system

Abstract

The system according to this embodiment aims to estimate the weight of an object by analyzing image data. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and an estimation unit. The acquisition unit acquires image data. The analysis unit analyzes the image data acquired by the acquisition unit. The estimation unit estimates the weight based on the results of the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] In the conventional technology, it may be difficult to directly measure the weight of an object, and there is room for improvement. [[ID=Z37]]

[0005] The system according to the embodiment aims to analyze image data to estimate the weight of an object.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and an estimation unit. The acquisition unit acquires image data. The analysis unit analyzes the image data acquired by the acquisition unit. The estimation unit estimates the weight based on the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can estimate the weight of an object by analyzing image data. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] 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.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The weight estimation system according to an embodiment of the present invention is a system in which AI estimates the weight of an object from image data captured by a smartphone camera. This weight estimation system is useful when it is not possible to directly measure the weight of an object for various reasons. For example, this may be the case when there is no weighing scale on site, when there are many objects to measure and it is inefficient to measure them one by one, when the object is too big or too small, when the object is moving (such as an animal), when it is dangerous to touch, or when the object is dirty and you do not want to touch it. This technology aims to develop an application that can estimate weight from an image by training the AI ​​with various image data and weights. Specifically, the object is photographed with a smartphone camera, and the AI ​​analyzes the image data to estimate the weight. This makes it possible to estimate the weight of an object using only a smartphone. This technology has a need in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. For example, in the construction industry, it can estimate the weight of steel frames, sand, gravel, and sludge, and in the fisheries industry, it can estimate the weight of tuna and farmed fish. Furthermore, in the livestock industry, the weight of cattle, pigs, and horses can be estimated, and in agriculture, the weight of fruits and crops can be estimated. In addition, in nutritional management, the calorie content of meals can be estimated, and in nature conservation, the weight of natural monuments and wild animals can be estimated. In school education, the weight of insects and medaka fish can be estimated. By providing this technology as an industry-specific application, it becomes possible to approach target industries. In particular, it becomes possible to reach industries that have not been reached before, and the target market can be expanded from Japan to the whole world. As a result, the weight estimation system can estimate the weight of objects using only a smartphone, according to the needs of various industries.

[0029] The weight estimation system according to this embodiment comprises an acquisition unit, an analysis unit, and an estimation unit. The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. The acquisition unit can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. The acquisition unit can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The analysis unit analyzes the image data acquired by the acquisition unit. The analysis unit can analyze the image data using, for example, AI. The analysis unit can analyze image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The estimation unit estimates the weight based on the results analyzed by the analysis unit. The estimation unit can estimate the weight based on the analysis results, for example. The estimation unit can optimize the estimation algorithm based on the shape and material of the object, for example. The estimation unit can improve the estimation accuracy by referring to past weight data of the object, for example. This enables the weight estimation system according to the embodiment to acquire and analyze image data and estimate weight.

[0030] The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. Specifically, it takes images of an object from multiple angles using a smartphone camera and acquires these images at high resolution. The acquisition unit can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. This allows the acquisition unit to acquire images of the entire animal or specific parts of an animal in order to estimate its weight. It can also safely acquire image data of dangerous objects from a distance. The acquisition unit can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. In the construction industry, it acquires on-site image data to estimate the weight of building materials and machinery. In the fisheries industry, it acquires image data of catches to estimate the weight of fish and shellfish. In agriculture, it acquires image data of crops to estimate crop yields. In nutrition management, it acquires image data of ingredients to estimate the weight of food. In livestock farming, it acquires image data of livestock to estimate their weight. In nature conservation, image data of wild animals is acquired to estimate their weight. In school education, image data of various objects is acquired for experiments and observations. This allows the acquisition unit to acquire image data that can be used in a variety of industries and applications, increasing the overall flexibility and versatility of the system.

[0031] The analysis unit analyzes image data acquired by the acquisition unit. The analysis unit can analyze image data using, for example, AI. Specifically, it uses deep learning technology to extract features of objects from image data and analyze their shape and size. The analysis unit can analyze image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. In the construction industry, it analyzes the shape and size of building materials and provides basic data for estimating their weight. In the fisheries industry, it analyzes the shape and size of fish and shellfish and provides data for estimating the weight of catches. In agriculture, it analyzes the shape and size of crops and provides data for estimating yields. In nutrition management, it analyzes the shape and size of food and provides data for estimating the weight of ingredients. In livestock farming, it analyzes the shape and size of livestock and provides data for estimating their weight. In nature conservation, it analyzes the shape and size of wild animals and provides data for estimating their weight. In school education, the system provides data for analyzing the shape and size of various objects and estimating their weight for experiments and observations. Furthermore, the analysis unit can use AI to analyze image data in real time and provide results quickly. This allows the analysis unit to perform highly accurate analyses that are suitable for diverse industries and applications, improving the overall performance of the system.

