System

Abstract

An object of the system according to the embodiment is to reproduce a figure and a future figure of a fetus based on an echo photograph and genetic information.SOLUTION: A system includes an echo photograph analysis part, a generation part, a genetic information analysis part, and a future figure projection part. The echo photograph analysis unit analyzes the echo photograph. The generation part reproduces the figure of the fetus from the echo photograph analyzed by the echo photograph analysis part. The genetic information analysis unit analyzes genetic information. A future figure projection part projects a figure after several years on the basis of the genetic information analyzed by the genetic information analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to recreate the appearance of a fetus or its future appearance based on ultrasound images and genetic information.

[0005] The system according to the embodiment aims to reproduce the appearance of a fetus and its future appearance based on ultrasound images and genetic information. [Means for solving the problem]

[0006] The system according to the embodiment includes an ultrasound image analysis unit, a generation unit, a genetic information analysis unit, and a future image projection unit. The ultrasound image analysis unit analyzes ultrasound images. The generation unit recreates the appearance of the fetus from the ultrasound images analyzed by the ultrasound image analysis unit. The genetic information analysis unit analyzes genetic information. The future image projection unit projects the appearance of the fetus several years from now based on the genetic information analyzed by the genetic information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the appearance of a fetus and its future appearance based on ultrasound images and genetic information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The fetus generation AI service according to an embodiment of the present invention is a system that automatically analyzes ultrasound images, recreates the appearance of the fetus, and analyzes genetic information to project what the fetus will look like in a few years' time, thereby providing future families with touching and meaningful moments.

[0029] The fetus generation AI service according to the embodiment includes an ultrasound image analysis unit, a generation unit, a genetic information analysis unit, and a future appearance projection unit. The ultrasound image analysis unit analyzes ultrasound images. For example, the ultrasound image analysis unit receives ultrasound images taken by a pregnant woman at a hospital as input and analyzes the image data. The ultrasound image analysis unit also learns the characteristics of ultrasound images and can reproduce the appearance of the fetus with high accuracy. The generation unit reproduces the appearance of the fetus from the ultrasound image analyzed by the ultrasound image analysis unit. For example, the generation unit reproduces the appearance of the fetus based on the image data of the ultrasound image using a generation AI. The generation AI can reproduce the appearance of the fetus using a text generation AI (e.g., LLM) or a multimodal generation AI. The genetic information analysis unit analyzes genetic information. For example, the genetic information analysis unit analyzes the genetic information of the fetus based on a genetic sample provided by the parents. The generation AI can predict the future appearance and health status based on the genetic information. The future appearance projection unit projects the appearance of the fetus several years in the future based on the genetic information analyzed by the genetic information analysis unit. For example, the future appearance projection unit uses the generation AI to predict the future appearance based on ultrasound images and genetic information data, and generates the result as an image or video. This allows the fetus generation AI service according to the embodiment to provide moving and meaningful moments for future families. For example, at a family gathering, the generation AI can project the future appearance of the baby in the form of "This baby will grow up like this," providing a touching moment for the whole family.

[0030] When analyzing ultrasound images, the ultrasound image analysis unit can reproduce fetal movements and facial expressions in real time and generate a dynamic 3D model. For example, the ultrasound image analysis unit uses a generative AI to analyze ultrasound images and reproduce fetal movements and facial expressions in real time. For example, the fetus moving its hands or frowning is displayed as a 3D model. The ultrasound image analysis unit also simulates fetal movements based on the results of ultrasound image analysis and generates a dynamic 3D model. For example, it reproduces the movements of a fetus in response to the mother's voice. The ultrasound image analysis unit also uses a generative AI to analyze ultrasound image data and reproduce fetal facial expressions and movements in real time. For example, it displays the fetus laughing or crying as a 3D model. This allows for more moving moments by reproducing fetal movements and facial expressions in real time.

[0031] The ultrasound image analysis unit can analyze ultrasound images to detect fetal health conditions and abnormalities and notify medical professionals. For example, the generative AI analyzes ultrasound images to detect fetal health conditions and abnormalities. For example, it analyzes heart rate and bone development and notifies medical professionals if abnormalities are found. The ultrasound image analysis unit also evaluates the fetus's health condition based on the results of ultrasound image analysis and sends an alert to medical professionals if abnormalities are detected. For example, it detects heart abnormalities and organ underdevelopment. The generative AI also analyzes ultrasound image data to monitor the fetus's health condition in real time. For example, it detects abnormal movements or developmental delays and notifies medical professionals. This allows for early medical response by detecting fetal health conditions and abnormalities and notifying medical professionals.

[0032] The ultrasound image analysis unit can reconstruct the appearance of a fetus using medical images other than ultrasound images as input. In the ultrasound image analysis unit, for example, a generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, the internal structure of a fetus is reconstructed in detail based on MRI or CT scan data. In addition, the ultrasound image analysis unit uses MRI or CT scan image data as input and the generative AI reconstructs the appearance of a fetus. For example, the detailed structure of the fetal brain and heart is displayed as a 3D model. In addition, the ultrasound image analysis unit adds a function whereby the generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, the detailed structure of the fetal skeleton and organs is reconstructed based on CT scan data. This makes it possible to provide more detailed information by reconstructing the appearance of a fetus using medical images other than ultrasound images.

