system

Abstract

We provide the system. [Solution] An input method for entering the user's physical information and desired experience scenario, A data processing means that generates feedback for simulating different stages of pregnancy based on the aforementioned physical information and desired experience scenarios, Output means for providing the aforementioned feedback to the user and for reproducing physical and emotional changes, A means of recording and collecting user feedback and data about their experiences, A system including a learning means for adjusting the simulation using the collected feedback and data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since pregnancy brings a direct physical experience only to some partners, there is a problem that it is difficult for partners who are not pregnant to understand the burden. For this reason, there may be insufficient support or communication due to lack of understanding of the burden. Furthermore, as part of the promotion of taking parental leave and the reform of work styles in companies, the importance of knowing the experience of pregnancy is increasing, but it is difficult to share the actual experience.

Means for Solving the Problems

[0005] This invention is a system that simulates different stages of pregnancy based on the user's physical information and desired experience scenario, allowing the user to experience physical and emotional changes. Specifically, it realistically recreates the pregnancy experience for the user by collecting user information through an input means, generating feedback through a data processing means, and providing that feedback through an output means. Furthermore, it provides a wider variety of pregnancy experiences by collecting feedback and data on the experience through a recording means and individually adjusting the simulation with a learning means. This system can also be used as an employee training tool in companies, promoting understanding of the pregnancy experience and improving communication between partners.

[0006] A "user" refers to a person who uses the system to simulate the experience of pregnancy.

[0007] "Physical information" refers to physiological and physical data such as the user's weight, height, and health status.

[0008] An "experience scenario" refers to a setting provided by the system to simulate different stages of pregnancy and specific situations related to those stages.

[0009] "Input means" refers to devices or interfaces that allow users to provide physical information or experience scenarios to a system.

[0010] "Data processing means" refers to devices or programs that generate feedback content based on user input information.

[0011] "Feedback" refers to the information and experiences provided by the system to simulate the physical and emotional experiences of pregnancy.

[0012] "Output means" refers to devices or methods for presenting generated feedback to the user.

[0013] "Recording means" refers to devices or programs used to collect user feedback and impressions about their experiences.

[0014] "Learning tools" refer to algorithms and programs used to improve system simulations based on collected feedback.

[0015] "Corporate employee training tools" refer to educational devices or programs used by companies to encourage employees to take childcare leave or to deepen their understanding of pregnancy experiences. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for simulating the experience of pregnancy, providing feedback at each stage of pregnancy based on the user's physical information and experience scenario. The system mainly consists of a server, terminals, and a user interface.

[0038] Server roles and functions

[0039] After receiving the user's inputted physical information and experience scenario, the server uses an AI model to generate real-time feedback. This feedback includes things like simulated abdominal weight and fetal movement, and emotional simulations. The server simultaneously processes data from numerous users, learning and improving the AI ​​model based on the collected feedback.

[0040] Terminal roles and functions

[0041] The device simulates a physical pregnancy experience by providing the user with feedback received from the server. It incorporates vibration and weight modules to realistically reproduce abdominal pressure and fetal movements. The device also provides an interface for the user to input their reactions and feedback, which is then returned to the server.

[0042] User interaction and experience

[0043] Users input necessary physical information and desired scenarios through the device's interface. For example, if a user wishes to experience early pregnancy, they will receive feedback focusing on mild morning sickness and emotional changes. As users receive this feedback and go about their daily lives, they can feel the reality of pregnancy and record their thoughts in response to questions from the device.

[0044] Specific example

[0045] For example, if a user selects "mid-pregnancy," the server generates feedback that includes the active movements of the fetus. The device reproduces the unpredictable movements (fetal movements) as vibrations, along with the sensation of increasing weight in the abdomen. As the user performs their daily activities, they experience sudden fetal movements, record the resulting emotional changes, and send this feedback to the server. This feedback is analyzed by the server and used to improve the user experience in the future.

[0046] This allows users, as non-pregnant partners or trainees, to more directly understand the pregnancy experience and use that understanding to improve communication.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user uses the device's interface to input physical information (e.g., weight, height) and the pregnancy scenario they wish to experience.

[0050] Step 2:

[0051] The terminal sends the user's input information to the server.

[0052] Step 3:

[0053] Based on the information received, the server uses an AI model to generate feedback that is appropriate for the user's experience scenario.

[0054] Step 4:

[0055] The server sends the generated feedback to the device. The feedback includes data on changes in abdominal weight, simulations of fetal movement, and changes in emotions.

[0056] Step 5:

[0057] Based on the feedback it receives, the device controls vibration devices and weight modules to provide the user with a physical pregnancy experience.

[0058] Step 6:

[0059] Users go about their daily lives while receiving feedback, and record the emotions and physical sensations they experience based on that feedback.

[0060] Step 7:

[0061] The device sends user reactions and recorded feedback to the server.

[0062] Step 8:

[0063] The server uses the collected user feedback data to train an AI model, updating it to provide a more accurate experience in the future.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Conventional pregnancy simulation systems have made it difficult for users to experience each stage of pregnancy realistically and in detail, and have been insufficient in reproducing emotions and physical changes in real time through feedback. Furthermore, there have been challenges in improving and adjusting the quality of feedback based on recorded experiences.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes an input device means for inputting the stages and physical elements that the user wishes to experience, a data processing device means for creating feedback using a generative AI model based on the stages and physical elements, and an output device means for presenting the feedback to the user and reproducing the physical experience. This allows the user to experience each stage of pregnancy in real time and to improve and adjust the quality of the feedback based on the results.

[0069] A "user" is someone who uses the system to input the stages and physical elements they wish to experience.

[0070] An "input device" refers to hardware or software used by a user to input information about the stage and physical elements they wish to experience.

[0071] A "generative AI model" is an algorithm or program that automatically generates feedback to provide to the user based on the input data.

[0072] "Feedback" refers to information and experiences that are generated by a generative AI model based on the user's experience and presented to the user.

[0073] A "data processing device" is hardware or software that has the function of processing input data and generating feedback.

[0074] An "output device" is hardware or software that provides generated feedback to the user, enabling the user to realistically recreate the experience.

[0075] A "recording device" is a device that has the function of recording user reactions and evaluations to the user experience and saving them in a format that can be used for future system improvements.

[0076] A "learning device" refers to hardware or software that uses recorded data to refine a generated AI model and improve the quality of feedback.

[0077] This invention is a system for users to simulate each stage of pregnancy, and mainly consists of a server, a terminal, and a user interface.

[0078] Server roles and functions

[0079] The server receives information about the user's experience stage and physical characteristics. Based on this information, it uses a generative AI model to generate optimal feedback for the user. The generative AI model learns from past data and provides real-time responses in response to the input prompts. An example of a prompt is the instruction, "Start the mid-pregnancy experience simulation and generate feedback on abdominal weight and fetal movement."

[0080] Terminal roles and functions

[0081] The device is responsible for receiving feedback sent from the server and providing it to the user. The device incorporates vibration devices and weight modules, which are operated to allow the user to experience abdominal pressure and fetal movements. The device also has an interface for recording the user's reactions and feedback, and this information is sent to the server.

[0082] User interaction and experience

[0083] Users can input their desired experience scenarios and stages of pregnancy through the device's interface. For example, when selecting early pregnancy, they will receive feedback on mild morning sickness and emotional changes. In their daily lives, users can feel the reality of the pregnancy experience based on the feedback received through the device and record their own thoughts and reactions.

[0084] This system allows the server to generate sophisticated feedback in real time using a generative AI model, providing users with a more realistic experience via their devices. This enables users to understand the pregnancy experience in more detail, and allows non-pregnant partners and training participants to share the experience from a new perspective.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users input the pregnancy stage and physical characteristics they wish to experience through the device's user interface. For example, if a user selects "mid-pregnancy," this information is sent from the device to the server. The input data includes the target pregnancy stage and the user's physical data.

[0088] Step 2:

[0089] The server generates feedback using a generative AI model based on the user's input data. Specifically, the AI ​​model selects the optimal response from past datasets to create feedback corresponding to the input pregnancy stage. In this process, data processing and calculations are performed according to prompts such as "Generate feedback for the second trimester," and the generated result is output as feedback.

[0090] Step 3:

[0091] The server sends the generated feedback to the terminal. The feedback includes physical and emotional changes that the user should experience, with detailed descriptions of vibration and pressure patterns.

[0092] Step 4:

[0093] The device controls its built-in vibration and weight modules based on the feedback it receives. For example, it generates vibrations on the user's abdomen to simulate fetal movement. This allows the user to realistically experience pregnancy according to their chosen stage.

[0094] Step 5:

[0095] Users input their reactions to the feedback experience into their device. For example, they record changes in their emotions and physical reactions when they feel fetal movement. The entered data is sent from the device to the server.

[0096] Step 6:

[0097] The server receives response data sent from users and uses it as training data to improve the generative AI model. Specifically, it analyzes this data and adjusts the model to make future feedback more individualized and accurate. The output is the adjusted model parameters.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Traditionally, simulations of pregnancy experiences have been limited to specific scenarios and devices, making it difficult for users to fully understand each stage of pregnancy concretely and realistically. Furthermore, there was a lack of reproduction of physical sensations and feedback on emotional changes, highlighting the need to improve user comprehension and the quality of the experience.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes receiving means for receiving the user's physical information and desired experience conditions, information processing means for simulating various stages of pregnancy based on the physical information and desired experience conditions, and output device for providing the user with the simulation results and reproducing physical phenomena and emotional changes. This allows the user to experience each stage of pregnancy in more detail and receive real-time adapted feedback, significantly improving the quality and understanding of the experience.

[0103] "Receiving means" refers to a device or process that receives the user's physical information and desired experience conditions as input.

[0104] An "information processing system" is a system that performs calculations and analyses to simulate each stage of pregnancy based on received physical information and experiential conditions.

[0105] An "output device" is a device that reproduces and provides the results of a simulation to the user as physical phenomena or emotional changes.

[0106] A "recording device" is a system or component used to store user reactions and information about their experience.

[0107] A "learning device" is a device that has the function of improving or adjusting the system simulation using collected user responses and information.

[0108] "Adaptive means" refers to methods and devices for providing optimal feedback based on the user's real-time location information and selection options.

[0109] A "multimodal output means" is a means of providing feedback through multiple senses, including sight, hearing, and touch, using a mobile display device or a haptic device.

[0110] This invention is a system for users to concretely simulate the experience of pregnancy. This system consists of a server, terminal devices, and a user interface.

[0111] The server receives the user's physical information and desired experience conditions via a receiving device, and uses an information processing device to generate simulations of each stage of pregnancy based on this information. This information processing utilizes deep learning frameworks such as TENSORFLOW® and PyTorch, generating simulation data in real time based on a generated AI model. The server then transfers this data to an output device to reproduce physical phenomena and emotional changes.

[0112] The terminal device provides feedback to the user from the server. Specifically, it provides visual and auditory feedback using mobile display devices such as Google® Glass® and Microsoft® HoloLens®, and reproduces physical phenomena through touch via a Bluetooth-connected vibration device.

[0113] Through this system, users can experience each stage of pregnancy. For example, by using a terminal installed in a commercial facility and giving the voice command "Start mid-pregnancy experience," the server generates physical and emotional feedback about the second trimester and conveys it to the user in real time. This allows users to deepen their understanding as a pregnant partner or parent.

[0114] For example, if a user specifies "late pregnancy," the system provides feedback such as a heavy belly and active fetal movements. The user can experience this and be influenced by their selection of related products.

[0115] An example prompt for the generating AI model is as follows: "User ID: X has requested a late-stage pregnancy experience. Generate and communicate the physical characteristics and emotional feedback of late-stage pregnancy, particularly emphasizing abdominal pressure and fetal movement when constructing vibration data."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server receives physical information and desired experience conditions transmitted from the user via a terminal. Input includes the user's health information and the stage of pregnancy they wish to experience. The received information is converted into an appropriate format as initial preprocessing.

[0119] Step 2:

[0120] The server uses a generative AI model, an information processing tool, to generate feedback data corresponding to each stage of pregnancy, based on the received physical information and experiential conditions. The input information is analyzed using deep learning, and emotional and physical scenarios aligned with the pregnancy stage are output as data. Specifically, data such as emotional changes, fetal movement, and abdominal weight are extracted.