[0032] The estimation unit estimates weight based on the results of the analysis performed by the analysis unit. For example, the estimation unit can estimate weight based on the analysis results. Specifically, it optimizes the estimation algorithm based on the shape, size, and material of the object to estimate weight with high accuracy. For example, the estimation unit can improve estimation accuracy by referring to past weight data of the object. This allows the estimation unit to perform more accurate weight estimation by combining past data with current analysis results. For example, in the construction industry, past weight data of building materials can be referred to estimate the current weight of building materials. In the fisheries industry, past weight data of catches can be referred to estimate the current weight of catches. In agriculture, past yield data of crops can be referred to estimate the current yield of crops. In nutritional management, past weight data of food can be referred to estimate the current weight of ingredients. In livestock farming, past weight data of livestock can be referred to estimate the current weight of livestock. In nature conservation, past weight data of wild animals can be referred to estimate the current weight of wild animals. In school education, past data is referenced to estimate the weight of a current object for experiments and observations. Furthermore, the estimation unit can continuously learn its estimation algorithm using AI to improve estimation accuracy. As a result, the estimation unit can perform highly accurate weight estimations that are suitable for various industries and applications, improving the reliability and accuracy of the entire system.

[0033] The acquisition unit can acquire image data using a smartphone's camera. For example, the acquisition unit can acquire high-resolution image data using a smartphone's camera. For example, the acquisition unit can acquire wide-angle image data using a smartphone's camera with a wide-angle lens. For example, the acquisition unit can acquire detailed image data using a macro lens using a smartphone's camera. This allows for easy image data acquisition using a smartphone's camera.

[0034] The analysis unit can analyze image data using AI. For example, the analysis unit can analyze image data using deep learning. For example, the analysis unit can analyze image data using a neural network. For example, the analysis unit can analyze image data using an image recognition algorithm. This improves the accuracy of image data analysis by using AI.

[0035] The estimation unit can estimate weight based on the analysis results. For example, the estimation unit can estimate weight using volume estimation from an image. For example, the estimation unit can estimate weight using density assumptions. For example, the estimation unit can estimate weight using feature extraction from image data. This allows for accurate weight estimation based on the analysis results.

[0036] The acquisition unit can acquire image data of objects that are difficult to touch directly, such as animals and dangerous objects. For example, the acquisition unit can acquire image data of wild animals. For example, the acquisition unit can acquire image data of toxic substances. For example, the acquisition unit can acquire image data of high-temperature objects. This allows for the safe acquisition of image data of objects that are difficult to touch directly.

[0037] The analysis unit can analyze image data of objects in various industries, including construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. For example, in the construction industry, the analysis unit can analyze image data of steel frames, sand, gravel, and sludge. For example, in the fisheries industry, the analysis unit can analyze image data of tuna and farmed fish. For example, in agriculture, the analysis unit can analyze image data of fruits and crops. This allows the system to handle a wide range of applications by analyzing image data of objects in various industries.

[0038] The acquisition unit can detect the movement of an object and acquire images at the optimal timing. For example, if the object is moving, the acquisition unit can acquire an image the moment the movement stops. For example, if the object is moving at a constant rhythm, the acquisition unit can acquire images in accordance with that rhythm. For example, if the object is moving in an unpredictable way, the acquisition unit can take continuous shots and select the optimal image. This allows for image acquisition at the optimal timing according to the movement of the object.

[0039] The acquisition unit can automatically recognize the size and shape of the object and select the optimal shooting angle. For example, if the object is large, the acquisition unit can shoot with a wide angle to capture the entire object. For example, if the object is small, the acquisition unit can zoom in to capture details. For example, if the object has a complex shape, the acquisition unit can shoot from multiple angles and select the best image. This allows the optimal shooting angle to be selected according to the size and shape of the object.

[0040] The acquisition unit can acquire multiple images under different lighting conditions and provide them to the analysis unit. For example, the acquisition unit can acquire images in both bright and dark locations. For example, the acquisition unit can acquire images under different lighting conditions during the day and at night. For example, the acquisition unit can acquire images under different lighting conditions indoors and outdoors. By acquiring image data under different lighting conditions, the accuracy of the analysis is improved.