[0033] The ultrasound image analysis unit can develop an app that uses AR to display the image of the fetus recreated from the ultrasound image on a family member's smartphone or tablet. For example, the ultrasound image analysis unit develops an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR. For example, a 3D model of the fetus is displayed superimposed on real space using a smartphone camera. The ultrasound image analysis unit can also develop an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR on a family member's smartphone or tablet. For example, the image of the fetus can be shared in AR at family gatherings. The ultrasound image analysis unit can also develop an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR. For example, a function can be added to display the fetus's movements and facial expressions in real time in AR. This allows the family to experience the fetus more realistically by displaying the fetus's appearance using AR.

[0034] The genetic information analysis unit can predict the fetus's future health risks and potential talents based on the results of the genetic information analysis, and provide advice to the family. In the genetic information analysis unit, for example, the generation AI analyzes genetic information and predicts the fetus's future health risks. For example, the risk of a specific disease is evaluated based on the genetic information, and advice is provided to the family. The genetic information analysis unit also predicts the fetus's potential talents based on the results of the genetic information analysis, and provides advice to the family. For example, talent in music or sports is evaluated based on the genetic information. In the genetic information analysis unit, the generation AI analyzes genetic information and predicts the fetus's future health risks and potential talents. For example, advice is provided to the family regarding future health management and educational policies based on the genetic information. In this way, predicting the fetus's future health risks and talents and providing appropriate advice to the family is useful for the family's health management and educational policies.

[0035] The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when analyzing genetic information, allowing for more accurate future predictions. For example, when the generation AI analyzes genetic information, the genetic information analysis unit takes into account the parents' lifestyle and environmental factors. For example, it predicts the fetus's future health risks based on the parents' eating habits and exercise habits. The genetic information analysis unit also combines the results of the genetic information analysis with the parents' lifestyle and environmental factors to make more accurate future predictions. For example, it evaluates the fetus's health risks by taking into account the parents' smoking and drinking habits. The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when the generation AI analyzes genetic information, allowing for more accurate future predictions. For example, it evaluates the fetus's future health risks based on the parents' occupations and living environment. In this way, by taking into account the parents' lifestyle and environmental factors, more accurate future predictions are possible.

[0036] The genetic information analysis unit can develop a health management app that provides future dietary and exercise advice for the fetus based on the results of genetic information analysis. For example, the genetic information analysis unit develops a health management app in which the generation AI analyzes genetic information and provides future dietary and exercise advice for the fetus. For example, if specific nutrients are needed, a meal plan is proposed. The genetic information analysis unit also develops an app that supports future health management for the fetus based on the results of genetic information analysis. For example, it adds functions to record exercise habits and dietary content and monitor health status. The genetic information analysis unit also develops a health management app in which the generation AI analyzes genetic information and provides future dietary and exercise advice for the fetus. For example, it proposes an individualized health management plan based on genetic information. In this way, by developing an app that supports future health management for the fetus, families can manage their health appropriately.

[0037] The genetic information analysis unit can compare the results of genetic information analysis with family history and genealogy to reveal the genetic characteristics of family members. For example, the generation AI analyzes genetic information and compares it with family history and genealogy to reveal genetic characteristics. For example, it analyzes the family's genetic medical history and characteristics. The genetic information analysis unit also adds a function to reveal the genetic characteristics of family members based on the results of genetic information analysis. For example, it analyzes common genetic characteristics based on the family's genealogy. The generation AI also analyzes genetic information and compares it with family history and genealogy to reveal genetic characteristics. For example, it evaluates future health risks based on the family's genetic characteristics. This reveals the genetic characteristics of family members, which is useful for managing the family's health and understanding their history.

[0038] The future image projection unit can display the growth process as an animation when projecting what the fetus will look like several years from now, visually showing how the fetus will grow. For example, the generation AI in the future image projection unit displays the fetus's growth process as an animation based on ultrasound images and genetic information. For example, it visually shows the fetus's growth as it ages 1, 2, and 3. The future image projection unit also adds a function to display the growth process as an animation when projecting what the fetus will look like several years from now. For example, it can recreate the moment the fetus starts walking or speaks for the first time in an animation. The generation AI in the future image projection unit also analyzes ultrasound images and genetic information to display the fetus's growth process as an animation. For example, it can show the physical changes and facial expressions that occur as the fetus grows. This allows families to visually understand the fetus's growth process by displaying the animation.

[0039] The future image projection unit can reflect family characteristics when projecting what the baby will look like several years from now, generating a more realistic image of the future. For example, the generation AI uses ultrasound images and genetic information to reflect family characteristics in what the baby will look like several years from now. For example, it recreates the future appearance of the fetus more realistically based on the facial features of the parents and siblings. The future image projection unit also adds a function to reflect family characteristics when projecting what the baby will look like several years from now. For example, it recreates the future facial features of the fetus based on the shape of the parents' eyes and mouth. The generation AI also analyzes ultrasound images and genetic information to reflect family characteristics in what the baby will look like several years from now. For example, it recreates the future appearance of the fetus based on the hair color and skin color of siblings. This allows for a more realistic image of the future to be generated by reflecting family characteristics.

[0040] The future vision projection unit can provide a projection of what the family will look like several years from now in a storytelling format that reflects the family's history and cultural background. For example, when the generation AI projects what the family will look like several years from now, the future vision projection unit provides it in a storytelling format that reflects the family's history and cultural background. For example, it generates a future image that incorporates the family's traditional events and cultural background. Furthermore, the future vision projection unit adds a storytelling format that reflects the family's history and cultural background when projecting what the family will look like several years from now. For example, it recreates the future image based on the family's historical events and cultural background. Furthermore, when the generation AI projects what the family will look like several years from now, the future vision projection unit provides it in a storytelling format that reflects the family's history and cultural background. For example, it generates a future image that incorporates the family's traditions and culture. This makes it possible to deepen family bonds by providing it in a storytelling format that reflects the family's history and cultural background.

