System

Abstract

An object of a system according to an embodiment is to provide a new perspective on fitness progress and training of a user.SOLUTION: A system includes a posing photograph acquisition part, a muscle analysis part, and a fortune-telling result generation part. The posing photograph obtaining unit obtains a posing photograph of a user. The muscle analyzing unit analyzes a state of a muscle from the posing photograph acquired by the posing photograph acquiring unit. A fortune-telling result generation part generates a fortune-telling result on the basis of the state of the muscle analyzed by the muscle analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not doing enough to provide users with new perspectives to help them maintain their fitness progress and motivation for training.

[0005] The system according to the embodiment aims to provide a new perspective on a user's fitness progress and training. [Means for solving the problem]

[0006] The system according to the embodiment includes a posing photo acquisition unit, a muscle analysis unit, and a fortune-telling result generation unit. The posing photo acquisition unit acquires a posing photo of a user. The muscle analysis unit analyzes the muscle condition from the posing photo acquired by the posing photo acquisition unit. The fortune-telling result generation unit generates a fortune-telling result based on the muscle condition analyzed by the muscle analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a new perspective on a user's fitness progress and training. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The fortune-telling system according to an embodiment of the present invention automatically analyzes a user's posed photos, uses a generating AI to analyze the condition of the user's muscles, and provides a unique and humorous fortune-telling result based on the analysis results. This allows the fortune-telling system to provide a new perspective on the user's fitness progress and training, and to keep the user motivated while having fun.

[0029] The fortune-telling system according to the embodiment includes a posing photo acquisition unit, a muscle analysis unit, and a fortune-telling result generation unit. The posing photo acquisition unit acquires a posing photo of a user. For example, the posing photo may be taken using a smartphone camera and input into the system. The posing photo acquisition unit may also acquire posing photos in real time using a webcam. For example, when a user strikes a specified pose, the webcam automatically takes a photo and transmits it to the system. The posing photo acquisition unit may also acquire posing photos from an existing photo database. For example, a user may upload previously taken photos to the system and use them for analysis. The muscle analysis unit analyzes the muscle condition from the posing photo acquired by the posing photo acquisition unit. For example, the generation AI may analyze muscle size and shape using a text generation AI (e.g., LLM). The generation AI may also analyze muscle balance and tone using a multimodal generation AI. The generation AI may also evaluate muscle development using image analysis technology. For example, the text generation AI may output muscle shape and size as text data to provide the analysis results. The multimodal generation AI combines image data and text data to perform a detailed analysis of muscle condition. Image analysis technology quantifies the muscle development level and visually displays the analysis results. The fortune-telling result generation unit generates a fortune-telling result based on the muscle condition analyzed by the muscle analysis unit. For example, the generation AI generates a humorous fortune-telling result based on the muscle condition. The generation AI can also provide fitness progress and training advice based on the user's muscle condition. The generation AI can also add humorous elements to the fortune-telling result. For example, the generation AI may describe the muscle condition as "superhero arms" and provide positive feedback to the user. This allows the fortune-telling system according to the embodiment to provide a new perspective on the user's fitness progress and training, and to maintain motivation while having fun. For example, by periodically taking posing photos and inputting them into the system, users can check their muscle development and enjoy humorous fortune-telling results.In addition, by receiving training advice, you can perform effective training.

[0030] The muscle analysis unit analyzes the minute muscle movements and tension state from the posing photo in real time, and can evaluate the user's muscle fatigue level and stress level. The muscle analysis unit, for example, uses generative AI to analyze the minute muscle movements and tension state from the posing photo in real time. For example, it analyzes the muscle contraction and relaxation patterns when the user poses to evaluate the muscle fatigue level. The muscle analysis unit also analyzes the muscle tension state to evaluate the user's stress level. For example, it analyzes changes in muscle tension and quantifies the stress level. The muscle analysis unit also analyzes the minute muscle movements to evaluate the user's muscle fatigue level. For example, it analyzes muscle movement patterns to evaluate the fatigue level. In this way, it is possible to provide more detailed feedback by analyzing the minute muscle movements and tension state in real time and evaluating the user's muscle fatigue level and stress level.