[0041] The acquisition unit can acquire background information of the target object and provide it to the analysis unit. For example, the acquisition unit can acquire information about objects in the background of the target object. For example, the acquisition unit can acquire the color and brightness of the background of the target object. For example, the acquisition unit can acquire the movement and changes of the background of the target object. As a result, the accuracy of the analysis is improved by acquiring background information of the target object.

[0042] The analysis unit can optimize the analysis algorithm by considering the material and surface condition of the object. For example, if the object is metal, the analysis unit can adjust the analysis algorithm by considering its reflectivity. For example, if the object is cloth, the analysis unit can adjust the analysis algorithm by considering the fiber density. For example, if the object is glass, the analysis unit can adjust the analysis algorithm by considering its transparency. This improves the accuracy of the analysis according to the material and surface condition of the object.

[0043] The analysis unit can improve analysis accuracy by referring to past analysis data. For example, the analysis unit can improve analysis accuracy by referring to data of similar objects that have been analyzed in the past. For example, the analysis unit can learn from past analysis data and apply new analysis algorithms. For example, the analysis unit can improve analysis accuracy by correcting errors based on past analysis data. In this way, analysis accuracy is improved by referring to past data.

[0044] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining an image analysis algorithm and a machine learning algorithm. For example, the analysis unit can improve analysis accuracy by combining different image analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining different machine learning algorithms. Thus, analysis accuracy is improved by combining different analysis algorithms.

[0045] The analysis unit can perform analysis while considering environmental information such as the temperature and humidity of the object. For example, the analysis unit can adjust the analysis algorithm considering the object's temperature. For example, the analysis unit can adjust the analysis algorithm considering the object's humidity. For example, the analysis unit can adjust the analysis algorithm considering the surrounding environmental information of the object. This improves the accuracy of the analysis by considering environmental information.

[0046] The estimation unit can improve its estimation accuracy by referring to past weight data of the object. For example, the estimation unit can improve its estimation accuracy by referring to data of similar objects that have been estimated in the past. For example, the estimation unit can learn from past weight data and apply a new estimation algorithm. For example, the estimation unit can improve its estimation accuracy by correcting errors based on past weight data. Thus, estimation accuracy is improved by referring to past data.

[0047] The prediction unit can optimize its prediction algorithm based on the shape and material of the object. For example, if the object is metal, the prediction unit can adjust the prediction algorithm considering reflectivity. For example, if the object is cloth, the prediction unit can adjust the prediction algorithm considering fiber density. For example, if the object is glass, the prediction unit can adjust the prediction algorithm considering transparency. This improves prediction accuracy according to the shape and material of the object.

[0048] The prediction unit can improve its prediction accuracy by combining different prediction algorithms. For example, the prediction unit can improve its prediction accuracy by combining an image analysis algorithm and a machine learning algorithm. For example, the prediction unit can improve its prediction accuracy by combining different image analysis algorithms. For example, the prediction unit can improve its prediction accuracy by combining different machine learning algorithms. Thus, prediction accuracy is improved by combining different prediction algorithms.

[0049] The prediction unit can customize the prediction results based on the intended use and application of the object. For example, if the object is a construction material, the prediction unit can customize the prediction results considering its use at a construction site. For example, if the object is food, the prediction unit can customize the prediction results considering nutritional management. For example, if the object is an animal, the prediction unit can customize the prediction results considering its use in the livestock industry. This allows the system to provide prediction results tailored to the intended use and application.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The weight estimation system can also be equipped with an environmental information acquisition unit. This unit can acquire environmental information such as temperature, humidity, and illuminance around the object. For example, in a construction site, temperature and humidity can affect the weight of steel frames, so acquiring this information can improve the accuracy of the estimation. In the fisheries industry, the temperature and water quality of the fish's growing environment affect its weight, so acquiring this information allows for more accurate weight estimation. Furthermore, in agriculture, the temperature and humidity of the growing environment for fruits and crops affect their weight, so acquiring this information can improve the accuracy of the estimation. In this way, by considering environmental information, more accurate weight estimation becomes possible.

[0052] The weight estimation system can also detect the movement of an object and acquire images at the optimal timing. For example, if the object is moving, it can acquire an image the moment it stops moving. If the object is moving at a constant rhythm, it can acquire images in accordance with that rhythm. If the object is moving in an unpredictable way, it can take continuous shots and select the best image. This allows for image acquisition at the optimal timing according to the object's movement.