[0041] The future image projection unit can add a function to display a projection of what the family will look like in several years' time on the family's smart home devices. For example, the future image projection unit adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is projected on a smart mirror. The future image projection unit also adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is projected onto a wall using a projector. The future image projection unit also adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is displayed on a smart display. This allows the family to experience the future more realistically by displaying it on a smart home device.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When analyzing ultrasound images, the ultrasound image analysis unit can reproduce fetal movements and facial expressions in real time and generate a dynamic 3D model. For example, the fetus moving its hands or frowning can be displayed as a 3D model. The ultrasound image analysis unit also simulates fetal movements based on the results of ultrasound image analysis and generates a dynamic 3D model. For example, it can reproduce the way a fetus moves in response to the mother's voice. The ultrasound image analysis unit also uses generative AI to analyze ultrasound image data and reproduce fetal facial expressions and movements in real time. For example, it can display the fetus laughing or crying as a 3D model. This allows for real-time reproduction of fetal movements and facial expressions, providing even more moving moments.

[0044] The ultrasound image analysis unit can analyze ultrasound images to detect fetal health conditions and abnormalities and notify medical professionals. For example, it analyzes heart rate and bone development and notifies medical professionals if abnormalities are found. The ultrasound image analysis unit also evaluates the fetus's health condition based on the results of ultrasound image analysis and sends an alert to medical professionals if abnormalities are detected. For example, it detects heart abnormalities and organ underdevelopment. The ultrasound image analysis unit also uses generative AI to analyze ultrasound image data and monitor the fetus's health condition in real time. For example, it detects abnormal movements or developmental delays and notifies medical professionals. This allows for early medical response by detecting fetal health conditions and abnormalities and notifying medical professionals.

[0045] The ultrasound image analysis unit can reconstruct the appearance of a fetus using medical images other than ultrasound images as input. For example, the generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, it reconstructs the internal structure of a fetus in detail based on MRI or CT scan data. The ultrasound image analysis unit also uses MRI or CT scan image data as input, and the generative AI reconstructs the appearance of a fetus. For example, it displays the detailed structure of the fetal brain and heart as a 3D model. The ultrasound image analysis unit also adds a function to the generative AI to analyze medical images other than ultrasound images and reconstruct the appearance of a fetus. For example, it reconstructs the detailed structure of the fetal skeleton and organs based on CT scan data. This makes it possible to provide more detailed information by reconstructing the appearance of a fetus using medical images other than ultrasound images.

[0046] The ultrasound image analysis unit can develop an app that displays the image of the fetus recreated from the ultrasound image in AR on a family member's smartphone or tablet. For example, the generation AI can analyze the ultrasound image and develop an app that displays the recreated image of the fetus in AR. For example, a 3D model of the fetus can be displayed superimposed on real space using the smartphone camera. The ultrasound image analysis unit can also develop an app that displays the image of the fetus recreated from the ultrasound image in AR on a family member's smartphone or tablet. For example, the image of the fetus can be shared in AR at family gatherings. The ultrasound image analysis unit can also develop an app that analyzes the ultrasound image in AR and displays the recreated image of the fetus in AR. For example, a function can be added to display the fetus's movements and facial expressions in real time in AR. This allows the family to experience the fetus more realistically by displaying the fetus's appearance using AR.

[0047] The genetic information analysis unit can predict the fetus's future health risks and potential talents based on the results of the genetic information analysis, and provide advice to the family. For example, the generation AI analyzes genetic information and predicts the fetus's future health risks. For example, it evaluates the risk of certain diseases based on the genetic information and provides advice to the family. The genetic information analysis unit can also predict the fetus's potential talents based on the results of the genetic information analysis, and provide advice to the family. For example, it can evaluate talent in music or sports based on the genetic information. The genetic information analysis unit can also analyze genetic information and predict the fetus's future health risks and potential talents. For example, it can provide advice to the family about future health management and educational policies based on the genetic information. In this way, predicting the fetus's future health risks and talents and providing appropriate advice to the family can be useful for family health management and educational policies.

[0048] The genetic information analysis unit takes into account the parents' lifestyle and environmental factors when analyzing genetic information, enabling more accurate future predictions. For example, when the generation AI analyzes genetic information, it takes into account the parents' lifestyle and environmental factors. For example, it predicts the fetus's future health risks based on the parents' eating habits and exercise habits. The genetic information analysis unit also combines the parents' lifestyle and environmental factors with the results of the genetic information analysis to make more accurate future predictions. For example, it evaluates the fetus's health risks by taking into account the parents' smoking and drinking habits. The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when analyzing genetic information, predicting future health risks and talents. For example, it evaluates the fetus's future health condition based on the parents' occupation and living environment. In this way, by taking into account the parents' lifestyle and environmental factors, more accurate future predictions are possible.