[0121] Step 3:

[0122] The server sends the generated feedback data to the terminal. The output feedback data includes visual information, auditory effects, and physical data for tactile reproduction. These are designed for multimodal output on the terminal device.

[0123] Step 4:

[0124] The device uses feedback data received from the server to recreate visual and auditory scenarios on devices such as Google Glass and HoloLens. Additionally, a Bluetooth-connected vibration device provides physical tactile feedback to the user's stomach. Visual data is displayed on the screen, while tactile data is felt as vibrations.

[0125] Step 5:

[0126] Users experience feedback from their devices and input their thoughts and reactions to the recreated scenarios into the device's interface. The input feedback data is sent to the server in real time.

[0127] Step 6:

[0128] The server stores user feedback and reaction data in a recording device. Based on the collected data, a learning device analyzes the information and forms a feedback loop to improve the generated AI model. This will improve the quality of future simulations and enable a more realistic experience.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention is a system for simulating different stages of pregnancy based on the user's physical information and desired experience scenarios, and is particularly characterized by its use of an emotion engine to recognize the user's emotional state and individually adjust feedback. The system consists of a server, a terminal, an emotion engine, and a user interface.

[0131] Server roles and functions

[0132] The server receives physical information and experience scenarios from the user and uses an AI model to generate appropriate feedback. This feedback includes not only a physical recreation of the pregnancy experience but also emotional changes. By also taking into account the results of the user's emotional analysis by the emotion engine, more personalized feedback is provided.

[0133] Terminal roles and functions

[0134] The device receives feedback from the server and uses its built-in vibration and weight modules to recreate a physical pregnancy experience for the user. It also uses a camera and microphone to capture the user's facial expressions and voice, which are then sent to the emotion engine.

[0135] The role of the emotional engine

[0136] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, and physiological data. This information is fed back to the server, enabling real-time adjustments.

[0137] User interaction and experience

[0138] Users input their information and desired pregnancy scenarios through the device's interface. They then receive personalized feedback based on the feedback received, along with a physical experience derived from the emotional engine's analysis. During this process, users record their emotions and physical sensations in response to the feedback, which are then used to improve future experiences.

[0139] Specific example

[0140] For example, if a user selects "Stressful Second Trimester," the server generates feedback on the fetus's active movements and emotional changes. The device records the user's facial expressions and words while providing increasing weight in the abdomen and irregular pulsations. The emotion engine analyzes this data and, if it determines the user is stressed, adds relaxation feedback or adjusts the scenario. In this way, the user experience is provided in a manner closer to that of actual pregnancy.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The user uses the device's interface to enter their physical information and the pregnancy scenario they wish to experience.

[0144] Step 2:

[0145] The terminal sends the entered information to the server.

[0146] Step 3:

[0147] Based on the information received, the server uses an AI model to generate appropriate feedback.

[0148] Step 4:

[0149] The server sends feedback to the device, including feedback such as the weight of the belly and the patterns of fetal movement.

[0150] Step 5:

[0151] The device uses an emotion engine to record the user's facial expressions, voice, and physiological responses, and sends them to a server.

[0152] Step 6:

[0153] The server analyzes data from the emotion engine to identify the user's emotional state.

[0154] Step 7:

[0155] The server adjusts or updates the feedback based on the emotional state and sends the new feedback to the device as needed.

[0156] Step 8:

[0157] The device presents the user with tuned feedback, providing emotional feedback along with the physical experience.

[0158] Step 9:

[0159] Users experience the feedback they receive and record their emotions and physical reactions to it.

[0160] Step 10:

[0161] The device sends user records back to the server, which then uses this information to improve the AI ​​model for future user experiences.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] This invention aims to solve the difficulties in providing personalized pregnancy experiences that meet the physical and emotional needs of users. Current technology makes it difficult to generate real-time feedback tailored to individual users and to further adjust the experience based on that feedback.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes information input means for biometric data and experience scenarios, data calculation means for generating responses, and information output means for reproducing physical and emotional changes. This makes it possible to provide users with a personalized pregnancy experience in real time and to further optimize the next experience based on that feedback.

[0167] "Biometric data" refers to information about a user's physical condition, including specific numerical values ​​such as body temperature, heart rate, and blood pressure.

[0168] An "experience scenario" refers to a scene designed to simulate a specific situation or condition desired by the user, such as specific experiences during the early, middle, and late stages of pregnancy.

[0169] "Information input means" refers to devices and systems for receiving biometric data and experience scenarios from users, and includes interfaces such as touch panels and keyboards.

[0170] "Data processing means" refers to devices or algorithms that perform necessary calculations and processing based on input information and generate appropriate feedback or responses.

[0171] "Response" refers to information and feedback generated based on user input, provided to the user through physical and emotional experiences.

[0172] "Information output means" refers to devices or methods for presenting the generated response to the user, and includes physical devices such as vibration devices and displays.

[0173] "Data recording means" refers to devices or systems that have the function of saving user experiences and feedback, and retaining information that can be used to improve future experiences.

[0174] "Learning adjustment means" refers to methods and devices for analyzing collected data and improving or adjusting feedback content and experience scenarios.

[0175] One embodiment of this invention is a system aimed at allowing a user to concretely simulate the experience of pregnancy and experience physical and emotional changes. The system consists of a server, a terminal, an emotion analysis engine, and a user interface.

[0176] server

[0177] The server functions as a central control, processing biometric data and experience scenarios obtained from the user. It leverages a generative AI model to generate appropriate feedback based on user input. Specifically, it constructs prompts and sends them to the generative AI model according to the user's selected stage of pregnancy and emotional needs. This feedback includes both physical and emotional adjustments and is individually customized.

[0178] terminal

[0179] The device receives feedback from a server and provides the user with a physical pregnancy experience. It incorporates vibration devices and weight modules to provide feedback to the user's body. In addition, the device's camera and microphone record the user's facial expressions and voice, sending this data to an emotion analysis engine for more accurate feedback adjustments.

[0180] User actions

[0181] Users input pregnancy experience scenarios and their own physical data using the device's interface. For example, if a user selects "stressful second trimester" as their scenario, the device will provide increasing weight and irregular vibrations in the abdomen, adjusting the experience in real time based on server feedback. This allows users to have a more realistic pregnancy experience.

[0182] Sentiment analysis engine

[0183] The emotion analysis engine analyzes the user's facial expressions, voice, and physiological data, and uses this information to determine the user's emotional state. This analysis result is sent to the server and used to adjust feedback in real time.

[0184] Example of a prompt

[0185] "Based on user information and scenarios, generate feedback that replicates the stressful experiences of mid-pregnancy. Adjust the feedback, including relaxation techniques, taking into account the user's emotional analysis results."

[0186] In this way, the system provides users with an optimized pregnancy experience and promotes deeper learning and understanding through feedback tailored to individual needs.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] Users input biometric data and desired experience scenarios through the device's interface, using a touchscreen or keyboard. The entered data includes lifestyle information and the selection of pregnancy stage. The device then transmits this information to a server.

[0190] Step 2:

[0191] The server receives biometric data and experience scenarios from the terminal. Based on this information, it generates prompts and sends them to a generation AI model. The AI ​​model analyzes the input prompts and generates personalized feedback. The feedback includes physical and emotional elements and designs a pregnancy experience according to the user's choices. The generated feedback is stored on the server.

[0192] Step 3:

[0193] The server sends the generated feedback to the device. This feedback includes physical experiences that are reproduced in the user's body and recommended emotional adjustments. The device receives the information from the server and uses its built-in vibration device and weight module to embody the specified feedback in the user's body.

[0194] Step 4:

[0195] The device uses its built-in camera and microphone to collect user facial expressions and voice data in real time. This collected data serves as foundational information for evaluating the user's emotional and sensory responses. The device then transmits the collected data to an emotion analysis engine.

[0196] Step 5:

[0197] The emotion analysis engine analyzes the user's facial expression data, voice information, and physiological data received from the device. This allows it to recognize the user's current emotional state and feed the analysis results back to the server. The analysis results include emotional indicators such as stress, joy, and relaxation.

[0198] Step 6:

[0199] The server receives feedback from the sentiment analysis engine and optimizes the generated feedback. If necessary, it uses the generative AI model again to create new prompts and revise the feedback content. This adjusted feedback is then sent back to the terminal.

[0200] Step 7:

[0201] The device receives updated feedback from the server and continuously provides the user with an optimized experience. This experience process is dynamically adjusted according to the user's needs and emotional responses.

[0202] (Application Example 2)

[0203] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0204] In modern society, opportunities to realistically experience the stages of pregnancy are limited. Furthermore, there is a lack of means to individually perceive the emotional and physical changes involved, necessitating the development of methods for simulating these experiences. In particular, there is a need for a system that allows individuals to experience the stages of pregnancy in a more realistic way through virtual environments.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0206] In this invention, the server includes input means for inputting the user's physical attributes and desired experience; information processing means for generating responses to simulate different stages of pregnancy based on the physical attributes and desired experience; and presentation means for providing the responses to the user and reproducing physical and emotional changes. This makes the user feel more realistic about the pregnancy experience in the virtual environment.

[0207] "Physical attributes" refer to a user's build, health status, or other physiological information specific to the individual.

[0208] "Experience content" refers to the conditions and settings used to simulate a specific scenario or situation desired by the user.

[0209] "Information processing means" refers to a computing device or program for analyzing input information and generating the response necessary for simulation.

[0210] "Response" refers to data or signals that reproduce physical and emotional changes generated based on user input.

[0211] "Presentation means" refers to devices used by users to perceive responses intuitively, such as displays, speakers, and vibration devices.

[0212] "Virtual reality delivery methods" refer to technologies and devices that enable users to immerse themselves in a virtual environment.

[0213] "Control means" refers to mechanisms or programs that adjust signals and data to operate a device and give the user a specific sensation.

[0214] This invention is a system that simulates different stages of pregnancy in a virtual environment based on the user's physical attributes and desired experience. The system is broadly composed of a server, terminal, user interface, and emotion engine.

[0215] The server uses cloud services (e.g., AWS®) to receive physical attributes and experience details entered by the user. The entered information is analyzed using information processing tools, and a generative AI model generates a response to provide to the user. The generated response is transmitted to the terminal.

[0216] The terminal is installed on the user's device, such as a smartphone or head-mounted display (e.g., Oculus Quest). The terminal provides physical sensations by controlling a vibrating belt or weighted vest as a means of presentation based on the received responses. Visually, it recreates virtual pregnancy stages through a virtual reality presentation system.

[0217] The user interface is designed to allow users to easily provide information through input methods. The emotion engine uses Google Cloud's emotion analysis API to analyze the user's facial expressions and voice, recognizing their emotional state in real time. This allows the server to dynamically adjust the responses it generates based on the user's state.

[0218] For example, if a user wants to experience "early pregnancy amidst busy workdays," the system will provide responses that correspond to this situation. The device visually recreates a virtual office environment, and a vibrating belt provides vibrations that realistically represent mild fatigue. If the user feels stressed, the emotion engine detects this, and the system automatically adds content that promotes relaxation.

[0219] An example of a prompt might be: "User X's current experience scenario is 'early pregnancy amidst busy workdays.' Please generate appropriate feedback, taking into account physical information and emotional state." This allows the system to provide a personalized pregnancy experience within the virtual environment.

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The user enters their physical attributes and desired experience through a device. This information is stored as digital data on the device and transmitted to the server. This data includes the user's physical information and selected experience scenario.

[0223] Step 2:

[0224] The server analyzes the received physical attributes and experience details using information processing tools. The analyzed data is input into a generative AI model, which generates appropriate responses based on prepared prompts. This model operates on a cloud-based system and performs data calculations based on pre-programmed scenarios. The output is user-specific response data.

[0225] Step 3:

[0226] The server sends the generated response data to the terminal. The terminal receives this response and presents it to the user through virtual reality provisioning and control means. Specifically, the terminal displays visual information on a display device and transmits physical effects to the vibration belt and weighted vest.

[0227] Step 4:

[0228] The device uses its camera and microphone to collect the user's facial expressions and voice in order to obtain emotional and physical feedback. This data is sent in real time to an emotion analysis API to analyze the user's emotional state. The input data consists of facial expressions and voice information, while the output data is the analysis result regarding the emotional state.

[0229] Step 5:

[0230] The server receives emotional state data from the emotion engine and adjusts the original response in real time. The adjusted data is then returned to the device, providing the user with further personalized feedback. Specifically, if stress levels are high, relaxation content may be added.

