System

Abstract

An object of the system according to the embodiment is to analyze an image of a concrete pillar and evaluate the strength.SOLUTION: A system includes an image analysis part, an intensity evaluation part, and a result display part. The image analysis part analyzes the image of the concrete pillar. The strength evaluation part evaluates the strength of the concrete pillar based on the result analyzed by the image analysis part. The result display unit displays a result of the strength examination evaluated by the strength evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, experience is required to evaluate the strength of concrete pillars, and there was a problem that it was difficult for inexperienced people or those who had just been transferred to a new position to make judgments.

[0005] The system of the embodiment aims to analyze images of concrete pillars and evaluate their strength. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a strength evaluation unit, and a result display unit. The image analysis unit analyzes an image of the concrete pillar. The strength evaluation unit evaluates the strength of the concrete pillar based on the results of the analysis by the image analysis unit. The result display unit displays the results of the strength evaluation performed by the strength evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze images of concrete pillars and assess their strength. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The strength assessment system according to an embodiment of the present invention is a system that automatically analyzes images of concrete pillars, evaluates their strength, and displays the results. As a result, the strength assessment system can easily assess the strength of concrete pillars and display the results.

[0029] The strength evaluation system according to the embodiment includes an image analysis unit, a strength evaluation unit, and a result display unit. The image analysis unit analyzes an image of a concrete pole. For example, the image analysis unit analyzes the shape and material of the concrete pole using an image recognition algorithm. The image analysis unit can also analyze the installation environment of the concrete pole using machine learning technology. The image analysis unit can also extract feature points within the image to evaluate the condition of the concrete pole. For example, the image analysis unit analyzes the shape of the concrete pole using an image recognition algorithm and identifies the material. The installation environment of the concrete pole is analyzed using machine learning technology to extract information necessary for strength evaluation. The feature points within the image are extracted and the condition of the concrete pole is evaluated. The strength evaluation unit evaluates the strength of the concrete pole based on the results of the analysis by the image analysis unit. For example, the strength evaluation unit evaluates the strength using data from a physical strength test. The strength evaluation unit can also evaluate the strength using simulation technology. The strength evaluation unit can also evaluate the strength based on past data. For example, the strength evaluation unit evaluates the strength of the concrete pole using data from a physical strength test. The strength evaluation unit evaluates the strength of the concrete pole using simulation technology. The strength of the concrete pillar is evaluated based on past data. The result display unit displays the results of the strength evaluation evaluated by the strength evaluation unit. For example, the result display unit visually displays the results of the strength evaluation using a graph display. The result display unit can also display the results of the strength evaluation in detail using a text display. The result display unit can also provide an interactive display to make the results easier for the user to understand. For example, the result display unit visually displays the results of the strength evaluation using a graph display. The strength evaluation results in detail using a text display. The interactive display makes the results easier for the user to understand. In this way, the strength evaluation system according to the embodiment can easily evaluate the strength of the concrete pillar and display the results. For example, the user can quickly grasp the strength of the concrete pillar and take appropriate measures. The strength evaluation system reduces the burden on the user when evaluating the strength of the concrete pillar and supports efficient work.

[0030] The image analysis unit can detect minute cracks or signs of corrosion in the image and reflect them in the strength evaluation by the strength evaluation unit. For example, when the generation AI analyzes an image of a concrete pillar, the image analysis unit detects minute cracks and signs of corrosion in the image at high resolution and performs a strength evaluation based on that information. For example, the length and depth of the crack and the degree of corrosion are quantified and reflected in the strength evaluation. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit identifies the location of the crack or corrosion and performs a strength evaluation based on that location information. For example, if a crack is located at the base of the concrete pillar, it determines that there is a high risk of strength deterioration. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit predicts the progression rate of the crack or corrosion and performs a strength evaluation based on the prediction result. For example, it calculates the progression rate of cracks and corrosion based on past data and predicts future strength deterioration. This allows the detection of minute cracks and signs of corrosion and reflects them in the strength evaluation, enabling more accurate strength evaluation.

[0031] The image analysis unit can perform strength evaluation by the strength evaluation unit while taking into account the surrounding environment. For example, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding wind speed data and performs a strength evaluation that takes the effects of wind into account. For example, it determines that the strength of concrete pillars is likely to decrease in areas with strong winds. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding humidity data and performs a strength evaluation that takes into account the effects of humidity. For example, it determines that corrosion is likely to progress in high humidity environments. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding temperature data and performs a strength evaluation that takes into account the effects of temperature. For example, it determines that concrete pillars will deteriorate quickly in areas with extreme temperature changes. This allows for more realistic strength evaluation by taking the surrounding environment into account.

[0032] The image analysis unit generates a 3D model, allowing users to visually confirm the strength. For example, the image analysis unit uses the generation AI to analyze images of a concrete pillar and generate a 3D model based on that data. Users can rotate and zoom in and out of the 3D model to check the condition of the concrete pillar in detail. The image analysis unit also uses the generation AI to analyze images of the concrete pillar and display the location of cracks and corrosion on the 3D model. Users can visually identify problem areas on the 3D model and use this information to evaluate the strength. The image analysis unit also uses the generation AI to analyze images of the concrete pillar and overlay surrounding environmental information (wind speed, humidity, etc.) on the 3D model. Users can visually understand the impact of environmental factors on the strength of the concrete pillar through the 3D model. This allows users to visually confirm the strength by generating a 3D model.

[0033] The image analysis unit integrates images from different viewpoints to perform a more accurate strength assessment. For example, the image analysis unit integrates images of a concrete pillar taken by the generation AI from multiple viewpoints to generate a 3D model. This allows for an overall understanding of the concrete pillar and a more accurate strength assessment. The image analysis unit also analyzes images from different viewpoints by the generation AI and integrates information from each viewpoint to perform a strength assessment. For example, it comprehensively evaluates the condition of the base, middle, and top of the concrete pillar. The image analysis unit also analyzes images from different viewpoints by the generation AI to identify the location of cracks and corrosion. This allows for an overall strength assessment of the concrete pillar and identifies problem areas. Integrating images from different viewpoints enables a more accurate strength assessment.

[0034] The strength evaluation unit can compare with past data and provide a graph showing the trend. For example, when the generation AI displays the results of a strength study, the strength evaluation unit provides a graph that compares with past study results. For example, it can display the strength study results for the past year in chronological order to grasp the trend. Furthermore, when the generation AI displays the results of the strength study, the strength evaluation unit compares them with past data to detect abnormal values. For example, it displays a warning if the results differ significantly from past data. Furthermore, when the generation AI displays the results of the strength study, the strength evaluation unit compares them with past data to identify areas for improvement. For example, it can identify areas where strength has decreased based on past data and propose countermeasures. In this way, by comparing with past data and providing a graph showing the trend, changes in strength can be visually grasped.