[0049] The genetic information analysis unit can develop a health management app that provides future dietary and exercise advice for the fetus based on the results of genetic information analysis. For example, the generative AI analyzes genetic information to develop a health management app that provides future dietary and exercise advice for the fetus. For example, if specific nutrients are needed, a meal plan may be proposed. The genetic information analysis unit also develops an app that supports the future health management of the fetus based on the results of genetic information analysis. For example, it adds functions to record exercise habits and dietary content and monitor health status. The genetic information analysis unit also develops a health management app that analyzes genetic information using the generative AI to develop a health management app that provides future dietary and exercise advice for the fetus. For example, it proposes an individualized health management plan based on genetic information. In this way, by developing an app that supports the future health management of the fetus, families can manage their health appropriately.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The ultrasound image analysis unit analyzes the ultrasound image. For example, it receives an ultrasound image taken by a pregnant woman at the hospital as input and analyzes the image data. The ultrasound image analysis unit has learned the characteristics of ultrasound images and can reproduce the appearance of the fetus with high accuracy. Step 2: The generator recreates the appearance of the fetus from the ultrasound image analyzed by the ultrasound image analyzer. For example, the generator uses a generation AI to recreate the appearance of the fetus based on the image data of the ultrasound image. The generation AI can recreate the appearance of the fetus using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The genetic information analysis unit analyzes the genetic information. For example, it analyzes the genetic information of the fetus based on genetic samples provided by the parents. The generation AI can then predict the future appearance and health status based on the genetic information. Step 4: The future vision projection unit projects what the family will look like in a few years' time based on the genetic information analyzed by the genetic information analysis unit. For example, the future vision projection unit uses the generation AI to predict what the family will look like in the future based on ultrasound images and genetic information data, and generates images and videos. This allows the future family to experience moving and meaningful moments.

[0052] (Example 2) The fetus generation AI service according to an embodiment of the present invention is a system that automatically analyzes ultrasound images, recreates the appearance of the fetus, and analyzes genetic information to project what the fetus will look like in a few years' time, thereby providing future families with touching and meaningful moments.

[0053] The fetus generation AI service according to the embodiment includes an ultrasound image analysis unit, a generation unit, a genetic information analysis unit, and a future appearance projection unit. The ultrasound image analysis unit analyzes ultrasound images. For example, the ultrasound image analysis unit receives ultrasound images taken by a pregnant woman at a hospital as input and analyzes the image data. The ultrasound image analysis unit also learns the characteristics of ultrasound images and can reproduce the appearance of the fetus with high accuracy. The generation unit reproduces the appearance of the fetus from the ultrasound image analyzed by the ultrasound image analysis unit. For example, the generation unit reproduces the appearance of the fetus based on the image data of the ultrasound image using a generation AI. The generation AI can reproduce the appearance of the fetus using a text generation AI (e.g., LLM) or a multimodal generation AI. The genetic information analysis unit analyzes genetic information. For example, the genetic information analysis unit analyzes the genetic information of the fetus based on a genetic sample provided by the parents. The generation AI can predict the future appearance and health status based on the genetic information. The future appearance projection unit projects the appearance of the fetus several years in the future based on the genetic information analyzed by the genetic information analysis unit. For example, the future appearance projection unit uses the generation AI to predict the future appearance based on ultrasound images and genetic information data, and generates the result as an image or video. This allows the fetus generation AI service according to the embodiment to provide moving and meaningful moments for future families. For example, at a family gathering, the generation AI can project the future appearance of the baby in the form of "This baby will grow up like this," providing a touching moment for the whole family.

[0054] When analyzing ultrasound images, the ultrasound image analysis unit can reproduce fetal movements and facial expressions in real time and generate a dynamic 3D model. For example, the ultrasound image analysis unit uses a generative AI to analyze ultrasound images and reproduce fetal movements and facial expressions in real time. For example, the fetus moving its hands or frowning is displayed as a 3D model. The ultrasound image analysis unit also simulates fetal movements based on the results of ultrasound image analysis and generates a dynamic 3D model. For example, it reproduces the movements of a fetus in response to the mother's voice. The ultrasound image analysis unit also uses a generative AI to analyze ultrasound image data and reproduce fetal facial expressions and movements in real time. For example, it displays the fetus laughing or crying as a 3D model. This allows for more moving moments by reproducing fetal movements and facial expressions in real time.

[0055] The ultrasound image analysis unit can analyze ultrasound images to detect fetal health conditions and abnormalities and notify medical professionals. For example, the generative AI analyzes ultrasound images to detect fetal health conditions and abnormalities. For example, it analyzes heart rate and bone development and notifies medical professionals if abnormalities are found. The ultrasound image analysis unit also evaluates the fetus's health condition based on the results of ultrasound image analysis and sends an alert to medical professionals if abnormalities are detected. For example, it detects heart abnormalities and organ underdevelopment. The generative AI also analyzes ultrasound image data to monitor the fetus's health condition in real time. For example, it detects abnormal movements or developmental delays and notifies medical professionals. This allows for early medical response by detecting fetal health conditions and abnormalities and notifying medical professionals.

[0056] The ultrasound image analysis unit uses an emotion estimation function to analyze the emotions of parents viewing ultrasound images and customize the reproduced image of the fetus based on those emotions. For example, the ultrasound image analysis unit uses a generation AI to analyze ultrasound images and analyze the emotions of parents in real time. For example, if the parents are happy, the unit generates a reproduced image that emphasizes the fetus's smiling face. The ultrasound image analysis unit also uses an emotion estimation function to analyze the emotions of parents viewing ultrasound images and customize the reproduced image of the fetus based on those emotions. For example, if the parents are moved, the unit reproduces the fetus's movements more realistically. The ultrasound image analysis unit also uses a generation AI to analyze ultrasound image data and customize the reproduced image based on the parents' emotions. For example, if the parents are feeling anxious, the unit generates a reproduced image that emphasizes the fetus's health. This allows the reproduced image to be customized based on the parents' emotions, creating a more touching moment.