[0231] Step 6:

[0232] Users can review their own impressions and experiences recorded on their devices. This feedback and data are used by the system to learn from subsequent experiences and provide a more accurate and improved experience.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] This invention is a system for simulating the experience of pregnancy, providing feedback at each stage of pregnancy based on the user's physical information and experience scenario. The system mainly consists of a server, terminals, and a user interface.

[0250] Server roles and functions

[0251] After receiving the user's inputted physical information and experience scenario, the server uses an AI model to generate real-time feedback. This feedback includes things like simulated abdominal weight and fetal movement, and emotional simulations. The server simultaneously processes data from numerous users, learning and improving the AI ​​model based on the collected feedback.

[0252] Terminal roles and functions

[0253] The device simulates a physical pregnancy experience by providing the user with feedback received from the server. It incorporates vibration and weight modules to realistically reproduce abdominal pressure and fetal movements. The device also provides an interface for the user to input their reactions and feedback, which is then returned to the server.

[0254] User interaction and experience

[0255] Users input necessary physical information and desired scenarios through the device's interface. For example, if a user wishes to experience early pregnancy, they will receive feedback focusing on mild morning sickness and emotional changes. As users receive this feedback and go about their daily lives, they can feel the reality of pregnancy and record their thoughts in response to questions from the device.

[0256] Specific example

[0257] For example, if a user selects "mid-pregnancy," the server generates feedback that includes the active movements of the fetus. The device reproduces the unpredictable movements (fetal movements) as vibrations, along with the sensation of increasing weight in the abdomen. As the user performs their daily activities, they experience sudden fetal movements, record the resulting emotional changes, and send this feedback to the server. This feedback is analyzed by the server and used to improve the user experience in the future.

[0258] This allows users, as non-pregnant partners or trainees, to more directly understand the pregnancy experience and use that understanding to improve communication.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The user uses the device's interface to input physical information (e.g., weight, height) and the pregnancy scenario they wish to experience.

[0262] Step 2:

[0263] The terminal sends the user's input information to the server.

[0264] Step 3:

[0265] Based on the information received, the server uses an AI model to generate feedback that is appropriate for the user's experience scenario.

[0266] Step 4:

[0267] The server sends the generated feedback to the device. The feedback includes data on changes in abdominal weight, simulations of fetal movement, and changes in emotions.

[0268] Step 5:

[0269] Based on the feedback it receives, the device controls vibration devices and weight modules to provide the user with a physical pregnancy experience.

[0270] Step 6:

[0271] Users go about their daily lives while receiving feedback, and record the emotions and physical sensations they experience based on that feedback.

[0272] Step 7:

[0273] The device sends user reactions and recorded feedback to the server.

[0274] Step 8:

[0275] The server uses the collected user feedback data to train an AI model, updating it to provide a more accurate experience in the future.

[0276] (Example 1)

[0277] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] Conventional pregnancy simulation systems have made it difficult for users to experience each stage of pregnancy realistically and in detail, and have been insufficient in reproducing emotions and physical changes in real time through feedback. Furthermore, there have been challenges in improving and adjusting the quality of feedback based on recorded experiences.

[0279] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes an input device means for inputting the stages and physical elements that the user wishes to experience, a data processing device means for creating feedback using a generative AI model based on the stages and physical elements, and an output device means for presenting the feedback to the user and reproducing the physical experience. This allows the user to experience each stage of pregnancy in real time and to improve and adjust the quality of the feedback based on the results.

[0281] A "user" is someone who uses the system to input the stages and physical elements they wish to experience.

[0282] The "input device" refers to hardware or software for inputting information about the stages and physical elements that the user wants to experience.

[0283] The "generative AI model" refers to an algorithm or program that automatically generates feedback to be provided to the user based on the input data.

[0284] "Feedback" refers to information and physical sensations created by the generative AI model according to the user's experience content and presented to the user.

[0285] The "data processing device" refers to hardware or software that has the function of processing the input data to generate feedback.

[0286] The "output device" refers to hardware or software for providing the generated feedback to the user so that the user can actually reproduce the experience.

[0287] The "recording device" has the function of recording the reactions and evaluations of the user's experience and storing them in a form that can be used for future system improvement.

[0288] The "learning device" refers to hardware or software that uses the recorded data to adjust the generative AI model and improve the quality of the feedback.

[0289] This invention is a system for the user to simulate each stage of pregnancy, mainly composed of a server, a terminal, and a user interface.

[0290] The role and function of the server

[0291] <000092G>The server receives information about the user's experience stage and physical characteristics. Based on this information, it uses a generative AI model to generate optimal feedback for the user. The generative AI model learns from past data and provides real-time responses in response to the input prompts. An example of a prompt is the instruction, "Start the mid-pregnancy experience simulation and generate feedback on abdominal weight and fetal movement."

[0292] Terminal roles and functions

[0293] The device is responsible for receiving feedback sent from the server and providing it to the user. The device incorporates vibration devices and weight modules, which are operated to allow the user to experience abdominal pressure and fetal movements. The device also has an interface for recording the user's reactions and feedback, and this information is sent to the server.

[0294] User interaction and experience

[0295] Users can input their desired experience scenarios and stages of pregnancy through the device's interface. For example, when selecting early pregnancy, they will receive feedback on mild morning sickness and emotional changes. In their daily lives, users can feel the reality of the pregnancy experience based on the feedback received through the device and record their own thoughts and reactions.

[0296] This system allows the server to generate sophisticated feedback in real time using a generative AI model, providing users with a more realistic experience via their devices. This enables users to understand the pregnancy experience in more detail, and allows non-pregnant partners and training participants to share the experience from a new perspective.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] Users input the pregnancy stage and physical characteristics they wish to experience through the device's user interface. For example, if a user selects "mid-pregnancy," this information is sent from the device to the server. The input data includes the target pregnancy stage and the user's physical data.

[0300] Step 2:

[0301] The server generates feedback using a generative AI model based on the user's input data. Specifically, the AI ​​model selects the optimal response from past datasets to create feedback corresponding to the input pregnancy stage. In this process, data processing and calculations are performed according to prompts such as "Generate feedback for the second trimester," and the generated result is output as feedback.

[0302] Step 3:

[0303] The server sends the generated feedback to the terminal. The feedback includes physical and emotional changes that the user should experience, with detailed descriptions of vibration and pressure patterns.

[0304] Step 4:

[0305] The device controls its built-in vibration and weight modules based on the feedback it receives. For example, it generates vibrations on the user's abdomen to simulate fetal movement. This allows the user to realistically experience pregnancy according to their chosen stage.

[0306] Step 5:

[0307] Users input their reactions to the feedback experience into their device. For example, they record changes in their emotions and physical reactions when they feel fetal movement. The entered data is sent from the device to the server.

[0308] Step 6:

[0309] The server receives the response data sent from the user and utilizes it as learning data for improving the generative AI model. Specifically, it analyzes this data and adjusts the model to make future feedback more individualized and accurate. As an output, adjusted model parameters are obtained.

[0310] (Application Example 1)

[0311] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] Conventionally, the simulation of the pregnancy experience could only be reproduced in limited scenarios and devices, making it difficult for users to fully and concretely understand each process of pregnancy in a real way. Also, the reproduction of physical sensations and the feedback of emotional changes were insufficient, and it was necessary to improve the user's understanding and the quality of the experience.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0314] In this invention, the server includes a receiving means for receiving the user's physical information and desired experience conditions, an information processing means for simulating various processes of pregnancy based on the physical information and desired experience conditions, and an output device for providing the simulation result to the user and reproducing physical phenomena and emotional changes. Thereby, the user can experience each stage of pregnancy in more detail and receive real-time adapted feedback, significantly improving the quality of the experience and the understanding.

[0315] The "receiving means" is a device or process that receives the user's physical information and desired experience conditions as inputs.

[0316] An "information processing system" is a system that performs calculations and analyses to simulate each stage of pregnancy based on received physical information and experiential conditions.

[0317] An "output device" is a device that reproduces and provides the results of a simulation to the user as physical phenomena or emotional changes.

[0318] A "recording device" is a system or component used to store user reactions and information about their experience.

[0319] A "learning device" is a device that has the function of improving or adjusting the system simulation using collected user responses and information.

[0320] "Adaptive means" refers to methods and devices for providing optimal feedback based on the user's real-time location information and selection options.

[0321] A "multimodal output means" is a means of providing feedback through multiple senses, including sight, hearing, and touch, using a mobile display device or a haptic device.

[0322] This invention is a system for users to concretely simulate the experience of pregnancy. This system consists of a server, terminal devices, and a user interface.

[0323] The server receives the user's physical information and desired experience conditions via a receiving device, and uses an information processing device to generate simulations of each stage of pregnancy based on this information. This information processing utilizes deep learning frameworks such as TensorFlow and PyTorch, generating simulation data in real time based on a generated AI model. The server then transfers this data to an output device, which reproduces physical phenomena and emotional changes.

[0324] The terminal device provides feedback to the user from the server. Specifically, it provides visual and auditory feedback using mobile display devices such as Google Glass and Microsoft HoloLens, and reproduces physical phenomena through touch via a Bluetooth-connected vibration device.

[0325] Through this system, users can experience each stage of pregnancy. For example, by using a terminal installed in a commercial facility and giving the voice command "Start mid-pregnancy experience," the server generates physical and emotional feedback about the second trimester and conveys it to the user in real time. This allows users to deepen their understanding as a pregnant partner or parent.

[0326] For example, if a user specifies "late pregnancy," the system provides feedback such as a heavy belly and active fetal movements. The user can experience this and be influenced by their selection of related products.

[0327] An example prompt for the generating AI model is as follows: "User ID: X has requested a late-stage pregnancy experience. Generate and communicate the physical characteristics and emotional feedback of late-stage pregnancy, particularly emphasizing abdominal pressure and fetal movement when constructing vibration data."

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The server receives physical information and desired experience conditions transmitted from the user via a terminal. Input includes the user's health information and the stage of pregnancy they wish to experience. The received information is converted into an appropriate format as initial preprocessing.

[0331] Step 2:

[0332] The server uses a generative AI model, an information processing tool, to generate feedback data corresponding to each stage of pregnancy, based on the received physical information and experiential conditions. The input information is analyzed using deep learning, and emotional and physical scenarios aligned with the pregnancy stage are output as data. Specifically, data such as emotional changes, fetal movement, and abdominal weight are extracted.

[0333] Step 3:

[0334] The server sends the generated feedback data to the terminal. The output feedback data includes visual information, auditory effects, and physical data for tactile reproduction. These are designed for multimodal output on the terminal device.

[0335] Step 4:

[0336] The device uses feedback data received from the server to recreate visual and auditory scenarios on devices such as Google Glass and HoloLens. Additionally, a Bluetooth-connected vibration device provides physical tactile feedback to the user's stomach. Visual data is displayed on the screen, while tactile data is felt as vibrations.

[0337] Step 5:

[0338] Users experience feedback from their devices and input their thoughts and reactions to the recreated scenarios into the device's interface. The input feedback data is sent to the server in real time.

[0339] Step 6:

[0340] The server stores user feedback and reaction data in a recording device. Based on the collected data, a learning device analyzes the information and forms a feedback loop to improve the generated AI model. This will improve the quality of future simulations and enable a more realistic experience.

[0341] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0342] This invention is a system for simulating different stages of pregnancy based on the user's physical information and desired experience scenarios, and is particularly characterized by its use of an emotion engine to recognize the user's emotional state and individually adjust feedback. The system consists of a server, a terminal, an emotion engine, and a user interface.

[0343] Server roles and functions

[0344] The server receives physical information and experience scenarios from the user and uses an AI model to generate appropriate feedback. This feedback includes not only a physical recreation of the pregnancy experience but also emotional changes. By also taking into account the results of the user's emotional analysis by the emotion engine, more personalized feedback is provided.

[0345] Terminal roles and functions

[0346] The device receives feedback from the server and uses its built-in vibration and weight modules to recreate a physical pregnancy experience for the user. It also uses a camera and microphone to capture the user's facial expressions and voice, which are then sent to the emotion engine.

[0347] The role of the emotional engine

[0348] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, and physiological data. This information is fed back to the server, enabling real-time adjustments.

[0349] User interaction and experience

[0350] Users input their information and desired pregnancy scenarios through the device's interface. They then receive personalized feedback based on the feedback received, along with a physical experience derived from the emotional engine's analysis. During this process, users record their emotions and physical sensations in response to the feedback, which are then used to improve future experiences.

