displaying important results. The prediction unit can also display all results evenly if the parent is relaxed. For example, if the parent is in a hurry, the prediction unit prioritizes displaying results that need to be displayed quickly. This allows the parent to prioritize important information by adjusting the order in which the prediction results are displayed based on the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, an AI. For example, the prediction unit may input the parent's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0095] The prediction unit can make predictions taking into account the geographical distribution of the child's growth data. For example, the prediction unit makes predictions taking into account the geographical distribution of the child's growth data. For example, if the child's growth data is concentrated in a specific region, the prediction unit makes predictions taking into account the characteristics of that region. Furthermore, if the child's growth data is distributed across multiple regions, the prediction unit can also make predictions taking into account the characteristics of each region. For example, the prediction unit improves the accuracy of the prediction by taking into account the geographical distribution of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the geographical distribution of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the geographical distribution of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0096] The prediction unit can improve the accuracy of the prediction by referring to literature related to the child's growth data when making a prediction. The prediction unit, for example, improves the accuracy of the prediction by referring to literature related to the child's growth data when making a prediction. For example, the prediction unit makes a prediction by referring to the latest research paper related to the child's growth data. The prediction unit can also make a prediction by referring to past research paper related to the child's growth data. For example, the prediction unit improves the accuracy of the prediction by referring to literature related to the child's growth data. In this way, the accuracy of the prediction is improved by referring to literature related to the child's growth data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input data from literature related to the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0097] The prediction unit can make a prediction taking into account the market value of the child's growth data. The prediction unit, for example, makes a prediction taking into account the market value of the child's growth data. For example, if the market value of the child's growth data is high, the prediction unit makes a prediction by prioritizing that data. Furthermore, if the market value of the child's growth data is low, the prediction unit can make a prediction by disregarding that data. For example, the prediction unit improves the accuracy of the prediction by taking into account the market value of the child's growth data. In this way, the accuracy of the prediction is improved by taking into account the market value of the child's growth data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on the market value of the child's growth data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0098] The presentation unit can estimate the parent's emotions and adjust the presentation display method based on the estimated parent's emotions. For example, the presentation unit can estimate the parent's emotions and adjust the presentation display method based on the estimated parent's emotions. For example, if the parent is stressed, the presentation unit can provide a simple, highly visible presentation method. Furthermore, if the parent is relaxed, the presentation unit can provide a presentation method including detailed information. For example, if the parent is in a hurry, the presentation unit can provide a presentation method that focuses on the main points. This allows the parent to easily understand the information by adjusting the presentation display method according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the presentation unit can be performed using, for example, an AI, or without an AI. For example, the presentation unit can input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The presentation unit can display the current growth prediction by referring to past trends in the child's growth data when presenting the data. The presentation unit, for example, displays the current growth prediction by referring to past trends in the child's growth data when presenting the data. For example, the presentation unit displays the child's past growth data in a graph and overlays the current growth prediction. The presentation unit can also display the child's past growth data in a chart and overlay the current growth prediction. For example, the presentation unit displays the child's past growth data in a table format and overlays the current growth prediction. This allows the current growth prediction to be displayed more accurately by referring to past trends in the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may input data on past trends in the child's growth data into the generation AI and cause the generation AI to display the current growth prediction.

[0100] The presentation unit can apply different display methods to each category of the child's growth data when presenting the data. For example, the presentation unit applies different display methods to each category of the child's growth data when presenting the data. For example, the presentation unit displays the child's height data in a graph and the weight data in a chart. The presentation unit can also display the child's development data in a table format and the health data in a graph. For example, the presentation unit applies an optimal display method to each category of the child's growth data. By applying different display methods to each category of the child's growth data, information can be presented in a more understandable manner. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data of the child's growth data category to the generation AI and cause the generation AI to apply different display methods.

[0101] The presentation unit can perform display taking into consideration attribute information of the submitter of the child's growth data when presenting the data. The presentation unit, for example, performs display taking into consideration attribute information of the submitter of the child's growth data when presenting the data. For example, if the submitter of the child's growth data is a parent, the presentation unit performs display taking into consideration the attribute information of the parent. Furthermore, if the submitter of the child's growth data is a doctor, the presentation unit can also perform display taking into consideration the attribute information of the doctor. For example, the presentation unit selects an optimal display method taking into consideration the attribute information of the submitter of the child's growth data. This allows information to be displayed more appropriately by taking into consideration the attribute information of the submitter of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data of attribute information of the submitter of the child's growth data to the generation AI and cause the generation AI to select a display method.

