System

Abstract

An object of a system according to an embodiment is to improve the efficiency of searching for a lost child or a wanderer and to reduce time and cost.SOLUTION: A system includes a reception part, an analysis part, a retrieval part, and a provision part. The reception unit receives an image of a subject from a guardian or a relative. The analysis unit analyzes the image received by the reception unit and the security camera video, and extracts a feature of the target. The search unit searches for the target person in the security camera video on the basis of the feature extracted by the analysis unit. The providing unit provides a search result obtained by the search 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, searching for lost or wandering children relied on analogue manual labor, which was time-consuming and expensive.

[0005] The system according to the embodiment aims to improve the efficiency of searches for lost children and wanderers, and to reduce time and costs involved. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives an image of a subject from a guardian or relative. The analysis unit analyzes the image received by the reception unit and security camera footage to extract features of the subject. The search unit searches for the subject within the security camera footage based on the features extracted by the analysis unit. The provision unit provides the search results obtained by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of searching for lost children or wanderers, and can reduce time and costs. [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) A system according to an embodiment of the present invention streamlines the search for lost or wandering children in shopping centers, amusement facilities, and other locations. This system accepts images of the subject from guardians or relatives, analyzes security camera footage and the subject's images using a generation AI, extracts the subject's characteristics, and automatically searches for the subject within the security camera footage. This reduces the time and cost required for searches. Furthermore, by regularly importing security camera footage into the generation AI, the attributes of facility visitors (e.g., number of visitors, gender, age, etc.) can be compiled. This data can be used to provide data that can be used to improve operational efficiency and increase sales at the facility. For example, a guardian or relative can provide an image clearly showing the subject's face. Next, the generation AI inputs the security camera footage and the subject's image, which then analyzes the footage and image to extract the subject's characteristics. The generation AI then analyzes facial features such as facial contours, eye position, and hairstyle, and identifies the location where the subject appears across multiple security camera footage. This significantly reduces the time and cost required for searches. Furthermore, by regularly feeding security camera footage into the generative AI, data such as the number, gender, and age of visitors can be collected and analyzed. This data can be used to provide useful information for improving operational efficiency and sales at the facility. This allows the system to streamline searches for lost children and wandering persons, and also utilizes visitor attribute data to improve operational efficiency and sales. This allows the system to streamline searches for lost children and wandering persons, reducing time and costs. For example, accepting images from guardians or relatives that clearly show the target's face improves the accuracy of the analysis. Furthermore, using the generative AI to analyze security camera footage and images of the target and extract the target's characteristics makes it easier to identify the target. Furthermore, analyzing multiple security camera footage allows the target's location to be quickly identified. This significantly reduces the time and cost required for searches for lost children and wandering persons. Furthermore, by regularly feeding security camera footage into the generative AI, attribute data of facility visitors can be collected and analyzed. This data can be used to provide information that can be used to improve operational efficiency and increase sales at facilities.For example, if it is known based on visitor attribute data that many visitors visit during a particular time period, work efficiency can be improved by allocating staff accordingly. Also, if there are many visitors of a certain age or gender, sales can be increased by offering products and services tailored to those attributes. This allows the system to not only make searches for lost children or wanderers more efficient, but also to use the attribute data of facility visitors to improve work efficiency and increase sales.

[0029] A search support system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives an image of a subject from a guardian or relative. Examples of guardians or relatives include, but are not limited to, parents, grandparents, and siblings. The reception unit can receive, for example, photos taken with a smartphone or previously taken photos. The reception unit can also automatically evaluate the resolution and quality of the images and select the most suitable image. For example, the reception unit can automatically select the image with the highest resolution from multiple images uploaded by a user. The analysis unit uses a generative AI to analyze the images received by the reception unit and security camera footage to extract features of the subject. Examples of the subject's features include, but are not limited to, facial contours, eye position, and hairstyle. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify the eye position based on the center point of the eyes or the distance between the eyes. The analysis unit can also analyze hairstyle features based on the length, color, and style of hair. The search unit searches for a target person in the security camera video based on the features extracted by the analysis unit. The search may involve, but is not limited to, using an image recognition algorithm or setting a search range. For example, the search unit may use cosine similarity to search for a portion of the security camera video that matches the target person's features. The search unit may also use Jaccard coefficients to search for a portion of the video that matches the target person's features. The search unit may also use TF-IDF to search for a portion of the video that matches the target person's features. The providing unit provides the search results obtained by the search unit. The providing may involve, but is not limited to, notifying parents or facility staff. For example, the providing unit may display the search results to parents or facility staff via a web application or a mobile application. The providing unit may also send the search results by email. This allows the search support system according to the embodiment to streamline searches for lost children or wandering persons, reducing time and costs.

[0030] The reception unit can accept images in which the subject's face is clearly visible. The reception unit, for example, accepts images in which the subject's face is clearly visible. Images in which the subject's face is clearly visible include, but are not limited to, images with high resolution, an appropriate face size, and good lighting conditions. For example, the reception unit preferentially accepts images with high resolution. The reception unit can also preferentially accept images with an appropriate face size. The reception unit can also preferentially accept images with good lighting conditions. As a result, by accepting images in which the subject's face is clearly visible, the accuracy of the analysis is improved. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the resolution and quality of images uploaded by the user into the generation AI, and cause the generation AI to select the optimal image.

