system

Abstract

We provide the system. [Solution] Means for acquiring an image including a reference object having known dimensions and an object to be measured, A means for obtaining the dimensions of the aforementioned reference object from a database based on its identification information, A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual dimensions of the object based on that measurement, A means for outputting the calculated dimensions of the object, A system that includes this.

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 and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a demand to simply and accurately measure the height of children in the growth stage at home. On the other hand, existing systems require measurement at school or in the hospital, which is difficult in daily life. Also, when fishing as a hobby, there is a lack of means to accurately and simply record the size of the caught fish. Considering these needs, it is necessary to provide a system that can simply measure the dimensions of an object based on commonly available items in daily life.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for acquiring an image containing a reference object with known dimensions and an object to be measured, retrieves the dimensions of the reference object from a database based on its identification information, and measures the pixel dimensions of the reference object in the image. Furthermore, it calculates the actual dimensions of the object based on the measurement results and outputs the calculated dimensions to the user, thereby solving the above problem. This makes it possible for users to easily measure the dimensions of objects using products they use on a daily basis as a reference.

[0006] A "reference object" is an object with known dimensions that is photographed together with the object being measured and is used as a dimensional reference.

[0007] A "measurement target" is a subject that is photographed together with a reference object, and whose dimensions are to be calculated.

[0008] "Means for acquiring an image" refers to a device or function capable of recording an image that includes a reference object and the object being measured.

[0009] "Identification information" refers to information used to identify a reference object, and is the information used when obtaining the dimensions of a reference object from a database.

[0010] A "database" is an information system that stores dimensional information based on the identification information of a reference object.

[0011] "Pixel dimension" refers to the number of pixels occupied by a reference object or object being measured within an image, and is information necessary for calculating dimensions.

[0012] "Means for calculating dimensions" refers to devices or algorithms that calculate the dimensions of an object being measured based on the pixel dimensions of a reference object and its actual dimensions. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system for calculating the dimensions of an object to be measured using an image that includes a reference object with known dimensions and the object to be measured. This system operates on a smartphone terminal, and by using a dedicated application, users can easily measure their height and the dimensions of objects in their daily lives.

[0035] Beyond simply taking an image, an application installed on the smartphone automatically performs image analysis and dimensional calculation. The user first uses their smartphone's camera to photograph both a reference object and the object to be measured together. The reference object is typically an everyday item with known dimensions, such as a plastic bottle or a door.

[0036] The device processes the captured image within the application. First, the application identifies a reference object from the image and, based on that identification information, retrieves the dimensional information of that reference object from a pre-registered database. Then, it measures the proportion of pixels occupied by the reference object in the image and calculates the ratio to the number of pixels of the target object based on that. Based on this ratio, it calculates the actual dimensions of the object to be measured.

[0037] The calculated dimensions are provided to the user as visual feedback on the device's display screen. For example, if a user takes a photo of themselves standing next to a plastic bottle (30 centimeters tall) as a reference object, the application can measure the pixel height of the plastic bottle in the image, calculate the user's height based on that, and display it on the screen.

[0038] This feature allows users to quickly measure the dimensions of objects in everyday situations, greatly improving convenience when, for example, measuring a child's height or recording the size of a fish caught.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses their smartphone's camera to capture an image that includes a reference object with known dimensions and the object to be measured. By including the reference object in the field of view, the user provides the reference information necessary for subsequent calculations.

[0042] Step 2:

[0043] The terminal receives image data provided by the user and begins the image analysis process. In this step, image processing algorithms are used to prepare the device for analyzing objects within the image.

[0044] Step 3:

[0045] The device detects a reference object within the image and analyzes its identification information. Here, the reference object is identified based on its shape and label, and this identification information is used to query a database.

[0046] Step 4:

[0047] The terminal retrieves the dimensional information of the identified reference object from a pre-registered database based on its identification information. This makes the actual dimensional data of the reference object available to the terminal.

[0048] Step 5:

[0049] The device measures the pixel size of a reference object within the image. This is done by detecting the vertical and horizontal edges of the reference object and counting the number of pixels between them.

[0050] Step 6:

[0051] The device derives a ratio for calculating the dimensions of an object based on the actual dimensions and pixel dimensions of a reference object. This prepares the device to calculate the actual size from any number of pixels in an image.

[0052] Step 7:

[0053] The device measures the pixel size of the object to be measured within the image and applies the ratio obtained in step 6 to calculate the actual dimensions of the object.

[0054] Step 8:

[0055] The terminal visually displays the calculated dimensions of the object being measured to the user. This allows the user to confirm the actual dimensions of the object and record or share them as needed.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In daily life, there is a problem in that it is difficult to measure the dimensions of objects simply and accurately without using special measuring tools. Furthermore, there is a demand for easy measurement of objects that are difficult to measure, such as living organisms.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring an image containing both a reference object and a measurement target using a mobile information terminal, means for identifying the reference object in the image using an image recognition algorithm, and means for acquiring the dimensions of the identified reference object from pre-recorded information. This makes it possible to easily measure the dimensions of objects in daily life using a mobile information terminal.

[0061] A "portable information terminal" is a general term for a small, portable information processing device equipped with communication and information management functions that can be carried by a user.

[0062] A "reference object" refers to an item that has known dimensions and is used as a reference point in the measurement process; it is an item that is used on a daily basis.

[0063] A "measurement target" refers to an object or living organism within an image that is designated for determining its dimensions, and is the subject of evaluation and analysis.

[0064] An "image recognition algorithm" refers to a set of computational methods for detecting and identifying specific patterns or objects within acquired image data.

[0065] "Pre-recorded information base" refers to a database in which identification information and dimensional data of reference objects are registered in advance and are available in a searchable format.

[0066] This invention is a system that uses a dedicated application running on a mobile device to easily determine the dimensions of an object to be measured using everyday reference objects. Specifically, the user first uses the camera function of the mobile device to photograph both the reference object and the object to be measured. As reference objects, items with known dimensions that are commonly found in daily life, such as plastic bottles or doors, can be used.

[0067] The device is equipped with a dedicated application for analyzing captured images. This application uses an image recognition algorithm to identify reference objects and retrieves dimensional information from a pre-recorded database based on the identification information.

[0068] Next, the device measures the number of pixels in a reference object within the image and calculates the ratio to the number of pixels of the object being measured. From this ratio, it calculates the actual dimensions of the object and provides visual feedback to the user. This allows the user to quickly and accurately determine the dimensions of an object in the real world.

[0069] For example, if a user wants to measure their height using a plastic bottle as a reference object, the application will measure the height of the bottle in the image, calculate the user's height based on that, and display it on the screen. This method can also be used as a prompt message, such as "Tell me how to measure my height using a plastic bottle as a reference object."

[0070] This system makes it possible to smoothly measure the dimensions of objects in various everyday situations without using special equipment.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user uses a mobile device to capture an image containing a reference object with known dimensions and the object to be measured. The acquired image is the input. The image data is sent to the application as output. It is important to ensure that the reference object and the object to be measured are clearly within the same image when taking the picture.

[0074] Step 2:

[0075] The device receives images captured via the application. The input is the acquired image data. The output is information about a reference object identified by an image recognition algorithm. The device accurately obtains the location and characteristics of the reference object and prepares for the next processing step.

[0076] Step 3:

[0077] The terminal retrieves dimensional data from a pre-recorded database based on the information of the identified reference object. The input is the identification information of the reference object. The output is the actual dimensional data of the reference object. At this stage, a query is made to the database and the corresponding dimensions are returned.

[0078] Step 4:

[0079] The device measures the number of pixels in a reference object within an image and calculates the ratio between that number of pixels and the number of pixels in the object being measured. The input is the image data and the dimensions of the reference object. The output is the actual dimensions of the object being measured. This ratio calculation accurately determines the size of the object the user is looking for.

[0080] Step 5:

[0081] The terminal visually presents the calculated dimensions to the user. The input is the calculated dimension information. The output is the dimension value displayed on the mobile device's screen. This allows the user to confirm the actual size of the object being measured.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] In daily life, there is a need to accurately and easily adjust and evaluate goods for consumption activities, but conventional measurement methods are time-consuming and make rapid evaluation difficult. This project aims to solve that problem. In particular, it is necessary to be able to quickly and accurately calculate the dimensions of the object being measured by using a reference object with known dimensions, and to utilize this information as useful information for consumption activities.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes means for acquiring an image including a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from an information storage means based on its identification information; and means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object to be measured based on these dimensions. This makes it possible to quickly and accurately adjust and evaluate goods for consumer activities.

[0087] A "reference object" is an object with known dimensions that is used as a comparison standard with the object being measured.

[0088] A "measured object" is an object whose dimensions are unknown and which is included in the image along with a reference object as the object to be measured.

[0089] "Information storage means" refers to a database that stores identification information and dimensional data of a reference object and makes it available for retrieval as needed.

[0090] "Pixel dimensions" refer to the number of pixels an object has within an image, and are the basic data used to calculate its actual dimensions.

[0091] "Consumption activity" refers to everyday actions related to the use or evaluation of products and services, and actions used to determine the purchase or necessity of goods.

[0092] The system for realizing this invention operates on a mobile device such as a smartphone or tablet. The device has an application installed for image processing and dimension calculation. This application utilizes the image analysis library OpenCV and a database that stores known dimension information.

[0093] First, the user takes a picture of the object whose dimensions they want to measure (the object to be measured) and an object with known dimensions (the reference object) together using the camera on their mobile device. The application analyzes this image and identifies the reference object. The information about the identified reference object is then used in a database to retrieve its dimensions.

[0094] Next, the application uses the OpenCV library to measure the number of pixels in a reference object within an image and calculates the ratio between that number and the number of pixels in the object being measured. Based on this ratio, the actual dimensions of the object being measured are calculated.

[0095] The calculated dimensions are displayed on the device screen as visual feedback to the user, which can be used to adjust items for consumption. For example, if a user wants to know the size of a pizza delivered via food delivery, they can use a plate with a known diameter placed next to the pizza as a reference. This method allows for easy measurement of the pizza's actual diameter.

[0096] For example, a possible prompt message could be: "Using this photograph and a table knife (20cm long) as a reference object, calculate the diameter of the cake on the plate." This information can then be provided to an AI model to perform appropriate dimensional calculations.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The user uses the camera on their mobile device to capture an image that includes the object to be measured and a reference object with known dimensions. This image is then input into the application.

[0100] Step 2:

[0101] The terminal uses the OpenCV image analysis library to identify reference objects within an image. Reference object identification information is output, and based on this information, the dimensions of the reference objects are retrieved from a database.

[0102] Step 3:

[0103] The terminal uses the acquired dimensional information of the reference object to measure the number of pixels in the reference object within the image. The ratio of the number of pixels in the reference object to the number of pixels in the object being measured is calculated using the pixel count as input.

[0104] Step 4:

[0105] The terminal uses the calculated ratio to determine the actual dimensions of the object being measured. The calculated dimensions are output and become input for the next processing step.

[0106] Step 5:

[0107] The terminal displays the calculated dimensions to the user. The display method provides information visually on a graphical interface in a format that is easy for the user to understand.

[0108] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0109] This invention combines a system that calculates the dimensions of an object using an image that includes a reference object and the object to be measured with an emotion engine that recognizes the user's emotions. Implemented via a smartphone application, the user can measure the dimensions of an object using a familiar object and receive personalized feedback on the results.

[0110] First, the user uses the camera to take an image that includes a reference object (e.g., a plastic bottle or a door) and the object to be measured. The device analyzes the captured image, identifies the reference object, and retrieves its dimensions from a database. Next, it measures the pixel dimensions of the reference object in the image, calculates the ratio, and then calculates the actual size of the object to be measured.