[0351] Specific example

[0352] For example, if a user selects "Stressful Second Trimester," the server generates feedback on the fetus's active movements and emotional changes. The device records the user's facial expressions and words while providing increasing weight in the abdomen and irregular pulsations. The emotion engine analyzes this data and, if it determines the user is stressed, adds relaxation feedback or adjusts the scenario. In this way, the user experience is provided in a manner closer to that of actual pregnancy.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The user uses the device's interface to enter their physical information and the pregnancy scenario they wish to experience.

[0356] Step 2:

[0357] The terminal sends the entered information to the server.

[0358] Step 3:

[0359] Based on the information received, the server uses an AI model to generate appropriate feedback.

[0360] Step 4:

[0361] The server sends feedback to the device, including feedback such as the weight of the belly and the patterns of fetal movement.

[0362] Step 5:

[0363] The device uses an emotion engine to record the user's facial expressions, voice, and physiological responses, and sends them to a server.

[0364] Step 6:

[0365] The server analyzes data from the emotion engine to identify the user's emotional state.

[0366] Step 7:

[0367] The server adjusts or updates the feedback based on the emotional state and sends the new feedback to the device as needed.

[0368] Step 8:

[0369] The device presents the user with tuned feedback, providing emotional feedback along with the physical experience.

[0370] Step 9:

[0371] Users experience the feedback they receive and record their emotions and physical reactions to it.

[0372] Step 10:

[0373] The device sends user records back to the server, which then uses this information to improve the AI ​​model for future user experiences.

[0374] (Example 2)

[0375] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0376] This invention aims to solve the difficulties in providing personalized pregnancy experiences that meet the physical and emotional needs of users. Current technology makes it difficult to generate real-time feedback tailored to individual users and to further adjust the experience based on that feedback.

[0377] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0378] In this invention, the server includes information input means for biometric data and experience scenarios, data calculation means for generating responses, and information output means for reproducing physical and emotional changes. This makes it possible to provide users with a personalized pregnancy experience in real time and to further optimize the next experience based on that feedback.

[0379] "Biometric data" refers to information about a user's physical condition, including specific numerical values ​​such as body temperature, heart rate, and blood pressure.

[0380] An "experience scenario" refers to a scene designed to simulate a specific situation or condition desired by the user, such as specific experiences during the early, middle, and late stages of pregnancy.

[0381] "Information input means" refers to devices and systems for receiving biometric data and experience scenarios from users, and includes interfaces such as touch panels and keyboards.

[0382] "Data processing means" refers to devices or algorithms that perform necessary calculations and processing based on input information and generate appropriate feedback or responses.

[0383] "Response" refers to information and feedback generated based on user input, provided to the user through physical and emotional experiences.

[0384] "Information output means" refers to devices or methods for presenting the generated response to the user, and includes physical devices such as vibration devices and displays.

[0385] "Data recording means" refers to devices or systems that have the function of saving user experiences and feedback, and retaining information that can be used to improve future experiences.

[0386] "Learning adjustment means" refers to methods and devices for analyzing collected data and improving or adjusting feedback content and experience scenarios.

[0387] One embodiment of this invention is a system aimed at allowing a user to concretely simulate the experience of pregnancy and experience physical and emotional changes. The system consists of a server, a terminal, an emotion analysis engine, and a user interface.

[0388] server

[0389] The server functions as a central control, processing biometric data and experience scenarios obtained from the user. It leverages a generative AI model to generate appropriate feedback based on user input. Specifically, it constructs prompts and sends them to the generative AI model according to the user's selected stage of pregnancy and emotional needs. This feedback includes both physical and emotional adjustments and is individually customized.

[0390] terminal

[0391] The device receives feedback from a server and provides the user with a physical pregnancy experience. It incorporates vibration devices and weight modules to provide feedback to the user's body. In addition, the device's camera and microphone record the user's facial expressions and voice, sending this data to an emotion analysis engine for more accurate feedback adjustments.

[0392] User actions

[0393] Users input pregnancy experience scenarios and their own physical data using the device's interface. For example, if a user selects "stressful second trimester" as their scenario, the device will provide increasing weight and irregular vibrations in the abdomen, adjusting the experience in real time based on server feedback. This allows users to have a more realistic pregnancy experience.

[0394] Sentiment analysis engine

[0395] The emotion analysis engine analyzes the user's facial expressions, voice, and physiological data, and uses this information to determine the user's emotional state. This analysis result is sent to the server and used to adjust feedback in real time.

[0396] Example of a prompt

[0397] "Based on user information and scenarios, generate feedback that replicates the stressful experiences of mid-pregnancy. Adjust the feedback, including relaxation techniques, taking into account the user's emotional analysis results."

[0398] In this way, the system provides users with an optimized pregnancy experience and promotes deeper learning and understanding through feedback tailored to individual needs.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] Users input biometric data and desired experience scenarios through the device's interface, using a touchscreen or keyboard. The entered data includes lifestyle information and the selection of pregnancy stage. The device then transmits this information to a server.

[0402] Step 2:

[0403] The server receives biometric data and experience scenarios from the terminal. Based on this information, it generates prompts and sends them to a generation AI model. The AI ​​model analyzes the input prompts and generates personalized feedback. The feedback includes physical and emotional elements and designs a pregnancy experience according to the user's choices. The generated feedback is stored on the server.

[0404] Step 3:

[0405] The server sends the generated feedback to the device. This feedback includes physical experiences that are reproduced in the user's body and recommended emotional adjustments. The device receives the information from the server and uses its built-in vibration device and weight module to embody the specified feedback in the user's body.

[0406] Step 4:

[0407] The device uses its built-in camera and microphone to collect user facial expressions and voice data in real time. This collected data serves as foundational information for evaluating the user's emotional and sensory responses. The device then transmits the collected data to an emotion analysis engine.

[0408] Step 5:

[0409] The emotion analysis engine analyzes the user's facial expression data, voice information, and physiological data received from the device. This allows it to recognize the user's current emotional state and feed the analysis results back to the server. The analysis results include emotional indicators such as stress, joy, and relaxation.

[0410] Step 6:

[0411] The server receives feedback from the sentiment analysis engine and optimizes the generated feedback. If necessary, it uses the generative AI model again to create new prompts and revise the feedback content. This adjusted feedback is then sent back to the terminal.

[0412] Step 7:

[0413] The device receives updated feedback from the server and continuously provides the user with an optimized experience. This experience process is dynamically adjusted according to the user's needs and emotional responses.

[0414] (Application Example 2)

[0415] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0416] In modern society, opportunities to realistically experience the stages of pregnancy are limited. Furthermore, there is a lack of means to individually perceive the emotional and physical changes involved, necessitating the development of methods for simulating these experiences. In particular, there is a need for a system that allows individuals to experience the stages of pregnancy in a more realistic way through virtual environments.

[0417] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0418] In this invention, the server includes input means for inputting the user's physical attributes and desired experience; information processing means for generating responses to simulate different stages of pregnancy based on the physical attributes and desired experience; and presentation means for providing the responses to the user and reproducing physical and emotional changes. This makes the user feel more realistic about the pregnancy experience in the virtual environment.

[0419] "Physical attributes" refer to a user's build, health status, or other physiological information specific to the individual.

[0420] "Experience content" refers to the conditions and settings used to simulate a specific scenario or situation desired by the user.

[0421] "Information processing means" refers to a computing device or program for analyzing input information and generating the response necessary for simulation.

[0422] "Response" refers to data or signals that reproduce physical and emotional changes generated based on user input.

[0423] "Presentation means" refers to devices used by users to perceive responses intuitively, such as displays, speakers, and vibration devices.

[0424] "Virtual reality delivery methods" refer to technologies and devices that enable users to immerse themselves in a virtual environment.

[0425] "Control means" refers to mechanisms or programs that adjust signals and data to operate a device and give the user a specific sensation.

[0426] This invention is a system that simulates different stages of pregnancy in a virtual environment based on the user's physical attributes and desired experience. The system is broadly composed of a server, terminal, user interface, and emotion engine.

[0427] The server uses cloud services (e.g., AWS) to receive physical attributes and experience details entered by the user. The entered information is analyzed using information processing tools, and a generative AI model generates a response to provide to the user. The generated response is transmitted to the terminal.

[0428] The terminal is installed on the user's device, such as a smartphone or head-mounted display (e.g., Oculus Quest). The terminal provides physical sensations by controlling a vibrating belt or weighted vest as a means of presentation based on the received responses. Visually, it recreates virtual pregnancy stages through a virtual reality presentation system.

[0429] The user interface is designed to allow users to easily provide information through input methods. The emotion engine uses Google Cloud's emotion analysis API to analyze the user's facial expressions and voice, recognizing their emotional state in real time. This allows the server to dynamically adjust the responses it generates based on the user's state.

[0430] For example, if a user wants to experience "early pregnancy amidst busy workdays," the system will provide responses that correspond to this situation. The device visually recreates a virtual office environment, and a vibrating belt provides vibrations that realistically represent mild fatigue. If the user feels stressed, the emotion engine detects this, and the system automatically adds content that promotes relaxation.

[0431] An example of a prompt might be: "User X's current experience scenario is 'early pregnancy amidst busy workdays.' Please generate appropriate feedback, taking into account physical information and emotional state." This allows the system to provide a personalized pregnancy experience within the virtual environment.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The user enters their physical attributes and desired experience through a device. This information is stored as digital data on the device and transmitted to the server. This data includes the user's physical information and selected experience scenario.

[0435] Step 2:

[0436] The server analyzes the received physical attributes and experience details using information processing tools. The analyzed data is input into a generative AI model, which generates appropriate responses based on prepared prompts. This model operates on a cloud-based system and performs data calculations based on pre-programmed scenarios. The output is user-specific response data.

[0437] Step 3:

[0438] The server sends the generated response data to the terminal. The terminal receives this response and presents it to the user through virtual reality provisioning and control means. Specifically, the terminal displays visual information on a display device and transmits physical effects to the vibration belt and weighted vest.

[0439] Step 4:

[0440] The device uses its camera and microphone to collect the user's facial expressions and voice in order to obtain emotional and physical feedback. This data is sent in real time to an emotion analysis API to analyze the user's emotional state. The input data consists of facial expressions and voice information, while the output data is the analysis result regarding the emotional state.

[0441] Step 5:

[0442] The server receives emotional state data from the emotion engine and adjusts the original response in real time. The adjusted data is then returned to the device, providing the user with further personalized feedback. Specifically, if stress levels are high, relaxation content may be added.

[0443] Step 6:

[0444] Users can review their own impressions and experiences recorded on their devices. This feedback and data are used by the system to learn from subsequent experiences and provide a more accurate and improved experience.

[0445] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0446] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0447] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0451] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0452] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0453] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0454] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0455] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0456] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0457] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0458] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0459] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0460] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0461] This invention is a system for simulating the experience of pregnancy, providing feedback at each stage of pregnancy based on the user's physical information and experience scenario. The system mainly consists of a server, terminals, and a user interface.

[0462] Server roles and functions

[0463] After receiving the user's inputted physical information and experience scenario, the server uses an AI model to generate real-time feedback. This feedback includes things like simulated abdominal weight and fetal movement, and emotional simulations. The server simultaneously processes data from numerous users, learning and improving the AI ​​model based on the collected feedback.

[0464] Terminal roles and functions

[0465] The device simulates a physical pregnancy experience by providing the user with feedback received from the server. It incorporates vibration and weight modules to realistically reproduce abdominal pressure and fetal movements. The device also provides an interface for the user to input their reactions and feedback, which is then returned to the server.

[0466] User interaction and experience

[0467] Users input necessary physical information and desired scenarios through the device's interface. For example, if a user wishes to experience early pregnancy, they will receive feedback focusing on mild morning sickness and emotional changes. As users receive this feedback and go about their daily lives, they can feel the reality of pregnancy and record their thoughts in response to questions from the device.

[0468] Specific example

[0469] For example, if a user selects "mid-pregnancy," the server generates feedback that includes the active movements of the fetus. The device reproduces the unpredictable movements (fetal movements) as vibrations, along with the sensation of increasing weight in the abdomen. As the user performs their daily activities, they experience sudden fetal movements, record the resulting emotional changes, and send this feedback to the server. This feedback is analyzed by the server and used to improve the user experience in the future.