[0053] The weight estimation system can acquire multiple images under different lighting conditions and provide them to the analysis unit. For example, it can acquire images in both bright and dark locations. It can acquire images under different lighting conditions during the day and at night. It can acquire images under different lighting conditions indoors and outdoors. This improves the accuracy of the analysis by acquiring image data under different lighting conditions.

[0054] The weight estimation system can also acquire background information about the object and provide it to the analysis unit. For example, it can acquire information about objects in the background of the object. It can acquire the color and brightness of the background of the object. It can acquire the movement and changes of the background of the object. As a result, acquiring background information about the object improves the accuracy of the analysis.

[0055] The weight estimation system can further improve its accuracy by combining different analysis algorithms. For example, it can improve accuracy by combining image analysis algorithms and machine learning algorithms. It can improve accuracy by combining different image analysis algorithms. It can improve accuracy by combining different machine learning algorithms. In short, combining different analysis algorithms improves accuracy.

[0056] The weight estimation system can further customize the estimation results based on the intended use and application of the object. For example, if the object is a construction material, the estimation results can be customized to take into account its use on a construction site. If the object is food, the estimation results can be customized to take into account nutritional management. If the object is an animal, the estimation results can be customized to take into account its use in the livestock industry. This allows the system to provide estimation results tailored to the intended use and application.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. It can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. Furthermore, it can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit. The analysis unit can analyze the image data using, for example, AI. Furthermore, it can analyze image data of objects in various industries such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. Step 3: The estimation unit estimates the weight based on the results analyzed by the analysis unit. For example, the estimation unit can estimate the weight based on the analysis results. Furthermore, the estimation algorithm can be optimized based on the shape and material of the object. In addition, the estimation accuracy can be improved by referring to past weight data of the object.

[0059] (Example of form 2) The weight estimation system according to an embodiment of the present invention is a system in which AI estimates the weight of an object from image data captured by a smartphone camera. This weight estimation system is useful when it is not possible to directly measure the weight of an object for various reasons. For example, this may be the case when there is no weighing scale on site, when there are many objects to measure and it is inefficient to measure them one by one, when the object is too big or too small, when the object is moving (such as an animal), when it is dangerous to touch, or when the object is dirty and you do not want to touch it. This technology aims to develop an application that can estimate weight from an image by training the AI ​​with various image data and weights. Specifically, the object is photographed with a smartphone camera, and the AI ​​analyzes the image data to estimate the weight. This makes it possible to estimate the weight of an object using only a smartphone. This technology has a need in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. For example, in the construction industry, it can estimate the weight of steel frames, sand, gravel, and sludge, and in the fisheries industry, it can estimate the weight of tuna and farmed fish. Furthermore, in the livestock industry, the weight of cattle, pigs, and horses can be estimated, and in agriculture, the weight of fruits and crops can be estimated. In addition, in nutritional management, the calorie content of meals can be estimated, and in nature conservation, the weight of natural monuments and wild animals can be estimated. In school education, the weight of insects and medaka fish can be estimated. By providing this technology as an industry-specific application, it becomes possible to approach target industries. In particular, it becomes possible to reach industries that have not been reached before, and the target market can be expanded from Japan to the whole world. As a result, the weight estimation system can estimate the weight of objects using only a smartphone, according to the needs of various industries.

[0060] The weight estimation system according to this embodiment comprises an acquisition unit, an analysis unit, and an estimation unit. The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. The acquisition unit can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. The acquisition unit can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The analysis unit analyzes the image data acquired by the acquisition unit. The analysis unit can analyze the image data using, for example, AI. The analysis unit can analyze image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The estimation unit estimates the weight based on the results analyzed by the analysis unit. The estimation unit can estimate the weight based on the analysis results, for example. The estimation unit can optimize the estimation algorithm based on the shape and material of the object, for example. The estimation unit can improve the estimation accuracy by referring to past weight data of the object, for example. This enables the weight estimation system according to the embodiment to acquire and analyze image data and estimate weight.

[0061] The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. Specifically, it takes images of an object from multiple angles using a smartphone camera and acquires these images at high resolution. The acquisition unit can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. This allows the acquisition unit to acquire images of the entire animal or specific parts of an animal in order to estimate its weight. It can also safely acquire image data of dangerous objects from a distance. The acquisition unit can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. In the construction industry, it acquires on-site image data to estimate the weight of building materials and machinery. In the fisheries industry, it acquires image data of catches to estimate the weight of fish and shellfish. In agriculture, it acquires image data of crops to estimate crop yields. In nutrition management, it acquires image data of ingredients to estimate the weight of food. In livestock farming, it acquires image data of livestock to estimate their weight. In nature conservation, image data of wild animals is acquired to estimate their weight. In school education, image data of various objects is acquired for experiments and observations. This allows the acquisition unit to acquire image data that can be used in a variety of industries and applications, increasing the overall flexibility and versatility of the system.