[0035] The strength evaluation unit can display a confidence interval or error range to indicate the reliability of the results. For example, when the generation AI displays the results of a strength study, the strength evaluation unit displays the confidence interval. For example, it displays the result in the form of, "The strength of this concrete pillar is in the range of 40 kg to 60 kg with a 95% confidence interval." The strength evaluation unit also displays the error range when the generation AI displays the results of a strength study. For example, it displays the strength of this concrete pillar is 50 kg ± 10 kg. The strength evaluation unit also visually displays the confidence interval or error range in a graph when the generation AI displays the results of a strength study. For example, it displays the results of the strength evaluation in a bar graph with the confidence interval or error range overlaid on top. This increases the reliability of the evaluation results by displaying the confidence interval or error range to indicate the reliability of the results.

[0036] The strength evaluation unit adds a function to read out the results aloud, making it possible to accommodate visually impaired people. For example, when the generation AI displays the results of a strength study, the strength evaluation unit adds a function to read out the results aloud. For example, the results can be communicated aloud in the form of, "An antenna weighing up to 50 kg can be installed on this concrete pillar." The strength evaluation unit also makes it possible to customize the voice readout function when the generation AI displays the results of a strength study. For example, it makes it possible to adjust the reading speed and tone of the voice. The strength evaluation unit also makes the voice readout function multilingual when the generation AI displays the results of a strength study. For example, it makes it possible to read out the results in multiple languages, such as English and Chinese. This makes it possible to accommodate visually impaired people by reading out the results aloud.

[0037] The strength evaluation unit can display the results as animations to allow the user to intuitively understand. For example, when the generation AI displays the results of a strength study, the strength evaluation unit displays the results as animations. For example, the weight of an antenna that can be installed on a concrete pole can be shown as an animation, making it easier to understand visually. Furthermore, when the generation AI displays the results of a strength study, the strength evaluation unit uses animations to explain the strength evaluation process. For example, an animation can show the flow from image analysis of a concrete pole to strength evaluation. Furthermore, when the generation AI displays the results of a strength study, the strength evaluation unit uses animations to visually show the results of the strength evaluation. For example, areas where the strength of the concrete pole has decreased can be shown in red to alert the user. In this way, displaying the results as animations allows the user to intuitively understand.

[0038] When suggesting installable equipment, the strength evaluation unit also takes into account the equipment's lifespan or maintenance frequency, allowing it to select the optimal equipment. For example, when the generation AI suggests installable equipment, the strength evaluation unit takes into account equipment lifespan data and prioritizes proposing equipment with a long lifespan. For example, it may recommend an antenna with a lifespan of 10 years or more. When the generation AI suggests installable equipment, the strength evaluation unit also takes into account maintenance frequency and prioritizes proposing equipment with low maintenance. For example, it may recommend a radio that requires maintenance once a year. When the generation AI suggests installable equipment, the strength evaluation unit also comprehensively evaluates the equipment's lifespan and maintenance frequency, allowing it to select the optimal equipment. For example, it may prioritize proposing equipment with a long lifespan and low maintenance. This allows the optimal equipment to be selected by taking into account the equipment's lifespan and maintenance frequency.

[0039] When suggesting installable equipment, the strength evaluation unit can also evaluate the energy efficiency or environmental impact of the equipment to provide sustainable options. For example, when the generation AI suggests installable equipment, the strength evaluation unit evaluates the energy efficiency of the equipment and prioritizes suggesting equipment with high energy efficiency. For example, it recommends antennas with low power consumption. When the generation AI suggests installable equipment, the strength evaluation unit evaluates the environmental impact of the equipment and prioritizes suggesting equipment with low environmental impact. For example, it recommends radios made from recyclable materials. When the generation AI suggests installable equipment, the strength evaluation unit comprehensively evaluates energy efficiency and environmental impact to provide sustainable options. For example, it prioritizes suggesting equipment with high energy efficiency and low environmental impact. This makes it possible to provide sustainable options by evaluating energy efficiency and environmental impact.

[0040] The strength evaluation unit can make personalized suggestions by taking into account the user's past selection history. For example, the generation AI in the strength evaluation unit analyzes the user's past selection history and makes personalized device suggestions based on that data. For example, it suggests products from the same manufacturer as devices selected in the past. The strength evaluation unit also considers the user's past selection history and suggests devices that match the user's preferences. For example, it suggests devices with similar functions based on the features of devices selected in the past. The strength evaluation unit also considers the user's past selection history and suggests devices that best suit the user's needs. For example, it infers the user's usage environment from the past selection history and suggests devices that are suitable for that environment. In this way, personalized suggestions can be made by taking into account the user's past selection history.

[0041] The strength evaluation unit can compare devices from different manufacturers and provide the optimal option. For example, the generation AI compares devices from different manufacturers and suggests the optimal option based on performance and price. For example, it prioritizes suggestions of devices with high performance and reasonable prices. The generation AI also compares devices from different manufacturers and suggests the device that best meets the user's needs. For example, it selects devices with specific functions from multiple manufacturers and presents the comparison results. The generation AI also compares devices from different manufacturers and makes suggestions tailored to the user's preferences. For example, it suggests devices from the manufacturer that the user prefers, taking into account design and brand image. This makes it possible to provide the optimal option by comparing devices from different manufacturers.

[0042] The strength evaluation unit can provide details of specific reinforcement methods or materials to be used when providing advice regarding strength. For example, the strength evaluation unit provides advice in the form of, "We recommend reinforcing this concrete pillar with additional rebar." When the generation AI provides advice regarding strength, the strength evaluation unit also provides details of the materials to be used. For example, the advice is provided in the form of, "We recommend reinforcing this concrete pillar with high-strength concrete." When the generation AI provides advice regarding strength, the strength evaluation unit also provides details of the reinforcement method and the materials to be used. For example, the advice is provided in the form of, "We recommend reinforcing this concrete pillar with additional rebar and high-strength concrete." This allows the user to take appropriate measures by providing details of specific reinforcement methods and materials to be used.

[0043] The strength evaluation unit can provide specific advice by referring to past success stories or failure stories. For example, when the generation AI provides advice regarding strength, the strength evaluation unit refers to past success stories and provides specific advice. For example, the advice may be provided in the form of, "We recommend using a reinforcement method that has been successful in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, the strength evaluation unit refers to past failure stories and provides specific advice. For example, the advice may be provided in the form of, "We recommend avoiding reinforcement methods that have failed in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, the strength evaluation unit comprehensively evaluates success stories and failure stories and provides optimal advice. For example, the advice may be provided in the form of, "Based on past data, we will propose the most effective reinforcement method." This allows specific advice to be provided by referring to past success stories and failure stories.

[0044] The strength evaluation unit provides advice in video format, making it easier for users to understand visually. For example, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in video format. For example, a video may be used to show how to reinforce a concrete pillar, making it easier to understand visually. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit uses a video to explain how to use reinforcing materials. For example, a video may be used to show how to install reinforcing bars and how to pour concrete. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit uses a video to explain the steps for reinforcement work. For example, a video may be used to show a step-by-step guide for reinforcement work, allowing the user to perform the work accurately. In this way, providing advice in video format makes it easier for users to understand visually.