[0031] The analysis unit can analyze facial contours, eye position, and hairstyle features. The analysis unit, for example, analyzes facial contours using edge detection technology. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify eye positions based on the center points of the eyes or the distance between the eyes. For example, the analysis unit can identify eye positions based on the center points of the eyes. The analysis unit can also identify eye positions based on the distance between the eyes. The analysis unit can also analyze hairstyle features based on hair length, color, and style. For example, the analysis unit analyzes hairstyle features based on hair length. The analysis unit can also analyze hairstyle features based on hair color. The analysis unit can also analyze hairstyle features based on hair style. This makes it easier to identify the target person by analyzing features such as facial contours, eye position, and hairstyle. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input an image of the subject's face into the generation AI and have the generation AI analyze the facial contours, eye position, and hairstyle features.

[0032] The search unit can analyze footage from multiple security cameras in a shopping center and identify the location where the target person is captured. For example, the search unit can analyze footage from multiple security cameras in a shopping center and identify the location where the target person is captured. The footage from multiple security cameras may include, but is not limited to, the camera's installation location, resolution, frame rate, etc. For example, the search unit analyzes the security camera footage based on the camera's installation location. The search unit can also prioritize analyzing security camera footage with high resolution. The search unit can also prioritize analyzing security camera footage with high frame rate. This allows the target person's location to be quickly identified by analyzing multiple security camera footage. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input security camera footage into the generation AI and have the generation AI identify the target person's location.

[0033] The providing unit can provide the search results to guardians or facility staff. For example, the providing unit provides the search results to guardians or facility staff. Examples of guardians or facility staff include, but are not limited to, parents, grandparents, siblings, and facility employees. For example, the providing unit can display the search results to guardians or facility staff via a web application or a mobile application. The providing unit can also send the search results by email. This allows for the prompt provision of search results, enabling early detection of lost children or wandering persons. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the search results into a generation AI and cause the generation AI to notify guardians or facility staff.

[0034] The reception unit can periodically capture security camera footage to collect attribute data of facility visitors. The reception unit, for example, periodically captures security camera footage to collect attribute data of facility visitors. The frequency of periodic capture includes, but is not limited to, daily, weekly, or real-time. For example, the reception unit can capture security camera footage daily to collect attribute data of facility visitors. The reception unit can also capture security camera footage weekly to collect attribute data of facility visitors. The reception unit can also capture security camera footage in real time to collect attribute data of facility visitors. The attribute data includes, for example, age, gender, number of visits, etc., but is not limited to, these examples. For example, the reception unit collects attribute data based on the visitor's age. The reception unit can also collect attribute data based on the visitor's gender. The reception unit can also collect attribute data based on the visitor's number of visits. In this way, collecting attribute data of facility visitors can be used to improve business efficiency and increase sales. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input security camera footage into the generation AI and cause the generation AI to collect attribute data of facility visitors.

[0035] The analysis unit can analyze data on the number, gender, and age of visitors. The analysis unit, for example, counts the number of visitors. Methods for counting the number of visitors include, but are not limited to, camera image analysis and the use of sensors. For example, the analysis unit counts the number of visitors using camera image analysis. The analysis unit can also count the number of visitors using sensors. The analysis unit also determines the gender of visitors. Methods for determining gender include, but are not limited to, facial recognition technology and self-reporting. For example, the analysis unit determines the gender of visitors using facial recognition technology. The analysis unit can also determine the gender of visitors based on self-reporting. The analysis unit also determines the age of visitors. Methods for determining age include, but are not limited to, facial recognition technology and self-reporting. For example, the analysis unit determines the age of visitors using facial recognition technology. The analysis unit can also determine the age of visitors based on self-reporting. By analyzing visitor attribute data, this helps improve business efficiency and increase sales at the facility. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input an image of a visitor's face into the generation AI and have the generation AI determine the visitor's gender and age.

[0036] The providing unit can provide the analysis results as data to be utilized for improving the facility's business efficiency or sales. The providing unit, for example, provides the analysis results as data to be utilized for improving the facility's business efficiency or sales. Business efficiency improvement includes, but is not limited to, shortening work time and reducing costs. For example, the providing unit can shorten work time based on the analysis results. The providing unit can also reduce costs based on the analysis results. Sales improvement includes, but is not limited to, increasing sales amount and the number of customers. For example, the providing unit can increase sales amount based on the analysis results. The providing unit can also increase the number of customers based on the analysis results. This allows the facility's business efficiency and sales to be improved by utilizing the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to provide data to be utilized for improving business efficiency and sales.