[0470] This allows users, as non-pregnant partners or trainees, to more directly understand the pregnancy experience and use that understanding to improve communication.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] The user uses the device's interface to input physical information (e.g., weight, height) and the pregnancy scenario they wish to experience.

[0474] Step 2:

[0475] The terminal sends the user's input information to the server.

[0476] Step 3:

[0477] Based on the information received, the server uses an AI model to generate feedback that is appropriate for the user's experience scenario.

[0478] Step 4:

[0479] The server sends the generated feedback to the device. The feedback includes data on changes in abdominal weight, simulations of fetal movement, and changes in emotions.

[0480] Step 5:

[0481] Based on the feedback it receives, the device controls vibration devices and weight modules to provide the user with a physical pregnancy experience.

[0482] Step 6:

[0483] Users go about their daily lives while receiving feedback, and record the emotions and physical sensations they experience based on that feedback.

[0484] Step 7:

[0485] The device sends user reactions and recorded feedback to the server.

[0486] Step 8:

[0487] The server uses the collected user feedback data to train an AI model, updating it to provide a more accurate experience in the future.

[0488] (Example 1)

[0489] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] Conventional pregnancy simulation systems have made it difficult for users to experience each stage of pregnancy realistically and in detail, and have been insufficient in reproducing emotions and physical changes in real time through feedback. Furthermore, there have been challenges in improving and adjusting the quality of feedback based on recorded experiences.

[0491] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0492] In this invention, the server includes an input device means for inputting the stages and physical elements that the user wishes to experience, a data processing device means for creating feedback using a generative AI model based on the stages and physical elements, and an output device means for presenting the feedback to the user and reproducing the physical experience. This allows the user to experience each stage of pregnancy in real time and to improve and adjust the quality of the feedback based on the results.

[0493] A "user" is someone who uses the system to input the stages and physical elements they wish to experience.

[0494] An "input device" refers to hardware or software used by a user to input information about the stage and physical elements they wish to experience.

[0495] A "generative AI model" is an algorithm or program that automatically generates feedback to provide to the user based on the input data.

[0496] "Feedback" refers to information and experiences that are generated by a generative AI model based on the user's experience and presented to the user.

[0497] A "data processing device" is hardware or software that has the function of processing input data and generating feedback.

[0498] An "output device" is hardware or software that provides generated feedback to the user, enabling the user to realistically recreate the experience.

[0499] A "recording device" is a device that has the function of recording user reactions and evaluations to the user experience and saving them in a format that can be used for future system improvements.

[0500] A "learning device" refers to hardware or software that uses recorded data to refine a generated AI model and improve the quality of feedback.

[0501] This invention is a system for users to simulate each stage of pregnancy, and mainly consists of a server, a terminal, and a user interface.

[0502] Server roles and functions

[0503] The server receives information about the user's experience stage and physical characteristics. Based on this information, it uses a generative AI model to generate optimal feedback for the user. The generative AI model learns from past data and provides real-time responses in response to the input prompts. An example of a prompt is the instruction, "Start the mid-pregnancy experience simulation and generate feedback on abdominal weight and fetal movement."

[0504] Terminal roles and functions

[0505] The device is responsible for receiving feedback sent from the server and providing it to the user. The device incorporates vibration devices and weight modules, which are operated to allow the user to experience abdominal pressure and fetal movements. The device also has an interface for recording the user's reactions and feedback, and this information is sent to the server.

[0506] User interaction and experience

[0507] Users can input their desired experience scenarios and stages of pregnancy through the device's interface. For example, when selecting early pregnancy, they will receive feedback on mild morning sickness and emotional changes. In their daily lives, users can feel the reality of the pregnancy experience based on the feedback received through the device and record their own thoughts and reactions.

[0508] This system allows the server to generate sophisticated feedback in real time using a generative AI model, providing users with a more realistic experience via their devices. This enables users to understand the pregnancy experience in more detail, and allows non-pregnant partners and training participants to share the experience from a new perspective.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] Users input the pregnancy stage and physical characteristics they wish to experience through the device's user interface. For example, if a user selects "mid-pregnancy," this information is sent from the device to the server. The input data includes the target pregnancy stage and the user's physical data.

[0512] Step 2:

[0513] The server generates feedback using a generative AI model based on the user's input data. Specifically, the AI ​​model selects the optimal response from past datasets to create feedback corresponding to the input pregnancy stage. In this process, data processing and calculations are performed according to prompts such as "Generate feedback for the second trimester," and the generated result is output as feedback.

[0514] Step 3:

[0515] The server sends the generated feedback to the terminal. The feedback includes physical and emotional changes that the user should experience, with detailed descriptions of vibration and pressure patterns.

[0516] Step 4:

[0517] The device controls its built-in vibration and weight modules based on the feedback it receives. For example, it generates vibrations on the user's abdomen to simulate fetal movement. This allows the user to realistically experience pregnancy according to their chosen stage.

[0518] Step 5:

[0519] Users input their reactions to the feedback experience into their device. For example, they record changes in their emotions and physical reactions when they feel fetal movement. The entered data is sent from the device to the server.

[0520] Step 6:

[0521] The server receives response data sent from users and uses it as training data to improve the generative AI model. Specifically, it analyzes this data and adjusts the model to make future feedback more individualized and accurate. The output is the adjusted model parameters.

[0522] (Application Example 1)

[0523] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Traditionally, simulations of pregnancy experiences have been limited to specific scenarios and devices, making it difficult for users to fully understand each stage of pregnancy concretely and realistically. Furthermore, there was a lack of reproduction of physical sensations and feedback on emotional changes, highlighting the need to improve user comprehension and the quality of the experience.

[0525] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0526] In this invention, the server includes receiving means for receiving the user's physical information and desired experience conditions, information processing means for simulating various stages of pregnancy based on the physical information and desired experience conditions, and output device for providing the user with the simulation results and reproducing physical phenomena and emotional changes. This allows the user to experience each stage of pregnancy in more detail and receive real-time adapted feedback, significantly improving the quality and understanding of the experience.

[0527] "Receiving means" refers to a device or process that receives the user's physical information and desired experience conditions as input.

[0528] An "information processing system" is a system that performs calculations and analyses to simulate each stage of pregnancy based on received physical information and experiential conditions.

[0529] An "output device" is a device that reproduces and provides the results of a simulation to the user as physical phenomena or emotional changes.

[0530] A "recording device" is a system or component used to store user reactions and information about their experience.

[0531] A "learning device" is a device that has the function of improving or adjusting the system simulation using collected user responses and information.

[0532] "Adaptive means" refers to methods and devices for providing optimal feedback based on the user's real-time location information and selection options.

[0533] A "multimodal output means" is a means of providing feedback through multiple senses, including sight, hearing, and touch, using a mobile display device or a haptic device.

[0534] This invention is a system for users to concretely simulate the experience of pregnancy. This system consists of a server, terminal devices, and a user interface.

[0535] The server receives the user's physical information and desired experience conditions via a receiving device, and uses an information processing device to generate simulations of each stage of pregnancy based on this information. This information processing utilizes deep learning frameworks such as TensorFlow and PyTorch, generating simulation data in real time based on a generated AI model. The server then transfers this data to an output device, which reproduces physical phenomena and emotional changes.

[0536] The terminal device provides feedback to the user from the server. Specifically, it provides visual and auditory feedback using mobile display devices such as Google Glass and Microsoft HoloLens, and reproduces physical phenomena through touch via a Bluetooth-connected vibration device.

[0537] Through this system, users can experience each stage of pregnancy. For example, by using a terminal installed in a commercial facility and giving the voice command "Start mid-pregnancy experience," the server generates physical and emotional feedback about the second trimester and conveys it to the user in real time. This allows users to deepen their understanding as a pregnant partner or parent.

[0538] For example, if a user specifies "late pregnancy," the system provides feedback such as a heavy belly and active fetal movements. The user can experience this and be influenced by their selection of related products.

[0539] An example prompt for the generating AI model is as follows: "User ID: X has requested a late-stage pregnancy experience. Generate and communicate the physical characteristics and emotional feedback of late-stage pregnancy, particularly emphasizing abdominal pressure and fetal movement when constructing vibration data."

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The server receives physical information and desired experience conditions transmitted from the user via a terminal. Input includes the user's health information and the stage of pregnancy they wish to experience. The received information is converted into an appropriate format as initial preprocessing.

[0543] Step 2:

[0544] The server uses a generative AI model, an information processing tool, to generate feedback data corresponding to each stage of pregnancy, based on the received physical information and experiential conditions. The input information is analyzed using deep learning, and emotional and physical scenarios aligned with the pregnancy stage are output as data. Specifically, data such as emotional changes, fetal movement, and abdominal weight are extracted.

[0545] Step 3:

[0546] The server sends the generated feedback data to the terminal. The output feedback data includes visual information, auditory effects, and physical data for tactile reproduction. These are designed for multimodal output on the terminal device.

[0547] Step 4:

[0548] The device uses feedback data received from the server to recreate visual and auditory scenarios on devices such as Google Glass and HoloLens. Additionally, a Bluetooth-connected vibration device provides physical tactile feedback to the user's stomach. Visual data is displayed on the screen, while tactile data is felt as vibrations.

[0549] Step 5:

[0550] Users experience feedback from their devices and input their thoughts and reactions to the recreated scenarios into the device's interface. The input feedback data is sent to the server in real time.

[0551] Step 6:

[0552] The server stores user feedback and reaction data in a recording device. Based on the collected data, a learning device analyzes the information and forms a feedback loop to improve the generated AI model. This will improve the quality of future simulations and enable a more realistic experience.

[0553] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0554] This invention is a system for simulating different stages of pregnancy based on the user's physical information and desired experience scenarios, and is particularly characterized by its use of an emotion engine to recognize the user's emotional state and individually adjust feedback. The system consists of a server, a terminal, an emotion engine, and a user interface.

[0555] Server roles and functions

[0556] The server receives physical information and experience scenarios from the user and uses an AI model to generate appropriate feedback. This feedback includes not only a physical recreation of the pregnancy experience but also emotional changes. By also taking into account the results of the user's emotional analysis by the emotion engine, more personalized feedback is provided.

[0557] Terminal roles and functions

[0558] The device receives feedback from the server and uses its built-in vibration and weight modules to recreate a physical pregnancy experience for the user. It also uses a camera and microphone to capture the user's facial expressions and voice, which are then sent to the emotion engine.

[0559] The role of the emotional engine

[0560] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, and physiological data. This information is fed back to the server, enabling real-time adjustments.

[0561] User interaction and experience

[0562] Users input their information and desired pregnancy scenarios through the device's interface. They then receive personalized feedback based on the feedback received, along with a physical experience derived from the emotional engine's analysis. During this process, users record their emotions and physical sensations in response to the feedback, which are then used to improve future experiences.

[0563] Specific example

[0564] For example, if a user selects "Stressful Second Trimester," the server generates feedback on the fetus's active movements and emotional changes. The device records the user's facial expressions and words while providing increasing weight in the abdomen and irregular pulsations. The emotion engine analyzes this data and, if it determines the user is stressed, adds relaxation feedback or adjusts the scenario. In this way, the user experience is provided in a manner closer to that of actual pregnancy.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The user uses the device's interface to enter their physical information and the pregnancy scenario they wish to experience.

[0568] Step 2:

[0569] The terminal sends the entered information to the server.

[0570] Step 3:

[0571] Based on the information received, the server uses an AI model to generate appropriate feedback.

[0572] Step 4:

[0573] The server sends feedback to the device, including feedback such as the weight of the belly and the patterns of fetal movement.

[0574] Step 5:

[0575] The device uses an emotion engine to record the user's facial expressions, voice, and physiological responses, and sends them to a server.

[0576] Step 6:

[0577] The server analyzes data from the emotion engine to identify the user's emotional state.

[0578] Step 7:

[0579] The server adjusts or updates the feedback based on the emotional state and sends the new feedback to the device as needed.

[0580] Step 8:

[0581] The device presents the user with tuned feedback, providing emotional feedback along with the physical experience.

[0582] Step 9:

[0583] Users experience the feedback they receive and record their emotions and physical reactions to it.

[0584] Step 10:

[0585] The device sends user records back to the server, which then uses this information to improve the AI ​​model for future user experiences.