[0045] The strength evaluation unit provides advice in multiple languages, making it possible to accommodate international users. For example, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in multiple languages. For example, the advice is displayed in major languages ​​such as English, Chinese, and Spanish. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit automatically translates the advice according to the user's language setting. For example, the advice is displayed in the language set by the user. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in a video format that supports multiple languages. For example, subtitles and audio are provided in multiple languages, making it possible to accommodate international users. This makes it possible to accommodate international users by providing advice in multiple languages.

[0046] The strength evaluation unit can display historical data in chronological order and visualize past trends. For example, when the generation AI manages the history of strength studies, the strength evaluation unit displays the historical data in chronological order. For example, it can display the strength study results for the past year in a chronological order graph to grasp trends. The strength evaluation unit also detects abnormal values ​​based on the historical data when the generation AI manages the history of strength studies. For example, it displays a warning if a result is found that differs significantly from past data. The strength evaluation unit also identifies areas for improvement based on the historical data when the generation AI manages the history of strength studies. For example, it can identify areas where strength has decreased based on past data and propose countermeasures. In this way, by displaying the historical data in chronological order and visualizing past trends, it becomes easier to grasp changes in strength.

[0047] The strength evaluation unit can predict future changes in strength based on historical data and propose preventive measures. For example, when the generation AI manages the history of strength studies, the strength evaluation unit predicts future changes in strength based on historical data. For example, it predicts the rate of crack and corrosion progression based on past data and predicts future strength decline. In addition, when the generation AI manages the history of strength studies, the strength evaluation unit proposes preventive measures based on the prediction results. For example, it makes a proposal such as, "The strength of this concrete pillar may decline over the next five years. Reinforcement is required." In addition, when the generation AI manages the history of strength studies, the strength evaluation unit predicts future changes in strength and proposes a regular maintenance schedule. For example, it makes a proposal such as, "This concrete pillar requires annual maintenance." In this way, by predicting future changes in strength based on historical data and proposing preventive measures, problems can be prevented before they occur.

[0048] The strength evaluation unit can share historical data on the cloud, allowing multiple users to access it simultaneously. In the strength evaluation unit, for example, the generation AI manages the strength study history on the cloud, allowing multiple users to access it simultaneously. For example, allowing all members of the project team to view the historical data in real time. The strength evaluation unit also shares the strength study history on the cloud, allowing users to access it from anywhere. For example, allowing historical data to be checked even from the site. The strength evaluation unit also manages the strength study history on the cloud, allowing automatic data backup. For example, backing up data regularly to prevent data loss. This allows multiple users to access the history data simultaneously by sharing it on the cloud.

[0049] The strength evaluation unit can link the historical data with other project management tools to achieve efficient project management. In the strength evaluation unit, for example, the generation AI links the strength study history with other project management tools to achieve efficient project management. For example, the generation AI automatically synchronizes the historical data with the project management tool. In addition, the strength evaluation unit links the strength study history with other project management tools to make task management and progress management more efficient. For example, the generation AI automatically sets task priorities based on the historical data. In addition, the strength evaluation unit links the strength study history with other project management tools to make it easier to grasp the overall picture of the project. For example, the generation AI displays the historical data on the dashboard of the project management tool. In this way, efficient project management can be achieved by linking the historical data with other project management tools.

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

[0051] When suggesting installable equipment, the strength evaluation unit also takes into account the equipment's lifespan or maintenance frequency, allowing it to select the most suitable equipment. For example, when the generation AI suggests installable equipment, it takes into account equipment lifespan data and prioritizes proposing equipment with a long lifespan. For example, it may recommend an antenna with a lifespan of 10 years or more. When the generation AI suggests installable equipment, it also takes into account maintenance frequency and prioritizes proposing equipment that requires little maintenance. For example, it may recommend a radio that only requires annual maintenance. When the generation AI suggests installable equipment, it also comprehensively evaluates the equipment's lifespan and maintenance frequency and selects the most suitable equipment. For example, it may prioritize proposing equipment with a long lifespan and low maintenance. This allows the optimal equipment to be selected by taking into account the equipment's lifespan and maintenance frequency.

[0052] When suggesting installable equipment, the strength evaluation unit can also evaluate the energy efficiency or environmental impact of the equipment to provide sustainable options. For example, when the generation AI suggests installable equipment, it evaluates the energy efficiency of the equipment and prioritizes the suggestion of equipment with high energy efficiency. For example, it can recommend antennas with low power consumption. When suggesting installable equipment, it evaluates the environmental impact of the equipment and prioritizes the suggestion of equipment with low environmental impact. For example, it can recommend radios made from recyclable materials. When suggesting installable equipment, the generation AI comprehensively evaluates energy efficiency and environmental impact to provide sustainable options. For example, it can prioritize the suggestion of equipment with high energy efficiency and low environmental impact. This makes it possible to provide sustainable options by evaluating energy efficiency and environmental impact.

[0053] The strength evaluation unit can provide specific advice by referring to past success stories or failure stories. For example, when the generation AI provides advice regarding strength, it refers to past success stories and provides specific advice. For example, it may provide advice in the form of, "We recommend using reinforcement methods that have been successful in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, it refers to past failure stories and provides specific advice. For example, it may provide advice in the form of, "We recommend avoiding reinforcement methods that have failed in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, it comprehensively evaluates success stories and failure stories and provides optimal advice. For example, it may provide advice in the form of, "Based on past data, we will propose the most effective reinforcement method." This allows specific advice to be provided by referring to past success stories and failure stories.

[0054] The strength evaluation unit can provide advice in video format, making it easier for users to understand visually. For example, when the generation AI provides advice regarding strength, it provides the advice in video format. For example, a video may be used to show how to reinforce a concrete pillar, making it easier to understand visually. Furthermore, when the generation AI provides advice regarding strength, it may use a video to explain how to use reinforcing materials. For example, a video may be used to show how to install rebar and how to pour concrete. Furthermore, when the generation AI provides advice regarding strength, it may use a video to explain the steps for reinforcement work. For example, a video may be used to show a step-by-step guide for reinforcement work, allowing users to perform the work accurately. In this way, providing advice in video format makes it easier for users to understand visually.

[0055] The strength evaluation unit can provide advice in multiple languages ​​to accommodate international users. For example, when the generation AI provides advice regarding strength, the advice is provided in multiple languages. For example, the advice is displayed in major languages ​​such as English, Chinese, and Spanish. In addition, when the generation AI provides advice regarding strength, the advice is automatically translated according to the user's language setting. For example, the advice is displayed in the language set by the user. In addition, when the generation AI provides advice regarding strength, the advice is provided in a video format that supports multiple languages. For example, subtitles and audio are provided in multiple languages ​​to accommodate international users. This allows advice to be provided in multiple languages, making it possible to accommodate international users.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image analysis unit analyzes the image of the concrete pillar. For example, the image analysis unit uses an image recognition algorithm to analyze the shape and material of the concrete pillar. It also uses machine learning technology to analyze the installation environment of the concrete pillar, extracting feature points in the image and evaluating the condition of the concrete pillar. Step 2: The strength evaluation unit evaluates the strength of the concrete pillar based on the results of the analysis by the image analysis unit. For example, the strength is evaluated using data from physical strength tests, simulation techniques, and past data. Step 3: The result display unit displays the results of the strength evaluation performed by the strength evaluation unit. For example, the results are displayed visually using graphs or text, and an interactive display is provided to make the results easier for the user to understand.