[0111] Simultaneously, an emotion engine built into the device analyzes the user's facial expressions and voice through sensors to evaluate the user's emotional state. This emotion engine evaluates the user's facial expressions and tone of voice in real time and recognizes the user's emotions based on that data. By understanding how the user is reacting to the measurements, a more personalized experience can be provided.

[0112] For example, if the measurement results please the user, the emotion engine will provide positive feedback tailored to the analysis. On the other hand, if the user is disappointed with the results, it can offer encouragement and advice on setting goals.

[0113] This system provides a more interactive and enjoyable experience for users, as feedback is adjusted based on their emotional state, not just the dimensions of an object. By leveraging the emotion engine, measurement becomes more than just a routine task.

[0114] The following describes the processing flow.

[0115] Step 1:

[0116] The user takes an image including a reference object and the object to be measured using the camera on their smartphone. Users can easily complete the preparation by using everyday items as reference objects.

[0117] Step 2:

[0118] The device receives the captured image and begins image analysis. Here, optical character recognition or shape detection algorithms are applied to identify reference objects within the image.

[0119] Step 3:

[0120] The terminal uses identification information to retrieve the dimensions of a reference object from a database. This information is extracted from the label and shape of the reference object.

[0121] Step 4:

[0122] The device measures the pixel dimensions of a reference object within the image. In this process, it measures the vertical and horizontal lengths of the reference object in pixels and determines the ratio between the actual size and the pixel dimensions.

[0123] Step 5:

[0124] The terminal measures the pixel dimensions of the object to be measured and uses the ratio obtained in step 4 to calculate the actual dimensions of the object. This calculation is performed based on the known dimensions of a reference object.

[0125] Step 6:

[0126] The device's built-in emotion engine analyzes the user's facial expressions and voice tone to assess their emotional state. Facial expressions are recorded via the camera, and audio is captured via the microphone.

[0127] Step 7:

[0128] The device combines the calculated dimensions of the object with information about the user's emotional state obtained from the emotion engine to generate feedback for the user. For example, if the user is satisfied, it will display a congratulatory message, and if they are dissatisfied, it will offer encouragement.

[0129] Step 8:

[0130] The device displays the generated feedback to the user visually and audibly. The user receives information through the device's screen and speaker, and can select the next action after confirming the results.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0133] Conventional dimension measurement systems are solely focused on accurately measuring the dimensions of an object and are unable to provide feedback that takes into account the user's emotional state. As a result, users cannot receive actions or information that consider their emotional reactions to the measurement results, making it difficult to provide an interactive user experience.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and a target to be measured; means for acquiring the dimensions of the reference object from information storage based on identification information of the reference object; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the target based on these dimensions; means for analyzing the user's facial expressions and voice and evaluating their emotional state; and means for generating feedback based on the evaluation of the emotions. This allows the user to perform dimension measurements while receiving interactive feedback corresponding to their emotional state in response to the results.

[0136] A "reference object" is an object with known dimensions that is used as a reference for scaling dimensions within an image.

[0137] "Measurement target" refers to the object or subject whose dimensions you want to measure.

[0138] "Information storage" refers to databases or memory devices that hold identification information and dimensional data for reference objects.

[0139] "Pixel dimensions" refer to the width and height of a specific object within an image, measured in pixels.

[0140] A "user" is the entity that utilizes the system, takes pictures, and receives feedback.

[0141] "Emotional state" refers to the psychological or emotional state recognized based on the user's facial expressions and voice analysis.

[0142] "Feedback" refers to messages and advice provided to users based on measurement results and emotional states.

[0143] This invention is a system that provides feedback by analyzing user emotions, in addition to an image-based dimensional measurement system. Specific embodiments are shown below.

[0144] The user takes an image including a reference object and the object to be measured using a mobile device such as a smartphone. The camera application built into the smartphone is used for this purpose. The reference object is an everyday item (e.g., a plastic bottle) whose dimensional information is pre-registered in a database. Based on this, the device identifies the reference object and calculates the actual dimensions of the object based on its dimensions. Examples of smartphones that can be used include iPhone® and Android® devices.

[0145] The terminal includes image analysis algorithms and access to a dimensional database. Image analysis measures the number of pixels in both a reference object and the object being measured, and uses this to determine the actual size of the object.

[0146] At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to evaluate their emotional state. This emotion engine uses sensors to capture the user's real-time facial expressions and voice tone to determine their psychological state.

[0147] For example, if a user measures the dimensions of a table at home, a plastic bottle is used as a reference object. The image taken by the user is analyzed, and the dimensions of the table are accurately calculated. If the user shows a satisfied expression with the measurement results, the emotion engine displays positive feedback such as, "You're happy with the results. Enjoy arranging your new furniture."

[0148] As an example of a prompt message, you can enter the following:

[0149] "I used a plastic bottle to measure the dimensions of the table. I'm happy with the results. Please let me know what kind of feedback would be appropriate."

[0150] In this way, the system provides feedback that responds to the user's emotions, resulting in an interactive and comfortable user experience.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The user uses their smartphone's camera application to capture an image containing both a reference object and the object to be measured. The input is an image containing the user-selected reference object and the object to be measured. As output, this image data is sent to the next analysis step. Specifically, the user launches the camera application, precisely frames the reference object and the object on the screen, and presses the "Capture" button.

[0154] Step 2:

[0155] The terminal analyzes the acquired image. Image data captured is provided as input. Using an image analysis algorithm, it identifies a reference object and retrieves its dimensions from the information storage. The output is the dimensional information of the reference object. Specifically, the terminal executes an image processing algorithm, recognizes the contours and features of the reference object, and retrieves size information from the database.

[0156] Step 3:

[0157] The device measures the pixel dimensions of a reference object in an image and calculates the actual size of the target object. The input requires the dimension information of the reference object and image data containing the target object. It counts the number of pixels in the image and determines the dimensions of the target object by ratio calculation based on the actual dimensions of the reference object. The output is the calculated dimensions of the target object. Specifically, the system compares the pixel dimensions of the reference object and the target object to calculate their actual size.

[0158] Step 4:

[0159] An emotion engine built into the device analyzes the user's facial expressions and voice. Image and audio data are used as input. The analysis is performed in real time to evaluate the user's emotional state. The output is the judgment result of the user's emotional state. Specifically, it analyzes the user's facial changes and voice tone captured by the camera and microphone.

[0160] Step 5:

[0161] The device generates feedback based on calculated dimensions and emotion assessment. The inputs provided are the object's dimension data and the user's emotion assessment. A generation AI model is used to create prompts and present appropriate feedback to the user. The output is a feedback message displayed to the user. Specifically, the feedback generation algorithm generates a message based on the emotional state and displays it on the device screen.

[0162] (Application Example 2)

[0163] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0164] Measuring the dimensions of objects in everyday environments often presents problems, requiring specialized equipment and considerable effort. Furthermore, providing feedback that considers the psychological state of the user receiving the measurement results is difficult, potentially leading to decreased user satisfaction and a less-than-ideal user experience. In this context, there is a need for a system that allows users to easily obtain object dimensions and receive feedback that reflects their emotional state during the process.

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

[0166] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from a database based on its identification information; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object based on that; means for outputting the calculated dimensions of the object; means for analyzing the user's emotional state; and means for providing feedback based on the user's emotional state. This allows users to easily acquire the dimensions of an object and receive feedback that corresponds to their emotions at the time.

[0167] A "reference object" is an object that has known dimensions and is used as a reference when measuring the dimensions of other objects.

[0168] The "object to be measured" is the object from which you intend to obtain dimensions.

[0169] "Means of acquiring images" refers to methods of capturing visual information of the actual environment as electronic data using cameras and sensors.

[0170] "Methods of obtaining information from a database" refer to methods of searching for and retrieving necessary information from pre-stored information sources.

[0171] "Methods for measuring pixel dimensions and calculating the actual dimensions of an object based on them" refers to methods that measure the size of a reference object in an image on a pixel-by-pixel basis and use that information to derive the actual physical dimensions of the object.

[0172] "Means for outputting dimensions" refers to methods for visualizing or notifying dimensional information obtained through calculations.

[0173] "Methods for analyzing a user's emotional state" refer to technologies that evaluate psychological characteristics from a user's facial expressions and voice, and estimate their emotions.

[0174] "Means of providing feedback" refers to methods of returning information or responses that correspond to the user's behavior and emotional state.

[0175] This system is primarily composed of mobile devices such as smartphones and tablets, equipped with cameras for users to photograph reference objects and objects to be measured. The devices use image analysis software such as OpenCV to process the captured images. This allows the system to determine the pixel dimensions of the reference object and calculate the dimensions of the object to be measured by retrieving the actual dimensions from a database.

[0176] Furthermore, this system includes a function to detect and analyze the user's emotional state. Emotional analysis utilizes emotion recognition models such as Amazon Rekognition and Google Cloud Vision. This allows for the analysis of the user's facial expressions and voice data obtained from the camera and microphone, enabling real-time evaluation of their emotional state.

[0177] Once the user has finished measuring dimensions, the device displays the calculated dimensions to the user. In addition, it generates feedback tailored to the user's emotional state, providing appropriate information and suggestions. This feedback aims to personalize the user's shopping experience and improve satisfaction.

[0178] As a concrete example, when a user measures the dimensions of a chair they are considering purchasing in a store, the application calculates the chair's dimensions and suggests related interior products if the user is smiling. In this case, the prompt used as input to the generative AI model is: "Measure the dimensions of this product and suggest related recommendations if the user is happy. Please also consider appropriate feedback for other emotional states."

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The user takes pictures of a reference object and the object to be measured with a camera. The input is the image data acquired by the user. The output is this image data that is passed to the image analysis software. Specifically, the user launches the camera app on their smartphone, adjusts the position on the screen so that the reference object (e.g., a plastic bottle) and the object to be measured (e.g., a chair) are included, and presses the shutter button.

[0182] Step 2:

[0183] The terminal analyzes the acquired image data using OpenCV to identify the reference object. The input is the image data obtained in step 1. As data processing, an image processing algorithm scans the pixel information and detects the features of the reference object. The output is the pixel dimensions of the reference object and their corresponding position information.

[0184] Step 3:

[0185] The terminal retrieves the actual dimensions of the reference object from the database. The input is the identification information of the reference object obtained in step 2. A database query is executed using this identification information. The output is the actual dimension data of the reference object.

[0186] Step 4:

[0187] The terminal calculates the actual dimensions of the object being measured based on the pixel dimensions of a reference object. The inputs are the pixel dimensions from step 2 and the actual dimensions from step 3. A ratio calculation is performed as part of the data calculation to derive the actual dimensions of the object being measured. The output is the calculated actual dimensions of the object.

[0188] Step 5:

[0189] The device collects user facial and voice data and passes it to the emotion engine. The input is real-time sensor data. Specifically, the device's front camera and microphone are activated to capture the user's face and voice. The output is a dataset for analysis by the emotion engine.

[0190] Step 6:

[0191] The server evaluates the user's emotional state using an emotion recognition model such as Amazon Rekognition. The input is the user's facial expressions and voice data from step 5. As a process, a generative AI model assigns emotion labels. The output is the evaluation result of the user's emotional state.

[0192] Step 7:

[0193] The device displays the dimensions of the object and generates feedback based on the evaluated emotional state. The inputs are the dimensional data from step 4 and the emotional evaluation from step 6. The feedback is created using prompts for the generating AI model. The output is the dimensional information and feedback message presented to the user. Specifically, the measurement results and an emotionally appropriate message are displayed as a pop-up on the device screen.

[0194] 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.

[0195] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0201] 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.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] 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.