[0062] The analysis unit analyzes image data acquired by the acquisition unit. The analysis unit can analyze image data using, for example, AI. Specifically, it uses deep learning technology to extract features of objects from image data and analyze their shape and size. The analysis unit can analyze image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. In the construction industry, it analyzes the shape and size of building materials and provides basic data for estimating their weight. In the fisheries industry, it analyzes the shape and size of fish and shellfish and provides data for estimating the weight of catches. In agriculture, it analyzes the shape and size of crops and provides data for estimating yields. In nutrition management, it analyzes the shape and size of food and provides data for estimating the weight of ingredients. In livestock farming, it analyzes the shape and size of livestock and provides data for estimating their weight. In nature conservation, it analyzes the shape and size of wild animals and provides data for estimating their weight. In school education, the system provides data for analyzing the shape and size of various objects and estimating their weight for experiments and observations. Furthermore, the analysis unit can use AI to analyze image data in real time and provide results quickly. This allows the analysis unit to perform highly accurate analyses that are suitable for diverse industries and applications, improving the overall performance of the system.

[0063] The estimation unit estimates weight based on the results of the analysis performed by the analysis unit. For example, the estimation unit can estimate weight based on the analysis results. Specifically, it optimizes the estimation algorithm based on the shape, size, and material of the object to estimate weight with high accuracy. For example, the estimation unit can improve estimation accuracy by referring to past weight data of the object. This allows the estimation unit to perform more accurate weight estimation by combining past data with current analysis results. For example, in the construction industry, past weight data of building materials can be referred to estimate the current weight of building materials. In the fisheries industry, past weight data of catches can be referred to estimate the current weight of catches. In agriculture, past yield data of crops can be referred to estimate the current yield of crops. In nutritional management, past weight data of food can be referred to estimate the current weight of ingredients. In livestock farming, past weight data of livestock can be referred to estimate the current weight of livestock. In nature conservation, past weight data of wild animals can be referred to estimate the current weight of wild animals. In school education, past data is referenced to estimate the weight of a current object for experiments and observations. Furthermore, the estimation unit can continuously learn its estimation algorithm using AI to improve estimation accuracy. As a result, the estimation unit can perform highly accurate weight estimations that are suitable for various industries and applications, improving the reliability and accuracy of the entire system.

[0064] The acquisition unit can acquire image data using a smartphone's camera. For example, the acquisition unit can acquire high-resolution image data using a smartphone's camera. For example, the acquisition unit can acquire wide-angle image data using a smartphone's camera with a wide-angle lens. For example, the acquisition unit can acquire detailed image data using a macro lens using a smartphone's camera. This allows for easy image data acquisition using a smartphone's camera.

[0065] The analysis unit can analyze image data using AI. For example, the analysis unit can analyze image data using deep learning. For example, the analysis unit can analyze image data using a neural network. For example, the analysis unit can analyze image data using an image recognition algorithm. This improves the accuracy of image data analysis by using AI.

[0066] The estimation unit can estimate weight based on the analysis results. For example, the estimation unit can estimate weight using volume estimation from an image. For example, the estimation unit can estimate weight using density assumptions. For example, the estimation unit can estimate weight using feature extraction from image data. This allows for accurate weight estimation based on the analysis results.

[0067] The acquisition unit can acquire image data of objects that are difficult to touch directly, such as animals and dangerous objects. For example, the acquisition unit can acquire image data of wild animals. For example, the acquisition unit can acquire image data of toxic substances. For example, the acquisition unit can acquire image data of high-temperature objects. This allows for the safe acquisition of image data of objects that are difficult to touch directly.

[0068] The analysis unit can analyze image data of objects in various industries, including construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. For example, in the construction industry, the analysis unit can analyze image data of steel frames, sand, gravel, and sludge. For example, in the fisheries industry, the analysis unit can analyze image data of tuna and farmed fish. For example, in agriculture, the analysis unit can analyze image data of fruits and crops. This allows the system to handle a wide range of applications by analyzing image data of objects in various industries.