[0037] The reception unit can automatically evaluate the resolution or quality of images and select an optimal image. For example, the reception unit automatically selects the image with the highest resolution from multiple images uploaded by a user. Methods for evaluating resolution include, but are not limited to, the number of pixels and image quality evaluation criteria. For example, the reception unit evaluates the resolution based on the number of pixels. The reception unit can also evaluate the resolution based on image quality evaluation criteria. The reception unit can also automatically select, from the images uploaded by the user, an image in which a face is most clearly visible. Methods for evaluating images in which a face is clearly visible include, but are not limited to, the size of the face and lighting conditions. For example, the reception unit can evaluate images based on the size of the face. The reception unit can also evaluate images based on the lighting conditions. The reception unit can also automatically select, from the images uploaded by the user, an image with a simple background that highlights facial features. Methods for evaluating the simplicity of the background include, but are not limited to, the color of the background and the presence or absence of an object in the background. For example, the reception unit can evaluate images based on the color of the background. The reception unit can also evaluate images based on the presence or absence of an object in the background. This improves the accuracy of analysis by selecting the most suitable image. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the resolution and quality of the image uploaded by the user to the generation AI, and cause the generation AI to select the most suitable image.

[0038] The reception unit can receive multiple images and automatically select the most distinctive image. For example, the reception unit automatically selects an image that most clearly shows facial features from multiple images uploaded by a user. Methods for evaluating facial features include, but are not limited to, facial contours and eye positions. For example, the reception unit can evaluate images based on facial contours. The reception unit can also evaluate images based on eye positions. The reception unit can also compare images taken from different angles among the images uploaded by the user and select the most distinctive image. Methods for evaluating different angles include, but are not limited to, facial orientation and shooting angle. For example, the reception unit can evaluate images based on facial orientation. The reception unit can also evaluate images based on shooting angle. The reception unit can also automatically select an image with optimal lighting conditions from the images uploaded by the user. Methods for evaluating lighting conditions include, but are not limited to, lighting intensity and the presence or absence of shadows. For example, the reception unit can evaluate images based on lighting intensity. The reception unit can also evaluate images based on the presence or absence of shadows. This improves the accuracy of analysis by selecting the most distinctive image. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input multiple images uploaded by a user to the generation AI and cause the generation AI to select the most distinctive image.

[0039] The reception unit can analyze background information of the image and perform processing to emphasize the features of the subject. The reception unit, for example, analyzes the background information of the image and performs preprocessing to emphasize the features of the subject. Methods of analyzing background information include, but are not limited to, the background color and the presence or absence of objects in the background. For example, the reception unit analyzes the image based on the background color. The reception unit can also analyze the image based on the presence or absence of objects in the background. For example, the reception unit automatically blurs the background of the image to emphasize the face of the subject. The reception unit can also automatically remove the background of the image and extract only the face of the subject. The reception unit can also automatically adjust the background color of the image to highlight the facial features of the subject. This enhances the features of the subject, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input background information of the image to the generation AI and cause the generation AI to perform preprocessing to emphasize the features of the subject.

[0040] The reception unit can preferentially accept highly relevant images based on the user's geographical location information. The reception unit, for example, preferentially accepts highly relevant images taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area within a facility, the reception unit may preferentially accept images taken in that area. Also, if the user is near an entrance to the facility, the reception unit may preferentially accept images taken near the entrance. Also, if the user is on a specific floor of the facility, the reception unit may preferentially accept images taken on that floor. This preferential acceptance of highly relevant images improves the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant images.

[0041] The reception unit can analyze the user's social media activity and receive relevant images. The reception unit, for example, analyzes the user's social media activity and receives relevant images. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and comments. For example, the reception unit automatically receives images posted by the user on social media. The reception unit can also automatically select relevant images from the user's social media activity. The reception unit can also receive relevant images by referring to images posted by the user's friends on social media. This allows for efficient reception of relevant images by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select relevant images.

[0042] The reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, adjusts the reception method by reflecting the user's past feedback. Methods for obtaining past feedback include, but are not limited to, survey results and user ratings. For example, the reception unit may preferentially provide an image upload method that the user has previously preferred. The reception unit may also provide an optimal image upload interface based on the user's past feedback. The reception unit may also provide a customized image upload method by improving issues previously pointed out by the user. This improves user convenience by reflecting past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into the generation AI and cause the generation AI to adjust the reception method.

[0043] The analysis unit can analyze the subject's features from multiple angles and combine multiple features to improve the accuracy of the analysis. The analysis unit, for example, combines features such as the subject's facial contour, eye position, and hairstyle for analysis. Multifaceted analysis methods include, but are not limited to, combining multiple features and applying different analysis techniques. For example, the analysis unit analyzes the facial contour using edge detection technology, identifies the eye position based on the center point of the eyes and the distance between the eyes, and analyzes hairstyle features based on the length, color, and style of the hair. The analysis unit can also incorporate additional information such as the subject's clothing and belongings into the analysis. For example, the analysis unit analyzes the color and design of the subject's clothing to complement the features. The analysis unit can also analyze the bag or accessories the subject carries to complement the features. The analysis unit can also analyze the subject's movements and posture to dynamically extract features. For example, the analysis unit analyzes the subject's walking pattern to extract features. The analysis unit can also analyze the subject's posture and movements to dynamically extract features. The analysis unit can also analyze changes in the subject's movements in real time and extract features. This allows for combining multiple features to improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input images of the subject's face and movement data into the generation AI and have the generation AI perform feature analysis.