[0205] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] This invention provides a system for calculating the dimensions of an object to be measured using an image that includes a reference object with known dimensions and the object to be measured. This system operates on a smartphone terminal, and by using a dedicated application, users can easily measure their height and the dimensions of objects in their daily lives.

[0211] Beyond simply taking an image, an application installed on the smartphone automatically performs image analysis and dimensional calculation. The user first uses their smartphone's camera to photograph both a reference object and the object to be measured together. The reference object is typically an everyday item with known dimensions, such as a plastic bottle or a door.

[0212] The device processes the captured image within the application. First, the application identifies a reference object from the image and, based on that identification information, retrieves the dimensional information of that reference object from a pre-registered database. Then, it measures the proportion of pixels occupied by the reference object in the image and calculates the ratio to the number of pixels of the target object based on that. Based on this ratio, it calculates the actual dimensions of the object to be measured.

[0213] The calculated dimensions are provided to the user as visual feedback on the device's display screen. For example, if a user takes a photo of themselves standing next to a plastic bottle (30 centimeters tall) as a reference object, the application can measure the pixel height of the plastic bottle in the image, calculate the user's height based on that, and display it on the screen.

[0214] This feature allows users to quickly measure the dimensions of objects in everyday situations, greatly improving convenience when, for example, measuring a child's height or recording the size of a fish caught.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] The user uses their smartphone's camera to capture an image that includes a reference object with known dimensions and the object to be measured. By including the reference object in the field of view, the user provides the reference information necessary for subsequent calculations.

[0218] Step 2:

[0219] The terminal receives image data provided by the user and begins the image analysis process. In this step, image processing algorithms are used to prepare the device for analyzing objects within the image.

[0220] Step 3:

[0221] The device detects a reference object within the image and analyzes its identification information. Here, the reference object is identified based on its shape and label, and this identification information is used to query a database.

[0222] Step 4:

[0223] The terminal retrieves the dimensional information of the identified reference object from a pre-registered database based on its identification information. This makes the actual dimensional data of the reference object available to the terminal.

[0224] Step 5:

[0225] The device measures the pixel size of a reference object within the image. This is done by detecting the vertical and horizontal edges of the reference object and counting the number of pixels between them.

[0226] Step 6:

[0227] The device derives a ratio for calculating the dimensions of an object based on the actual dimensions and pixel dimensions of a reference object. This prepares the device to calculate the actual size from any number of pixels in an image.

[0228] Step 7:

[0229] The device measures the pixel size of the object to be measured within the image and applies the ratio obtained in step 6 to calculate the actual dimensions of the object.

[0230] Step 8:

[0231] The terminal visually displays the calculated dimensions of the object being measured to the user. This allows the user to confirm the actual dimensions of the object and record or share them as needed.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] In daily life, there is a problem in that it is difficult to measure the dimensions of objects simply and accurately without using special measuring tools. Furthermore, there is a demand for easy measurement of objects that are difficult to measure, such as living organisms.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for acquiring an image containing both a reference object and a measurement target using a mobile information terminal, means for identifying the reference object in the image using an image recognition algorithm, and means for acquiring the dimensions of the identified reference object from pre-recorded information. This makes it possible to easily measure the dimensions of objects in daily life using a mobile information terminal.

[0237] A "portable information terminal" is a general term for a small, portable information processing device equipped with communication and information management functions that can be carried by a user.

[0238] A "reference object" refers to an item that has known dimensions and is used as a reference point in the measurement process; it is an item that is used on a daily basis.

[0239] A "measurement target" refers to an object or living organism within an image that is designated for determining its dimensions, and is the subject of evaluation and analysis.

[0240] An "image recognition algorithm" refers to a set of computational methods for detecting and identifying specific patterns or objects within acquired image data.

[0241] "Pre-recorded information base" refers to a database in which identification information and dimensional data of reference objects are registered in advance and are available in a searchable format.

[0242] This invention is a system that uses a dedicated application running on a mobile device to easily determine the dimensions of an object to be measured using everyday reference objects. Specifically, the user first uses the camera function of the mobile device to photograph both the reference object and the object to be measured. As reference objects, items with known dimensions that are commonly found in daily life, such as plastic bottles or doors, can be used.

[0243] The device is equipped with a dedicated application for analyzing captured images. This application uses an image recognition algorithm to identify reference objects and retrieves dimensional information from a pre-recorded database based on the identification information.

[0244] Next, the device measures the number of pixels in a reference object within the image and calculates the ratio to the number of pixels of the object being measured. From this ratio, it calculates the actual dimensions of the object and provides visual feedback to the user. This allows the user to quickly and accurately determine the dimensions of an object in the real world.

[0245] For example, if a user wants to measure their height using a plastic bottle as a reference object, the application will measure the height of the bottle in the image, calculate the user's height based on that, and display it on the screen. This method can also be used as a prompt message, such as "Tell me how to measure my height using a plastic bottle as a reference object."

[0246] This system makes it possible to smoothly measure the dimensions of objects in various everyday situations without using special equipment.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The user uses a mobile device to capture an image containing a reference object with known dimensions and the object to be measured. The acquired image is the input. The image data is sent to the application as output. It is important to ensure that the reference object and the object to be measured are clearly within the same image when taking the picture.

[0250] Step 2:

[0251] The device receives images captured via the application. The input is the acquired image data. The output is information about a reference object identified by an image recognition algorithm. The device accurately obtains the location and characteristics of the reference object and prepares for the next processing step.

[0252] Step 3:

[0253] The terminal retrieves dimensional data from a pre-recorded database based on the information of the identified reference object. The input is the identification information of the reference object. The output is the actual dimensional data of the reference object. At this stage, a query is made to the database and the corresponding dimensions are returned.

[0254] Step 4:

[0255] The device measures the number of pixels in a reference object within an image and calculates the ratio between that number of pixels and the number of pixels in the object being measured. The input is the image data and the dimensions of the reference object. The output is the actual dimensions of the object being measured. This ratio calculation accurately determines the size of the object the user is looking for.

[0256] Step 5:

[0257] The terminal visually presents the calculated dimensions to the user. The input is the calculated dimension information. The output is the dimension value displayed on the mobile device's screen. This allows the user to confirm the actual size of the object being measured.

[0258] (Application Example 1)

[0259] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] In daily life, there is a need to accurately and easily adjust and evaluate goods for consumption activities, but conventional measurement methods are time-consuming and make rapid evaluation difficult. This project aims to solve that problem. In particular, it is necessary to be able to quickly and accurately calculate the dimensions of the object being measured by using a reference object with known dimensions, and to utilize this information as useful information for consumption activities.

[0261] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0262] In this invention, the server includes means for acquiring an image including a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from an information storage means based on its identification information; and means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object to be measured based on these dimensions. This makes it possible to quickly and accurately adjust and evaluate goods for consumer activities.

[0263] A "reference object" is an object with known dimensions that is used as a comparison standard with the object being measured.

[0264] A "measured object" is an object whose dimensions are unknown and which is included in the image along with a reference object as the object to be measured.

[0265] "Information storage means" refers to a database that stores identification information and dimensional data of a reference object and makes it available for retrieval as needed.

[0266] "Pixel dimensions" refer to the number of pixels an object has within an image, and are the basic data used to calculate its actual dimensions.

[0267] "Consumption activity" refers to everyday actions related to the use or evaluation of products and services, and actions used to determine the purchase or necessity of goods.

[0268] The system for realizing this invention operates on a mobile device such as a smartphone or tablet. The device has an application installed for image processing and dimension calculation. This application utilizes the image analysis library OpenCV and a database that stores known dimension information.

[0269] First, the user takes a picture of the object whose dimensions they want to measure (the object to be measured) and an object with known dimensions (the reference object) together using the camera on their mobile device. The application analyzes this image and identifies the reference object. The information about the identified reference object is then used in a database to retrieve its dimensions.

[0270] Next, the application uses the OpenCV library to measure the number of pixels in a reference object within an image and calculates the ratio between that number and the number of pixels in the object being measured. Based on this ratio, the actual dimensions of the object being measured are calculated.

[0271] The calculated dimensions are displayed on the device screen as visual feedback to the user, which can be used to adjust items for consumption. For example, if a user wants to know the size of a pizza delivered via food delivery, they can use a plate with a known diameter placed next to the pizza as a reference. This method allows for easy measurement of the pizza's actual diameter.

[0272] For example, a possible prompt message could be: "Using this photograph and a table knife (20cm long) as a reference object, calculate the diameter of the cake on the plate." This information can then be provided to an AI model to perform appropriate dimensional calculations.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0274] Step 1:

[0275] The user uses the camera on their mobile device to capture an image that includes the object to be measured and a reference object with known dimensions. This image is then input into the application.

[0276] Step 2:

[0277] The terminal uses the OpenCV image analysis library to identify reference objects within an image. Reference object identification information is output, and based on this information, the dimensions of the reference objects are retrieved from a database.

[0278] Step 3:

[0279] The terminal uses the acquired dimensional information of the reference object to measure the number of pixels in the reference object within the image. The ratio of the number of pixels in the reference object to the number of pixels in the object being measured is calculated using the pixel count as input.

[0280] Step 4:

[0281] The terminal uses the calculated ratio to determine the actual dimensions of the object being measured. The calculated dimensions are output and become input for the next processing step.

[0282] Step 5:

[0283] The terminal displays the calculated dimensions to the user. The display method provides information visually on a graphical interface in a format that is easy for the user to understand.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0285] This invention combines an emotion engine that recognizes the user's emotion with a system that calculates the dimensions of an object using an image including a reference object and a measurement target object. It is implemented by an application on a smartphone terminal, and the user can measure the dimensions of an object using a nearby article and receive individual feedback on the result.

[0286] First, the user uses the camera to take an image including a reference object (e.g., a plastic bottle or a door) and a measurement target object. The terminal analyzes the captured image, identifies the reference object, and obtains its dimensions from the database. Next, the pixel dimensions of the reference object in the image are measured, and the actual dimensions of the target object are calculated after calculating the ratio.

[0287] At the same time, an emotion engine incorporated in the terminal analyzes the user's facial expressions and voice through sensors and evaluates the user's emotional state. This emotion engine evaluates the user's facial expressions and voice tones in real time and recognizes the user's emotion based on that data. By understanding how the user is reacting to the measurement, a more personalized experience can be provided.

[0288] For example, if the measurement result pleases the user, the emotion engine provides positive feedback according to the analysis result. On the other hand, if the user is disappointed with the result, encouragement or advice on goal setting can be presented.

[0289] With this system, the user not only knows the dimensions of an object, but also receives feedback adjusted according to their emotional state, providing a more interactive and enjoyable experience. By utilizing the emotion engine, the measurement activity becomes more than just a routine task performed daily.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] The user takes an image including a reference object and the object to be measured using the camera on their smartphone. Users can easily complete the preparation by using everyday items as reference objects.

[0293] Step 2:

[0294] The device receives the captured image and begins image analysis. Here, optical character recognition or shape detection algorithms are applied to identify reference objects within the image.

[0295] Step 3:

[0296] The terminal uses identification information to retrieve the dimensions of a reference object from a database. This information is extracted from the label and shape of the reference object.

[0297] Step 4:

[0298] The device measures the pixel dimensions of a reference object within the image. In this process, it measures the vertical and horizontal lengths of the reference object in pixels and determines the ratio between the actual size and the pixel dimensions.

[0299] Step 5:

[0300] The terminal measures the pixel dimensions of the object to be measured and uses the ratio obtained in step 4 to calculate the actual dimensions of the object. This calculation is performed based on the known dimensions of a reference object.