[0069] The acquisition unit can estimate the user's emotions and adjust the timing of image data acquisition based on the estimated emotions. For example, if the user is tense, the acquisition unit can wait until the user relaxes before acquiring image data. For example, if the user is in a hurry, the acquisition unit can acquire image data quickly. For example, if the user is excited, the acquisition unit can temporarily suspend shooting to acquire a stable image. This allows image data to be acquired at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The acquisition unit can detect the movement of an object and acquire images at the optimal timing. For example, if the object is moving, the acquisition unit can acquire an image the moment the movement stops. For example, if the object is moving at a constant rhythm, the acquisition unit can acquire images in accordance with that rhythm. For example, if the object is moving in an unpredictable way, the acquisition unit can take continuous shots and select the optimal image. This allows for image acquisition at the optimal timing according to the movement of the object.

[0071] The acquisition unit can automatically recognize the size and shape of the object and select the optimal shooting angle. For example, if the object is large, the acquisition unit can shoot with a wide angle to capture the entire object. For example, if the object is small, the acquisition unit can zoom in to capture details. For example, if the object has a complex shape, the acquisition unit can shoot from multiple angles and select the best image. This allows the optimal shooting angle to be selected according to the size and shape of the object.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of image data to acquire based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can prioritize acquiring important image data. For example, if the user is relaxed, the acquisition unit can prioritize acquiring detailed image data. For example, if the user is in a hurry, the acquisition unit can prioritize image data that can be acquired quickly. This allows for the prioritization of important image data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The acquisition unit can acquire multiple images under different lighting conditions and provide them to the analysis unit. For example, the acquisition unit can acquire images in both bright and dark locations. For example, the acquisition unit can acquire images under different lighting conditions during the day and at night. For example, the acquisition unit can acquire images under different lighting conditions indoors and outdoors. By acquiring image data under different lighting conditions, the accuracy of the analysis is improved.

[0074] The acquisition unit can acquire background information of the target object and provide it to the analysis unit. For example, the acquisition unit can acquire information about objects in the background of the target object. For example, the acquisition unit can acquire the color and brightness of the background of the target object. For example, the acquisition unit can acquire the movement and changes of the background of the target object. As a result, the accuracy of the analysis is improved by acquiring background information of the target object.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the system to provide the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can optimize the analysis algorithm by considering the material and surface condition of the object. For example, if the object is metal, the analysis unit can adjust the analysis algorithm by considering its reflectivity. For example, if the object is cloth, the analysis unit can adjust the analysis algorithm by considering the fiber density. For example, if the object is glass, the analysis unit can adjust the analysis algorithm by considering its transparency. This improves the accuracy of the analysis according to the material and surface condition of the object.

[0077] The analysis unit can improve analysis accuracy by referring to past analysis data. For example, the analysis unit can improve analysis accuracy by referring to data of similar objects that have been analyzed in the past. For example, the analysis unit can learn from past analysis data and apply new analysis algorithms. For example, the analysis unit can improve analysis accuracy by correcting errors based on past analysis data. In this way, analysis accuracy is improved by referring to past data.

[0078] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize important analyses. For example, if the user is relaxed, the analysis unit can perform detailed analyses. For example, if the user is in a hurry, the analysis unit can prioritize items that can be analyzed quickly. This allows for prioritizing important analyses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can improve analysis accuracy by combining different analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining an image analysis algorithm and a machine learning algorithm. For example, the analysis unit can improve analysis accuracy by combining different image analysis algorithms. For example, the analysis unit can improve analysis accuracy by combining different machine learning algorithms. Thus, analysis accuracy is improved by combining different analysis algorithms.

[0080] The analysis unit can perform analysis while considering environmental information such as the temperature and humidity of the object. For example, the analysis unit can adjust the analysis algorithm considering the object's temperature. For example, the analysis unit can adjust the analysis algorithm considering the object's humidity. For example, the analysis unit can adjust the analysis algorithm considering the surrounding environmental information of the object. This improves the accuracy of the analysis by considering environmental information.

[0081] The inference unit can estimate the user's emotions and adjust the display method of the inference results based on the estimated emotions. For example, if the user is nervous, the inference unit can provide a simple and highly visible display method. For example, if the user is relaxed, the inference unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the inference unit can provide a display method that gets straight to the point. This allows for the provision of the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The estimation unit can improve its estimation accuracy by referring to past weight data of the object. For example, the estimation unit can improve its estimation accuracy by referring to data of similar objects that have been estimated in the past. For example, the estimation unit can learn from past weight data and apply a new estimation algorithm. For example, the estimation unit can improve its estimation accuracy by correcting errors based on past weight data. Thus, estimation accuracy is improved by referring to past data.