[0044] The analysis unit can analyze the subject's movements and posture and dynamically extract features. The analysis unit can, for example, analyze the subject's movements and posture and dynamically extract features. Methods for analyzing movements and posture include, but are not limited to, motion analysis algorithms and posture estimation techniques. For example, the analysis unit can analyze the subject's walking pattern and extract features. The analysis unit can also analyze the subject's posture and movements and dynamically extract features. The analysis unit can also analyze changes in the subject's movements in real time and extract features. In this way, features can be dynamically extracted by analyzing movements and posture. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the subject's movements and posture into the generation AI and cause the generation AI to dynamically extract features.

[0045] The analysis unit can analyze additional information about the subject's clothing and belongings to complement the characteristics. The analysis unit can analyze additional information, such as the subject's clothing and belongings, to complement the characteristics. Methods for analyzing clothing and belongings include, but are not limited to, color, shape, and brand. For example, the analysis unit can analyze the color and design of the subject's clothing to complement the characteristics. The analysis unit can also analyze bags and accessories carried by the subject to complement the characteristics. The analysis unit can also analyze changes in the subject's clothing and belongings to dynamically complement the characteristics. In this way, analyzing the additional information complements the characteristics and improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data about the subject's clothing and belongings into the generation AI and have the generation AI complement the characteristics.

[0046] The analysis unit can improve the accuracy of the analysis based on the subject's past visit history. The analysis unit improves the accuracy of the analysis based on, for example, the subject's past visit history. Methods for acquiring the past visit history include, but are not limited to, the date and time of visit and the number of visits. For example, the analysis unit complements characteristics based on the subject's past visit history. The analysis unit can also improve the accuracy of the analysis by referring to information on places the subject has visited in the past. The analysis unit can also incorporate the subject's past behavioral patterns into the analysis to improve accuracy. In this way, referring to the past visit history improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the subject's past visit history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0047] The analysis unit can complement the characteristics by referring to literature and databases related to the subject. The analysis unit complements the characteristics by, for example, referring to literature and databases related to the subject. Methods for referencing related literature and databases include, but are not limited to, academic papers and patent databases. For example, the analysis unit complements the characteristics by referring to literature related to the subject. The analysis unit can also complement the characteristics by referring to database information about the subject. The analysis unit can also complement the characteristics by referring to past research data about the subject. This complements the characteristics by referring to related literature and databases, improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data from literature and databases related to the subject into the generation AI and have the generation AI complement the characteristics.

[0048] The analysis unit can perform analysis taking into account the market value and influence of the subject. The analysis unit performs analysis taking into account, for example, the market value and influence of the subject. Methods for evaluating market value and influence include, but are not limited to, sales revenue and number of followers. For example, the analysis unit determines the priority of analysis based on the market value of the subject. The analysis unit can also adjust the accuracy of analysis taking into account the influence of the subject. The analysis unit can also adjust the display method of the analysis results based on the market value and influence of the subject. This improves the priority and accuracy of analysis by taking market value and influence into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the market value and influence of the subject into the generation AI and have the generation AI adjust the priority and accuracy of the analysis.

[0049] The search unit can improve search accuracy by taking into account the image quality and angle of the security camera footage. The search unit improves search accuracy by taking into account, for example, the image quality and angle of the security camera footage. Methods for evaluating image quality and angle include, but are not limited to, resolution and camera installation angle. For example, the search unit prioritizes searching for security camera footage with high resolution. The search unit can also prioritize searching for footage in which the camera installation angle captures the target's face from the front. The search unit can also prioritize searching for security camera footage with good lighting conditions. This improves search accuracy by taking image quality and angle into account. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input data on the image quality and angle of the security camera footage into the generation AI and cause the generation AI to improve search accuracy.

[0050] The search unit can analyze the movement pattern of the subject and set a predicted location. The search unit, for example, analyzes the movement pattern of the subject and sets a predicted location. Methods for analyzing movement patterns include, but are not limited to, past movement history and movement speed. For example, the search unit can analyze the subject's past movement pattern and identify a predicted location. The search unit can also analyze the subject's current movement pattern in real time and identify a predicted location. The search unit can also analyze changes in the subject's movement pattern and identify a predicted location. This makes it easier to identify a predicted location by analyzing the movement pattern. Some or all of the above-mentioned processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input the subject's movement pattern data into the generation AI and cause the generation AI to identify a predicted location.

[0051] The search unit can analyze environmental information about the subject and improve search accuracy. The search unit can analyze, for example, environmental information about the subject's surroundings and improve search accuracy. Methods for analyzing environmental information include, but are not limited to, lighting conditions and surrounding objects. For example, the search unit can analyze lighting conditions about the subject and improve search accuracy. The search unit can also analyze the movements of people around the subject and improve search accuracy. The search unit can also analyze objects and backgrounds around the subject and improve search accuracy. In this way, analyzing environmental information improves search accuracy. Some or all of the above-described processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input environmental information data about the subject to the generation AI and cause the generation AI to improve search accuracy.

[0052] The search unit can perform a search based on the geographic distribution of the target. The search unit performs a search, for example, taking into account the geographic distribution of the target. Methods for acquiring the geographic distribution include, but are not limited to, GPS data and location information services. For example, if the target is in a specific area, the search unit can prioritize searching security camera footage within that area. Also, if the target is on a specific floor of a facility, the search unit can prioritize searching security camera footage within that floor. Also, if the target is near the entrance to a facility, the search unit can prioritize searching security camera footage near the entrance. This improves search accuracy by taking geographic distribution into account. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input geographic distribution data of the target into the generation AI and cause the generation AI to improve search accuracy.