[0301] Step 6:

[0302] The device's built-in emotion engine analyzes the user's facial expressions and voice tone to assess their emotional state. Facial expressions are recorded via the camera, and audio is captured via the microphone.

[0303] Step 7:

[0304] The terminal combines the calculated dimension of the object and the information on the user's emotional state obtained from the emotion engine to generate feedback for the user. For example, when the user is satisfied, a congratulatory message is presented, and when there is dissatisfaction, encouragement is given.

[0305] Step 8:

[0306] The terminal visually and auditorily displays the generated feedback to the user. The user can obtain information through the terminal's screen and speaker and select the next action while confirming the result.

[0307] (Example 2)

[0308] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0309] A conventional dimension measurement system is only aimed at accurately measuring the dimension of an object and cannot provide feedback according to the user's emotional state. For this reason, the user cannot receive actions or information considering their emotional reaction to the measurement result, and there is a problem that it is difficult to provide an interactive user experience.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0311] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and a target to be measured; means for acquiring the dimensions of the reference object from information storage based on identification information of the reference object; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the target based on these dimensions; means for analyzing the user's facial expressions and voice and evaluating their emotional state; and means for generating feedback based on the evaluation of the emotions. This allows the user to perform dimension measurements while receiving interactive feedback corresponding to their emotional state in response to the results.

[0312] A "reference object" is an object with known dimensions that is used as a reference for scaling dimensions within an image.

[0313] "Measurement target" refers to the object or subject whose dimensions you want to measure.

[0314] "Information storage" refers to databases or memory devices that hold identification information and dimensional data for reference objects.

[0315] "Pixel dimensions" refer to the width and height of a specific object within an image, measured in pixels.

[0316] A "user" is the entity that utilizes the system, takes pictures, and receives feedback.

[0317] "Emotional state" refers to the psychological or emotional state recognized based on the user's facial expressions and voice analysis.

[0318] "Feedback" refers to messages and advice provided to users based on measurement results and emotional states.

[0319] This invention is a system that provides feedback by analyzing user emotions, in addition to an image-based dimensional measurement system. Specific embodiments are shown below.

[0320] The user takes an image including a reference object and the object to be measured using a mobile device such as a smartphone. The camera application built into the smartphone is used for this purpose. The reference object is an everyday item (e.g., a plastic bottle) whose dimensional information is pre-registered in a database. Based on this, the device identifies the reference object and calculates the actual dimensions of the object based on its dimensions. Examples of smartphones that can be used include iPhones and Android devices.

[0321] The terminal includes image analysis algorithms and access to a dimensional database. Image analysis measures the number of pixels in both a reference object and the object being measured, and uses this to determine the actual size of the object.

[0322] At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to evaluate their emotional state. This emotion engine uses sensors to capture the user's real-time facial expressions and voice tone to determine their psychological state.

[0323] For example, if a user measures the dimensions of a table at home, a plastic bottle is used as a reference object. The image taken by the user is analyzed, and the dimensions of the table are accurately calculated. If the user shows a satisfied expression with the measurement results, the emotion engine displays positive feedback such as, "You're happy with the results. Enjoy arranging your new furniture."

[0324] As an example of a prompt message, you can enter the following:

[0325] "I used a plastic bottle to measure the dimensions of the table. I'm happy with the results. Please let me know what kind of feedback would be appropriate."

[0326] In this way, the system provides feedback that responds to the user's emotions, resulting in an interactive and comfortable user experience.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The user uses their smartphone's camera application to capture an image containing both a reference object and the object to be measured. The input is an image containing the user-selected reference object and the object to be measured. As output, this image data is sent to the next analysis step. Specifically, the user launches the camera application, precisely frames the reference object and the object on the screen, and presses the "Capture" button.

[0330] Step 2:

[0331] The terminal analyzes the acquired image. Image data captured is provided as input. Using an image analysis algorithm, it identifies a reference object and retrieves its dimensions from the information storage. The output is the dimensional information of the reference object. Specifically, the terminal executes an image processing algorithm, recognizes the contours and features of the reference object, and retrieves size information from the database.

[0332] Step 3:

[0333] The device measures the pixel dimensions of a reference object in an image and calculates the actual size of the target object. The input requires the dimension information of the reference object and image data containing the target object. It counts the number of pixels in the image and determines the dimensions of the target object by ratio calculation based on the actual dimensions of the reference object. The output is the calculated dimensions of the target object. Specifically, the system compares the pixel dimensions of the reference object and the target object to calculate their actual size.

[0334] Step 4:

[0335] An emotion engine built into the device analyzes the user's facial expressions and voice. Image and audio data are used as input. The analysis is performed in real time to evaluate the user's emotional state. The output is the judgment result of the user's emotional state. Specifically, it analyzes the user's facial changes and voice tone captured by the camera and microphone.

[0336] Step 5:

[0337] The device generates feedback based on calculated dimensions and emotion assessment. The inputs provided are the object's dimension data and the user's emotion assessment. A generation AI model is used to create prompts and present appropriate feedback to the user. The output is a feedback message displayed to the user. Specifically, the feedback generation algorithm generates a message based on the emotional state and displays it on the device screen.

[0338] (Application Example 2)

[0339] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0340] Measuring the dimensions of objects in everyday environments often presents problems, requiring specialized equipment and considerable effort. Furthermore, providing feedback that considers the psychological state of the user receiving the measurement results is difficult, potentially leading to decreased user satisfaction and a less-than-ideal user experience. In this context, there is a need for a system that allows users to easily obtain object dimensions and receive feedback that reflects their emotional state during the process.

[0341] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0342] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from a database based on its identification information; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object based on that; means for outputting the calculated dimensions of the object; means for analyzing the user's emotional state; and means for providing feedback based on the user's emotional state. This allows users to easily acquire the dimensions of an object and receive feedback that corresponds to their emotions at the time.

[0343] A "reference object" is an object that has known dimensions and is used as a reference when measuring the dimensions of other objects.

[0344] The "object to be measured" is the object from which you intend to obtain dimensions.

[0345] "Means of acquiring images" refers to methods of capturing visual information of the actual environment as electronic data using cameras and sensors.

[0346] "Methods of obtaining information from a database" refer to methods of searching for and retrieving necessary information from pre-stored information sources.

[0347] "Methods for measuring pixel dimensions and calculating the actual dimensions of an object based on them" refers to methods that measure the size of a reference object in an image on a pixel-by-pixel basis and use that information to derive the actual physical dimensions of the object.

[0348] "Means for outputting dimensions" refers to methods for visualizing or notifying dimensional information obtained through calculations.

[0349] "Methods for analyzing a user's emotional state" refer to technologies that evaluate psychological characteristics from a user's facial expressions and voice, and estimate their emotions.

[0350] "Means of providing feedback" refers to methods of returning information or responses that correspond to the user's behavior and emotional state.

[0351] This system is primarily composed of mobile devices such as smartphones and tablets, equipped with cameras for users to photograph reference objects and objects to be measured. The devices use image analysis software such as OpenCV to process the captured images. This allows the system to determine the pixel dimensions of the reference object and calculate the dimensions of the object to be measured by retrieving the actual dimensions from a database.

[0352] Furthermore, this system includes a function to detect and analyze the user's emotional state. Emotional analysis utilizes emotion recognition models such as Amazon Rekognition and Google Cloud Vision. This allows for the analysis of the user's facial expressions and voice data obtained from the camera and microphone, enabling real-time evaluation of their emotional state.

[0353] Once the user has finished measuring dimensions, the device displays the calculated dimensions to the user. In addition, it generates feedback tailored to the user's emotional state, providing appropriate information and suggestions. This feedback aims to personalize the user's shopping experience and improve satisfaction.

[0354] As a concrete example, when a user measures the dimensions of a chair they are considering purchasing in a store, the application calculates the chair's dimensions and suggests related interior products if the user is smiling. In this case, the prompt used as input to the generative AI model is: "Measure the dimensions of this product and suggest related recommendations if the user is happy. Please also consider appropriate feedback for other emotional states."

[0355] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0356] Step 1:

[0357] The user takes pictures of a reference object and the object to be measured with a camera. The input is the image data acquired by the user. The output is this image data that is passed to the image analysis software. Specifically, the user launches the camera app on their smartphone, adjusts the position on the screen so that the reference object (e.g., a plastic bottle) and the object to be measured (e.g., a chair) are included, and presses the shutter button.

[0358] Step 2:

[0359] The terminal analyzes the acquired image data using OpenCV to identify the reference object. The input is the image data obtained in step 1. As data processing, an image processing algorithm scans the pixel information and detects the features of the reference object. The output is the pixel dimensions of the reference object and their corresponding position information.

[0360] Step 3:

[0361] The terminal retrieves the actual dimensions of the reference object from the database. The input is the identification information of the reference object obtained in step 2. A database query is executed using this identification information. The output is the actual dimension data of the reference object.

[0362] Step 4:

[0363] The terminal calculates the actual dimensions of the object being measured based on the pixel dimensions of a reference object. The inputs are the pixel dimensions from step 2 and the actual dimensions from step 3. A ratio calculation is performed as part of the data calculation to derive the actual dimensions of the object being measured. The output is the calculated actual dimensions of the object.

[0364] Step 5:

[0365] The device collects user facial and voice data and passes it to the emotion engine. The input is real-time sensor data. Specifically, the device's front camera and microphone are activated to capture the user's face and voice. The output is a dataset for analysis by the emotion engine.

[0366] Step 6:

[0367] The server evaluates the user's emotional state using an emotion recognition model such as Amazon Rekognition. The input is the user's facial expressions and voice data from step 5. As a process, a generative AI model assigns emotion labels. The output is the evaluation result of the user's emotional state.

[0368] Step 7:

[0369] The device displays the dimensions of the object and generates feedback based on the evaluated emotional state. The inputs are the dimensional data from step 4 and the emotional evaluation from step 6. The feedback is created using prompts for the generating AI model. The output is the dimensional information and feedback message presented to the user. Specifically, the measurement results and an emotionally appropriate message are displayed as a pop-up on the device screen.

[0370] 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.

[0371] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0372] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0373] [Third Embodiment]

[0374] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0375] 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.

[0376] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0377] 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.

[0378] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0380] 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.

[0381] 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.

[0382] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0383] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0384] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0385] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0386] This invention provides a system for calculating the dimensions of an object to be measured using an image that includes a reference object with known dimensions and the object to be measured. This system operates on a smartphone terminal, and by using a dedicated application, users can easily measure their height and the dimensions of objects in their daily lives.

[0387] Beyond simply taking an image, an application installed on the smartphone automatically performs image analysis and dimensional calculation. The user first uses their smartphone's camera to photograph both a reference object and the object to be measured together. The reference object is typically an everyday item with known dimensions, such as a plastic bottle or a door.

[0388] The device processes the captured image within the application. First, the application identifies a reference object from the image and, based on that identification information, retrieves the dimensional information of that reference object from a pre-registered database. Then, it measures the proportion of pixels occupied by the reference object in the image and calculates the ratio to the number of pixels of the target object based on that. Based on this ratio, it calculates the actual dimensions of the object to be measured.

[0389] The calculated dimensions are provided to the user as visual feedback on the device's display screen. For example, if a user takes a photo of themselves standing next to a plastic bottle (30 centimeters tall) as a reference object, the application can measure the pixel height of the plastic bottle in the image, calculate the user's height based on that, and display it on the screen.

[0390] This feature allows users to quickly measure the dimensions of objects in everyday situations, greatly improving convenience when, for example, measuring a child's height or recording the size of a fish caught.

[0391] The following describes the processing flow.