[0586] (Example 2)

[0587] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0588] This invention aims to solve the difficulties in providing personalized pregnancy experiences that meet the physical and emotional needs of users. Current technology makes it difficult to generate real-time feedback tailored to individual users and to further adjust the experience based on that feedback.

[0589] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0590] In this invention, the server includes information input means for biometric data and experience scenarios, data calculation means for generating responses, and information output means for reproducing physical and emotional changes. This makes it possible to provide users with a personalized pregnancy experience in real time and to further optimize the next experience based on that feedback.

[0591] "Biometric data" refers to information about a user's physical condition, including specific numerical values ​​such as body temperature, heart rate, and blood pressure.

[0592] An "experience scenario" refers to a scene designed to simulate a specific situation or condition desired by the user, such as specific experiences during the early, middle, and late stages of pregnancy.

[0593] "Information input means" refers to devices and systems for receiving biometric data and experience scenarios from users, and includes interfaces such as touch panels and keyboards.

[0594] "Data processing means" refers to devices or algorithms that perform necessary calculations and processing based on input information and generate appropriate feedback or responses.

[0595] "Response" refers to information and feedback generated based on user input, provided to the user through physical and emotional experiences.

[0596] "Information output means" refers to devices or methods for presenting the generated response to the user, and includes physical devices such as vibration devices and displays.

[0597] "Data recording means" refers to devices or systems that have the function of saving user experiences and feedback, and retaining information that can be used to improve future experiences.

[0598] "Learning adjustment means" refers to methods and devices for analyzing collected data and improving or adjusting feedback content and experience scenarios.

[0599] One embodiment of this invention is a system aimed at allowing a user to concretely simulate the experience of pregnancy and experience physical and emotional changes. The system consists of a server, a terminal, an emotion analysis engine, and a user interface.

[0600] server

[0601] The server functions as a central control, processing biometric data and experience scenarios obtained from the user. It leverages a generative AI model to generate appropriate feedback based on user input. Specifically, it constructs prompts and sends them to the generative AI model according to the user's selected stage of pregnancy and emotional needs. This feedback includes both physical and emotional adjustments and is individually customized.

[0602] terminal

[0603] The device receives feedback from a server and provides the user with a physical pregnancy experience. It incorporates vibration devices and weight modules to provide feedback to the user's body. In addition, the device's camera and microphone record the user's facial expressions and voice, sending this data to an emotion analysis engine for more accurate feedback adjustments.

[0604] User actions

[0605] Users input pregnancy experience scenarios and their own physical data using the device's interface. For example, if a user selects "stressful second trimester" as their scenario, the device will provide increasing weight and irregular vibrations in the abdomen, adjusting the experience in real time based on server feedback. This allows users to have a more realistic pregnancy experience.

[0606] Sentiment analysis engine

[0607] The emotion analysis engine analyzes the user's facial expressions, voice, and physiological data, and uses this information to determine the user's emotional state. This analysis result is sent to the server and used to adjust feedback in real time.

[0608] Example of a prompt

[0609] "Based on user information and scenarios, generate feedback that replicates the stressful experiences of mid-pregnancy. Adjust the feedback, including relaxation techniques, taking into account the user's emotional analysis results."

[0610] In this way, the system provides users with an optimized pregnancy experience and promotes deeper learning and understanding through feedback tailored to individual needs.

[0611] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0612] Step 1:

[0613] Users input biometric data and desired experience scenarios through the device's interface, using a touchscreen or keyboard. The entered data includes lifestyle information and the selection of pregnancy stage. The device then transmits this information to a server.

[0614] Step 2:

[0615] The server receives biometric data and experience scenarios from the terminal. Based on this information, it generates prompts and sends them to a generation AI model. The AI ​​model analyzes the input prompts and generates personalized feedback. The feedback includes physical and emotional elements and designs a pregnancy experience according to the user's choices. The generated feedback is stored on the server.

[0616] Step 3:

[0617] The server sends the generated feedback to the device. This feedback includes physical experiences that are reproduced in the user's body and recommended emotional adjustments. The device receives the information from the server and uses its built-in vibration device and weight module to embody the specified feedback in the user's body.

[0618] Step 4:

[0619] The device uses its built-in camera and microphone to collect user facial expressions and voice data in real time. This collected data serves as foundational information for evaluating the user's emotional and sensory responses. The device then transmits the collected data to an emotion analysis engine.

[0620] Step 5:

[0621] The emotion analysis engine analyzes the user's facial expression data, voice information, and physiological data received from the device. This allows it to recognize the user's current emotional state and feed the analysis results back to the server. The analysis results include emotional indicators such as stress, joy, and relaxation.

[0622] Step 6:

[0623] The server receives feedback from the sentiment analysis engine and optimizes the generated feedback. If necessary, it uses the generative AI model again to create new prompts and revise the feedback content. This adjusted feedback is then sent back to the terminal.

[0624] Step 7:

[0625] The device receives updated feedback from the server and continuously provides the user with an optimized experience. This experience process is dynamically adjusted according to the user's needs and emotional responses.

[0626] (Application Example 2)

[0627] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0628] In modern society, opportunities to realistically experience the stages of pregnancy are limited. Furthermore, there is a lack of means to individually perceive the emotional and physical changes involved, necessitating the development of methods for simulating these experiences. In particular, there is a need for a system that allows individuals to experience the stages of pregnancy in a more realistic way through virtual environments.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0630] In this invention, the server includes input means for inputting the user's physical attributes and desired experience; information processing means for generating responses to simulate different stages of pregnancy based on the physical attributes and desired experience; and presentation means for providing the responses to the user and reproducing physical and emotional changes. This makes the user feel more realistic about the pregnancy experience in the virtual environment.

[0631] "Physical attributes" refer to a user's build, health status, or other physiological information specific to the individual.

[0632] "Experience content" refers to the conditions and settings used to simulate a specific scenario or situation desired by the user.

[0633] "Information processing means" refers to a computing device or program for analyzing input information and generating the response necessary for simulation.

[0634] "Response" refers to data or signals that reproduce physical and emotional changes generated based on user input.

[0635] "Presentation means" refers to devices used by users to perceive responses intuitively, such as displays, speakers, and vibration devices.

[0636] "Virtual reality delivery methods" refer to technologies and devices that enable users to immerse themselves in a virtual environment.

[0637] "Control means" refers to mechanisms or programs that adjust signals and data to operate a device and give the user a specific sensation.

[0638] This invention is a system that simulates different stages of pregnancy in a virtual environment based on the user's physical attributes and desired experience. The system is broadly composed of a server, terminal, user interface, and emotion engine.

[0639] The server uses cloud services (e.g., AWS) to receive physical attributes and experience details entered by the user. The entered information is analyzed using information processing tools, and a generative AI model generates a response to provide to the user. The generated response is transmitted to the terminal.

[0640] The terminal is installed on the user's device, such as a smartphone or head-mounted display (e.g., Oculus Quest). The terminal provides physical sensations by controlling a vibrating belt or weighted vest as a means of presentation based on the received responses. Visually, it recreates virtual pregnancy stages through a virtual reality presentation system.

[0641] The user interface is designed to allow users to easily provide information through input methods. The emotion engine uses Google Cloud's emotion analysis API to analyze the user's facial expressions and voice, recognizing their emotional state in real time. This allows the server to dynamically adjust the responses it generates based on the user's state.

[0642] For example, if a user wants to experience "early pregnancy amidst busy workdays," the system will provide responses that correspond to this situation. The device visually recreates a virtual office environment, and a vibrating belt provides vibrations that realistically represent mild fatigue. If the user feels stressed, the emotion engine detects this, and the system automatically adds content that promotes relaxation.

[0643] An example of a prompt might be: "User X's current experience scenario is 'early pregnancy amidst busy workdays.' Please generate appropriate feedback, taking into account physical information and emotional state." This allows the system to provide a personalized pregnancy experience within the virtual environment.

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The user enters their physical attributes and desired experience through a device. This information is stored as digital data on the device and transmitted to the server. This data includes the user's physical information and selected experience scenario.

[0647] Step 2:

[0648] The server analyzes the received physical attributes and experience details using information processing tools. The analyzed data is input into a generative AI model, which generates appropriate responses based on prepared prompts. This model operates on a cloud-based system and performs data calculations based on pre-programmed scenarios. The output is user-specific response data.

[0649] Step 3:

[0650] The server sends the generated response data to the terminal. The terminal receives this response and presents it to the user through virtual reality provisioning and control means. Specifically, the terminal displays visual information on a display device and transmits physical effects to the vibration belt and weighted vest.

[0651] Step 4:

[0652] The device uses its camera and microphone to collect the user's facial expressions and voice in order to obtain emotional and physical feedback. This data is sent in real time to an emotion analysis API to analyze the user's emotional state. The input data consists of facial expressions and voice information, while the output data is the analysis result regarding the emotional state.

[0653] Step 5:

[0654] The server receives emotional state data from the emotion engine and adjusts the original response in real time. The adjusted data is then returned to the device, providing the user with further personalized feedback. Specifically, if stress levels are high, relaxation content may be added.

[0655] Step 6:

[0656] Users can review their own impressions and experiences recorded on their devices. This feedback and data are used by the system to learn from subsequent experiences and provide a more accurate and improved experience.

[0657] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0663] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0665] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0667] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0668] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0669] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0670] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0671] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0673] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] This invention is a system for simulating the experience of pregnancy, providing feedback at each stage of pregnancy based on the user's physical information and experience scenario. The system mainly consists of a server, terminals, and a user interface.

[0675] Server roles and functions

[0676] After receiving the user's inputted physical information and experience scenario, the server uses an AI model to generate real-time feedback. This feedback includes things like simulated abdominal weight and fetal movement, and emotional simulations. The server simultaneously processes data from numerous users, learning and improving the AI ​​model based on the collected feedback.

[0677] Terminal roles and functions

[0678] The device simulates a physical pregnancy experience by providing the user with feedback received from the server. It incorporates vibration and weight modules to realistically reproduce abdominal pressure and fetal movements. The device also provides an interface for the user to input their reactions and feedback, which is then returned to the server.

[0679] User interaction and experience

[0680] Users input necessary physical information and desired scenarios through the device's interface. For example, if a user wishes to experience early pregnancy, they will receive feedback focusing on mild morning sickness and emotional changes. As users receive this feedback and go about their daily lives, they can feel the reality of pregnancy and record their thoughts in response to questions from the device.

[0681] Specific example

[0682] For example, if a user selects "mid-pregnancy," the server generates feedback that includes the active movements of the fetus. The device reproduces the unpredictable movements (fetal movements) as vibrations, along with the sensation of increasing weight in the abdomen. As the user performs their daily activities, they experience sudden fetal movements, record the resulting emotional changes, and send this feedback to the server. This feedback is analyzed by the server and used to improve the user experience in the future.

[0683] This allows users, as non-pregnant partners or trainees, to more directly understand the pregnancy experience and use that understanding to improve communication.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The user uses the device's interface to input physical information (e.g., weight, height) and the pregnancy scenario they wish to experience.

[0687] Step 2:

[0688] The terminal sends the user's input information to the server.

[0689] Step 3:

[0690] Based on the information received, the server uses an AI model to generate feedback that is appropriate for the user's experience scenario.

[0691] Step 4:

[0692] The server sends the generated feedback to the device. The feedback includes data on changes in abdominal weight, simulations of fetal movement, and changes in emotions.

[0693] Step 5:

[0694] Based on the feedback it receives, the device controls vibration devices and weight modules to provide the user with a physical pregnancy experience.

[0695] Step 6:

[0696] Users go about their daily lives while receiving feedback, and record the emotions and physical sensations they experience based on that feedback.

[0697] Step 7:

[0698] The device sends user reactions and recorded feedback to the server.

[0699] Step 8:

[0700] The server uses the collected user feedback data to train an AI model, updating it to provide a more accurate experience in the future.

[0701] (Example 1)

[0702] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] Conventional pregnancy simulation systems have made it difficult for users to experience each stage of pregnancy realistically and in detail, and have been insufficient in reproducing emotions and physical changes in real time through feedback. Furthermore, there have been challenges in improving and adjusting the quality of feedback based on recorded experiences.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0705] In this invention, the server includes an input device means for inputting the stages and physical elements that the user wishes to experience, a data processing device means for creating feedback using a generative AI model based on the stages and physical elements, and an output device means for presenting the feedback to the user and reproducing the physical experience. This allows the user to experience each stage of pregnancy in real time and to improve and adjust the quality of the feedback based on the results.