[0058] (Example 2) The strength assessment system according to an embodiment of the present invention is a system that automatically analyzes images of concrete pillars, evaluates their strength, and displays the results. As a result, the strength assessment system can easily assess the strength of concrete pillars and display the results.

[0059] The strength evaluation system according to the embodiment includes an image analysis unit, a strength evaluation unit, and a result display unit. The image analysis unit analyzes an image of a concrete pole. For example, the image analysis unit analyzes the shape and material of the concrete pole using an image recognition algorithm. The image analysis unit can also analyze the installation environment of the concrete pole using machine learning technology. The image analysis unit can also extract feature points within the image to evaluate the condition of the concrete pole. For example, the image analysis unit analyzes the shape of the concrete pole using an image recognition algorithm and identifies the material. The installation environment of the concrete pole is analyzed using machine learning technology to extract information necessary for strength evaluation. The feature points within the image are extracted and the condition of the concrete pole is evaluated. The strength evaluation unit evaluates the strength of the concrete pole based on the results of the analysis by the image analysis unit. For example, the strength evaluation unit evaluates the strength using data from a physical strength test. The strength evaluation unit can also evaluate the strength using simulation technology. The strength evaluation unit can also evaluate the strength based on past data. For example, the strength evaluation unit evaluates the strength of the concrete pole using data from a physical strength test. The strength evaluation unit evaluates the strength of the concrete pole using simulation technology. The strength of the concrete pillar is evaluated based on past data. The result display unit displays the results of the strength evaluation evaluated by the strength evaluation unit. For example, the result display unit visually displays the results of the strength evaluation using a graph display. The result display unit can also display the results of the strength evaluation in detail using a text display. The result display unit can also provide an interactive display to make the results easier for the user to understand. For example, the result display unit visually displays the results of the strength evaluation using a graph display. The strength evaluation results in detail using a text display. The interactive display makes the results easier for the user to understand. In this way, the strength evaluation system according to the embodiment can easily evaluate the strength of the concrete pillar and display the results. For example, the user can quickly grasp the strength of the concrete pillar and take appropriate measures. The strength evaluation system reduces the burden on the user when evaluating the strength of the concrete pillar and supports efficient work.

[0060] The image analysis unit can detect minute cracks or signs of corrosion in the image and reflect them in the strength evaluation by the strength evaluation unit. For example, when the generation AI analyzes an image of a concrete pillar, the image analysis unit detects minute cracks and signs of corrosion in the image at high resolution and performs a strength evaluation based on that information. For example, the length and depth of the crack and the degree of corrosion are quantified and reflected in the strength evaluation. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit identifies the location of the crack or corrosion and performs a strength evaluation based on that location information. For example, if a crack is located at the base of the concrete pillar, it determines that there is a high risk of strength deterioration. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit predicts the progression rate of the crack or corrosion and performs a strength evaluation based on the prediction result. For example, it calculates the progression rate of cracks and corrosion based on past data and predicts future strength deterioration. This allows the detection of minute cracks and signs of corrosion and reflects them in the strength evaluation, enabling more accurate strength evaluation.

[0061] The image analysis unit can perform strength evaluation by the strength evaluation unit while taking into account the surrounding environment. For example, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding wind speed data and performs a strength evaluation that takes the effects of wind into account. For example, it determines that the strength of concrete pillars is likely to decrease in areas with strong winds. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding humidity data and performs a strength evaluation that takes into account the effects of humidity. For example, it determines that corrosion is likely to progress in high humidity environments. In addition, when the generation AI analyzes an image of a concrete pillar, the image analysis unit acquires surrounding temperature data and performs a strength evaluation that takes into account the effects of temperature. For example, it determines that concrete pillars will deteriorate quickly in areas with extreme temperature changes. This allows for more realistic strength evaluation by taking the surrounding environment into account.

[0062] The image analysis unit generates a 3D model, allowing users to visually confirm the strength. For example, the image analysis unit uses the generation AI to analyze images of a concrete pillar and generate a 3D model based on that data. Users can rotate and zoom in and out of the 3D model to check the condition of the concrete pillar in detail. The image analysis unit also uses the generation AI to analyze images of the concrete pillar and display the location of cracks and corrosion on the 3D model. Users can visually identify problem areas on the 3D model and use this information to evaluate the strength. The image analysis unit also uses the generation AI to analyze images of the concrete pillar and overlay surrounding environmental information (wind speed, humidity, etc.) on the 3D model. Users can visually understand the impact of environmental factors on the strength of the concrete pillar through the 3D model. This allows users to visually confirm the strength by generating a 3D model.

[0063] The image analysis unit integrates images from different viewpoints to perform a more accurate strength assessment. For example, the image analysis unit integrates images of a concrete pillar taken by the generation AI from multiple viewpoints to generate a 3D model. This allows for an overall understanding of the concrete pillar and a more accurate strength assessment. The image analysis unit also analyzes images from different viewpoints by the generation AI and integrates information from each viewpoint to perform a strength assessment. For example, it comprehensively evaluates the condition of the base, middle, and top of the concrete pillar. The image analysis unit also analyzes images from different viewpoints by the generation AI to identify the location of cracks and corrosion. This allows for an overall strength assessment of the concrete pillar and identifies problem areas. Integrating images from different viewpoints enables a more accurate strength assessment.

[0064] The strength evaluation unit can compare with past data and provide a graph showing the trend. For example, when the generation AI displays the results of a strength study, the strength evaluation unit provides a graph that compares with past study results. For example, it can display the strength study results for the past year in chronological order to grasp the trend. Furthermore, when the generation AI displays the results of the strength study, the strength evaluation unit compares them with past data to detect abnormal values. For example, it displays a warning if the results differ significantly from past data. Furthermore, when the generation AI displays the results of the strength study, the strength evaluation unit compares them with past data to identify areas for improvement. For example, it can identify areas where strength has decreased based on past data and propose countermeasures. In this way, by comparing with past data and providing a graph showing the trend, changes in strength can be visually grasped.

[0065] The strength evaluation unit can display a confidence interval or error range to indicate the reliability of the results. For example, when the generation AI displays the results of a strength study, the strength evaluation unit displays the confidence interval. For example, it displays the result in the form of, "The strength of this concrete pillar is in the range of 40 kg to 60 kg with a 95% confidence interval." The strength evaluation unit also displays the error range when the generation AI displays the results of a strength study. For example, it displays the strength of this concrete pillar is 50 kg ± 10 kg. The strength evaluation unit also visually displays the confidence interval or error range in a graph when the generation AI displays the results of a strength study. For example, it displays the results of the strength evaluation in a bar graph with the confidence interval or error range overlaid on top. This increases the reliability of the evaluation results by displaying the confidence interval or error range to indicate the reliability of the results.