[0392] Step 1:

[0393] The user uses their smartphone's camera to capture an image that includes a reference object with known dimensions and the object to be measured. By including the reference object in the field of view, the user provides the reference information necessary for subsequent calculations.

[0394] Step 2:

[0395] The terminal receives image data provided by the user and begins the image analysis process. In this step, image processing algorithms are used to prepare the device for analyzing objects within the image.

[0396] Step 3:

[0397] The device detects a reference object within the image and analyzes its identification information. Here, the reference object is identified based on its shape and label, and this identification information is used to query a database.

[0398] Step 4:

[0399] The terminal retrieves the dimensional information of the identified reference object from a pre-registered database based on its identification information. This makes the actual dimensional data of the reference object available to the terminal.

[0400] Step 5:

[0401] The device measures the pixel size of a reference object within the image. This is done by detecting the vertical and horizontal edges of the reference object and counting the number of pixels between them.

[0402] Step 6:

[0403] The device derives a ratio for calculating the dimensions of an object based on the actual dimensions and pixel dimensions of a reference object. This prepares the device to calculate the actual size from any number of pixels in an image.

[0404] Step 7:

[0405] The device measures the pixel size of the object to be measured within the image and applies the ratio obtained in step 6 to calculate the actual dimensions of the object.

[0406] Step 8:

[0407] The terminal visually displays the calculated dimensions of the object being measured to the user. This allows the user to confirm the actual dimensions of the object and record or share them as needed.

[0408] (Example 1)

[0409] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0410] In daily life, there is a problem in that it is difficult to measure the dimensions of objects simply and accurately without using special measuring tools. Furthermore, there is a demand for easy measurement of objects that are difficult to measure, such as living organisms.

[0411] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0412] In this invention, the server includes means for acquiring an image containing both a reference object and a measurement target using a mobile information terminal, means for identifying the reference object in the image using an image recognition algorithm, and means for acquiring the dimensions of the identified reference object from pre-recorded information. This makes it possible to easily measure the dimensions of objects in daily life using a mobile information terminal.

[0413] A "portable information terminal" is a general term for a small, portable information processing device equipped with communication and information management functions that can be carried by a user.

[0414] A "reference object" refers to an item that has known dimensions and is used as a reference point in the measurement process; it is an item that is used on a daily basis.

[0415] A "measurement target" refers to an object or living organism within an image that is designated for determining its dimensions, and is the subject of evaluation and analysis.

[0416] An "image recognition algorithm" refers to a set of computational methods for detecting and identifying specific patterns or objects within acquired image data.

[0417] "Pre-recorded information base" refers to a database in which identification information and dimensional data of reference objects are registered in advance and are available in a searchable format.

[0418] This invention is a system that uses a dedicated application running on a mobile device to easily determine the dimensions of an object to be measured using everyday reference objects. Specifically, the user first uses the camera function of the mobile device to photograph both the reference object and the object to be measured. As reference objects, items with known dimensions that are commonly found in daily life, such as plastic bottles or doors, can be used.

[0419] The device is equipped with a dedicated application for analyzing captured images. This application uses an image recognition algorithm to identify reference objects and retrieves dimensional information from a pre-recorded database based on the identification information.

[0420] Next, the device measures the number of pixels in a reference object within the image and calculates the ratio to the number of pixels of the object being measured. From this ratio, it calculates the actual dimensions of the object and provides visual feedback to the user. This allows the user to quickly and accurately determine the dimensions of an object in the real world.

[0421] For example, if a user wants to measure their height using a plastic bottle as a reference object, the application will measure the height of the bottle in the image, calculate the user's height based on that, and display it on the screen. This method can also be used as a prompt message, such as "Tell me how to measure my height using a plastic bottle as a reference object."

[0422] This system makes it possible to smoothly measure the dimensions of objects in various everyday situations without using special equipment.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The user uses a mobile device to capture an image containing a reference object with known dimensions and the object to be measured. The acquired image is the input. The image data is sent to the application as output. It is important to ensure that the reference object and the object to be measured are clearly within the same image when taking the picture.

[0426] Step 2:

[0427] The device receives images captured via the application. The input is the acquired image data. The output is information about a reference object identified by an image recognition algorithm. The device accurately obtains the location and characteristics of the reference object and prepares for the next processing step.

[0428] Step 3:

[0429] The terminal retrieves dimensional data from a pre-recorded database based on the information of the identified reference object. The input is the identification information of the reference object. The output is the actual dimensional data of the reference object. At this stage, a query is made to the database and the corresponding dimensions are returned.

[0430] Step 4:

[0431] The device measures the number of pixels in a reference object within an image and calculates the ratio between that number of pixels and the number of pixels in the object being measured. The input is the image data and the dimensions of the reference object. The output is the actual dimensions of the object being measured. This ratio calculation accurately determines the size of the object the user is looking for.

[0432] Step 5:

[0433] The terminal visually presents the calculated dimensions to the user. The input is the calculated dimension information. The output is the dimension value displayed on the mobile device's screen. This allows the user to confirm the actual size of the object being measured.

[0434] (Application Example 1)

[0435] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0436] In daily life, there is a need to accurately and easily adjust and evaluate goods for consumption activities, but conventional measurement methods are time-consuming and make rapid evaluation difficult. This project aims to solve that problem. In particular, it is necessary to be able to quickly and accurately calculate the dimensions of the object being measured by using a reference object with known dimensions, and to utilize this information as useful information for consumption activities.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0438] In this invention, the server includes means for acquiring an image including a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from an information storage means based on its identification information; and means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object to be measured based on these dimensions. This makes it possible to quickly and accurately adjust and evaluate goods for consumer activities.

[0439] A "reference object" is an object with known dimensions that is used as a comparison standard with the object being measured.

[0440] A "measured object" is an object whose dimensions are unknown and which is included in the image along with a reference object as the object to be measured.

[0441] "Information storage means" refers to a database that stores identification information and dimensional data of a reference object and makes it available for retrieval as needed.

[0442] "Pixel dimensions" refer to the number of pixels an object has within an image, and are the basic data used to calculate its actual dimensions.

[0443] "Consumption activity" refers to everyday actions related to the use or evaluation of products and services, and actions used to determine the purchase or necessity of goods.

[0444] The system for realizing this invention operates on a mobile device such as a smartphone or tablet. The device has an application installed for image processing and dimension calculation. This application utilizes the image analysis library OpenCV and a database that stores known dimension information.

[0445] First, the user takes a picture of the object whose dimensions they want to measure (the object to be measured) and an object with known dimensions (the reference object) together using the camera on their mobile device. The application analyzes this image and identifies the reference object. The information about the identified reference object is then used in a database to retrieve its dimensions.

[0446] Next, the application uses the OpenCV library to measure the number of pixels in a reference object within an image and calculates the ratio between that number and the number of pixels in the object being measured. Based on this ratio, the actual dimensions of the object being measured are calculated.

[0447] The calculated dimensions are displayed on the device screen as visual feedback to the user, which can be used to adjust items for consumption. For example, if a user wants to know the size of a pizza delivered via food delivery, they can use a plate with a known diameter placed next to the pizza as a reference. This method allows for easy measurement of the pizza's actual diameter.

[0448] For example, a possible prompt message could be: "Using this photograph and a table knife (20cm long) as a reference object, calculate the diameter of the cake on the plate." This information can then be provided to an AI model to perform appropriate dimensional calculations.

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The user uses the camera on their mobile device to capture an image that includes the object to be measured and a reference object with known dimensions. This image is then input into the application.

[0452] Step 2:

[0453] The terminal uses the OpenCV image analysis library to identify reference objects within an image. Reference object identification information is output, and based on this information, the dimensions of the reference objects are retrieved from a database.

[0454] Step 3:

[0455] The terminal uses the acquired dimensional information of the reference object to measure the number of pixels in the reference object within the image. The ratio of the number of pixels in the reference object to the number of pixels in the object being measured is calculated using the pixel count as input.

[0456] Step 4:

[0457] The terminal uses the calculated ratio to determine the actual dimensions of the object being measured. The calculated dimensions are output and become input for the next processing step.

[0458] Step 5:

[0459] The terminal displays the calculated dimensions to the user. The display method provides information visually on a graphical interface in a format that is easy for the user to understand.

[0460] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0461] This invention combines a system that calculates the dimensions of an object using an image that includes a reference object and the object to be measured with an emotion engine that recognizes the user's emotions. Implemented via a smartphone application, the user can measure the dimensions of an object using a familiar object and receive personalized feedback on the results.

[0462] First, the user uses the camera to take an image that includes a reference object (e.g., a plastic bottle or a door) and the object to be measured. The device analyzes the captured image, identifies the reference object, and retrieves its dimensions from a database. Next, it measures the pixel dimensions of the reference object in the image, calculates the ratio, and then calculates the actual size of the object to be measured.

[0463] Simultaneously, an emotion engine built into the device analyzes the user's facial expressions and voice through sensors to evaluate the user's emotional state. This emotion engine evaluates the user's facial expressions and tone of voice in real time and recognizes the user's emotions based on that data. By understanding how the user is reacting to the measurements, a more personalized experience can be provided.

[0464] For example, if the measurement results please the user, the emotion engine will provide positive feedback tailored to the analysis. On the other hand, if the user is disappointed with the results, it can offer encouragement and advice on setting goals.

[0465] This system provides a more interactive and enjoyable experience for users, as feedback is adjusted based on their emotional state, not just the dimensions of an object. By leveraging the emotion engine, measurement becomes more than just a routine task.

[0466] The following describes the processing flow.

[0467] Step 1:

[0468] The user takes an image including a reference object and the object to be measured using the camera on their smartphone. Users can easily complete the preparation by using everyday items as reference objects.

[0469] Step 2:

[0470] The device receives the captured image and begins image analysis. Here, optical character recognition or shape detection algorithms are applied to identify reference objects within the image.

[0471] Step 3:

[0472] The terminal uses identification information to retrieve the dimensions of a reference object from a database. This information is extracted from the label and shape of the reference object.

[0473] Step 4:

[0474] The device measures the pixel dimensions of a reference object within the image. In this process, it measures the vertical and horizontal lengths of the reference object in pixels and determines the ratio between the actual size and the pixel dimensions.

[0475] Step 5:

[0476] The terminal measures the pixel dimensions of the object to be measured and uses the ratio obtained in step 4 to calculate the actual dimensions of the object. This calculation is performed based on the known dimensions of a reference object.

[0477] Step 6:

[0478] The device's built-in emotion engine analyzes the user's facial expressions and voice tone to assess their emotional state. Facial expressions are recorded via the camera, and audio is captured via the microphone.

[0479] Step 7:

[0480] The device combines the calculated dimensions of the object with information about the user's emotional state obtained from the emotion engine to generate feedback for the user. For example, if the user is satisfied, it will display a congratulatory message, and if they are dissatisfied, it will offer encouragement.

[0481] Step 8:

[0482] The device displays the generated feedback to the user visually and audibly. The user receives information through the device's screen and speaker, and can select the next action after confirming the results.

[0483] (Example 2)

[0484] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0485] Conventional dimension measurement systems are solely focused on accurately measuring the dimensions of an object and are unable to provide feedback that takes into account the user's emotional state. As a result, users cannot receive actions or information that consider their emotional reactions to the measurement results, making it difficult to provide an interactive user experience.

[0486] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0487] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and a target to be measured; means for acquiring the dimensions of the reference object from information storage based on identification information of the reference object; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the target based on these dimensions; means for analyzing the user's facial expressions and voice and evaluating their emotional state; and means for generating feedback based on the evaluation of the emotions. This allows the user to perform dimension measurements while receiving interactive feedback corresponding to their emotional state in response to the results.

[0488] A "reference object" is an object with known dimensions that is used as a reference for scaling dimensions within an image.