[0057] The ultrasound image analysis unit can reconstruct the appearance of a fetus using medical images other than ultrasound images as input. In the ultrasound image analysis unit, for example, a generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, the internal structure of a fetus is reconstructed in detail based on MRI or CT scan data. In addition, the ultrasound image analysis unit uses MRI or CT scan image data as input and the generative AI reconstructs the appearance of a fetus. For example, the detailed structure of the fetal brain and heart is displayed as a 3D model. In addition, the ultrasound image analysis unit adds a function whereby the generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, the detailed structure of the fetal skeleton and organs is reconstructed based on CT scan data. This makes it possible to provide more detailed information by reconstructing the appearance of a fetus using medical images other than ultrasound images.

[0058] The ultrasound image analysis unit can develop an app that uses AR to display the image of the fetus recreated from the ultrasound image on a family member's smartphone or tablet. For example, the ultrasound image analysis unit develops an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR. For example, a 3D model of the fetus is displayed superimposed on real space using a smartphone camera. The ultrasound image analysis unit can also develop an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR on a family member's smartphone or tablet. For example, the image of the fetus can be shared in AR at family gatherings. The ultrasound image analysis unit can also develop an app in which a generative AI analyzes ultrasound images and displays the recreated image of the fetus in AR. For example, a function can be added to display the fetus's movements and facial expressions in real time in AR. This allows the family to experience the fetus more realistically by displaying the fetus's appearance using AR.

[0059] The ultrasound photo analysis unit uses the emotion estimation function to analyze the emotions of all family members who view the ultrasound photo in real time and make suggestions to create the most moving moments. For example, the generation AI in the ultrasound photo analysis unit analyzes the ultrasound photo and analyzes the emotions of all family members in real time. For example, it detects moments when family members are moved and generates a re-created image that emphasizes those moments. The ultrasound photo analysis unit also uses the emotion estimation function to analyze the emotions of all family members who view the ultrasound photo in real time and make suggestions to create the most moving moments. For example, it generates a re-created image that emphasizes moments when family members are happy. The ultrasound photo analysis unit also uses the generation AI to analyze the ultrasound photo data and make suggestions to create the most moving moments based on the emotions of all family members. For example, it generates a re-created image that emphasizes moments when family members are moved. This allows the family to deepen their bonds by analyzing the emotions of all family members and creating the most moving moments.

[0060] The genetic information analysis unit can predict the fetus's future health risks and potential talents based on the results of the genetic information analysis, and provide advice to the family. In the genetic information analysis unit, for example, the generation AI analyzes genetic information and predicts the fetus's future health risks. For example, the risk of a specific disease is evaluated based on the genetic information, and advice is provided to the family. The genetic information analysis unit also predicts the fetus's potential talents based on the results of the genetic information analysis, and provides advice to the family. For example, talent in music or sports is evaluated based on the genetic information. In the genetic information analysis unit, the generation AI analyzes genetic information and predicts the fetus's future health risks and potential talents. For example, advice is provided to the family regarding future health management and educational policies based on the genetic information. In this way, predicting the fetus's future health risks and talents and providing appropriate advice to the family is useful for the family's health management and educational policies.

[0061] The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when analyzing genetic information, allowing for more accurate future predictions. For example, when the generation AI analyzes genetic information, the genetic information analysis unit takes into account the parents' lifestyle and environmental factors. For example, it predicts the fetus's future health risks based on the parents' eating habits and exercise habits. The genetic information analysis unit also combines the results of the genetic information analysis with the parents' lifestyle and environmental factors to make more accurate future predictions. For example, it evaluates the fetus's health risks by taking into account the parents' smoking and drinking habits. The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when the generation AI analyzes genetic information, allowing for more accurate future predictions. For example, it evaluates the fetus's future health risks based on the parents' occupations and living environment. In this way, by taking into account the parents' lifestyle and environmental factors, more accurate future predictions are possible.

[0062] The genetic information analysis unit uses the emotion estimation function to analyze the emotions of parents who view the genetic information analysis results, and can customize the presentation method of the analysis results based on those emotions. For example, the genetic information analysis unit analyzes the emotions of parents in real time when the generation AI presents the genetic information analysis results. For example, if the parent is feeling anxious, it selects a presentation method of the analysis results that gives a sense of security. The genetic information analysis unit also uses the emotion estimation function to analyze the emotions of parents who view the genetic information analysis results, and customizes the presentation method of the analysis results based on those emotions. For example, if the parent is happy, it emphasizes positive information. The genetic information analysis unit also customizes the presentation method based on the parent's emotions when the generation AI presents the genetic information analysis results. For example, if the parent is surprised, it adds a detailed explanation. In this way, by customizing the presentation method of the analysis results based on the parent's emotions, it is possible to provide the parent with the most appropriate information.