[0706] A "user" is someone who uses the system to input the stages and physical elements they wish to experience.

[0707] An "input device" refers to hardware or software used by a user to input information about the stage and physical elements they wish to experience.

[0708] A "generative AI model" is an algorithm or program that automatically generates feedback to provide to the user based on the input data.

[0709] "Feedback" refers to information and experiences that are generated by a generative AI model based on the user's experience and presented to the user.

[0710] A "data processing device" is hardware or software that has the function of processing input data and generating feedback.

[0711] An "output device" is hardware or software that provides generated feedback to the user, enabling the user to realistically recreate the experience.

[0712] A "recording device" is a device that has the function of recording user reactions and evaluations to the user experience and saving them in a format that can be used for future system improvements.

[0713] A "learning device" refers to hardware or software that uses recorded data to refine a generated AI model and improve the quality of feedback.

[0714] This invention is a system for users to simulate each stage of pregnancy, and mainly consists of a server, a terminal, and a user interface.

[0715] Server roles and functions

[0716] The server receives information about the user's experience stage and physical characteristics. Based on this information, it uses a generative AI model to generate optimal feedback for the user. The generative AI model learns from past data and provides real-time responses in response to the input prompts. An example of a prompt is the instruction, "Start the mid-pregnancy experience simulation and generate feedback on abdominal weight and fetal movement."

[0717] Terminal roles and functions

[0718] The device is responsible for receiving feedback sent from the server and providing it to the user. The device incorporates vibration devices and weight modules, which are operated to allow the user to experience abdominal pressure and fetal movements. The device also has an interface for recording the user's reactions and feedback, and this information is sent to the server.

[0719] User interaction and experience

[0720] Users can input their desired experience scenarios and stages of pregnancy through the device's interface. For example, when selecting early pregnancy, they will receive feedback on mild morning sickness and emotional changes. In their daily lives, users can feel the reality of the pregnancy experience based on the feedback received through the device and record their own thoughts and reactions.

[0721] This system allows the server to generate sophisticated feedback in real time using a generative AI model, providing users with a more realistic experience via their devices. This enables users to understand the pregnancy experience in more detail, and allows non-pregnant partners and training participants to share the experience from a new perspective.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] Users input the pregnancy stage and physical characteristics they wish to experience through the device's user interface. For example, if a user selects "mid-pregnancy," this information is sent from the device to the server. The input data includes the target pregnancy stage and the user's physical data.

[0725] Step 2:

[0726] The server generates feedback using a generative AI model based on the user's input data. Specifically, the AI ​​model selects the optimal response from past datasets to create feedback corresponding to the input pregnancy stage. In this process, data processing and calculations are performed according to prompts such as "Generate feedback for the second trimester," and the generated result is output as feedback.

[0727] Step 3:

[0728] The server sends the generated feedback to the terminal. The feedback includes physical and emotional changes that the user should experience, with detailed descriptions of vibration and pressure patterns.

[0729] Step 4:

[0730] The device controls its built-in vibration and weight modules based on the feedback it receives. For example, it generates vibrations on the user's abdomen to simulate fetal movement. This allows the user to realistically experience pregnancy according to their chosen stage.

[0731] Step 5:

[0732] Users input their reactions to the feedback experience into their device. For example, they record changes in their emotions and physical reactions when they feel fetal movement. The entered data is sent from the device to the server.

[0733] Step 6:

[0734] The server receives response data sent from users and uses it as training data to improve the generative AI model. Specifically, it analyzes this data and adjusts the model to make future feedback more individualized and accurate. The output is the adjusted model parameters.

[0735] (Application Example 1)

[0736] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0737] Traditionally, simulations of pregnancy experiences have been limited to specific scenarios and devices, making it difficult for users to fully understand each stage of pregnancy concretely and realistically. Furthermore, there was a lack of reproduction of physical sensations and feedback on emotional changes, highlighting the need to improve user comprehension and the quality of the experience.

[0738] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0739] In this invention, the server includes receiving means for receiving the user's physical information and desired experience conditions, information processing means for simulating various stages of pregnancy based on the physical information and desired experience conditions, and output device for providing the user with the simulation results and reproducing physical phenomena and emotional changes. This allows the user to experience each stage of pregnancy in more detail and receive real-time adapted feedback, significantly improving the quality and understanding of the experience.

[0740] "Receiving means" refers to a device or process that receives the user's physical information and desired experience conditions as input.

[0741] An "information processing system" is a system that performs calculations and analyses to simulate each stage of pregnancy based on received physical information and experiential conditions.

[0742] An "output device" is a device that reproduces and provides the results of a simulation to the user as physical phenomena or emotional changes.

[0743] A "recording device" is a system or component used to store user reactions and information about their experience.

[0744] A "learning device" is a device that has the function of improving or adjusting the system simulation using collected user responses and information.

[0745] "Adaptive means" refers to methods and devices for providing optimal feedback based on the user's real-time location information and selection options.

[0746] A "multimodal output means" is a means of providing feedback through multiple senses, including sight, hearing, and touch, using a mobile display device or a haptic device.

[0747] This invention is a system for users to concretely simulate the experience of pregnancy. This system consists of a server, terminal devices, and a user interface.

[0748] The server receives the user's physical information and desired experience conditions via a receiving device, and uses an information processing device to generate simulations of each stage of pregnancy based on this information. This information processing utilizes deep learning frameworks such as TensorFlow and PyTorch, generating simulation data in real time based on a generated AI model. The server then transfers this data to an output device, which reproduces physical phenomena and emotional changes.

[0749] The terminal device provides feedback to the user from the server. Specifically, it provides visual and auditory feedback using mobile display devices such as Google Glass and Microsoft HoloLens, and reproduces physical phenomena through touch via a Bluetooth-connected vibration device.

[0750] Through this system, users can experience each stage of pregnancy. For example, by using a terminal installed in a commercial facility and giving the voice command "Start mid-pregnancy experience," the server generates physical and emotional feedback about the second trimester and conveys it to the user in real time. This allows users to deepen their understanding as a pregnant partner or parent.

[0751] For example, if a user specifies "late pregnancy," the system provides feedback such as a heavy belly and active fetal movements. The user can experience this and be influenced by their selection of related products.

[0752] An example prompt for the generating AI model is as follows: "User ID: X has requested a late-stage pregnancy experience. Generate and communicate the physical characteristics and emotional feedback of late-stage pregnancy, particularly emphasizing abdominal pressure and fetal movement when constructing vibration data."

[0753] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0754] Step 1:

[0755] The server receives physical information and desired experience conditions transmitted from the user via a terminal. Input includes the user's health information and the stage of pregnancy they wish to experience. The received information is converted into an appropriate format as initial preprocessing.

[0756] Step 2:

[0757] The server uses a generative AI model, an information processing tool, to generate feedback data corresponding to each stage of pregnancy, based on the received physical information and experiential conditions. The input information is analyzed using deep learning, and emotional and physical scenarios aligned with the pregnancy stage are output as data. Specifically, data such as emotional changes, fetal movement, and abdominal weight are extracted.

[0758] Step 3:

[0759] The server sends the generated feedback data to the terminal. The output feedback data includes visual information, auditory effects, and physical data for tactile reproduction. These are designed for multimodal output on the terminal device.

[0760] Step 4:

[0761] The device uses feedback data received from the server to recreate visual and auditory scenarios on devices such as Google Glass and HoloLens. Additionally, a Bluetooth-connected vibration device provides physical tactile feedback to the user's stomach. Visual data is displayed on the screen, while tactile data is felt as vibrations.

[0762] Step 5:

[0763] Users experience feedback from their devices and input their thoughts and reactions to the recreated scenarios into the device's interface. The input feedback data is sent to the server in real time.

[0764] Step 6:

[0765] The server stores user feedback and reaction data in a recording device. Based on the collected data, a learning device analyzes the information and forms a feedback loop to improve the generated AI model. This will improve the quality of future simulations and enable a more realistic experience.

[0766] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0767] This invention is a system for simulating different stages of pregnancy based on the user's physical information and desired experience scenarios, and is particularly characterized by its use of an emotion engine to recognize the user's emotional state and individually adjust feedback. The system consists of a server, a terminal, an emotion engine, and a user interface.

[0768] Server roles and functions

[0769] The server receives physical information and experience scenarios from the user and uses an AI model to generate appropriate feedback. This feedback includes not only a physical recreation of the pregnancy experience but also emotional changes. By also taking into account the results of the user's emotional analysis by the emotion engine, more personalized feedback is provided.

[0770] Terminal roles and functions

[0771] The device receives feedback from the server and uses its built-in vibration and weight modules to recreate a physical pregnancy experience for the user. It also uses a camera and microphone to capture the user's facial expressions and voice, which are then sent to the emotion engine.

[0772] The role of the emotional engine

[0773] The emotion engine recognizes the user's emotional state by analyzing their facial expressions, voice, and physiological data. This information is fed back to the server, enabling real-time adjustments.

[0774] User interaction and experience

[0775] Users input their information and desired pregnancy scenarios through the device's interface. They then receive personalized feedback based on the feedback received, along with a physical experience derived from the emotional engine's analysis. During this process, users record their emotions and physical sensations in response to the feedback, which are then used to improve future experiences.

[0776] Specific example

[0777] For example, if a user selects "Stressful Second Trimester," the server generates feedback on the fetus's active movements and emotional changes. The device records the user's facial expressions and words while providing increasing weight in the abdomen and irregular pulsations. The emotion engine analyzes this data and, if it determines the user is stressed, adds relaxation feedback or adjusts the scenario. In this way, the user experience is provided in a manner closer to that of actual pregnancy.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] The user uses the device's interface to enter their physical information and the pregnancy scenario they wish to experience.

[0781] Step 2:

[0782] The terminal sends the entered information to the server.

[0783] Step 3:

[0784] Based on the information received, the server uses an AI model to generate appropriate feedback.

[0785] Step 4:

[0786] The server sends feedback to the device, including feedback such as the weight of the belly and the patterns of fetal movement.

[0787] Step 5:

[0788] The device uses an emotion engine to record the user's facial expressions, voice, and physiological responses, and sends them to a server.

[0789] Step 6:

[0790] The server analyzes data from the emotion engine to identify the user's emotional state.

[0791] Step 7:

[0792] The server adjusts or updates the feedback based on the emotional state and sends the new feedback to the device as needed.

[0793] Step 8:

[0794] The device presents the user with tuned feedback, providing emotional feedback along with the physical experience.

[0795] Step 9:

[0796] Users experience the feedback they receive and record their emotions and physical reactions to it.

[0797] Step 10:

[0798] The device sends user records back to the server, which then uses this information to improve the AI ​​model for future user experiences.

[0799] (Example 2)

[0800] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0801] This invention aims to solve the difficulties in providing personalized pregnancy experiences that meet the physical and emotional needs of users. Current technology makes it difficult to generate real-time feedback tailored to individual users and to further adjust the experience based on that feedback.

[0802] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0803] In this invention, the server includes information input means for biometric data and experience scenarios, data calculation means for generating responses, and information output means for reproducing physical and emotional changes. This makes it possible to provide users with a personalized pregnancy experience in real time and to further optimize the next experience based on that feedback.

[0804] "Biometric data" refers to information about a user's physical condition, including specific numerical values ​​such as body temperature, heart rate, and blood pressure.

[0805] An "experience scenario" refers to a scene designed to simulate a specific situation or condition desired by the user, such as specific experiences during the early, middle, and late stages of pregnancy.

[0806] "Information input means" refers to devices and systems for receiving biometric data and experience scenarios from users, and includes interfaces such as touch panels and keyboards.

[0807] "Data processing means" refers to devices or algorithms that perform necessary calculations and processing based on input information and generate appropriate feedback or responses.

[0808] "Response" refers to information and feedback generated based on user input, provided to the user through physical and emotional experiences.

[0809] "Information output means" refers to devices or methods for presenting the generated response to the user, and includes physical devices such as vibration devices and displays.

[0810] "Data recording means" refers to devices or systems that have the function of saving user experiences and feedback, and retaining information that can be used to improve future experiences.

[0811] "Learning adjustment means" refers to methods and devices for analyzing collected data and improving or adjusting feedback content and experience scenarios.