[0031] The muscle analysis unit can compare the muscle condition with the user's past training data and perform a detailed analysis of muscle growth patterns and the effects of training. For example, the muscle analysis unit compares the muscle condition analyzed by the generation AI with the user's past training data. For example, it compares past posing photos with current photos to analyze muscle growth patterns. The muscle analysis unit also compares past training data with the current muscle condition and performs a detailed analysis of the effects of training. For example, it evaluates the muscle growth rate and training effects based on past training history. The muscle analysis unit also analyzes muscle growth patterns and evaluates the effects of the user's training. For example, it analyzes muscle growth rate and growth indicators to evaluate the effects of training. This allows for a detailed analysis of muscle growth patterns and the effects of training by comparing with past training data.

[0032] The muscle analysis unit can simultaneously analyze not only the muscles but also the condition of the skeleton and joints from posed photos, making it possible to evaluate overall body balance. The muscle analysis unit, for example, uses generative AI to simultaneously analyze not only the muscles but also the condition of the skeleton and joints from posed photos. For example, it analyzes the position of the skeleton and the angle of the joints to evaluate overall body balance. The muscle analysis unit also analyzes the condition of the muscles and the position of the skeleton to evaluate body balance. For example, it compares the level of muscle development with the position of the skeleton to evaluate body balance. The muscle analysis unit also analyzes the condition of the joints to evaluate body balance. For example, it analyzes the range of motion of the joints and the condition of the joints to evaluate body balance. In this way, by analyzing not only the muscles but also the condition of the skeleton and joints, it is possible to evaluate overall body balance.

[0033] The muscle analysis unit can combine and analyze photos of different poses and visualize the user's muscle movements and posture changes in a 3D model. The muscle analysis unit, for example, combines and analyzes photos of different poses and visualizes the user's muscle movements and posture changes in a 3D model. For example, a 3D model is generated based on photos of a plurality of poses to visualize muscle movements. The muscle analysis unit also visualizes the user's posture changes in a 3D model based on photos of different poses. For example, posture changes are displayed in a 3D model based on photos taken from different angles. The muscle analysis unit also visualizes the user's muscle movements in a 3D model based on photos of different poses. For example, muscle movement patterns are analyzed and displayed in a 3D model. In this way, by combining and analyzing photos of different poses and visualizing muscle movements and posture changes in a 3D model, the user's body movements can be understood in detail.

[0034] The fortune-telling result generation unit can combine the fortune-telling result based on the muscle condition with the user's past training data and lifestyle habit data to provide a more personalized fortune-telling result. The fortune-telling result generation unit, for example, uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's past training data. For example, a fortune-telling result according to the muscle condition is provided based on past training history. The fortune-telling result generation unit also uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's lifestyle habit data. For example, a personalized fortune-telling result is provided based on the user's diet and sleep data. The fortune-telling result generation unit also uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's past training data and lifestyle habit data. For example, a more personalized fortune-telling result is provided based on the user's training history and lifestyle habit data. In this way, by combining past training data and lifestyle habit data, a more personalized fortune-telling result can be provided.

[0035] The fortune-telling result generation unit can provide comprehensive health advice by reflecting health data such as the user's nutritional state and sleep patterns in the fortune-telling result based on the muscle state, for example, using a generation AI to reflect the user's nutritional state in the fortune-telling result based on the muscle state. For example, a fortune-telling result that takes into account the content of meals and nutritional balance is provided. The fortune-telling result generation unit can also use a generation AI to reflect the user's sleep patterns in the fortune-telling result based on the muscle state. For example, a fortune-telling result that takes into account the quality and duration of sleep is provided. The fortune-telling result generation unit can also use a generation AI to reflect the user's health data in the fortune-telling result based on the muscle state. For example, comprehensive health advice is provided based on the user's nutritional state and sleep patterns. In this way, comprehensive health advice can be provided by reflecting health data such as the nutritional state and sleep patterns.

[0036] The fortune-telling result generation unit customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures and regions, thereby enabling global user support. The fortune-telling result generation unit, for example, customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures. For example, a fortune-telling result incorporating an Oriental medicine perspective is provided. The fortune-telling result generation unit also customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different regions. For example, a fortune-telling result incorporating a Western astrology perspective is provided. The fortune-telling result generation unit also customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures and regions. For example, a fortune-telling result incorporating a tarot card perspective is provided. This allows global user support by customizing to suit the fortune-telling style of different cultures and regions.