[0063] The genetic information analysis unit can develop a health management app that provides future dietary and exercise advice for the fetus based on the results of genetic information analysis. For example, the genetic information analysis unit develops a health management app in which the generation AI analyzes genetic information and provides future dietary and exercise advice for the fetus. For example, if specific nutrients are needed, a meal plan is proposed. The genetic information analysis unit also develops an app that supports future health management for the fetus based on the results of genetic information analysis. For example, it adds functions to record exercise habits and dietary content and monitor health status. The genetic information analysis unit also develops a health management app in which the generation AI analyzes genetic information and provides future dietary and exercise advice for the fetus. For example, it proposes an individualized health management plan based on genetic information. In this way, by developing an app that supports future health management for the fetus, families can manage their health appropriately.

[0064] The genetic information analysis unit can compare the results of genetic information analysis with family history and genealogy to reveal the genetic characteristics of family members. For example, the generation AI analyzes genetic information and compares it with family history and genealogy to reveal genetic characteristics. For example, it analyzes the family's genetic medical history and characteristics. The genetic information analysis unit also adds a function to reveal the genetic characteristics of family members based on the results of genetic information analysis. For example, it analyzes common genetic characteristics based on the family's genealogy. The generation AI also analyzes genetic information and compares it with family history and genealogy to reveal genetic characteristics. For example, it evaluates future health risks based on the family's genetic characteristics. This reveals the genetic characteristics of family members, which is useful for managing the family's health and understanding their history.

[0065] The genetic information analysis unit uses the emotion estimation function to analyze the emotions of all family members who view the genetic information analysis results in real time and make suggestions to elicit the most positive response. For example, when the generation AI presents the genetic information analysis results, the genetic information analysis unit analyzes the emotions of all family members in real time. For example, if a family member is happy, the genetic information analysis unit presents analysis results that emphasize positive information. The genetic information analysis unit also uses the emotion estimation function to analyze the emotions of all family members who view the genetic information analysis results in real time and make suggestions to elicit the most positive response. For example, if a family member is moved, the genetic information analysis unit presents analysis results that emphasize that emotion. The genetic information analysis unit also makes suggestions to elicit the most positive response based on the emotions of all family members when the generation AI presents the genetic information analysis results. For example, if a family member is surprised, a detailed explanation is added. This allows the family bonds to be deepened by analyzing the emotions of all family members and eliciting the most positive response.

[0066] The future image projection unit can display the growth process as an animation when projecting what the fetus will look like several years from now, visually showing how the fetus will grow. For example, the generation AI in the future image projection unit displays the fetus's growth process as an animation based on ultrasound images and genetic information. For example, it visually shows the fetus's growth as it ages 1, 2, and 3. The future image projection unit also adds a function to display the growth process as an animation when projecting what the fetus will look like several years from now. For example, it can recreate the moment the fetus starts walking or speaks for the first time in an animation. The generation AI in the future image projection unit also analyzes ultrasound images and genetic information to display the fetus's growth process as an animation. For example, it can show the physical changes and facial expressions that occur as the fetus grows. This allows families to visually understand the fetus's growth process by displaying the animation.

[0067] The future image projection unit can reflect family characteristics when projecting what the baby will look like several years from now, generating a more realistic image of the future. For example, the generation AI uses ultrasound images and genetic information to reflect family characteristics in what the baby will look like several years from now. For example, it recreates the future appearance of the fetus more realistically based on the facial features of the parents and siblings. The future image projection unit also adds a function to reflect family characteristics when projecting what the baby will look like several years from now. For example, it recreates the future facial features of the fetus based on the shape of the parents' eyes and mouth. The generation AI also analyzes ultrasound images and genetic information to reflect family characteristics in what the baby will look like several years from now. For example, it recreates the future appearance of the fetus based on the hair color and skin color of siblings. This allows for a more realistic image of the future to be generated by reflecting family characteristics.

[0068] The future image projection unit uses the emotion estimation function to analyze the emotions of the family members when they see what they will look like in several years, and can customize the future image based on those emotions. For example, the future image projection unit analyzes the emotions of the family members in real time when the generation AI projects their appearance in several years. For example, if the family members are moved, it customizes the future image to be more moving. The future image projection unit also uses the emotion estimation function to analyze the emotions of the family members when they see their appearance in several years, and customizes the future image based on those emotions. For example, if the family members are happy, it adds smiling faces to the future image. The future image projection unit also customizes the future image based on the emotions of the family members when the generation AI projects their appearance in several years. For example, if the family members are surprised, it adds surprised expressions to the future image. In this way, by customizing the future image based on the emotions of the family members, it is possible to provide a more moving future image.

[0069] The future vision projection unit can provide a projection of what the family will look like several years from now in a storytelling format that reflects the family's history and cultural background. For example, when the generation AI projects what the family will look like several years from now, the future vision projection unit provides it in a storytelling format that reflects the family's history and cultural background. For example, it generates a future image that incorporates the family's traditional events and cultural background. Furthermore, the future vision projection unit adds a storytelling format that reflects the family's history and cultural background when projecting what the family will look like several years from now. For example, it recreates the future image based on the family's historical events and cultural background. Furthermore, when the generation AI projects what the family will look like several years from now, the future vision projection unit provides it in a storytelling format that reflects the family's history and cultural background. For example, it generates a future image that incorporates the family's traditions and culture. This makes it possible to deepen family bonds by providing it in a storytelling format that reflects the family's history and cultural background.

[0070] The future image projection unit can add a function to display a projection of what the family will look like in several years' time on the family's smart home devices. For example, the future image projection unit adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is projected on a smart mirror. The future image projection unit also adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is projected onto a wall using a projector. The future image projection unit also adds a function to have the generation AI project the family's appearance in several years' time and display it on the family's smart home devices. For example, the future image is displayed on a smart display. This allows the family to experience the future more realistically by displaying it on a smart home device.