[0053] The search unit can improve search accuracy by referring to literature and databases related to the subject. The search unit can improve search accuracy by, for example, referring to literature and databases related to the subject. Methods for referencing related literature and databases include, but are not limited to, academic papers and patent databases. For example, the search unit can improve search accuracy by referring to literature related to the subject. The search unit can also improve search accuracy by referring to database information about the subject. The search unit can also improve search accuracy by referring to past research data about the subject. In this way, by referring to related literature and databases, search accuracy is improved. Some or all of the above-mentioned processing in the search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the search unit can input literature and database data related to the subject into the generation AI and have the generation AI improve search accuracy.

[0054] The search unit can conduct a search taking into account the market value and influence of the target person. The search unit, for example, conducts a search taking into account the market value and influence of the target person. Methods for evaluating market value and influence include, but are not limited to, sales revenue and number of followers. For example, the search unit determines search priority based on the market value of the target person. The search unit can also adjust search accuracy taking into account the influence of the target person. The search unit can also adjust the display method of search results based on the market value and influence of the target person. This improves search priority and accuracy by taking market value and influence into account. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input data on the market value and influence of the target person into the generation AI and have the generation AI adjust the search priority and accuracy.

[0055] The providing unit can change the level of detail of the information provided based on the importance of the search results. The providing unit, for example, changes the level of detail of the information provided based on the importance of the search results. Methods for evaluating the importance include, but are not limited to, the relevance and urgency of the search results. For example, the providing unit provides detailed information for search results with high importance. The providing unit can also provide concise information for search results with low importance. The providing unit can also adjust the display order of the information based on the importance of the search results. This improves the visibility of the information by providing the level of detail of the information according to the importance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the search results to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0056] The providing unit can apply an appropriate providing algorithm depending on the category of the search result. The providing unit applies an appropriate providing algorithm depending on, for example, the category of the search result. Examples of categorization methods include, but are not limited to, the type and relevance of the search result. For example, if the search result is related to a person, the providing unit can provide information that emphasizes facial features. Furthermore, if the search result is related to an object, the providing unit can provide detailed information about the object. Furthermore, if the search result is related to a place, the providing unit can provide detailed information about the place. This improves the visibility of the information by applying a providing algorithm depending on the category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the search result to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0057] The providing unit can improve the accuracy of the provision based on the user's past provision results. The providing unit improves the accuracy of the provision based on, for example, the user's past provision results. Methods for acquiring past provision results include, but are not limited to, user ratings and feedback. For example, the providing unit improves the accuracy of the provision based on information received by the user in the past. The providing unit can also improve the accuracy of the provision by referring to the user's past feedback. The providing unit can also analyze the user's past behavioral patterns and improve the accuracy of the provision. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0058] The providing unit can set the priority of provision based on the submission time of the search results. The providing unit sets the priority of provision based on, for example, the submission time of the search results. Methods for obtaining the submission time include, but are not limited to, for example, the submission date and time or the submission frequency. For example, the providing unit prioritizes the provision of the most recent search results. The providing unit can also provide concise information for search results that were submitted earlier. The providing unit can also adjust the display order of information based on the submission time. This improves the visibility of information by providing priority according to the submission time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the submission time of the search results to the generation AI and cause the generation AI to set the priority of provision.

[0059] The providing unit can change the order of providing the search results based on the relevance of the search results. The providing unit changes the order of providing the search results based on, for example, the relevance of the search results. Methods for evaluating the relevance include, but are not limited to, the degree of match or relevance of the search results. For example, the providing unit prioritizes providing highly relevant search results. The providing unit can also provide concise information for less relevant search results. The providing unit can also adjust the display order of the information based on the relevance of the search results. This improves the visibility of the information by providing the order based on the relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the search results to the generation AI and cause the generation AI to change the order of providing the information.

[0060] The providing unit can change the use of provided terminology depending on the user's level of expertise. The providing unit, for example, changes the use of provided terminology depending on the user's level of expertise. Methods for evaluating the level of expertise include, but are not limited to, the user's occupation, educational background, and years of experience. For example, if the user has specialized knowledge, the providing unit can provide information using specialized terminology. If the user does not have specialized knowledge, the providing unit can also provide information in simple language. The providing unit can also adjust the level of detail of the information depending on the user's level of expertise. This improves understanding of the information by providing terminology appropriate to the level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to change the use of terminology.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can analyze the subject's movements and posture and dynamically extract features. For example, it can analyze the subject's walking pattern and extract features. It can also analyze the subject's posture and movements and dynamically extract features. It can also analyze changes in the subject's movements in real time and extract features. This allows features to be dynamically extracted by analyzing movements and posture.

[0063] The analysis unit can analyze additional information about the subject's clothing and belongings to complement the characteristics. For example, it can analyze the color and design of the subject's clothing to complement the characteristics. It can also analyze the bags and accessories the subject carries to complement the characteristics. It can also analyze changes in the subject's clothing and belongings to dynamically complement the characteristics. In this way, by analyzing additional information, the characteristics can be complemented and the accuracy of the analysis can be improved.