[0083] The prediction unit can optimize its prediction algorithm based on the shape and material of the object. For example, if the object is metal, the prediction unit can adjust the prediction algorithm considering reflectivity. For example, if the object is cloth, the prediction unit can adjust the prediction algorithm considering fiber density. For example, if the object is glass, the prediction unit can adjust the prediction algorithm considering transparency. This improves prediction accuracy according to the shape and material of the object.

[0084] The inference unit can estimate the user's emotions and prioritize the inference results based on the estimated emotions. For example, if the user is stressed, the inference unit can prioritize displaying important inference results. For example, if the user is relaxed, the inference unit can display detailed inference results. For example, if the user is in a hurry, the inference unit can quickly display the inference results. This allows for prioritizing the display of important inference results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The prediction unit can improve its prediction accuracy by combining different prediction algorithms. For example, the prediction unit can improve its prediction accuracy by combining an image analysis algorithm and a machine learning algorithm. For example, the prediction unit can improve its prediction accuracy by combining different image analysis algorithms. For example, the prediction unit can improve its prediction accuracy by combining different machine learning algorithms. Thus, prediction accuracy is improved by combining different prediction algorithms.

[0086] The prediction unit can customize the prediction results based on the intended use and application of the object. For example, if the object is a construction material, the prediction unit can customize the prediction results considering its use at a construction site. For example, if the object is food, the prediction unit can customize the prediction results considering nutritional management. For example, if the object is an animal, the prediction unit can customize the prediction results considering its use in the livestock industry. This allows the system to provide prediction results tailored to the intended use and application.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The weight estimation system can also be equipped with an environmental information acquisition unit. This unit can acquire environmental information such as temperature, humidity, and illuminance around the object. For example, in a construction site, temperature and humidity can affect the weight of steel frames, so acquiring this information can improve the accuracy of the estimation. In the fisheries industry, the temperature and water quality of the fish's growing environment affect its weight, so acquiring this information allows for more accurate weight estimation. Furthermore, in agriculture, the temperature and humidity of the growing environment for fruits and crops affect their weight, so acquiring this information can improve the accuracy of the estimation. In this way, by considering environmental information, more accurate weight estimation becomes possible.

[0089] The weight estimation system can further estimate the user's emotions and adjust the display method of the estimation results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. This allows the system to provide the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The weight estimation system can also detect the movement of an object and acquire images at the optimal timing. For example, if the object is moving, it can acquire an image the moment it stops moving. If the object is moving at a constant rhythm, it can acquire images in accordance with that rhythm. If the object is moving in an unpredictable way, it can take continuous shots and select the best image. This allows for image acquisition at the optimal timing according to the object's movement.

[0091] The weight estimation system can acquire multiple images under different lighting conditions and provide them to the analysis unit. For example, it can acquire images in both bright and dark locations. It can acquire images under different lighting conditions during the day and at night. It can acquire images under different lighting conditions indoors and outdoors. This improves the accuracy of the analysis by acquiring image data under different lighting conditions.

[0092] The weight estimation system can also acquire background information about the object and provide it to the analysis unit. For example, it can acquire information about objects in the background of the object. It can acquire the color and brightness of the background of the object. It can acquire the movement and changes of the background of the object. As a result, acquiring background information about the object improves the accuracy of the analysis.

[0093] The weight estimation system can further estimate the user's emotions and determine the priority of image data to acquire based on the estimated emotions. For example, if the user is stressed, important image data can be prioritized. If the user is relaxed, detailed image data can be acquired. If the user is in a hurry, image data that can be acquired quickly can be prioritized. This allows for the prioritization of important image data according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The weight estimation system can further improve its accuracy by combining different analysis algorithms. For example, it can improve accuracy by combining image analysis algorithms and machine learning algorithms. It can improve accuracy by combining different image analysis algorithms. It can improve accuracy by combining different machine learning algorithms. In short, combining different analysis algorithms improves accuracy.

[0095] The weight estimation system can further estimate the user's emotions and prioritize analysis based on those emotions. For example, if the user is stressed, important analyses can be prioritized. If the user is relaxed, detailed analyses can be performed. If the user is in a hurry, items that can be analyzed quickly can be prioritized. This allows for prioritizing important analyses according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The weight estimation system can further customize the estimation results based on the intended use and application of the object. For example, if the object is a construction material, the estimation results can be customized to take into account its use on a construction site. If the object is food, the estimation results can be customized to take into account nutritional management. If the object is an animal, the estimation results can be customized to take into account its use in the livestock industry. This allows the system to provide estimation results tailored to the intended use and application.