[0071] The future image projection unit uses the emotion estimation function to analyze the emotions of all family members in real time when they see their appearances several years from now, and can make suggestions to create the most moving moments. For example, when the generation AI projects their appearances several years from now, the future image projection unit analyzes the emotions of all family members in real time. For example, it detects moments when family members are moved and generates a future image that emphasizes those moments. The future image projection unit also uses the emotion estimation function to analyze the emotions of all family members in real time when they see their appearances several years from now, and makes suggestions to create the most moving moments. For example, it generates a future image that emphasizes moments when the family is happy. The future image projection unit also makes suggestions to create the most moving moments based on the emotions of all family members when the generation AI projects their appearances several years from now. For example, it generates a future image that emphasizes moments when the family is moved. This allows the family bonds to be deepened by analyzing the emotions of all family members and creating the most moving moments.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When analyzing ultrasound images, the ultrasound image analysis unit can reproduce fetal movements and facial expressions in real time and generate a dynamic 3D model. For example, the fetus moving its hands or frowning can be displayed as a 3D model. The ultrasound image analysis unit also simulates fetal movements based on the results of ultrasound image analysis and generates a dynamic 3D model. For example, it can reproduce the way a fetus moves in response to the mother's voice. The ultrasound image analysis unit also uses generative AI to analyze ultrasound image data and reproduce fetal facial expressions and movements in real time. For example, it can display the fetus laughing or crying as a 3D model. This allows for real-time reproduction of fetal movements and facial expressions, providing even more moving moments.

[0074] The ultrasound image analysis unit can analyze ultrasound images to detect fetal health conditions and abnormalities and notify medical professionals. For example, it analyzes heart rate and bone development and notifies medical professionals if abnormalities are found. The ultrasound image analysis unit also evaluates the fetus's health condition based on the results of ultrasound image analysis and sends an alert to medical professionals if abnormalities are detected. For example, it detects heart abnormalities and organ underdevelopment. The ultrasound image analysis unit also uses generative AI to analyze ultrasound image data and monitor the fetus's health condition in real time. For example, it detects abnormal movements or developmental delays and notifies medical professionals. This allows for early medical response by detecting fetal health conditions and abnormalities and notifying medical professionals.

[0075] The ultrasound image analysis unit uses the emotion estimation function to analyze the emotions of parents viewing ultrasound images and customize the reproduced image of the fetus based on those emotions. For example, if the parents are happy, it generates a reproduced image that emphasizes the fetus's smiling face. The ultrasound image analysis unit also uses the emotion estimation function to analyze the emotions of parents viewing ultrasound images and customize the reproduced image of the fetus based on those emotions. For example, if the parents are moved, it will more realistically reproduce the fetal movements. The ultrasound image analysis unit also uses the generation AI to analyze the ultrasound image data and customize the reproduced image based on the parents' emotions. For example, if the parents are feeling anxious, it will generate a reproduced image that emphasizes the fetus's health. This makes it possible to provide a more touching moment by customizing the reproduced image based on the parents' emotions.

[0076] The ultrasound image analysis unit can reconstruct the appearance of a fetus using medical images other than ultrasound images as input. For example, the generative AI analyzes medical images other than ultrasound images to reconstruct the appearance of a fetus. For example, it reconstructs the internal structure of a fetus in detail based on MRI or CT scan data. The ultrasound image analysis unit also uses MRI or CT scan image data as input, and the generative AI reconstructs the appearance of a fetus. For example, it displays the detailed structure of the fetal brain and heart as a 3D model. The ultrasound image analysis unit also adds a function to the generative AI to analyze medical images other than ultrasound images and reconstruct the appearance of a fetus. For example, it reconstructs the detailed structure of the fetal skeleton and organs based on CT scan data. This makes it possible to provide more detailed information by reconstructing the appearance of a fetus using medical images other than ultrasound images.

[0077] The ultrasound image analysis unit can develop an app that displays the image of the fetus recreated from the ultrasound image in AR on a family member's smartphone or tablet. For example, the generation AI can analyze the ultrasound image and develop an app that displays the recreated image of the fetus in AR. For example, a 3D model of the fetus can be displayed superimposed on real space using the smartphone camera. The ultrasound image analysis unit can also develop an app that displays the image of the fetus recreated from the ultrasound image in AR on a family member's smartphone or tablet. For example, the image of the fetus can be shared in AR at family gatherings. The ultrasound image analysis unit can also develop an app that analyzes the ultrasound image in AR and displays the recreated image of the fetus in AR. For example, a function can be added to display the fetus's movements and facial expressions in real time in AR. This allows the family to experience the fetus more realistically by displaying the fetus's appearance using AR.

[0078] The ultrasound photo analysis unit uses the emotion estimation function to analyze the emotions of all family members who view the ultrasound photo in real time and make suggestions to create the most moving moments. For example, the generation AI analyzes the ultrasound photo and analyzes the emotions of all family members in real time. For example, it detects moments when family members are moved and generates a re-created image that emphasizes those moments. The ultrasound photo analysis unit also uses the emotion estimation function to analyze the emotions of all family members who view the ultrasound photo in real time and make suggestions to create the most moving moments. For example, it generates a re-created image that emphasizes moments when family members are happy. The ultrasound photo analysis unit also uses the generation AI to analyze the ultrasound photo data and make suggestions to create the most moving moments based on the emotions of all family members. For example, it generates a re-created image that emphasizes moments when family members are moved. This allows the family to deepen their bonds by analyzing the emotions of all family members and creating the most moving moments.