[0066] The strength evaluation unit adds a function to read out the results aloud, making it possible to accommodate visually impaired people. For example, when the generation AI displays the results of a strength study, the strength evaluation unit adds a function to read out the results aloud. For example, the results can be communicated aloud in the form of, "An antenna weighing up to 50 kg can be installed on this concrete pillar." The strength evaluation unit also makes it possible to customize the voice readout function when the generation AI displays the results of a strength study. For example, it makes it possible to adjust the reading speed and tone of the voice. The strength evaluation unit also makes the voice readout function multilingual when the generation AI displays the results of a strength study. For example, it makes it possible to read out the results in multiple languages, such as English and Chinese. This makes it possible to accommodate visually impaired people by reading out the results aloud.

[0067] The strength evaluation unit can display the results as animations to allow the user to intuitively understand. For example, when the generation AI displays the results of a strength study, the strength evaluation unit displays the results as animations. For example, the weight of an antenna that can be installed on a concrete pole can be shown as an animation, making it easier to understand visually. Furthermore, when the generation AI displays the results of a strength study, the strength evaluation unit uses animations to explain the strength evaluation process. For example, an animation can show the flow from image analysis of a concrete pole to strength evaluation. Furthermore, when the generation AI displays the results of a strength study, the strength evaluation unit uses animations to visually show the results of the strength evaluation. For example, areas where the strength of the concrete pole has decreased can be shown in red to alert the user. In this way, displaying the results as animations allows the user to intuitively understand.

[0068] The intensity evaluation unit can use the emotion estimation function to analyze the user's emotions when receiving the results and provide additional support information if a negative reaction is detected. For example, the intensity evaluation unit can use the emotion estimation function to analyze the user's emotions when receiving the results of the intensity evaluation and provide additional support information if a negative reaction is detected. For example, the intensity evaluation unit can display a message such as, "If you are dissatisfied with the results, please see this support page." The intensity evaluation unit can also use the emotion estimation function to calculate an emotion score when the user receives the results of the intensity evaluation and provide additional support information if the user's emotions are strong. For example, the intensity evaluation unit can display a message such as, "If you have any questions about the results, please see this FAQ." The intensity evaluation unit can also use the emotion estimation function to monitor the user's emotions when receiving the results of the intensity evaluation in real time and automatically contact customer support if a negative reaction is detected. For example, the intensity evaluation unit can display a message such as, "If you need support, please contact us here." This allows the user's anxiety to be alleviated by analyzing the user's emotions and providing additional support information if a negative reaction is detected.

[0069] The strength evaluation unit can use the emotion estimation function to analyze the emotions felt by the user when they receive the results and display a message designed to elicit positive emotions. For example, the strength evaluation unit uses the emotion estimation function to analyze the emotions felt by the user when they receive the results of the strength evaluation and display a message designed to elicit positive emotions. For example, the strength evaluation unit displays a message such as, "Great results! This concrete pillar is very strong." The strength evaluation unit also uses the emotion estimation function to calculate an emotion score felt by the user when they receive the results of the strength evaluation and customize a message designed to elicit positive emotions. For example, the strength evaluation unit displays a message such as, "Your choice is correct. This concrete pillar has sufficient strength." The strength evaluation unit also uses the emotion estimation function to monitor the emotions felt by the user when they receive the results of the strength evaluation in real time and provide feedback designed to elicit positive emotions. For example, the strength evaluation unit displays a message such as, "Analysis completed. The results are very good!" This analysis of the user's emotions and displaying a message designed to elicit positive emotions improves user satisfaction.

[0070] When suggesting installable equipment, the strength evaluation unit also takes into account the equipment's lifespan or maintenance frequency, allowing it to select the optimal equipment. For example, when the generation AI suggests installable equipment, the strength evaluation unit takes into account equipment lifespan data and prioritizes proposing equipment with a long lifespan. For example, it may recommend an antenna with a lifespan of 10 years or more. When the generation AI suggests installable equipment, the strength evaluation unit also takes into account maintenance frequency and prioritizes proposing equipment with low maintenance. For example, it may recommend a radio that requires maintenance once a year. When the generation AI suggests installable equipment, the strength evaluation unit also comprehensively evaluates the equipment's lifespan and maintenance frequency, allowing it to select the optimal equipment. For example, it may prioritize proposing equipment with a long lifespan and low maintenance. This allows the optimal equipment to be selected by taking into account the equipment's lifespan and maintenance frequency.

[0071] When suggesting installable equipment, the strength evaluation unit can also evaluate the energy efficiency or environmental impact of the equipment to provide sustainable options. For example, when the generation AI suggests installable equipment, the strength evaluation unit evaluates the energy efficiency of the equipment and prioritizes suggesting equipment with high energy efficiency. For example, it recommends antennas with low power consumption. When the generation AI suggests installable equipment, the strength evaluation unit evaluates the environmental impact of the equipment and prioritizes suggesting equipment with low environmental impact. For example, it recommends radios made from recyclable materials. When the generation AI suggests installable equipment, the strength evaluation unit comprehensively evaluates energy efficiency and environmental impact to provide sustainable options. For example, it prioritizes suggesting equipment with high energy efficiency and low environmental impact. This makes it possible to provide sustainable options by evaluating energy efficiency and environmental impact.

[0072] The intensity evaluation unit uses the emotion estimation function to analyze the emotion of the user when receiving the proposal and can make a proposal tailored to the user's preferences. For example, the intensity evaluation unit uses the emotion estimation function to analyze the emotion of the user when receiving the proposal and suggest devices tailored to the user's preferences. For example, it prioritizes suggesting devices for which the user expressed positive emotions. The intensity evaluation unit also uses the emotion estimation function to calculate an emotion score when the user received the proposal and customize the proposal content based on the score. For example, it suggests devices with designs and functions that the user prefers. The intensity evaluation unit also uses the emotion estimation function to monitor the emotion of the user when receiving the proposal in real time and provide feedback tailored to the user's preferences. For example, it displays a message such as, "This device is ideal for your needs." In this way, the intensity evaluation unit analyzes the user's emotion and makes suggestions tailored to their preferences, thereby improving user satisfaction.

[0073] The strength evaluation unit can make personalized suggestions by taking into account the user's past selection history. For example, the generation AI in the strength evaluation unit analyzes the user's past selection history and makes personalized device suggestions based on that data. For example, it suggests products from the same manufacturer as devices selected in the past. The strength evaluation unit also considers the user's past selection history and suggests devices that match the user's preferences. For example, it suggests devices with similar functions based on the features of devices selected in the past. The strength evaluation unit also considers the user's past selection history and suggests devices that best suit the user's needs. For example, it infers the user's usage environment from the past selection history and suggests devices that are suitable for that environment. In this way, personalized suggestions can be made by taking into account the user's past selection history.