[0102] The presentation unit can estimate the parent's emotions and adjust the importance of presentation based on the estimated parent's emotions. The presentation unit, for example, estimates the parent's emotions and adjusts the importance of presentation based on the estimated parent's emotions. For example, if the parent is stressed, the presentation unit prioritizes displaying important information. Furthermore, if the parent is relaxed, the presentation unit can equally display all information. For example, if the parent is in a hurry, the presentation unit prioritizes displaying information that needs to be displayed quickly. This allows the parent to prioritize checking important information by adjusting the importance of presentation based on the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit may input the parent's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0103] The presentation unit can display a change in presentation based on the time of submission of the child's growth data at the time of presentation. The presentation unit, for example, displays a change in presentation based on the time of submission of the child's growth data at the time of presentation. For example, if the child's growth data has been recently submitted, the presentation unit highlights the data. Furthermore, if the child's growth data has been submitted in the past, the presentation unit can dim the data. For example, the presentation unit displays a change in presentation based on the time of submission of the child's growth data. This makes it possible to emphasize the newness of the information by displaying a change in presentation based on the time of submission of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input data on the time of submission of the child's growth data to the generation AI and cause the generation AI to display a change in presentation.

[0104] The presentation unit can refer to market data related to the child's growth data when presenting the information. The presentation unit, for example, refers to market data related to the child's growth data when presenting the information. For example, the presentation unit displays market data related to the child's growth data in a graph. The presentation unit can also display market data related to the child's growth data in a chart. For example, the presentation unit displays market data related to the child's growth data in a table format. This increases the relevance of the information by referring to the market data related to the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit inputs market data related to the child's growth data into the generation AI and causes the generation AI to display the presentation.

[0105] The presentation unit may present the child's growth data while taking into consideration the technical maturity of the data. For example, the presentation unit may present the child's growth data while taking into consideration the technical maturity of the data. For example, if the technical maturity of the child's growth data is high, the presentation unit may display the data in detail. Furthermore, if the technical maturity of the child's growth data is low, the presentation unit may display the data in a concise manner. For example, the presentation unit may select an optimal display method while taking into consideration the technical maturity of the child's growth data. This enables appropriate display of information by taking into consideration the technical maturity of the child's growth data. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input data on the technical maturity of the child's growth data to a generation AI and cause the generation AI to select a display method. === Hard Collateral 1-1 === Each of the multiple elements including the data accepting unit, information acquiring unit, prediction unit, and presentation 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 data accepting unit allows a parent to upload data regarding their child's growth using the accepting device 38 of the smart device 14. The information acquiring unit acquires image and video data using the camera 42 of the smart device 14, and the generation AI analyzes the data using the specific processing unit 290 of the data processing device 12. The prediction unit performs growth prediction using the generation AI via the specific processing unit 290 of the data processing device 12. The presentation unit visually displays the results using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned data acceptance unit, information acquisition unit, prediction unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data acceptance unit uses the microphone 238 of the smart glasses 214 to allow a parent to voice-input data related to their child's growth. The information acquisition unit uses the camera 42 of the smart glasses 214 to acquire image and video data, which is then analyzed by the generation AI using the specific processing unit 290 of the data processing device 12. The prediction unit uses the generation AI to perform growth prediction using the specific processing unit 290 of the data processing device 12. The presentation unit provides the result by voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned data acceptance unit, information acquisition unit, prediction unit, and presentation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data acceptance unit uses the microphone 238 of the headset-type terminal 314 to input data related to the child's growth by voice. The information acquisition unit uses the camera 42 of the headset-type terminal 314 to acquire image and video data, which is analyzed by the generation AI using the specific processing unit 290 of the data processing device 12. The prediction unit uses the generation AI to predict growth using the specific processing unit 290 of the data processing device 12. The presentation unit visually displays the results using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the data accepting unit, information acquiring unit, prediction unit, and presentation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data accepting unit uses the microphone 238 of the robot 414 to allow a parent to input data related to their child's growth by voice. The information acquiring unit uses the camera 42 of the robot 414 to acquire image and video data, which is analyzed by the generation AI using the specific processing unit 290 of the data processing device 12. The prediction unit uses the generation AI to predict growth using the specific processing unit 290 of the data processing device 12. The presentation unit provides the result by voice using the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The growth prediction system can also collect data on parents' lifestyle habits and reflect this in growth predictions. For example, it can collect data on parents' diet, exercise habits, sleep patterns, etc., and use this data to predict the impact on a child's growth. Growth predictions can also be made more accurately by taking into account the parents' stress levels and work situations. Furthermore, it can provide advice recommending healthy lifestyle habits based on the parents' lifestyle data. This supports parents in managing their own health and has a positive impact on their child's growth.

[0108] Growth prediction systems can also take into account a child's genetic information. For example, based on the parents' genetic information, genetic factors that may affect a child's growth can be identified and reflected in growth predictions. Genetic information can also be used to predict the risk of certain diseases and disabilities and take early measures. Genetic information can also be used to predict the development of a child's specific talents and abilities and provide appropriate education and training. This allows for more comprehensive support for a child's development.

[0109] The growth prediction system can also collect data on a child's social environment and reflect this in its growth predictions. For example, it can collect data on the child's school, local environment, friendships, and other factors, and use this data to predict the impact on a child's growth. Based on this data, it can also predict social problems and stress that a child may face, allowing for early intervention. Furthermore, based on this data, it can provide advice to support the development of a child's social skills and communication abilities. This allows for more comprehensive support for a child's growth.