[0037] The fortune-telling result generation unit can adjust the fortune-telling result to match the user's fitness goals and training plan, and include specific training advice. The fortune-telling result generation unit, for example, adjusts the fortune-telling result to match the user's fitness goals. For example, it provides specific training advice based on the condition of the muscles. The fortune-telling result generation unit also adjusts the fortune-telling result to match the user's training plan. For example, it provides advice based on a weekly training schedule. The fortune-telling result generation unit also adjusts the fortune-telling result to match the user's fitness goals and training plan, and provides specific training advice. For example, it provides advice based on the type of exercise and the frequency of training. In this way, it is possible to adjust the fortune-telling result to match the fitness goals and training plan, and provide specific training advice.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The fortune-telling system further includes an environmental data acquisition unit. The environmental data acquisition unit acquires environmental data about the surroundings when the user takes a posed photo and can reflect this in the fortune-telling results. For example, data such as the brightness, temperature, and humidity of the shooting location can be acquired and considered as factors that affect the condition of the user's muscles. The environmental data acquisition unit can also analyze the sounds and background conditions around the user and reflect this in the fortune-telling results. This makes it possible to provide more personalized fortune-telling results that take into account the user's environmental data.

[0040] The fortune-telling system further includes a social data analysis unit. The social data analysis unit can analyze the user's social media posts and activity data and reflect this in the fortune-telling results. For example, the social data analysis unit can estimate the user's current interests and concerns from recently posted photos and comments and provide fortune-telling results based on these. The system can also analyze the user's friendships and frequency of interactions on social media to provide fortune-telling results that take the user's social situation into account. This makes it possible to utilize social data to provide more personalized fortune-telling results.

[0041] The fortune-telling system further includes a biometrics data acquisition unit. The biometrics data acquisition unit can acquire biometric data such as the user's heart rate and blood pressure and reflect it in the fortune-telling results. For example, if the user's heart rate is high, it can be assumed to indicate tension or stress, and advice on how to relax can be provided. It can also analyze blood pressure fluctuations and provide fortune-telling results that take the user's health condition into account. This makes it possible to utilize biometric data to provide more detailed feedback.

[0042] The fortune-telling system also has a customization function based on the user's hobbies and interests. For example, if a user has a particular hobby such as sports, music, or art, fortune-telling results related to that hobby can be provided. For example, a user who likes sports can be provided with sports performance advice based on the condition of their muscles. Also, for a user who likes music, the fortune-telling results can include suggestions for relaxing music. This makes it possible to provide fortune-telling results tailored to the user's hobbies and interests.

[0043] The fortune-telling system also has the ability to analyze a user's fitness data and reflect it in the fortune-telling results. For example, it can analyze a user's training history and exercise volume and generate fortune-telling results based on that data. For example, if a user's recent training has been going well, it can provide a positive fortune-telling result. Also, if a user's training has stagnated, it can provide advice to increase motivation. In this way, it can utilize the user's fitness data to provide more personalized fortune-telling results.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The posing photo acquisition unit acquires the user's posing photo. For example, the posing photo can be taken using a smartphone camera and entered into the system. Alternatively, the posing photo can be acquired in real time using a webcam. Furthermore, the posing photo can be acquired from an existing photo database. Step 2: The muscle analysis unit analyzes the muscle condition from the posing photos acquired by the posing photo acquisition unit. For example, it uses generative AI to analyze muscle size, shape, balance, tone, and development. It provides the analysis results using text generation AI, multimodal generation AI, and image analysis technology. Step 3: The fortune-telling result generator generates fortune-telling results based on the muscle condition analyzed by the muscle analysis unit. For example, it uses a generation AI to provide humorous fortune-telling results based on the muscle condition, fitness progress, and training advice.

[0046] (Example 2) The fortune-telling system according to an embodiment of the present invention automatically analyzes a user's posed photos, uses a generating AI to analyze the condition of the user's muscles, and provides a unique and humorous fortune-telling result based on the analysis results. This allows the fortune-telling system to provide a new perspective on the user's fitness progress and training, and to keep the user motivated while having fun.