[0812] One embodiment of this invention is a system aimed at allowing a user to concretely simulate the experience of pregnancy and experience physical and emotional changes. The system consists of a server, a terminal, an emotion analysis engine, and a user interface.

[0813] server

[0814] The server functions as a central control, processing biometric data and experience scenarios obtained from the user. It leverages a generative AI model to generate appropriate feedback based on user input. Specifically, it constructs prompts and sends them to the generative AI model according to the user's selected stage of pregnancy and emotional needs. This feedback includes both physical and emotional adjustments and is individually customized.

[0815] terminal

[0816] The device receives feedback from a server and provides the user with a physical pregnancy experience. It incorporates vibration devices and weight modules to provide feedback to the user's body. In addition, the device's camera and microphone record the user's facial expressions and voice, sending this data to an emotion analysis engine for more accurate feedback adjustments.

[0817] User actions

[0818] Users input pregnancy experience scenarios and their own physical data using the device's interface. For example, if a user selects "stressful second trimester" as their scenario, the device will provide increasing weight and irregular vibrations in the abdomen, adjusting the experience in real time based on server feedback. This allows users to have a more realistic pregnancy experience.

[0819] Sentiment analysis engine

[0820] The emotion analysis engine analyzes the user's facial expressions, voice, and physiological data, and uses this information to determine the user's emotional state. This analysis result is sent to the server and used to adjust feedback in real time.

[0821] Example of a prompt

[0822] "Based on user information and scenarios, generate feedback that replicates the stressful experiences of mid-pregnancy. Adjust the feedback, including relaxation techniques, taking into account the user's emotional analysis results."

[0823] In this way, the system provides users with an optimized pregnancy experience and promotes deeper learning and understanding through feedback tailored to individual needs.

[0824] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0825] Step 1:

[0826] Users input biometric data and desired experience scenarios through the device's interface, using a touchscreen or keyboard. The entered data includes lifestyle information and the selection of pregnancy stage. The device then transmits this information to a server.

[0827] Step 2:

[0828] The server receives biometric data and experience scenarios from the terminal. Based on this information, it generates prompts and sends them to a generation AI model. The AI ​​model analyzes the input prompts and generates personalized feedback. The feedback includes physical and emotional elements and designs a pregnancy experience according to the user's choices. The generated feedback is stored on the server.

[0829] Step 3:

[0830] The server sends the generated feedback to the device. This feedback includes physical experiences that are reproduced in the user's body and recommended emotional adjustments. The device receives the information from the server and uses its built-in vibration device and weight module to embody the specified feedback in the user's body.

[0831] Step 4:

[0832] The device uses its built-in camera and microphone to collect user facial expressions and voice data in real time. This collected data serves as foundational information for evaluating the user's emotional and sensory responses. The device then transmits the collected data to an emotion analysis engine.

[0833] Step 5:

[0834] The emotion analysis engine analyzes the user's facial expression data, voice information, and physiological data received from the device. This allows it to recognize the user's current emotional state and feed the analysis results back to the server. The analysis results include emotional indicators such as stress, joy, and relaxation.

[0835] Step 6:

[0836] The server receives feedback from the sentiment analysis engine and optimizes the generated feedback. If necessary, it uses the generative AI model again to create new prompts and revise the feedback content. This adjusted feedback is then sent back to the terminal.

[0837] Step 7:

[0838] The device receives updated feedback from the server and continuously provides the user with an optimized experience. This experience process is dynamically adjusted according to the user's needs and emotional responses.

[0839] (Application Example 2)

[0840] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0841] In modern society, opportunities to realistically experience the stages of pregnancy are limited. Furthermore, there is a lack of means to individually perceive the emotional and physical changes involved, necessitating the development of methods for simulating these experiences. In particular, there is a need for a system that allows individuals to experience the stages of pregnancy in a more realistic way through virtual environments.

[0842] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0843] In this invention, the server includes input means for inputting the user's physical attributes and desired experience; information processing means for generating responses to simulate different stages of pregnancy based on the physical attributes and desired experience; and presentation means for providing the responses to the user and reproducing physical and emotional changes. This makes the user feel more realistic about the pregnancy experience in the virtual environment.

[0844] "Physical attributes" refer to a user's build, health status, or other physiological information specific to the individual.

[0845] "Experience content" refers to the conditions and settings used to simulate a specific scenario or situation desired by the user.

[0846] "Information processing means" refers to a computing device or program for analyzing input information and generating the response necessary for simulation.

[0847] "Response" refers to data or signals that reproduce physical and emotional changes generated based on user input.

[0848] "Presentation means" refers to devices used by users to perceive responses intuitively, such as displays, speakers, and vibration devices.

[0849] "Virtual reality delivery methods" refer to technologies and devices that enable users to immerse themselves in a virtual environment.

[0850] "Control means" refers to mechanisms or programs that adjust signals and data to operate a device and give the user a specific sensation.

[0851] This invention is a system that simulates different stages of pregnancy in a virtual environment based on the user's physical attributes and desired experience. The system is broadly composed of a server, terminal, user interface, and emotion engine.

[0852] The server uses cloud services (e.g., AWS) to receive physical attributes and experience details entered by the user. The entered information is analyzed using information processing tools, and a generative AI model generates a response to provide to the user. The generated response is transmitted to the terminal.

[0853] The terminal is installed on the user's device, such as a smartphone or head-mounted display (e.g., Oculus Quest). The terminal provides physical sensations by controlling a vibrating belt or weighted vest as a means of presentation based on the received responses. Visually, it recreates virtual pregnancy stages through a virtual reality presentation system.

[0854] The user interface is designed to allow users to easily provide information through input methods. The emotion engine uses Google Cloud's emotion analysis API to analyze the user's facial expressions and voice, recognizing their emotional state in real time. This allows the server to dynamically adjust the responses it generates based on the user's state.

[0855] For example, if a user wants to experience "early pregnancy amidst busy workdays," the system will provide responses that correspond to this situation. The device visually recreates a virtual office environment, and a vibrating belt provides vibrations that realistically represent mild fatigue. If the user feels stressed, the emotion engine detects this, and the system automatically adds content that promotes relaxation.

[0856] An example of a prompt might be: "User X's current experience scenario is 'early pregnancy amidst busy workdays.' Please generate appropriate feedback, taking into account physical information and emotional state." This allows the system to provide a personalized pregnancy experience within the virtual environment.

[0857] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0858] Step 1:

[0859] The user enters their physical attributes and desired experience through a device. This information is stored as digital data on the device and transmitted to the server. This data includes the user's physical information and selected experience scenario.

[0860] Step 2:

[0861] The server analyzes the received physical attributes and experience details using information processing tools. The analyzed data is input into a generative AI model, which generates appropriate responses based on prepared prompts. This model operates on a cloud-based system and performs data calculations based on pre-programmed scenarios. The output is user-specific response data.

[0862] Step 3:

[0863] The server sends the generated response data to the terminal. The terminal receives this response and presents it to the user through virtual reality provisioning and control means. Specifically, the terminal displays visual information on a display device and transmits physical effects to the vibration belt and weighted vest.

[0864] Step 4:

[0865] The device uses its camera and microphone to collect the user's facial expressions and voice in order to obtain emotional and physical feedback. This data is sent in real time to an emotion analysis API to analyze the user's emotional state. The input data consists of facial expressions and voice information, while the output data is the analysis result regarding the emotional state.

[0866] Step 5:

[0867] The server receives emotional state data from the emotion engine and adjusts the original response in real time. The adjusted data is then returned to the device, providing the user with further personalized feedback. Specifically, if stress levels are high, relaxation content may be added.

[0868] Step 6:

[0869] Users can review their own impressions and experiences recorded on their devices. This feedback and data are used by the system to learn from subsequent experiences and provide a more accurate and improved experience.

[0870] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0871] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0872] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0873] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0874] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0875] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0876] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0877] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0878] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0879] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0880] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0881] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0882] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0883] 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.

[0884] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0885] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0886] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0887] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0888] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0889] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0890] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] An input method for entering the user's physical information and desired experience scenario,

[0894] A data processing means that generates feedback for simulating different stages of pregnancy based on the aforementioned physical information and desired experience scenarios,

[0895] Output means for providing the aforementioned feedback to the user and for reproducing physical and emotional changes,

[0896] A means of recording and collecting user feedback and data about their experiences,

[0897] A system including a learning means for adjusting the simulation using the collected feedback and data.

[0898] (Claim 2)

[0899] The system according to claim 1, configured to provide feedback to the user in real time regarding fetal movements and emotional changes.

[0900] (Claim 3)

[0901] The system according to claim 1, configured to be usable as a corporate employee training tool.

[0902] "Example 1"

[0903] (Claim 1)

[0904] An input device for users to input the stages and physical elements they want to experience,

[0905] A data processing device that creates feedback using a generative AI model based on the aforementioned stages and physical elements,

[0906] An output device for presenting the aforementioned feedback to the user and reproducing the physical experience,

[0907] A recording device that records user reactions and evaluations of the user experience,

[0908] A system including a learning device that optimizes the feedback of a generated AI model using the recorded data.

[0909] (Claim 2)

[0910] The system according to claim 1, configured so that the feedback transmits a sense of movement and emotional changes in response to changes over time.

[0911] (Claim 3)

[0912] The system according to claim 1, configured for use as training equipment in public facilities and educational institutions.

[0913] "Application Example 1"

[0914] (Claim 1)

[0915] A means of receiving the user's physical information and desired experience conditions,

[0916] Information processing means for simulating various stages of pregnancy based on the aforementioned physical information and desired experience conditions,

[0917] The system provides the user with the aforementioned simulation results and includes an output device for reproducing physical phenomena and emotional changes.

[0918] A recording device for collecting user reactions and information regarding the user experience,

[0919] A learning device for adjusting the simulation using the collected reactions and information,

[0920] Adaptive means for providing optimal feedback based on the user's location information and selection options in real time,

[0921] A multimodal output means for providing visual and auditory feedback using a mobile display device and for reproducing physical phenomena via a haptic device,

[0922] ...

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, configured such that feedback communicates the fetal movements and emotional changes to the user moment by moment.

[0926] (Claim 3)

[0927] The system according to claim 1, configured for use as an experiential exhibit within a commercial facility.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] Information input means for inputting the user's biometric data and desired experience scenario,

[0931] A data calculation means that generates responses to simulate different stages of pregnancy based on the aforementioned biometric data and desired experience scenarios,

[0932] Information output means for providing the aforementioned response to the user and for reproducing physical and emotional changes,

[0933] A data recording method for collecting opinions and information about user experiences,

[0934] A system including a learning adjustment means that adjusts the simulated experience using the opinions and information collected as described above.

[0935] (Claim 2)

[0936] The system according to claim 1, wherein the response is configured to sequentially convey to the user the movements and emotional changes of the fetus.

[0937] (Claim 3)

[0938] The system according to claim 1, configured to be available for use as a tool for training the staff of an organization.

[0939] "Application example 2 when combining with an emotional engine"

[0940] (Claim 1)

[0941] An input method for entering the user's physical attributes and desired experience,

[0942] Information processing means for generating responses to simulate different stages of pregnancy based on the aforementioned physical attributes and desired experiences,

[0943] A presentation means for providing the aforementioned response to the user and for reproducing physical and emotional changes,

[0944] A means of recording user feedback and information about their experiences,

[0945] A learning method that adjusts the simulation using the collected feedback and information,

[0946] A virtual reality provisioning means for enabling the aforementioned response to be experienced in a virtual environment,

[0947] A system including control means for controlling a device that works in conjunction with the virtual reality providing means and for reproducing sensations of vibration and weight.

[0948] (Claim 2)

[0949] The system according to claim 1, wherein the response is configured to immediately communicate to the user fetal movements and emotional changes.

[0950] (Claim 3)

[0951] The system according to claim 1, configured to be used as a training tool that allows users to experience the characteristics of early pregnancy in diverse environments. [Explanation of Symbols]

[0952] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for entering the user's physical information and desired experience scenario, A data processing means that generates feedback for simulating different stages of pregnancy based on the aforementioned physical information and desired experience scenarios, Output means for providing the aforementioned feedback to the user and for reproducing physical and emotional changes, A means of recording and collecting user feedback and data about their experiences, A system including a learning means for adjusting the simulation using the collected feedback and data.

2. The system according to claim 1, configured to provide feedback to the user in real time regarding fetal movements and emotional changes.

3. The system according to claim 1, configured to be usable as a training tool for corporate employees.

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