[0110] The growth prediction system can also collect psychological data about children and incorporate it into growth predictions. For example, it can collect data on children's emotions, moods, stress levels, and other factors and use this data to predict the impact on a child's growth. It can also use psychological data to predict psychological problems and stress a child may face and take early action. Furthermore, it can provide advice to support a child's psychological health based on the psychological data. This allows for more comprehensive support for a child's growth.

[0111] The growth prediction system can also collect children's learning data and reflect this in its growth predictions. For example, it can collect data on children's learning outcomes, learning styles, learning environments, etc., and use this data to predict the impact on children's growth. It can also use learning data to predict learning problems and stress that children may face, allowing for early countermeasures. It can also use learning data to provide advice to support children's learning abilities and motivation. This allows for more comprehensive support for children's growth.

[0112] The growth prediction system can estimate the parent's emotions and customize the results of the growth prediction based on the estimated parent's emotions. For example, if the parent is feeling anxious, it can emphasize positive information that will reassure the parent. If the parent is excited, it can provide detailed information that will help the parent make a calm decision. Furthermore, if the parent is tired, it can provide concise, to-the-point information. In this way, by customizing the results of the growth prediction according to the parent's emotions, the information can be more easily understood by the parent.

[0113] The growth prediction system can estimate the parent's emotions and adjust the notification method for growth predictions based on the estimated parent's emotions. For example, if the parent is feeling stressed, the system can reduce notifications and send them again when the parent is relaxed. Also, if the parent is busy, the system can temporarily stop notifications and send them again later. Furthermore, if the parent is relaxed, the system can notify the parent immediately and provide information smoothly. In this way, adjusting the notification method according to the parent's emotions makes it easier for the parent to receive information.

[0114] The growth prediction system can estimate the parent's emotions and customize the growth prediction feedback based on the estimated parent's emotions. For example, if the parent is feeling anxious, it can provide positive feedback that gives reassurance. If the parent is excited, it can provide detailed feedback that helps the parent to make calm judgments. Furthermore, if the parent is tired, it can provide concise and to-the-point feedback. In this way, by customizing the feedback according to the parent's emotions, it becomes easier for the parent to understand the information.

[0115] The growth prediction system can estimate the parent's emotions and customize the growth prediction advice based on the estimated parent's emotions. For example, if the parent is feeling anxious, it can provide positive advice that reassures the parent. If the parent is excited, it can provide detailed advice that helps the parent make calm decisions. Furthermore, if the parent is tired, it can provide concise and to-the-point advice. In this way, by customizing advice according to the parent's emotions, it becomes easier for the parent to understand the information.

[0116] The growth prediction system can estimate the parent's emotions and customize the growth prediction interface based on the estimated parent's emotions. For example, if the parent is feeling stressed, a simple, highly visible interface can be provided. If the parent is relaxed, an interface containing detailed information can be provided. Furthermore, if the parent is in a hurry, an interface that focuses on the main points can be provided. In this way, customizing the interface according to the parent's emotions makes it easier for the parent to understand the information.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The data receiving unit allows parents to upload data about their child's growth. The data uploaded by parents includes data about height, weight, and development. For example, parents can upload data such as records of their child's height and weight, and developmental progress. Step 2: The information acquisition unit acquires growth-related information using image or video data based on the data uploaded by the data acceptance unit. For example, when a parent uploads a photo or video of their child, the generative AI analyzes facial features and body movements to acquire growth-related information. Step 3: The prediction unit analyzes the child's past data based on the information acquired by the information acquisition unit and predicts future growth. For example, it uses generation AI to predict future changes in height and weight based on past data. It can also make predictions about development. Step 4: The presentation unit visually presents the prediction results obtained by the prediction unit. For example, it may visually display future changes in height and weight using graphs or charts. It may also provide alerts regarding growth at specific points in time.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data reception unit where parents upload data about their child's growth; an information acquisition unit that acquires information about growth using image or video data based on the data uploaded by the data acceptance unit; a prediction unit that analyzes past data of the child based on the information acquired by the information acquisition unit and predicts future growth; a presentation unit that visually presents the prediction result obtained by the prediction unit. A system characterized by:

2. The prediction unit Predicting future height and weight trends based on past data 2. The system of claim 1.

3. The prediction unit Making developmental predictions based on past data 2. The system of claim 1.

4. The presentation unit Visually display the forecast results using graphs or charts 2. The system of claim 1.

5. The presentation unit Provides point-in-time growth alerts 2. The system of claim 1.

6. The presentation unit Alerts recommending consultation with a specialist when growth abnormalities are detected 2. The system of claim 1.

7. The data receiving unit Estimate the parent's emotions and adjust the timing of data reception based on the estimated parent's emotions 2. The system of claim 1.

8. The data receiving unit Analyze the parent's past data submission history and select the appropriate reception method 2. The system of claim 1.

9. The data receiving unit When data is received, filtering is performed based on the parent's current living situation or areas of interest.

2. The system of claim 1.

Images