[0047] The fortune-telling system according to the embodiment includes a posing photo acquisition unit, a muscle analysis unit, and a fortune-telling result generation unit. The posing photo acquisition unit acquires a posing photo of a user. For example, the posing photo may be taken using a smartphone camera and input into the system. The posing photo acquisition unit may also acquire posing photos in real time using a webcam. For example, when a user strikes a specified pose, the webcam automatically takes a photo and transmits it to the system. The posing photo acquisition unit may also acquire posing photos from an existing photo database. For example, a user may upload previously taken photos to the system and use them for analysis. The muscle analysis unit analyzes the muscle condition from the posing photo acquired by the posing photo acquisition unit. For example, the generation AI may analyze muscle size and shape using a text generation AI (e.g., LLM). The generation AI may also analyze muscle balance and tone using a multimodal generation AI. The generation AI may also evaluate muscle development using image analysis technology. For example, the text generation AI may output muscle shape and size as text data to provide the analysis results. The multimodal generation AI combines image data and text data to perform a detailed analysis of muscle condition. Image analysis technology quantifies the muscle development level and visually displays the analysis results. The fortune-telling result generation unit generates a fortune-telling result based on the muscle condition analyzed by the muscle analysis unit. For example, the generation AI generates a humorous fortune-telling result based on the muscle condition. The generation AI can also provide fitness progress and training advice based on the user's muscle condition. The generation AI can also add humorous elements to the fortune-telling result. For example, the generation AI may describe the muscle condition as "superhero arms" and provide positive feedback to the user. This allows the fortune-telling system according to the embodiment to provide a new perspective on the user's fitness progress and training, and to maintain motivation while having fun. For example, by periodically taking posing photos and inputting them into the system, users can check their muscle development and enjoy humorous fortune-telling results.In addition, by receiving training advice, you can perform effective training.

[0048] The muscle analysis unit analyzes the minute muscle movements and tension state from the posing photo in real time, and can evaluate the user's muscle fatigue level and stress level. The muscle analysis unit, for example, uses generative AI to analyze the minute muscle movements and tension state from the posing photo in real time. For example, it analyzes the muscle contraction and relaxation patterns when the user poses to evaluate the muscle fatigue level. The muscle analysis unit also analyzes the muscle tension state to evaluate the user's stress level. For example, it analyzes changes in muscle tension and quantifies the stress level. The muscle analysis unit also analyzes the minute muscle movements to evaluate the user's muscle fatigue level. For example, it analyzes muscle movement patterns to evaluate the fatigue level. In this way, it is possible to provide more detailed feedback by analyzing the minute muscle movements and tension state in real time and evaluating the user's muscle fatigue level and stress level.

[0049] The muscle analysis unit can compare the muscle condition with the user's past training data and perform a detailed analysis of muscle growth patterns and the effects of training. For example, the muscle analysis unit compares the muscle condition analyzed by the generation AI with the user's past training data. For example, it compares past posing photos with current photos to analyze muscle growth patterns. The muscle analysis unit also compares past training data with the current muscle condition and performs a detailed analysis of the effects of training. For example, it evaluates the muscle growth rate and training effects based on past training history. The muscle analysis unit also analyzes muscle growth patterns and evaluates the effects of the user's training. For example, it analyzes muscle growth rate and growth indicators to evaluate the effects of training. This allows for a detailed analysis of muscle growth patterns and the effects of training by comparing with past training data.

[0050] The muscle analysis unit can use the emotion estimation function to analyze the user's emotion when the posed photo was taken and evaluate the effect of the emotion on the muscle condition. The muscle analysis unit, for example, uses the emotion estimation function to analyze the user's emotion when the posed photo was taken. For example, the emotion is estimated from the user's facial expression and posture, and the effect of the emotion on the muscle condition is evaluated. The muscle analysis unit also uses the emotion estimation function to evaluate the effect of the user's emotion on muscle tension. For example, the muscle analysis unit evaluates changes in muscle tension based on the user's emotion score. The muscle analysis unit also uses the emotion estimation function to evaluate the effect of the user's emotion on muscle performance. For example, the muscle analysis unit evaluates changes in muscle performance based on the user's emotion score. This makes it possible to provide more personalized feedback by evaluating the effect of the user's emotion on the muscle condition.

[0051] The muscle analysis unit can simultaneously analyze not only the muscles but also the condition of the skeleton and joints from posed photos, making it possible to evaluate overall body balance. The muscle analysis unit, for example, uses generative AI to simultaneously analyze not only the muscles but also the condition of the skeleton and joints from posed photos. For example, it analyzes the position of the skeleton and the angle of the joints to evaluate overall body balance. The muscle analysis unit also analyzes the condition of the muscles and the position of the skeleton to evaluate body balance. For example, it compares the level of muscle development with the position of the skeleton to evaluate body balance. The muscle analysis unit also analyzes the condition of the joints to evaluate body balance. For example, it analyzes the range of motion of the joints and the condition of the joints to evaluate body balance. In this way, by analyzing not only the muscles but also the condition of the skeleton and joints, it is possible to evaluate overall body balance.