[0064] The search unit can analyze the movement pattern of the subject and set a predicted location. For example, it can analyze the subject's past movement pattern to identify the predicted location. It can also analyze the subject's current movement pattern in real time to identify the predicted location. It can also analyze changes in the subject's movement pattern to identify the predicted location. This makes it easier to identify the predicted location by analyzing the movement pattern.

[0065] The analysis unit can improve the accuracy of the analysis based on the subject's past visit history. For example, it complements features based on the subject's past visit history. It can also improve the accuracy of the analysis by referring to information about places the subject has visited in the past. It can also improve accuracy by incorporating the subject's past behavioral patterns into the analysis. In this way, the accuracy of the analysis can be improved by referring to the past visit history.

[0066] The search unit can improve search accuracy by taking into account the image quality and angle of security camera footage. For example, it can prioritize searches for security camera footage with high resolution. It can also prioritize searches for footage where the camera's installation angle captures the target's face from the front. It can also prioritize searches for security camera footage with good lighting conditions. In this way, by taking image quality and angle into account, search accuracy is improved.

[0067] The providing unit can change the order of providing search results based on the relevance of the search results. For example, highly relevant search results are provided preferentially. For less relevant search results, concise information can be provided. Furthermore, the display order of information can be adjusted according to the relevance of the search results. This improves the visibility of information by providing an order according to relevance.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The reception unit receives an image of the subject from a guardian or relative. Examples of guardians or relatives include, but are not limited to, parents, grandparents, siblings, etc. The reception unit can receive, for example, photos taken with a smartphone or photos taken in the past. The reception unit can also automatically evaluate the resolution and quality of the images and select the optimal image. For example, the reception unit can automatically select the image with the highest resolution from multiple images uploaded by the user. Step 2: The analysis unit uses the generation AI to analyze the images and security camera footage received by the reception unit and extract features of the subject. The features of the subject include, but are not limited to, facial contours, eye position, and hairstyle. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify the eye position based on the center point of the eyes or the distance between the eyes. The analysis unit can also analyze hairstyle features based on hair length, color, and style. Step 3: The search unit searches for the target person in the security camera video based on the features extracted by the analysis unit. The search may include, but is not limited to, an image recognition algorithm and setting a search range. For example, the search unit may use cosine similarity to search for parts of the security camera video that match the target person's features. The search unit may also use Jaccard coefficients to search for parts that match the target person's features. The search unit may also use TF-IDF to search for parts that match the target person's features. Step 4: The providing unit provides the search results obtained by the search unit. Providing the results includes, but is not limited to, notifying parents or facility staff. For example, the providing unit displays the search results to parents or facility staff through a web application or a mobile application. The providing unit can also send the search results by email.

[0070] (Example 2) A system according to an embodiment of the present invention streamlines the search for lost or wandering children in shopping centers, amusement facilities, and other locations. This system accepts images of the subject from guardians or relatives, analyzes security camera footage and the subject's images using a generation AI, extracts the subject's characteristics, and automatically searches for the subject within the security camera footage. This reduces the time and cost required for searches. Furthermore, by regularly importing security camera footage into the generation AI, the attributes of facility visitors (e.g., number of visitors, gender, age, etc.) can be compiled. This data can be used to provide data that can be used to improve operational efficiency and increase sales at the facility. For example, a guardian or relative can provide an image clearly showing the subject's face. Next, the generation AI inputs the security camera footage and the subject's image, which then analyzes the footage and image to extract the subject's characteristics. The generation AI then analyzes facial features such as facial contours, eye position, and hairstyle, and identifies the location where the subject appears across multiple security camera footage. This significantly reduces the time and cost required for searches. Furthermore, by regularly feeding security camera footage into the generative AI, data such as the number, gender, and age of visitors can be collected and analyzed. This data can be used to provide useful information for improving operational efficiency and sales at the facility. This allows the system to streamline searches for lost children and wandering persons, and also utilizes visitor attribute data to improve operational efficiency and sales. This allows the system to streamline searches for lost children and wandering persons, reducing time and costs. For example, accepting images from guardians or relatives that clearly show the target's face improves the accuracy of the analysis. Furthermore, using the generative AI to analyze security camera footage and images of the target and extract the target's characteristics makes it easier to identify the target. Furthermore, analyzing multiple security camera footage allows the target's location to be quickly identified. This significantly reduces the time and cost required for searches for lost children and wandering persons. Furthermore, by regularly feeding security camera footage into the generative AI, attribute data of facility visitors can be collected and analyzed. This data can be used to provide information that can be used to improve operational efficiency and increase sales at facilities.For example, if it is known based on visitor attribute data that many visitors visit during a particular time period, work efficiency can be improved by allocating staff accordingly. Also, if there are many visitors of a certain age or gender, sales can be increased by offering products and services tailored to those attributes. This allows the system to not only make searches for lost children or wanderers more efficient, but also to use the attribute data of facility visitors to improve work efficiency and increase sales.