[0489] "Measurement target" refers to the object or subject whose dimensions you want to measure.

[0490] "Information storage" refers to databases or memory devices that hold identification information and dimensional data for reference objects.

[0491] "Pixel dimensions" refer to the width and height of a specific object within an image, measured in pixels.

[0492] A "user" is the entity that utilizes the system, takes pictures, and receives feedback.

[0493] "Emotional state" refers to the psychological or emotional state recognized based on the user's facial expressions and voice analysis.

[0494] "Feedback" refers to messages and advice provided to users based on measurement results and emotional states.

[0495] This invention is a system that provides feedback by analyzing user emotions, in addition to an image-based dimensional measurement system. Specific embodiments are shown below.

[0496] The user takes an image including a reference object and the object to be measured using a mobile device such as a smartphone. The camera application built into the smartphone is used for this purpose. The reference object is an everyday item (e.g., a plastic bottle) whose dimensional information is pre-registered in a database. Based on this, the device identifies the reference object and calculates the actual dimensions of the object based on its dimensions. Examples of smartphones that can be used include iPhones and Android devices.

[0497] The terminal includes image analysis algorithms and access to a dimensional database. Image analysis measures the number of pixels in both a reference object and the object being measured, and uses this to determine the actual size of the object.

[0498] At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to evaluate their emotional state. This emotion engine uses sensors to capture the user's real-time facial expressions and voice tone to determine their psychological state.

[0499] For example, if a user measures the dimensions of a table at home, a plastic bottle is used as a reference object. The image taken by the user is analyzed, and the dimensions of the table are accurately calculated. If the user shows a satisfied expression with the measurement results, the emotion engine displays positive feedback such as, "You're happy with the results. Enjoy arranging your new furniture."

[0500] As an example of a prompt message, you can enter the following:

[0501] "I used a plastic bottle to measure the dimensions of the table. I'm happy with the results. Please let me know what kind of feedback would be appropriate."

[0502] In this way, the system provides feedback that responds to the user's emotions, resulting in an interactive and comfortable user experience.

[0503] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0504] Step 1:

[0505] The user uses their smartphone's camera application to capture an image containing both a reference object and the object to be measured. The input is an image containing the user-selected reference object and the object to be measured. As output, this image data is sent to the next analysis step. Specifically, the user launches the camera application, precisely frames the reference object and the object on the screen, and presses the "Capture" button.

[0506] Step 2:

[0507] The terminal analyzes the acquired image. Image data captured is provided as input. Using an image analysis algorithm, it identifies a reference object and retrieves its dimensions from the information storage. The output is the dimensional information of the reference object. Specifically, the terminal executes an image processing algorithm, recognizes the contours and features of the reference object, and retrieves size information from the database.

[0508] Step 3:

[0509] The device measures the pixel dimensions of a reference object in an image and calculates the actual size of the target object. The input requires the dimension information of the reference object and image data containing the target object. It counts the number of pixels in the image and determines the dimensions of the target object by ratio calculation based on the actual dimensions of the reference object. The output is the calculated dimensions of the target object. Specifically, the system compares the pixel dimensions of the reference object and the target object to calculate their actual size.

[0510] Step 4:

[0511] An emotion engine built into the device analyzes the user's facial expressions and voice. Image and audio data are used as input. The analysis is performed in real time to evaluate the user's emotional state. The output is the judgment result of the user's emotional state. Specifically, it analyzes the user's facial changes and voice tone captured by the camera and microphone.

[0512] Step 5:

[0513] The device generates feedback based on calculated dimensions and emotion assessment. The inputs provided are the object's dimension data and the user's emotion assessment. A generation AI model is used to create prompts and present appropriate feedback to the user. The output is a feedback message displayed to the user. Specifically, the feedback generation algorithm generates a message based on the emotional state and displays it on the device screen.

[0514] (Application Example 2)

[0515] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] Measuring the dimensions of objects in everyday environments often presents problems, requiring specialized equipment and considerable effort. Furthermore, providing feedback that considers the psychological state of the user receiving the measurement results is difficult, potentially leading to decreased user satisfaction and a less-than-ideal user experience. In this context, there is a need for a system that allows users to easily obtain object dimensions and receive feedback that reflects their emotional state during the process.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0518] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from a database based on its identification information; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object based on that; means for outputting the calculated dimensions of the object; means for analyzing the user's emotional state; and means for providing feedback based on the user's emotional state. This allows users to easily acquire the dimensions of an object and receive feedback that corresponds to their emotions at the time.

[0519] A "reference object" is an object that has known dimensions and is used as a reference when measuring the dimensions of other objects.

[0520] The "object to be measured" is the object from which you intend to obtain dimensions.

[0521] "Means of acquiring images" refers to methods of capturing visual information of the actual environment as electronic data using cameras and sensors.

[0522] "Methods of obtaining information from a database" refer to methods of searching for and retrieving necessary information from pre-stored information sources.

[0523] "Methods for measuring pixel dimensions and calculating the actual dimensions of an object based on them" refers to methods that measure the size of a reference object in an image on a pixel-by-pixel basis and use that information to derive the actual physical dimensions of the object.

[0524] "Means for outputting dimensions" refers to methods for visualizing or notifying dimensional information obtained through calculations.

[0525] "Methods for analyzing a user's emotional state" refer to technologies that evaluate psychological characteristics from a user's facial expressions and voice, and estimate their emotions.

[0526] "Means of providing feedback" refers to methods of returning information or responses that correspond to the user's behavior and emotional state.

[0527] This system is primarily composed of mobile devices such as smartphones and tablets, equipped with cameras for users to photograph reference objects and objects to be measured. The devices use image analysis software such as OpenCV to process the captured images. This allows the system to determine the pixel dimensions of the reference object and calculate the dimensions of the object to be measured by retrieving the actual dimensions from a database.

[0528] Furthermore, this system includes a function to detect and analyze the user's emotional state. Emotional analysis utilizes emotion recognition models such as Amazon Rekognition and Google Cloud Vision. This allows for the analysis of the user's facial expressions and voice data obtained from the camera and microphone, enabling real-time evaluation of their emotional state.

[0529] Once the user has finished measuring dimensions, the device displays the calculated dimensions to the user. In addition, it generates feedback tailored to the user's emotional state, providing appropriate information and suggestions. This feedback aims to personalize the user's shopping experience and improve satisfaction.

[0530] As a concrete example, when a user measures the dimensions of a chair they are considering purchasing in a store, the application calculates the chair's dimensions and suggests related interior products if the user is smiling. In this case, the prompt used as input to the generative AI model is: "Measure the dimensions of this product and suggest related recommendations if the user is happy. Please also consider appropriate feedback for other emotional states."

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The user takes pictures of a reference object and the object to be measured with a camera. The input is the image data acquired by the user. The output is this image data that is passed to the image analysis software. Specifically, the user launches the camera app on their smartphone, adjusts the position on the screen so that the reference object (e.g., a plastic bottle) and the object to be measured (e.g., a chair) are included, and presses the shutter button.

[0534] Step 2:

[0535] The terminal analyzes the acquired image data using OpenCV to identify the reference object. The input is the image data obtained in step 1. As data processing, an image processing algorithm scans the pixel information and detects the features of the reference object. The output is the pixel dimensions of the reference object and their corresponding position information.

[0536] Step 3:

[0537] The terminal retrieves the actual dimensions of the reference object from the database. The input is the identification information of the reference object obtained in step 2. A database query is executed using this identification information. The output is the actual dimension data of the reference object.

[0538] Step 4:

[0539] The terminal calculates the actual dimensions of the object being measured based on the pixel dimensions of a reference object. The inputs are the pixel dimensions from step 2 and the actual dimensions from step 3. A ratio calculation is performed as part of the data calculation to derive the actual dimensions of the object being measured. The output is the calculated actual dimensions of the object.

[0540] Step 5:

[0541] The device collects user facial and voice data and passes it to the emotion engine. The input is real-time sensor data. Specifically, the device's front camera and microphone are activated to capture the user's face and voice. The output is a dataset for analysis by the emotion engine.

[0542] Step 6:

[0543] The server evaluates the user's emotional state using an emotion recognition model such as Amazon Rekognition. The input is the user's facial expressions and voice data from step 5. As a process, a generative AI model assigns emotion labels. The output is the evaluation result of the user's emotional state.

[0544] Step 7:

[0545] The device displays the dimensions of the object and generates feedback based on the evaluated emotional state. The inputs are the dimensional data from step 4 and the emotional evaluation from step 6. The feedback is created using prompts for the generating AI model. The output is the dimensional information and feedback message presented to the user. Specifically, the measurement results and an emotionally appropriate message are displayed as a pop-up on the device screen.

[0546] 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.

[0547] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0549] [Fourth Embodiment]

[0550] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0551] 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.

[0552] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0553] 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.

[0554] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0555] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0556] 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.

[0557] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0558] 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.

[0559] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0560] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0561] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0562] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0563] This invention provides a system for calculating the dimensions of an object to be measured using an image that includes a reference object with known dimensions and the object to be measured. This system operates on a smartphone terminal, and by using a dedicated application, users can easily measure their height and the dimensions of objects in their daily lives.

[0564] Beyond simply taking an image, an application installed on the smartphone automatically performs image analysis and dimensional calculation. The user first uses their smartphone's camera to photograph both a reference object and the object to be measured together. The reference object is typically an everyday item with known dimensions, such as a plastic bottle or a door.

[0565] The device processes the captured image within the application. First, the application identifies a reference object from the image and, based on that identification information, retrieves the dimensional information of that reference object from a pre-registered database. Then, it measures the proportion of pixels occupied by the reference object in the image and calculates the ratio to the number of pixels of the target object based on that. Based on this ratio, it calculates the actual dimensions of the object to be measured.

[0566] The calculated dimensions are provided to the user as visual feedback on the device's display screen. For example, if a user takes a photo of themselves standing next to a plastic bottle (30 centimeters tall) as a reference object, the application can measure the pixel height of the plastic bottle in the image, calculate the user's height based on that, and display it on the screen.

[0567] This feature allows users to quickly measure the dimensions of objects in everyday situations, greatly improving convenience when, for example, measuring a child's height or recording the size of a fish caught.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] The user uses their smartphone's camera to capture an image that includes a reference object with known dimensions and the object to be measured. By including the reference object in the field of view, the user provides the reference information necessary for subsequent calculations.

[0571] Step 2:

[0572] The terminal receives image data provided by the user and begins the image analysis process. In this step, image processing algorithms are used to prepare the device for analyzing objects within the image.

[0573] Step 3:

[0574] The device detects a reference object within the image and analyzes its identification information. Here, the reference object is identified based on its shape and label, and this identification information is used to query a database.

[0575] Step 4:

[0576] The terminal retrieves the dimensional information of the identified reference object from a pre-registered database based on its identification information. This makes the actual dimensional data of the reference object available to the terminal.

[0577] Step 5:

[0578] The device measures the pixel size of a reference object within the image. This is done by detecting the vertical and horizontal edges of the reference object and counting the number of pixels between them.

[0579] Step 6:

[0580] The device derives a ratio for calculating the dimensions of an object based on the actual dimensions and pixel dimensions of a reference object. This prepares the device to calculate the actual size from any number of pixels in an image.

[0581] Step 7:

[0582] The device measures the pixel size of the object to be measured within the image and applies the ratio obtained in step 6 to calculate the actual dimensions of the object.

[0583] Step 8:

[0584] The terminal visually displays the calculated dimensions of the object being measured to the user. This allows the user to confirm the actual dimensions of the object and record or share them as needed.