[0079] The genetic information analysis unit can predict the fetus's future health risks and potential talents based on the results of the genetic information analysis, and provide advice to the family. For example, the generation AI analyzes genetic information and predicts the fetus's future health risks. For example, it evaluates the risk of certain diseases based on the genetic information and provides advice to the family. The genetic information analysis unit can also predict the fetus's potential talents based on the results of the genetic information analysis, and provide advice to the family. For example, it can evaluate talent in music or sports based on the genetic information. The genetic information analysis unit can also analyze genetic information and predict the fetus's future health risks and potential talents. For example, it can provide advice to the family about future health management and educational policies based on the genetic information. In this way, predicting the fetus's future health risks and talents and providing appropriate advice to the family can be useful for family health management and educational policies.

[0080] The genetic information analysis unit takes into account the parents' lifestyle and environmental factors when analyzing genetic information, enabling more accurate future predictions. For example, when the generation AI analyzes genetic information, it takes into account the parents' lifestyle and environmental factors. For example, it predicts the fetus's future health risks based on the parents' eating habits and exercise habits. The genetic information analysis unit also combines the parents' lifestyle and environmental factors with the results of the genetic information analysis to make more accurate future predictions. For example, it evaluates the fetus's health risks by taking into account the parents' smoking and drinking habits. The genetic information analysis unit also takes into account the parents' lifestyle and environmental factors when analyzing genetic information, predicting future health risks and talents. For example, it evaluates the fetus's future health condition based on the parents' occupation and living environment. In this way, by taking into account the parents' lifestyle and environmental factors, more accurate future predictions are possible.

[0081] The genetic information analysis unit uses the emotion estimation function to analyze the emotions of parents who view the genetic information analysis results, and can customize the presentation method of the analysis results based on those emotions. For example, when the generation AI presents the genetic information analysis results, it analyzes the parents' emotions in real time. For example, if the parents are feeling anxious, it selects a presentation method of the analysis results that gives them a sense of security. The genetic information analysis unit also uses the emotion estimation function to analyze the parents' emotions when they view the genetic information analysis results, and customizes the presentation method of the analysis results based on those emotions. For example, if the parents are happy, it emphasizes positive information. The genetic information analysis unit also customizes the presentation method based on the parents' emotions when the generation AI presents the genetic information analysis results. For example, if the parents are surprised, it adds a detailed explanation. In this way, by customizing the presentation method of the analysis results based on the parents' emotions, it is possible to provide the parents with the most appropriate information.

[0082] The genetic information analysis unit can develop a health management app that provides future dietary and exercise advice for the fetus based on the results of genetic information analysis. For example, the generative AI analyzes genetic information to develop a health management app that provides future dietary and exercise advice for the fetus. For example, if specific nutrients are needed, a meal plan may be proposed. The genetic information analysis unit also develops an app that supports the future health management of the fetus based on the results of genetic information analysis. For example, it adds functions to record exercise habits and dietary content and monitor health status. The genetic information analysis unit also develops a health management app that analyzes genetic information using the generative AI to develop a health management app that provides future dietary and exercise advice for the fetus. For example, it proposes an individualized health management plan based on genetic information. In this way, by developing an app that supports the future health management of the fetus, families can manage their health appropriately.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The ultrasound image analysis unit analyzes the ultrasound image. For example, it receives an ultrasound image taken by a pregnant woman at the hospital as input and analyzes the image data. The ultrasound image analysis unit has learned the characteristics of ultrasound images and can reproduce the appearance of the fetus with high accuracy. Step 2: The generator recreates the appearance of the fetus from the ultrasound image analyzed by the ultrasound image analyzer. For example, the generator uses a generation AI to recreate the appearance of the fetus based on the image data of the ultrasound image. The generation AI can recreate the appearance of the fetus using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The genetic information analysis unit analyzes the genetic information. For example, it analyzes the genetic information of the fetus based on genetic samples provided by the parents. The generation AI can then predict the future appearance and health status based on the genetic information. Step 4: The future vision projection unit projects what the family will look like in a few years' time based on the genetic information analyzed by the genetic information analysis unit. For example, the future vision projection unit uses the generation AI to predict what the family will look like in the future based on ultrasound images and genetic information data, and generates images and videos. This allows the future family to experience moving and meaningful moments.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an echo image analysis unit that analyzes the echo image; a generator that reproduces the appearance of the fetus from the ultrasound image analyzed by the ultrasound image analyzer; a genetic information analysis unit that analyzes genetic information; a future image projection unit that projects what the person will look like several years from now based on the genetic information analyzed by the genetic information analysis unit. A system characterized by:

2. The echogram analysis unit During analysis of the ultrasound image, the fetus's movements and facial expressions are reproduced in real time to generate a dynamic 3D model.

2. The system of claim 1.

3. The echogram analysis unit The ultrasound image is analyzed to detect the fetal health status or abnormalities and notify a medical professional.

2. The system of claim 1.

4. The echogram analysis unit Analyzing the emotions of the parents who viewed the ultrasound image and customizing the reproduced image of the fetus based on the emotions.

2. The system of claim 1.

5. The echogram analysis unit Reconstruct the appearance of the fetus using medical images other than ultrasound images as input.

2. The system of claim 1.

Images