[0052] The muscle analysis unit can combine and analyze photos of different poses and visualize the user's muscle movements and posture changes in a 3D model. The muscle analysis unit, for example, combines and analyzes photos of different poses and visualizes the user's muscle movements and posture changes in a 3D model. For example, a 3D model is generated based on photos of a plurality of poses to visualize muscle movements. The muscle analysis unit also visualizes the user's posture changes in a 3D model based on photos of different poses. For example, posture changes are displayed in a 3D model based on photos taken from different angles. The muscle analysis unit also visualizes the user's muscle movements in a 3D model based on photos of different poses. For example, muscle movement patterns are analyzed and displayed in a 3D model. In this way, by combining and analyzing photos of different poses and visualizing muscle movements and posture changes in a 3D model, the user's body movements can be understood in detail.

[0053] The muscle analysis unit uses the emotion estimation function to analyze the emotion of the user when taking a posed photo in real time, and can provide posing advice to elicit positive emotions. The muscle analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when taking a posed photo in real time. For example, it estimates the emotion from the user's facial expression and posture, and provides posing advice to elicit positive emotions. The muscle analysis unit also uses the emotion estimation function to analyze the user's emotion in real time, and provides advice to elicit positive emotions. For example, it suggests a pose to elicit positive emotions based on the user's emotion score. The muscle analysis unit also uses the emotion estimation function to analyze the user's emotion in real time, and provides feedback to elicit positive emotions. For example, it provides positive feedback based on the user's emotion score. In this way, by analyzing the user's emotion in real time and providing posing advice to elicit positive emotions, it is possible to improve the user's motivation.

[0054] The fortune-telling result generation unit can combine the fortune-telling result based on the muscle condition with the user's past training data and lifestyle habit data to provide a more personalized fortune-telling result. The fortune-telling result generation unit, for example, uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's past training data. For example, a fortune-telling result according to the muscle condition is provided based on past training history. The fortune-telling result generation unit also uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's lifestyle habit data. For example, a personalized fortune-telling result is provided based on the user's diet and sleep data. The fortune-telling result generation unit also uses a generation AI to combine the fortune-telling result based on the muscle condition with the user's past training data and lifestyle habit data. For example, a more personalized fortune-telling result is provided based on the user's training history and lifestyle habit data. In this way, by combining past training data and lifestyle habit data, a more personalized fortune-telling result can be provided.

[0055] The fortune-telling result generation unit can provide comprehensive health advice by reflecting health data such as the user's nutritional state and sleep patterns in the fortune-telling result based on the muscle state, for example, using a generation AI to reflect the user's nutritional state in the fortune-telling result based on the muscle state. For example, a fortune-telling result that takes into account the content of meals and nutritional balance is provided. The fortune-telling result generation unit can also use a generation AI to reflect the user's sleep patterns in the fortune-telling result based on the muscle state. For example, a fortune-telling result that takes into account the quality and duration of sleep is provided. The fortune-telling result generation unit can also use a generation AI to reflect the user's health data in the fortune-telling result based on the muscle state. For example, comprehensive health advice is provided based on the user's nutritional state and sleep patterns. In this way, comprehensive health advice can be provided by reflecting health data such as the nutritional state and sleep patterns.

[0056] The fortune-telling result generation unit uses the emotion estimation function to reflect the user's emotional state in the fortune-telling result, thereby generating a fortune-telling result that will make the user feel the most positive emotion. The fortune-telling result generation unit, for example, uses the emotion estimation function to reflect the user's emotional state in the fortune-telling result. For example, a fortune-telling result that will elicit a positive emotion is provided based on the user's emotion score. The fortune-telling result generation unit also uses the emotion estimation function to analyze the user's emotional state and generate a fortune-telling result that will elicit the most positive emotion. For example, positive feedback is provided based on the user's emotion score. The fortune-telling result generation unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide a fortune-telling result that will elicit a positive emotion. For example, a positive fortune-telling result is generated in real time based on the user's emotion score. In this way, by reflecting the user's emotional state, it is possible to provide a fortune-telling result that will make the user feel the most positive emotion.