[0585] (Example 1)

[0586] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0587] In daily life, there is a problem in that it is difficult to measure the dimensions of objects simply and accurately without using special measuring tools. Furthermore, there is a demand for easy measurement of objects that are difficult to measure, such as living organisms.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0589] In this invention, the server includes means for acquiring an image containing both a reference object and a measurement target using a mobile information terminal, means for identifying the reference object in the image using an image recognition algorithm, and means for acquiring the dimensions of the identified reference object from pre-recorded information. This makes it possible to easily measure the dimensions of objects in daily life using a mobile information terminal.

[0590] A "portable information terminal" is a general term for a small, portable information processing device equipped with communication and information management functions that can be carried by a user.

[0591] A "reference object" refers to an item that has known dimensions and is used as a reference point in the measurement process; it is an item that is used on a daily basis.

[0592] A "measurement target" refers to an object or living organism within an image that is designated for determining its dimensions, and is the subject of evaluation and analysis.

[0593] An "image recognition algorithm" refers to a set of computational methods for detecting and identifying specific patterns or objects within acquired image data.

[0594] "Pre-recorded information base" refers to a database in which identification information and dimensional data of reference objects are registered in advance and are available in a searchable format.

[0595] This invention is a system that uses a dedicated application running on a mobile device to easily determine the dimensions of an object to be measured using everyday reference objects. Specifically, the user first uses the camera function of the mobile device to photograph both the reference object and the object to be measured. As reference objects, items with known dimensions that are commonly found in daily life, such as plastic bottles or doors, can be used.

[0596] The device is equipped with a dedicated application for analyzing captured images. This application uses an image recognition algorithm to identify reference objects and retrieves dimensional information from a pre-recorded database based on the identification information.

[0597] Next, the device measures the number of pixels in a reference object within the image and calculates the ratio to the number of pixels of the object being measured. From this ratio, it calculates the actual dimensions of the object and provides visual feedback to the user. This allows the user to quickly and accurately determine the dimensions of an object in the real world.

[0598] For example, if a user wants to measure their height using a plastic bottle as a reference object, the application will measure the height of the bottle in the image, calculate the user's height based on that, and display it on the screen. This method can also be used as a prompt message, such as "Tell me how to measure my height using a plastic bottle as a reference object."

[0599] This system makes it possible to smoothly measure the dimensions of objects in various everyday situations without using special equipment.

[0600] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0601] Step 1:

[0602] The user uses a mobile device to capture an image containing a reference object with known dimensions and the object to be measured. The acquired image is the input. The image data is sent to the application as output. It is important to ensure that the reference object and the object to be measured are clearly within the same image when taking the picture.

[0603] Step 2:

[0604] The device receives images captured via the application. The input is the acquired image data. The output is information about a reference object identified by an image recognition algorithm. The device accurately obtains the location and characteristics of the reference object and prepares for the next processing step.

[0605] Step 3:

[0606] The terminal retrieves dimensional data from a pre-recorded database based on the information of the identified reference object. The input is the identification information of the reference object. The output is the actual dimensional data of the reference object. At this stage, a query is made to the database and the corresponding dimensions are returned.

[0607] Step 4:

[0608] The device measures the number of pixels in a reference object within an image and calculates the ratio between that number of pixels and the number of pixels in the object being measured. The input is the image data and the dimensions of the reference object. The output is the actual dimensions of the object being measured. This ratio calculation accurately determines the size of the object the user is looking for.

[0609] Step 5:

[0610] The terminal visually presents the calculated dimensions to the user. The input is the calculated dimension information. The output is the dimension value displayed on the mobile device's screen. This allows the user to confirm the actual size of the object being measured.

[0611] (Application Example 1)

[0612] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] In daily life, there is a need to accurately and easily adjust and evaluate goods for consumption activities, but conventional measurement methods are time-consuming and make rapid evaluation difficult. This project aims to solve that problem. In particular, it is necessary to be able to quickly and accurately calculate the dimensions of the object being measured by using a reference object with known dimensions, and to utilize this information as useful information for consumption activities.

[0614] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0615] In this invention, the server includes means for acquiring an image including a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from an information storage means based on its identification information; and means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object to be measured based on these dimensions. This makes it possible to quickly and accurately adjust and evaluate goods for consumer activities.

[0616] A "reference object" is an object with known dimensions that is used as a comparison standard with the object being measured.

[0617] A "measured object" is an object whose dimensions are unknown and which is included in the image along with a reference object as the object to be measured.

[0618] "Information storage means" refers to a database that stores identification information and dimensional data of a reference object and makes it available for retrieval as needed.

[0619] "Pixel dimensions" refer to the number of pixels an object has within an image, and are the basic data used to calculate its actual dimensions.

[0620] "Consumption activity" refers to everyday actions related to the use or evaluation of products and services, and actions used to determine the purchase or necessity of goods.

[0621] The system for realizing this invention operates on a mobile device such as a smartphone or tablet. The device has an application installed for image processing and dimension calculation. This application utilizes the image analysis library OpenCV and a database that stores known dimension information.

[0622] First, the user takes a picture of the object whose dimensions they want to measure (the object to be measured) and an object with known dimensions (the reference object) together using the camera on their mobile device. The application analyzes this image and identifies the reference object. The information about the identified reference object is then used in a database to retrieve its dimensions.

[0623] Next, the application uses the OpenCV library to measure the number of pixels in a reference object within an image and calculates the ratio between that number and the number of pixels in the object being measured. Based on this ratio, the actual dimensions of the object being measured are calculated.

[0624] The calculated dimensions are displayed on the device screen as visual feedback to the user, which can be used to adjust items for consumption. For example, if a user wants to know the size of a pizza delivered via food delivery, they can use a plate with a known diameter placed next to the pizza as a reference. This method allows for easy measurement of the pizza's actual diameter.

[0625] For example, a possible prompt message could be: "Using this photograph and a table knife (20cm long) as a reference object, calculate the diameter of the cake on the plate." This information can then be provided to an AI model to perform appropriate dimensional calculations.

[0626] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0627] Step 1:

[0628] The user uses the camera on their mobile device to capture an image that includes the object to be measured and a reference object with known dimensions. This image is then input into the application.

[0629] Step 2:

[0630] The terminal uses the OpenCV image analysis library to identify reference objects within an image. Reference object identification information is output, and based on this information, the dimensions of the reference objects are retrieved from a database.

[0631] Step 3:

[0632] The terminal uses the acquired dimensional information of the reference object to measure the number of pixels in the reference object within the image. The ratio of the number of pixels in the reference object to the number of pixels in the object being measured is calculated using the pixel count as input.

[0633] Step 4:

[0634] The terminal uses the calculated ratio to determine the actual dimensions of the object being measured. The calculated dimensions are output and become input for the next processing step.

[0635] Step 5:

[0636] The terminal displays the calculated dimensions to the user. The display method provides information visually on a graphical interface in a format that is easy for the user to understand.

[0637] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0638] This invention combines a system that calculates the dimensions of an object using an image that includes a reference object and the object to be measured with an emotion engine that recognizes the user's emotions. Implemented via a smartphone application, the user can measure the dimensions of an object using a familiar object and receive personalized feedback on the results.

[0639] First, the user uses the camera to take an image that includes a reference object (e.g., a plastic bottle or a door) and the object to be measured. The device analyzes the captured image, identifies the reference object, and retrieves its dimensions from a database. Next, it measures the pixel dimensions of the reference object in the image, calculates the ratio, and then calculates the actual size of the object to be measured.

[0640] Simultaneously, an emotion engine built into the device analyzes the user's facial expressions and voice through sensors to evaluate the user's emotional state. This emotion engine evaluates the user's facial expressions and tone of voice in real time and recognizes the user's emotions based on that data. By understanding how the user is reacting to the measurements, a more personalized experience can be provided.

[0641] For example, if the measurement results please the user, the emotion engine will provide positive feedback tailored to the analysis. On the other hand, if the user is disappointed with the results, it can offer encouragement and advice on setting goals.

[0642] This system provides a more interactive and enjoyable experience for users, as feedback is adjusted based on their emotional state, not just the dimensions of an object. By leveraging the emotion engine, measurement becomes more than just a routine task.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user takes an image including a reference object and the object to be measured using the camera on their smartphone. Users can easily complete the preparation by using everyday items as reference objects.

[0646] Step 2:

[0647] The device receives the captured image and begins image analysis. Here, optical character recognition or shape detection algorithms are applied to identify reference objects within the image.

[0648] Step 3:

[0649] The terminal uses identification information to retrieve the dimensions of a reference object from a database. This information is extracted from the label and shape of the reference object.

[0650] Step 4:

[0651] The device measures the pixel dimensions of a reference object within the image. In this process, it measures the vertical and horizontal lengths of the reference object in pixels and determines the ratio between the actual size and the pixel dimensions.

[0652] Step 5:

[0653] The terminal measures the pixel dimensions of the object to be measured and uses the ratio obtained in step 4 to calculate the actual dimensions of the object. This calculation is performed based on the known dimensions of a reference object.

[0654] Step 6:

[0655] The device's built-in emotion engine analyzes the user's facial expressions and voice tone to assess their emotional state. Facial expressions are recorded via the camera, and audio is captured via the microphone.

[0656] Step 7:

[0657] The device combines the calculated dimensions of the object with information about the user's emotional state obtained from the emotion engine to generate feedback for the user. For example, if the user is satisfied, it will display a congratulatory message, and if they are dissatisfied, it will offer encouragement.

[0658] Step 8:

[0659] The device displays the generated feedback to the user visually and audibly. The user receives information through the device's screen and speaker, and can select the next action after confirming the results.

[0660] (Example 2)

[0661] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] Conventional dimension measurement systems are solely focused on accurately measuring the dimensions of an object and are unable to provide feedback that takes into account the user's emotional state. As a result, users cannot receive actions or information that consider their emotional reactions to the measurement results, making it difficult to provide an interactive user experience.

[0663] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0664] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and a target to be measured; means for acquiring the dimensions of the reference object from information storage based on identification information of the reference object; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the target based on these dimensions; means for analyzing the user's facial expressions and voice and evaluating their emotional state; and means for generating feedback based on the evaluation of the emotions. This allows the user to perform dimension measurements while receiving interactive feedback corresponding to their emotional state in response to the results.

[0665] A "reference object" is an object with known dimensions that is used as a reference for scaling dimensions within an image.

[0666] "Measurement target" refers to the object or subject whose dimensions you want to measure.

[0667] "Information storage" refers to databases or memory devices that hold identification information and dimensional data for reference objects.

[0668] "Pixel dimensions" refer to the width and height of a specific object within an image, measured in pixels.

[0669] A "user" is the entity that utilizes the system, takes pictures, and receives feedback.

[0670] "Emotional state" refers to the psychological or emotional state recognized based on the user's facial expressions and voice analysis.

[0671] "Feedback" refers to messages and advice provided to users based on measurement results and emotional states.

[0672] This invention is a system that provides feedback by analyzing user emotions, in addition to an image-based dimensional measurement system. Specific embodiments are shown below.

[0673] The user takes an image including a reference object and the object to be measured using a mobile device such as a smartphone. The camera application built into the smartphone is used for this purpose. The reference object is an everyday item (e.g., a plastic bottle) whose dimensional information is pre-registered in a database. Based on this, the device identifies the reference object and calculates the actual dimensions of the object based on its dimensions. Examples of smartphones that can be used include iPhones and Android devices.

[0674] The terminal includes image analysis algorithms and access to a dimensional database. Image analysis measures the number of pixels in both a reference object and the object being measured, and uses this to determine the actual size of the object.