[0074] The strength evaluation unit can compare devices from different manufacturers and provide the optimal option. For example, the generation AI compares devices from different manufacturers and suggests the optimal option based on performance and price. For example, it prioritizes suggestions of devices with high performance and reasonable prices. The generation AI also compares devices from different manufacturers and suggests the device that best meets the user's needs. For example, it selects devices with specific functions from multiple manufacturers and presents the comparison results. The generation AI also compares devices from different manufacturers and makes suggestions tailored to the user's preferences. For example, it suggests devices from the manufacturer that the user prefers, taking into account design and brand image. This makes it possible to provide the optimal option by comparing devices from different manufacturers.

[0075] The intensity evaluation unit can use the emotion estimation function to analyze the emotion of the user when receiving the proposal and make a suggestion to elicit positive emotions. The intensity evaluation unit, for example, uses the emotion estimation function to analyze the emotion of the user when receiving the proposal and make a suggestion to elicit positive emotions. For example, it displays a message such as, "This device is ideal for your needs." The intensity evaluation unit also uses the emotion estimation function to calculate an emotion score when the user receives the proposal and make a positive suggestion based on the score. For example, it displays a message such as, "This device has been highly rated." The intensity evaluation unit also uses the emotion estimation function to monitor the emotion of the user when receiving the proposal in real time and provide feedback to elicit positive emotions. For example, it displays a message such as, "This device is popular with many users." In this way, the intensity evaluation unit analyzes the user's emotion and makes a suggestion to elicit positive emotions, thereby improving user satisfaction.

[0076] The strength evaluation unit can provide details of specific reinforcement methods or materials to be used when providing advice regarding strength. For example, the strength evaluation unit provides advice in the form of, "We recommend reinforcing this concrete pillar with additional rebar." When the generation AI provides advice regarding strength, the strength evaluation unit also provides details of the materials to be used. For example, the advice is provided in the form of, "We recommend reinforcing this concrete pillar with high-strength concrete." When the generation AI provides advice regarding strength, the strength evaluation unit also provides details of the reinforcement method and the materials to be used. For example, the advice is provided in the form of, "We recommend reinforcing this concrete pillar with additional rebar and high-strength concrete." This allows the user to take appropriate measures by providing details of specific reinforcement methods and materials to be used.

[0077] The strength evaluation unit can provide specific advice by referring to past success stories or failure stories. For example, when the generation AI provides advice regarding strength, the strength evaluation unit refers to past success stories and provides specific advice. For example, the advice may be provided in the form of, "We recommend using a reinforcement method that has been successful in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, the strength evaluation unit refers to past failure stories and provides specific advice. For example, the advice may be provided in the form of, "We recommend avoiding reinforcement methods that have failed in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, the strength evaluation unit comprehensively evaluates success stories and failure stories and provides optimal advice. For example, the advice may be provided in the form of, "Based on past data, we will propose the most effective reinforcement method." This allows specific advice to be provided by referring to past success stories and failure stories.

[0078] The intensity evaluation unit can use the emotion estimation function to analyze the emotion of the user when receiving advice and provide additional support information if a negative reaction occurs. For example, the intensity evaluation unit can use the emotion estimation function to analyze the emotion of the user when receiving advice related to the intensity, and provide additional support information if a negative reaction is detected. For example, the intensity evaluation unit can display a message such as, "If you are dissatisfied with the advice, please see this support page." The intensity evaluation unit can also use the emotion estimation function to calculate an emotion score when the user receives advice related to the intensity, and provide additional support information if the user's negative emotion is strong. For example, the intensity evaluation unit can display a message such as, "If you have any questions about the advice, please see this FAQ." The intensity evaluation unit can also use the emotion estimation function to monitor the emotion of the user when receiving advice related to the intensity in real time, and automatically contact customer support if a negative reaction is detected. For example, the intensity evaluation unit can display a message such as, "If you need support, please contact us here." In this way, the intensity evaluation unit can analyze the user's emotion and provide additional support information if a negative reaction occurs, thereby reducing the user's anxiety.

[0079] The strength evaluation unit provides advice in video format, making it easier for users to understand visually. For example, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in video format. For example, a video may be used to show how to reinforce a concrete pillar, making it easier to understand visually. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit uses a video to explain how to use reinforcing materials. For example, a video may be used to show how to install reinforcing bars and how to pour concrete. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit uses a video to explain the steps for reinforcement work. For example, a video may be used to show a step-by-step guide for reinforcement work, allowing the user to perform the work accurately. In this way, providing advice in video format makes it easier for users to understand visually.

[0080] The strength evaluation unit provides advice in multiple languages, making it possible to accommodate international users. For example, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in multiple languages. For example, the advice is displayed in major languages ​​such as English, Chinese, and Spanish. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit automatically translates the advice according to the user's language setting. For example, the advice is displayed in the language set by the user. Furthermore, when the generation AI provides advice regarding strength, the strength evaluation unit provides the advice in a video format that supports multiple languages. For example, subtitles and audio are provided in multiple languages, making it possible to accommodate international users. This makes it possible to accommodate international users by providing advice in multiple languages.

[0081] The intensity evaluation unit can use the emotion estimation function to analyze the emotion of the user when receiving advice and display a message designed to elicit positive emotions. For example, the intensity evaluation unit can use the emotion estimation function to analyze the emotion of the user when receiving strength-related advice and display a message designed to elicit positive emotions. For example, the intensity evaluation unit can display a message such as, "Great choice! This reinforcement method is very effective." The intensity evaluation unit can also use the emotion estimation function to calculate an emotion score for the user when receiving strength-related advice and customize a positive message based on the score. For example, the intensity evaluation unit can display a message such as, "Your choice is correct. This reinforcement method is very reliable." The intensity evaluation unit can also use the emotion estimation function to monitor the emotion of the user when receiving strength-related advice in real time and provide feedback designed to elicit positive emotions. For example, the intensity evaluation unit can display a message such as, "Analysis completed. The results are very good!" This improves user satisfaction by analyzing the user's emotion and displaying a message designed to elicit positive emotions.

[0082] The strength evaluation unit can display historical data in chronological order and visualize past trends. For example, when the generation AI manages the history of strength studies, the strength evaluation unit displays the historical data in chronological order. For example, it can display the strength study results for the past year in a chronological order graph to grasp trends. The strength evaluation unit also detects abnormal values ​​based on the historical data when the generation AI manages the history of strength studies. For example, it displays a warning if a result is found that differs significantly from past data. The strength evaluation unit also identifies areas for improvement based on the historical data when the generation AI manages the history of strength studies. For example, it can identify areas where strength has decreased based on past data and propose countermeasures. In this way, by displaying the historical data in chronological order and visualizing past trends, it becomes easier to grasp changes in strength.