[0097] The weight estimation system can further estimate the user's emotions and prioritize the estimation results based on those emotions. For example, if the user is stressed, important estimation results can be displayed first. If the user is relaxed, detailed estimation results can be displayed. If the user is in a hurry, estimation results can be displayed quickly. This allows for prioritizing important estimation results according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The acquisition unit acquires image data. The acquisition unit can acquire image data using, for example, a smartphone camera. It can also acquire image data of objects that are difficult to touch directly, such as animals or dangerous objects. Furthermore, it can acquire image data of objects in various industries, such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. Step 2: The analysis unit analyzes the image data acquired by the acquisition unit. The analysis unit can analyze the image data using, for example, AI. Furthermore, it can analyze image data of objects in various industries such as construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. Step 3: The estimation unit estimates the weight based on the results analyzed by the analysis unit. For example, the estimation unit can estimate the weight based on the analysis results. Furthermore, the estimation algorithm can be optimized based on the shape and material of the object. In addition, the estimation accuracy can be improved by referring to past weight data of the object.

[0100] 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.

[0101] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the acquisition unit, analysis unit, and estimation unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire image data using the camera 42 of the smart device 14. The analysis unit can analyze the image data using AI by, for example, the specific processing unit 290 of the data processing unit 12. The estimation unit can estimate the weight based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0107] 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.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0109] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] 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.

[0111] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the acquisition unit, analysis unit, and estimation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire image data using the camera 42 of the smart glasses 214. The analysis unit can analyze the image data using AI, for example, in the identification processing unit 290 of the data processing unit 12. The estimation unit can estimate the weight based on the analysis results, for example, in the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0123] 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.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0125] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] 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.

[0127] 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.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the acquisition unit, analysis unit, and estimation unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire image data using the camera 42 of the headset terminal 314. The analysis unit can analyze the image data using AI by, for example, the specific processing unit 290 of the data processing unit 12. The estimation unit can estimate the weight based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0139] 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.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0141] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] 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.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the acquisition unit, analysis unit, and estimation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire image data using the camera 42 of the robot 414. The analysis unit can analyze the image data using AI, for example, by the specific processing unit 290 of the data processing unit 12. The estimation unit can estimate the weight based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] Figure 9 shows the 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.

[0155] 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.

[0156] 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.

[0157] 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, and motorcycles, 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 based, for example, 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.

[0158] 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."

[0159] 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.

[0160] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] 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 other things 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.

[0170] 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 to be incorporated by reference.

[0171] (Note 1) An acquisition unit that acquires image data, An analysis unit analyzes the image data acquired by the acquisition unit, The system includes an estimation unit that estimates the weight based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The acquisition unit is, Acquire image data using your smartphone's camera. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, AI analyzes image data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned inference unit, Estimate the weight based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Acquire image data of objects that are difficult to touch directly, such as animals and dangerous objects. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze image data of objects in various industries, including construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system detects the movement of the object and acquires images at the optimal timing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It automatically recognizes the size and shape of the object and selects the optimal shooting angle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of image data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, Multiple images are acquired under different lighting conditions and provided to the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, The system acquires background information about the target object and provides it to the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Optimize the analysis algorithm considering the material and surface condition of the object. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Referencing past analysis data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Combining different analysis algorithms improves analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The analysis takes into account environmental information such as the temperature and humidity of the object. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inference unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inference unit, Referencing past weight data of the object improves estimation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inference unit, Optimize the prediction algorithm based on the shape and material of the object. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inference unit, It estimates the user's emotions and prioritizes the estimation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inference unit, Combining different prediction algorithms improves prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned inference unit, Customize the prediction results based on the intended use and purpose of the object. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires image data, An analysis unit analyzes the image data acquired by the acquisition unit, The system includes an estimation unit that estimates the weight based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The acquisition unit is, Acquire image data using your smartphone's camera. The system according to feature 1.

3. The aforementioned analysis unit, AI analyzes image data. The system according to feature 1.

4. The aforementioned inference unit, Estimate the weight based on the analysis results. The system according to feature 1.

5. The acquisition unit is, Acquire image data of objects that are difficult to touch directly, such as animals and dangerous objects. The system according to feature 1.

6. The aforementioned analysis unit, We analyze image data of objects in various industries, including construction, fisheries, agriculture, nutrition management, livestock farming, nature conservation, and school education. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, The system detects the movement of the object and acquires images at the optimal timing. The system according to feature 1.

Images