[0071] A search support system according to an embodiment includes a reception unit, an analysis unit, a search unit, and a provision unit. The reception unit receives an image of a subject from a guardian or relative. Examples of guardians or relatives include, but are not limited to, parents, grandparents, and siblings. The reception unit can receive, for example, photos taken with a smartphone or previously taken photos. The reception unit can also automatically evaluate the resolution and quality of the images and select the most suitable image. For example, the reception unit can automatically select the image with the highest resolution from multiple images uploaded by a user. The analysis unit uses a generative AI to analyze the images received by the reception unit and security camera footage to extract features of the subject. Examples of the subject's features include, but are not limited to, facial contours, eye position, and hairstyle. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify the eye position based on the center point of the eyes or the distance between the eyes. The analysis unit can also analyze hairstyle features based on the length, color, and style of hair. The search unit searches for a target person in the security camera video based on the features extracted by the analysis unit. The search may involve, but is not limited to, using an image recognition algorithm or setting a search range. For example, the search unit may use cosine similarity to search for a portion of the security camera video that matches the target person's features. The search unit may also use Jaccard coefficients to search for a portion of the video that matches the target person's features. The search unit may also use TF-IDF to search for a portion of the video that matches the target person's features. The providing unit provides the search results obtained by the search unit. The providing may involve, but is not limited to, notifying parents or facility staff. For example, the providing unit may display the search results to parents or facility staff via a web application or a mobile application. The providing unit may also send the search results by email. This allows the search support system according to the embodiment to streamline searches for lost children or wandering persons, reducing time and costs.

[0072] The reception unit can accept images in which the subject's face is clearly visible. The reception unit, for example, accepts images in which the subject's face is clearly visible. Images in which the subject's face is clearly visible include, but are not limited to, images with high resolution, an appropriate face size, and good lighting conditions. For example, the reception unit preferentially accepts images with high resolution. The reception unit can also preferentially accept images with an appropriate face size. The reception unit can also preferentially accept images with good lighting conditions. As a result, by accepting images in which the subject's face is clearly visible, the accuracy of the analysis is improved. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the resolution and quality of images uploaded by the user into the generation AI, and cause the generation AI to select the optimal image.

[0073] The analysis unit can analyze facial contours, eye position, and hairstyle features. The analysis unit, for example, analyzes facial contours using edge detection technology. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify eye positions based on the center points of the eyes or the distance between the eyes. For example, the analysis unit can identify eye positions based on the center points of the eyes. The analysis unit can also identify eye positions based on the distance between the eyes. The analysis unit can also analyze hairstyle features based on hair length, color, and style. For example, the analysis unit analyzes hairstyle features based on hair length. The analysis unit can also analyze hairstyle features based on hair color. The analysis unit can also analyze hairstyle features based on hair style. This makes it easier to identify the target person by analyzing features such as facial contours, eye position, and hairstyle. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input an image of the subject's face into the generation AI and have the generation AI analyze the facial contours, eye position, and hairstyle features.

[0074] The search unit can analyze footage from multiple security cameras in a shopping center and identify the location where the target person is captured. For example, the search unit can analyze footage from multiple security cameras in a shopping center and identify the location where the target person is captured. The footage from multiple security cameras may include, but is not limited to, the camera's installation location, resolution, frame rate, etc. For example, the search unit analyzes the security camera footage based on the camera's installation location. The search unit can also prioritize analyzing security camera footage with high resolution. The search unit can also prioritize analyzing security camera footage with high frame rate. This allows the target person's location to be quickly identified by analyzing multiple security camera footage. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input security camera footage into the generation AI and have the generation AI identify the target person's location.

[0075] The providing unit can provide the search results to guardians or facility staff. For example, the providing unit provides the search results to guardians or facility staff. Examples of guardians or facility staff include, but are not limited to, parents, grandparents, siblings, and facility employees. For example, the providing unit can display the search results to guardians or facility staff via a web application or a mobile application. The providing unit can also send the search results by email. This allows for the prompt provision of search results, enabling early detection of lost children or wandering persons. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the search results into a generation AI and cause the generation AI to notify guardians or facility staff.

[0076] The reception unit can periodically capture security camera footage to collect attribute data of facility visitors. The reception unit, for example, periodically captures security camera footage to collect attribute data of facility visitors. The frequency of periodic capture includes, but is not limited to, daily, weekly, or real-time. For example, the reception unit can capture security camera footage daily to collect attribute data of facility visitors. The reception unit can also capture security camera footage weekly to collect attribute data of facility visitors. The reception unit can also capture security camera footage in real time to collect attribute data of facility visitors. The attribute data includes, for example, age, gender, number of visits, etc., but is not limited to, these examples. For example, the reception unit collects attribute data based on the visitor's age. The reception unit can also collect attribute data based on the visitor's gender. The reception unit can also collect attribute data based on the visitor's number of visits. In this way, collecting attribute data of facility visitors can be used to improve business efficiency and increase sales. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input security camera footage into the generation AI and cause the generation AI to collect attribute data of facility visitors.