[0083] The strength evaluation unit can predict future changes in strength based on historical data and propose preventive measures. For example, when the generation AI manages the history of strength studies, the strength evaluation unit predicts future changes in strength based on historical data. For example, it predicts the rate of crack and corrosion progression based on past data and predicts future strength decline. In addition, when the generation AI manages the history of strength studies, the strength evaluation unit proposes preventive measures based on the prediction results. For example, it makes a proposal such as, "The strength of this concrete pillar may decline over the next five years. Reinforcement is required." In addition, when the generation AI manages the history of strength studies, the strength evaluation unit predicts future changes in strength and proposes a regular maintenance schedule. For example, it makes a proposal such as, "This concrete pillar requires annual maintenance." In this way, by predicting future changes in strength based on historical data and proposing preventive measures, problems can be prevented before they occur.

[0084] The intensity evaluation unit can use the emotion estimation function to analyze the emotion a user feels when reviewing the history and provide an interface for eliciting positive emotions. For example, the intensity evaluation unit uses the emotion estimation function to analyze the emotion a user feels when reviewing the history of intensity consideration and provide an interface for eliciting positive emotions. For example, the intensity evaluation unit displays a message such as, "We will propose optimal measures based on past data." The intensity evaluation unit also uses the emotion estimation function to calculate an emotion score for the user when reviewing the history of intensity consideration and customizes a positive interface based on the score. For example, the intensity evaluation unit displays a message such as, "Your choice is correct. We will propose optimal measures based on past data." The intensity evaluation unit also uses the emotion estimation function to monitor the emotion a user feels when reviewing the history of intensity consideration in real time and provide feedback for eliciting positive emotions. For example, the intensity evaluation unit displays a message such as, "We will propose optimal measures based on past data. The results are very good!" This analysis of the user's emotion and the provision of an interface for eliciting positive emotions improves user satisfaction.

[0085] The strength evaluation unit can share historical data on the cloud, allowing multiple users to access it simultaneously. In the strength evaluation unit, for example, the generation AI manages the strength study history on the cloud, allowing multiple users to access it simultaneously. For example, allowing all members of the project team to view the historical data in real time. The strength evaluation unit also shares the strength study history on the cloud, allowing users to access it from anywhere. For example, allowing historical data to be checked even from the site. The strength evaluation unit also manages the strength study history on the cloud, allowing automatic data backup. For example, backing up data regularly to prevent data loss. This allows multiple users to access the history data simultaneously by sharing it on the cloud.

[0086] The strength evaluation unit can link the historical data with other project management tools to achieve efficient project management. In the strength evaluation unit, for example, the generation AI links the strength study history with other project management tools to achieve efficient project management. For example, the generation AI automatically synchronizes the historical data with the project management tool. In addition, the strength evaluation unit links the strength study history with other project management tools to make task management and progress management more efficient. For example, the generation AI automatically sets task priorities based on the historical data. In addition, the strength evaluation unit links the strength study history with other project management tools to make it easier to grasp the overall picture of the project. For example, the generation AI displays the historical data on the dashboard of the project management tool. In this way, efficient project management can be achieved by linking the historical data with other project management tools.

[0087] The intensity evaluation unit can use the emotion estimation function to analyze the emotion a user feels when reviewing the history and display a message designed to elicit positive emotions. For example, the intensity evaluation unit uses the emotion estimation function to analyze the emotion a user feels when reviewing the history of intensity consideration and display a message designed to elicit positive emotions. For example, the intensity evaluation unit displays a message such as, "We will propose optimal measures based on past data." The intensity evaluation unit also uses the emotion estimation function to calculate an emotion score for the user when reviewing the history of intensity consideration and customize a positive message based on the score. For example, the intensity evaluation unit displays a message such as, "Your choice is correct. We will propose optimal measures based on past data." The intensity evaluation unit also uses the emotion estimation function to monitor the emotion a user feels when reviewing the history of intensity consideration in real time and provide feedback designed to elicit positive emotions. For example, the intensity evaluation unit displays a message such as, "We will propose optimal measures based on past data. The results are very good!" This analysis of the user's emotion and displaying a message designed to elicit positive emotions improves user satisfaction.

[0088] The intensity evaluation unit can use the emotion estimation function to analyze the emotions of a user when uploading a picture of a container pillar and provide an interface for reducing stress and anxiety. For example, the intensity evaluation unit can use the emotion estimation function to analyze the user's facial expressions and voice when uploading a picture of a container pillar and detect stress or anxiety. For example, if the user is nervous, the intensity evaluation unit can display a message to help the user relax. The intensity evaluation unit can also use the emotion estimation function to calculate an emotion score when the user uploads a picture of a container pillar and customize the interface based on the score. For example, the intensity evaluation unit can change the color scheme or design to elicit positive emotions. The intensity evaluation unit can also use the emotion estimation function to monitor the user's emotions in real time when uploading a picture of a container pillar and display a support message if a negative emotion is detected. For example, the intensity evaluation unit can display a message such as "Don't worry, you'll get the results soon." This reduces the burden on the user by analyzing the user's emotions and providing an interface for reducing stress and anxiety.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The strength evaluation unit can analyze the emotions expressed by the user when they receive the results of the strength examination and display a message designed to elicit positive emotions. For example, when a user receives the results of the strength examination, the emotion estimation function can be used to calculate an emotion score and a positive message can be displayed based on the score. For example, a message such as "Great results! This concrete pillar is very strong" can be displayed. The unit can also monitor the emotions expressed by the user when they receive the results of the strength examination in real time and provide feedback designed to elicit positive emotions. For example, a message such as "Analysis completed. The results are very good!" can be displayed. In this way, by analyzing the user's emotions and displaying a message designed to elicit positive emotions, user satisfaction can be improved.

[0091] The intensity evaluation unit can analyze the emotions of the user when receiving a proposal and make a proposal tailored to the user's preferences. For example, the emotion estimation function can be used to analyze the emotions of the user when receiving a proposal and suggest devices that match the user's preferences. For example, devices for which the user expressed positive emotions can be prioritized. The intensity evaluation unit can also calculate an emotion score when the user received a proposal and customize the proposal content based on that score. For example, devices with designs and functions that the user prefers can be suggested. The intensity evaluation unit can also monitor the emotions of the user when receiving a proposal in real time and provide feedback tailored to the user's preferences. For example, a message such as "This device is ideal for your needs" can be displayed. This allows the user's emotions to be analyzed and suggestions tailored to their preferences to improve user satisfaction.

[0092] The intensity evaluation unit can analyze the user's emotions when receiving advice and provide additional support information if a negative reaction occurs. For example, the emotion estimation function can be used to analyze the user's emotions when receiving strength advice, and if a negative reaction is detected, additional support information can be provided. For example, a message such as "If you are dissatisfied with the advice, please see this support page" can be displayed. The intensity evaluation unit can also calculate an emotion score when the user receives strength advice and provide additional support information if the user's negative emotion is strong. For example, a message such as "If you have any questions about the advice, please see this FAQ" can be displayed. The intensity evaluation unit can also monitor the user's emotions when receiving strength advice in real time, and if a negative reaction is detected, automatically contact customer support. For example, a message such as "If you need support, please contact us here" can be displayed. In this way, the user's anxiety can be reduced by analyzing the user's emotions and providing additional support information if a negative reaction occurs.