[0057] The fortune-telling result generation unit customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures and regions, thereby enabling global user support. The fortune-telling result generation unit, for example, customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures. For example, a fortune-telling result incorporating an Oriental medicine perspective is provided. The fortune-telling result generation unit also customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different regions. For example, a fortune-telling result incorporating a Western astrology perspective is provided. The fortune-telling result generation unit also customizes the fortune-telling result based on the muscle condition to suit the fortune-telling style of different cultures and regions. For example, a fortune-telling result incorporating a tarot card perspective is provided. This allows global user support by customizing to suit the fortune-telling style of different cultures and regions.

[0058] The fortune-telling result generation unit can adjust the fortune-telling result to match the user's fitness goals and training plan, and include specific training advice. The fortune-telling result generation unit, for example, adjusts the fortune-telling result to match the user's fitness goals. For example, it provides specific training advice based on the condition of the muscles. The fortune-telling result generation unit also adjusts the fortune-telling result to match the user's training plan. For example, it provides advice based on a weekly training schedule. The fortune-telling result generation unit also adjusts the fortune-telling result to match the user's fitness goals and training plan, and provides specific training advice. For example, it provides advice based on the type of exercise and the frequency of training. In this way, it is possible to adjust the fortune-telling result to match the fitness goals and training plan, and provide specific training advice.

[0059] The fortune-telling result generation unit uses the emotion estimation function to monitor the user's emotional response to the fortune-telling result in real time, and can continuously improve the fortune-telling result. The fortune-telling result generation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the fortune-telling result in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The fortune-telling result generation unit also uses the emotion estimation function to analyze the user's emotional response in real time and continuously improve the fortune-telling result. For example, it adjusts the fortune-telling result based on the user's emotion score. The fortune-telling result generation unit also uses the emotion estimation function to monitor the user's emotional response in real time and continuously improve the fortune-telling result. For example, it builds a feedback loop based on the user's emotion score and adjusts the fortune-telling result. In this way, by monitoring the user's emotional response in real time and continuously improving the fortune-telling result, it is possible to provide a fortune-telling result that is more suitable for the user.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The fortune-telling system further includes a voice analysis unit. The voice analysis unit analyzes the voice when the user takes a posed photo and can infer emotions from the tone and tempo of the user's voice. For example, if the user's voice tone is high when taking a photo, it can be inferred to indicate excitement or joy, adding a positive element to the fortune-telling result. Also, if the voice tempo is fast, it can be inferred to indicate tension or impatience, and advice on how to relax can be provided. This allows the user's emotions to be understood in more detail through voice analysis and reflected in the fortune-telling result.

[0062] The fortune-telling system further includes an environmental data acquisition unit. The environmental data acquisition unit acquires environmental data about the surroundings when the user takes a posed photo and can reflect this in the fortune-telling results. For example, data such as the brightness, temperature, and humidity of the shooting location can be acquired and considered as factors that affect the condition of the user's muscles. The environmental data acquisition unit can also analyze the sounds and background conditions around the user and reflect this in the fortune-telling results. This makes it possible to provide more personalized fortune-telling results that take into account the user's environmental data.

[0063] The fortune-telling system further includes a social data analysis unit. The social data analysis unit can analyze the user's social media posts and activity data and reflect this in the fortune-telling results. For example, the social data analysis unit can estimate the user's current interests and concerns from recently posted photos and comments and provide fortune-telling results based on these. The system can also analyze the user's friendships and frequency of interactions on social media to provide fortune-telling results that take the user's social situation into account. This makes it possible to utilize social data to provide more personalized fortune-telling results.

[0064] The fortune-telling system further includes a biometrics data acquisition unit. The biometrics data acquisition unit can acquire biometric data such as the user's heart rate and blood pressure and reflect it in the fortune-telling results. For example, if the user's heart rate is high, it can be assumed to indicate tension or stress, and advice on how to relax can be provided. It can also analyze blood pressure fluctuations and provide fortune-telling results that take the user's health condition into account. This makes it possible to utilize biometric data to provide more detailed feedback.

[0065] The fortune-telling system also has a customization function based on the user's hobbies and interests. For example, if a user has a particular hobby such as sports, music, or art, fortune-telling results related to that hobby can be provided. For example, a user who likes sports can be provided with sports performance advice based on the condition of their muscles. Also, for a user who likes music, the fortune-telling results can include suggestions for relaxing music. This makes it possible to provide fortune-telling results tailored to the user's hobbies and interests.