[0675] At the same time, the device is equipped with an emotion engine that analyzes the user's facial expressions and voice to evaluate their emotional state. This emotion engine uses sensors to capture the user's real-time facial expressions and voice tone to determine their psychological state.

[0676] For example, if a user measures the dimensions of a table at home, a plastic bottle is used as a reference object. The image taken by the user is analyzed, and the dimensions of the table are accurately calculated. If the user shows a satisfied expression with the measurement results, the emotion engine displays positive feedback such as, "You're happy with the results. Enjoy arranging your new furniture."

[0677] As an example of a prompt message, you can enter the following:

[0678] "I used a plastic bottle to measure the dimensions of the table. I'm happy with the results. Please let me know what kind of feedback would be appropriate."

[0679] In this way, the system provides feedback that responds to the user's emotions, resulting in an interactive and comfortable user experience.

[0680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0681] Step 1:

[0682] The user uses their smartphone's camera application to capture an image containing both a reference object and the object to be measured. The input is an image containing the user-selected reference object and the object to be measured. As output, this image data is sent to the next analysis step. Specifically, the user launches the camera application, precisely frames the reference object and the object on the screen, and presses the "Capture" button.

[0683] Step 2:

[0684] The terminal analyzes the acquired image. Image data captured is provided as input. Using an image analysis algorithm, it identifies a reference object and retrieves its dimensions from the information storage. The output is the dimensional information of the reference object. Specifically, the terminal executes an image processing algorithm, recognizes the contours and features of the reference object, and retrieves size information from the database.

[0685] Step 3:

[0686] The device measures the pixel dimensions of a reference object in an image and calculates the actual size of the target object. The input requires the dimension information of the reference object and image data containing the target object. It counts the number of pixels in the image and determines the dimensions of the target object by ratio calculation based on the actual dimensions of the reference object. The output is the calculated dimensions of the target object. Specifically, the system compares the pixel dimensions of the reference object and the target object to calculate their actual size.

[0687] Step 4:

[0688] An emotion engine built into the device analyzes the user's facial expressions and voice. Image and audio data are used as input. The analysis is performed in real time to evaluate the user's emotional state. The output is the judgment result of the user's emotional state. Specifically, it analyzes the user's facial changes and voice tone captured by the camera and microphone.

[0689] Step 5:

[0690] The device generates feedback based on calculated dimensions and emotion assessment. The inputs provided are the object's dimension data and the user's emotion assessment. A generation AI model is used to create prompts and present appropriate feedback to the user. The output is a feedback message displayed to the user. Specifically, the feedback generation algorithm generates a message based on the emotional state and displays it on the device screen.

[0691] (Application Example 2)

[0692] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] Measuring the dimensions of objects in everyday environments often presents problems, requiring specialized equipment and considerable effort. Furthermore, providing feedback that considers the psychological state of the user receiving the measurement results is difficult, potentially leading to decreased user satisfaction and a less-than-ideal user experience. In this context, there is a need for a system that allows users to easily obtain object dimensions and receive feedback that reflects their emotional state during the process.

[0694] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0695] In this invention, the server includes means for acquiring an image containing a reference object having known dimensions and an object to be measured; means for acquiring the dimensions of the reference object from a database based on its identification information; means for measuring the pixel dimensions of the reference object in the image and calculating the actual dimensions of the object based on that; means for outputting the calculated dimensions of the object; means for analyzing the user's emotional state; and means for providing feedback based on the user's emotional state. This allows users to easily acquire the dimensions of an object and receive feedback that corresponds to their emotions at the time.

[0696] A "reference object" is an object that has known dimensions and is used as a reference when measuring the dimensions of other objects.

[0697] The "object to be measured" is the object from which you intend to obtain dimensions.

[0698] "Means of acquiring images" refers to methods of capturing visual information of the actual environment as electronic data using cameras and sensors.

[0699] "Methods of obtaining information from a database" refer to methods of searching for and retrieving necessary information from pre-stored information sources.

[0700] "Methods for measuring pixel dimensions and calculating the actual dimensions of an object based on them" refers to methods that measure the size of a reference object in an image on a pixel-by-pixel basis and use that information to derive the actual physical dimensions of the object.

[0701] "Means for outputting dimensions" refers to methods for visualizing or notifying dimensional information obtained through calculations.

[0702] "Methods for analyzing a user's emotional state" refer to technologies that evaluate psychological characteristics from a user's facial expressions and voice, and estimate their emotions.

[0703] "Means of providing feedback" refers to methods of returning information or responses that correspond to the user's behavior and emotional state.

[0704] This system is primarily composed of mobile devices such as smartphones and tablets, equipped with cameras for users to photograph reference objects and objects to be measured. The devices use image analysis software such as OpenCV to process the captured images. This allows the system to determine the pixel dimensions of the reference object and calculate the dimensions of the object to be measured by retrieving the actual dimensions from a database.

[0705] Furthermore, this system includes a function to detect and analyze the user's emotional state. Emotional analysis utilizes emotion recognition models such as Amazon Rekognition and Google Cloud Vision. This allows for the analysis of the user's facial expressions and voice data obtained from the camera and microphone, enabling real-time evaluation of their emotional state.

[0706] Once the user has finished measuring dimensions, the device displays the calculated dimensions to the user. In addition, it generates feedback tailored to the user's emotional state, providing appropriate information and suggestions. This feedback aims to personalize the user's shopping experience and improve satisfaction.

[0707] As a concrete example, when a user measures the dimensions of a chair they are considering purchasing in a store, the application calculates the chair's dimensions and suggests related interior products if the user is smiling. In this case, the prompt used as input to the generative AI model is: "Measure the dimensions of this product and suggest related recommendations if the user is happy. Please also consider appropriate feedback for other emotional states."

[0708] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0709] Step 1:

[0710] The user takes pictures of a reference object and the object to be measured with a camera. The input is the image data acquired by the user. The output is this image data that is passed to the image analysis software. Specifically, the user launches the camera app on their smartphone, adjusts the position on the screen so that the reference object (e.g., a plastic bottle) and the object to be measured (e.g., a chair) are included, and presses the shutter button.

[0711] Step 2:

[0712] The terminal analyzes the acquired image data using OpenCV to identify the reference object. The input is the image data obtained in step 1. As data processing, an image processing algorithm scans the pixel information and detects the features of the reference object. The output is the pixel dimensions of the reference object and their corresponding position information.

[0713] Step 3:

[0714] The terminal retrieves the actual dimensions of the reference object from the database. The input is the identification information of the reference object obtained in step 2. A database query is executed using this identification information. The output is the actual dimension data of the reference object.

[0715] Step 4:

[0716] The terminal calculates the actual dimensions of the object being measured based on the pixel dimensions of a reference object. The inputs are the pixel dimensions from step 2 and the actual dimensions from step 3. A ratio calculation is performed as part of the data calculation to derive the actual dimensions of the object being measured. The output is the calculated actual dimensions of the object.

[0717] Step 5:

[0718] The device collects user facial and voice data and passes it to the emotion engine. The input is real-time sensor data. Specifically, the device's front camera and microphone are activated to capture the user's face and voice. The output is a dataset for analysis by the emotion engine.

[0719] Step 6:

[0720] The server evaluates the user's emotional state using an emotion recognition model such as Amazon Rekognition. The input is the user's facial expressions and voice data from step 5. As a process, a generative AI model assigns emotion labels. The output is the evaluation result of the user's emotional state.

[0721] Step 7:

[0722] The device displays the dimensions of the object and generates feedback based on the evaluated emotional state. The inputs are the dimensional data from step 4 and the emotional evaluation from step 6. The feedback is created using prompts for the generating AI model. The output is the dimensional information and feedback message presented to the user. Specifically, the measurement results and an emotionally appropriate message are displayed as a pop-up on the device screen.

[0723] 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.

[0724] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0725] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0726] 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.

[0727] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0728] 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.

[0729] 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.

[0730] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0731] 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."

[0732] 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.

[0733] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0734] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0743] 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.

[0744] The following is further disclosed regarding the embodiments described above.

[0745] (Claim 1)

[0746] Means for acquiring an image including a reference object having known dimensions and an object to be measured,

[0747] A means for obtaining the dimensions of the aforementioned reference object from a database based on its identification information,

[0748] A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual dimensions of the object based on that measurement,

[0749] A means for outputting the calculated dimensions of the object,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] The system according to claim 1, characterized in that the reference material is a product used on a daily basis.

[0753] (Claim 3)

[0754] The system according to claim 1, characterized in that the object to be measured is a living organism.

[0755] "Example 1"

[0756] (Claim 1)

[0757] A means by which a user uses a mobile device to acquire an image that includes both a reference object and the object to be measured,

[0758] An image recognition algorithm provides means for identifying the reference object within the image,

[0759] Means for obtaining the dimensions of an identified reference object from a pre-recorded information base,

[0760] A means for measuring the number of pixels in the aforementioned reference object and calculating the actual size of the object to be measured based on that ratio,

[0761] A means of visually presenting the calculated dimensions to the user,

[0762] A system that includes this.

[0763] (Claim 2)

[0764] The system according to claim 1, characterized in that the reference material is an article widely used in daily life.

[0765] (Claim 3)

[0766] The system according to claim 1, characterized in that the object of measurement is an animal species.

[0767] "Application Example 1"

[0768] (Claim 1)

[0769] Means for acquiring an image including a reference object having known dimensions and an object to be measured,

[0770] A means for obtaining the dimensions of the aforementioned reference object from an information storage means, based on the identification information of the reference object,

[0771] A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual dimensions of the object to be measured based on that measurement,

[0772] A means for outputting the calculated dimensions of the object to be measured,

[0773] The aforementioned output dimensions are used for adjusting goods for consumer activities,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, characterized in that the referenced object is an item that is used on a regular basis and is applied to a situation in which consumption activities take place.

[0777] (Claim 3)

[0778] The system according to claim 1, characterized in that the object to be measured is an organic body.

[0779] "Example 2 of combining an emotion engine"

[0780] (Claim 1)

[0781] A reference object having known dimensions, and means for acquiring an image including the object to be measured.

[0782] A means for obtaining the dimensions of the aforementioned reference object from information storage based on its identification information,

[0783] A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual size of the object based on that measurement,

[0784] A means for outputting the calculated dimensions of the target,

[0785] A means for analyzing the user's facial expressions and voice to evaluate their emotional state,

[0786] Means for generating feedback based on the aforementioned emotional evaluation,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, characterized in that the reference material is an article used on a daily basis.

[0790] (Claim 3)

[0791] The system according to claim 1, characterized in that the object of measurement is a living organism.

[0792] "Application example 2 when combining with an emotional engine"

[0793] (Claim 1)

[0794] Means for acquiring an image including a reference object having known dimensions and an object to be measured,

[0795] A means for obtaining the dimensions of the aforementioned reference object from a database based on its identification information,

[0796] A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual dimensions of the object based on that measurement,

[0797] A means for outputting the calculated dimensions of the object,

[0798] A means of analyzing the emotional state of users,

[0799] A means of providing feedback based on the user's emotional state,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, characterized in that the reference material is an article that is commonly used.

[0803] (Claim 3)

[0804] The system according to claim 1, characterized in that the object to be measured is an organic body. [Explanation of Symbols]

[0805] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring an image including a reference object having known dimensions and an object to be measured, A means for obtaining the dimensions of the aforementioned reference object from a database based on its identification information, A means for measuring the pixel dimensions of a reference object in the aforementioned image and calculating the actual dimensions of the object based on that measurement, A means for outputting the calculated dimensions of the object, A system that includes this.

2. The system according to claim 1, characterized in that the reference material is a product used on a daily basis.

3. The system according to claim 1, characterized in that the object to be measured is a living organism.

Images