[0093] The strength evaluation unit can analyze the emotions of a user when referencing the history and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when referencing the strength consideration history and provide an interface for eliciting positive emotions. For example, a message such as "We will propose optimal measures based on past data" can be displayed. The unit can also calculate an emotion score when the user refers to the strength consideration history and customize a positive interface based on the score. For example, a message such as "Your choice is correct. We will propose optimal measures based on past data" can be displayed. The unit can also monitor the emotions of a user when referencing the strength consideration history in real time and provide feedback to elicit positive emotions. For example, a message such as "We will propose optimal measures based on past data. The results are very good!" can be displayed. In this way, by analyzing the user's emotions and providing an interface for eliciting positive emotions, user satisfaction can be improved.

[0094] The intensity evaluation unit can analyze the emotions felt by users when they upload images of the container pillars and provide an interface for reducing stress and anxiety. For example, the emotion estimation function can be used to analyze facial expressions and voices of users when they upload images of the container pillars to detect stress and anxiety. For example, if the user is nervous, a message encouraging them to relax can be displayed. The intensity evaluation unit can also calculate an emotion score when the user uploads images of the container pillars and customize the interface based on the score. For example, the color and design can be changed to elicit positive emotions. The intensity evaluation unit can also monitor the emotions felt by users when they upload images of the container pillars in real time and display a supportive message if negative emotions are detected. For example, a message such as "Don't worry, you'll get results soon" can be displayed. This reduces the burden on users by analyzing their emotions and providing an interface for reducing stress and anxiety.

[0095] When suggesting installable equipment, the strength evaluation unit also takes into account the equipment's lifespan or maintenance frequency, allowing it to select the most suitable equipment. For example, when the generation AI suggests installable equipment, it takes into account equipment lifespan data and prioritizes proposing equipment with a long lifespan. For example, it may recommend an antenna with a lifespan of 10 years or more. When the generation AI suggests installable equipment, it also takes into account maintenance frequency and prioritizes proposing equipment that requires little maintenance. For example, it may recommend a radio that only requires annual maintenance. When the generation AI suggests installable equipment, it also comprehensively evaluates the equipment's lifespan and maintenance frequency and selects the most suitable equipment. For example, it may prioritize proposing equipment with a long lifespan and low maintenance. This allows the optimal equipment to be selected by taking into account the equipment's lifespan and maintenance frequency.

[0096] When suggesting installable equipment, the strength evaluation unit can also evaluate the energy efficiency or environmental impact of the equipment to provide sustainable options. For example, when the generation AI suggests installable equipment, it evaluates the energy efficiency of the equipment and prioritizes the suggestion of equipment with high energy efficiency. For example, it can recommend antennas with low power consumption. When suggesting installable equipment, it evaluates the environmental impact of the equipment and prioritizes the suggestion of equipment with low environmental impact. For example, it can recommend radios made from recyclable materials. When suggesting installable equipment, the generation AI comprehensively evaluates energy efficiency and environmental impact to provide sustainable options. For example, it can prioritize the suggestion of equipment with high energy efficiency and low environmental impact. This makes it possible to provide sustainable options by evaluating energy efficiency and environmental impact.

[0097] The strength evaluation unit can provide specific advice by referring to past success stories or failure stories. For example, when the generation AI provides advice regarding strength, it refers to past success stories and provides specific advice. For example, it may provide advice in the form of, "We recommend using reinforcement methods that have been successful in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, it refers to past failure stories and provides specific advice. For example, it may provide advice in the form of, "We recommend avoiding reinforcement methods that have failed in the past with similar concrete pillars." In addition, when the generation AI provides advice regarding strength, it comprehensively evaluates success stories and failure stories and provides optimal advice. For example, it may provide advice in the form of, "Based on past data, we will propose the most effective reinforcement method." This allows specific advice to be provided by referring to past success stories and failure stories.

[0098] The strength evaluation unit can provide advice in video format, making it easier for users to understand visually. For example, when the generation AI provides advice regarding strength, it provides the advice in video format. For example, a video may be used to show how to reinforce a concrete pillar, making it easier to understand visually. Furthermore, when the generation AI provides advice regarding strength, it may use a video to explain how to use reinforcing materials. For example, a video may be used to show how to install rebar and how to pour concrete. Furthermore, when the generation AI provides advice regarding strength, it may use a video to explain the steps for reinforcement work. For example, a video may be used to show a step-by-step guide for reinforcement work, allowing users to perform the work accurately. In this way, providing advice in video format makes it easier for users to understand visually.

[0099] The strength evaluation unit can provide advice in multiple languages ​​to accommodate international users. For example, when the generation AI provides advice regarding strength, the advice is provided in multiple languages. For example, the advice is displayed in major languages ​​such as English, Chinese, and Spanish. In addition, when the generation AI provides advice regarding strength, the advice is automatically translated according to the user's language setting. For example, the advice is displayed in the language set by the user. In addition, when the generation AI provides advice regarding strength, the advice is provided in a video format that supports multiple languages. For example, subtitles and audio are provided in multiple languages ​​to accommodate international users. This allows advice to be provided in multiple languages, making it possible to accommodate international users.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The image analysis unit analyzes the image of the concrete pillar. For example, the image analysis unit uses an image recognition algorithm to analyze the shape and material of the concrete pillar. It also uses machine learning technology to analyze the installation environment of the concrete pillar, extracting feature points in the image and evaluating the condition of the concrete pillar. Step 2: The strength evaluation unit evaluates the strength of the concrete pillar based on the results of the analysis by the image analysis unit. For example, the strength is evaluated using data from physical strength tests, simulation techniques, and past data. Step 3: The result display unit displays the results of the strength evaluation performed by the strength evaluation unit. For example, the results are displayed visually using graphs or text, and an interactive display is provided to make the results easier for the user to understand.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image analysis unit that analyzes images of concrete pillars; a strength evaluation unit that evaluates the strength of the concrete pillar based on the results of the analysis by the image analysis unit; a result display unit that displays the results of the strength evaluation performed by the strength evaluation unit. A system characterized by:

2. The image analysis unit Detecting signs of minute cracks or corrosion in the image and reflecting them in the strength evaluation by the strength evaluation unit 2. The system of claim 1.

3. The image analysis unit The strength evaluation unit performs strength evaluation while taking into consideration the surrounding environment.

2. The system of claim 1.

4. The image analysis unit Generate a 3D model to allow users to visually check the strength 2. The system of claim 1.

5. The image analysis unit Integrating images from different perspectives for more accurate intensity assessment 2. The system of claim 1.

6. The strength evaluation unit Provide graphs that compare with historical data and show trends 2. The system of claim 1.

Images