[0066] The fortune-telling system further has a function of estimating the user's emotions and adjusting the fortune-telling results based on those emotions. For example, the system can estimate the user's emotions from their facial expressions and posture when taking a posed photo, and generate fortune-telling results based on those emotions. For example, if the user takes a photo with a smile, a positive fortune-telling result can be provided. Also, if the user looks tired, advice on how to relax can be provided. In this way, fortune-telling results based on the user's emotions can be provided.

[0067] The fortune-telling system also has a function to estimate the user's emotions and provide training advice based on those emotions. For example, the system can estimate the user's emotions from their facial expressions and posture when taking posed photos, and provide training advice based on those emotions. For example, if the user is tired, it can suggest lighter training. On the other hand, if the user is energetic, it can suggest harder training. In this way, it is possible to provide training advice based on the user's emotions.

[0068] The fortune-telling system further has a function of estimating the user's emotions and providing feedback based on those emotions. For example, the system can estimate the user's emotions from their facial expressions and postures when taking posed photos, and provide feedback based on those emotions. For example, if the user is feeling down, the system can provide an encouraging message. Also, if the user is happy, the system can provide more positive feedback. In this way, the system can provide feedback based on the user's emotions.

[0069] The fortune-telling system further has a function of estimating the user's emotions and personalizing the fortune-telling results based on those emotions. For example, the system can estimate the user's emotions from their facial expressions and postures when taking posed photos, and generate fortune-telling results based on those emotions. For example, if the user is relaxed, the system can provide advice on how to maintain a relaxed state. Also, if the user is tense, the system can provide advice on how to relieve tension. This makes it possible to provide personalized fortune-telling results based on the user's emotions.

[0070] The fortune-telling system also has the ability to analyze a user's fitness data and reflect it in the fortune-telling results. For example, it can analyze a user's training history and exercise volume and generate fortune-telling results based on that data. For example, if a user's recent training has been going well, it can provide a positive fortune-telling result. Also, if a user's training has stagnated, it can provide advice to increase motivation. In this way, it can utilize the user's fitness data to provide more personalized fortune-telling results.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The posing photo acquisition unit acquires the user's posing photo. For example, the posing photo can be taken using a smartphone camera and entered into the system. Alternatively, the posing photo can be acquired in real time using a webcam. Furthermore, the posing photo can be acquired from an existing photo database. Step 2: The muscle analysis unit analyzes the muscle condition from the posing photos acquired by the posing photo acquisition unit. For example, it uses generative AI to analyze muscle size, shape, balance, tone, and development. It provides the analysis results using text generation AI, multimodal generation AI, and image analysis technology. Step 3: The fortune-telling result generator generates fortune-telling results based on the muscle condition analyzed by the muscle analysis unit. For example, it uses a generation AI to provide humorous fortune-telling results based on the muscle condition, fitness progress, and training advice.

[0073] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0079] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0080] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0083] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0114] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0123] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0124] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0125] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0127] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0128] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0129] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0130] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0131] 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.

[0132] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0133] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0134] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0135] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0136] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0137] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a posing photo acquisition unit that acquires a posing photo of a user; a muscle analysis unit that analyzes muscle conditions from the posing photo acquired by the posing photo acquisition unit; a fortune-telling result generating unit that generates a fortune-telling result based on the muscle condition analyzed by the muscle analysis unit. A system characterized by:

2. The muscle analysis unit The minute muscle movements and tension state are analyzed in real time from the posed photos, and the user's muscle fatigue and stress level are evaluated.

2. The system of claim 1.

3. The muscle analysis unit From the posed photos, we analyze not only the muscles but also the condition of the skeleton and joints at the same time to evaluate the overall balance of the body.

2. The system of claim 1.

4. The fortune-telling result generating unit The fortune-telling results based on the muscle condition are combined with the user's past training data and lifestyle data to provide more personalized fortune-telling results.

2. The system of claim 1.

5. The muscle analysis unit Using an emotion estimation function, the emotion of the user when the posed photo was taken is analyzed, and the effect of the emotion on the muscle condition is evaluated.

2. The system of claim 1.

6. The muscle analysis unit The different posed photos are combined and analyzed to visualize the user's muscle movements and posture changes in the 3D model.

2. The system of claim 1.

7. The fortune-telling result generating unit Using an emotion estimation function, the emotional state of the user is reflected in the fortune-telling result, and the fortune-telling result that gives the user the most positive emotion is generated.

2. The system of claim 1.

8. The fortune-telling result generating unit Tailoring the fortune-telling results to the user's fitness goals and training plan and including specific training advice 2. The system of claim 1.

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