[0077] The analysis unit can analyze data on the number, gender, and age of visitors. The analysis unit, for example, counts the number of visitors. Methods for counting the number of visitors include, but are not limited to, camera image analysis and the use of sensors. For example, the analysis unit counts the number of visitors using camera image analysis. The analysis unit can also count the number of visitors using sensors. The analysis unit also determines the gender of visitors. Methods for determining gender include, but are not limited to, facial recognition technology and self-reporting. For example, the analysis unit determines the gender of visitors using facial recognition technology. The analysis unit can also determine the gender of visitors based on self-reporting. The analysis unit also determines the age of visitors. Methods for determining age include, but are not limited to, facial recognition technology and self-reporting. For example, the analysis unit determines the age of visitors using facial recognition technology. The analysis unit can also determine the age of visitors based on self-reporting. By analyzing visitor attribute data, this helps improve business efficiency and increase sales at the facility. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input an image of a visitor's face into the generation AI and have the generation AI determine the visitor's gender and age.

[0078] The providing unit can provide the analysis results as data to be utilized for improving the facility's business efficiency or sales. The providing unit, for example, provides the analysis results as data to be utilized for improving the facility's business efficiency or sales. Business efficiency improvement includes, but is not limited to, shortening work time and reducing costs. For example, the providing unit can shorten work time based on the analysis results. The providing unit can also reduce costs based on the analysis results. Sales improvement includes, but is not limited to, increasing sales amount and the number of customers. For example, the providing unit can increase sales amount based on the analysis results. The providing unit can also increase the number of customers based on the analysis results. This allows the facility's business efficiency and sales to be improved by utilizing the analysis results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to provide data to be utilized for improving business efficiency and sales.

[0079] The reception unit can estimate the user's emotions and change the image reception method based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and changes the image reception method based on the estimated user emotions. Methods for determining the user's emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the reception unit can estimate the user's emotions using facial expression recognition technology. The reception unit can also estimate the user's emotions using voice analysis technology. For example, if the user is anxious, the reception unit can provide a simple and intuitive interface to enable quick image uploading. If the user is relaxed, the reception unit can provide detailed explanations and options to carefully support the user in selecting and uploading images. If the user is anxious, the reception unit can display reassuring messages and guides to enable smooth image uploading. This improves user convenience by providing an image reception method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0080] The reception unit can automatically evaluate the resolution or quality of images and select an optimal image. For example, the reception unit automatically selects the image with the highest resolution from multiple images uploaded by a user. Methods for evaluating resolution include, but are not limited to, the number of pixels and image quality evaluation criteria. For example, the reception unit evaluates the resolution based on the number of pixels. The reception unit can also evaluate the resolution based on image quality evaluation criteria. The reception unit can also automatically select, from the images uploaded by the user, an image in which a face is most clearly visible. Methods for evaluating images in which a face is clearly visible include, but are not limited to, the size of the face and lighting conditions. For example, the reception unit can evaluate images based on the size of the face. The reception unit can also evaluate images based on the lighting conditions. The reception unit can also automatically select, from the images uploaded by the user, an image with a simple background that highlights facial features. Methods for evaluating the simplicity of the background include, but are not limited to, the color of the background and the presence or absence of an object in the background. For example, the reception unit can evaluate images based on the color of the background. The reception unit can also evaluate images based on the presence or absence of an object in the background. This improves the accuracy of analysis by selecting the most suitable image. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the resolution and quality of the image uploaded by the user to the generation AI, and cause the generation AI to select the most suitable image.

[0081] The reception unit can receive multiple images and automatically select the most distinctive image. For example, the reception unit automatically selects an image that most clearly shows facial features from multiple images uploaded by a user. Methods for evaluating facial features include, but are not limited to, facial contours and eye positions. For example, the reception unit can evaluate images based on facial contours. The reception unit can also evaluate images based on eye positions. The reception unit can also compare images taken from different angles among the images uploaded by the user and select the most distinctive image. Methods for evaluating different angles include, but are not limited to, facial orientation and shooting angle. For example, the reception unit can evaluate images based on facial orientation. The reception unit can also evaluate images based on shooting angle. The reception unit can also automatically select an image with optimal lighting conditions from the images uploaded by the user. Methods for evaluating lighting conditions include, but are not limited to, lighting intensity and the presence or absence of shadows. For example, the reception unit can evaluate images based on lighting intensity. The reception unit can also evaluate images based on the presence or absence of shadows. This improves the accuracy of analysis by selecting the most distinctive image. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input multiple images uploaded by a user to the generation AI and cause the generation AI to select the most distinctive image.

[0082] The reception unit can analyze background information of the image and perform processing to emphasize the features of the subject. The reception unit, for example, analyzes the background information of the image and performs preprocessing to emphasize the features of the subject. Methods of analyzing background information include, but are not limited to, the background color and the presence or absence of objects in the background. For example, the reception unit analyzes the image based on the background color. The reception unit can also analyze the image based on the presence or absence of objects in the background. For example, the reception unit automatically blurs the background of the image to emphasize the face of the subject. The reception unit can also automatically remove the background of the image and extract only the face of the subject. The reception unit can also automatically adjust the background color of the image to highlight the facial features of the subject. This enhances the features of the subject, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input background information of the image to the generation AI and cause the generation AI to perform preprocessing to emphasize the features of the subject.

[0083] The reception unit can estimate the user's emotion and set a priority order for the images to be accepted based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and sets a priority order for the images to be accepted based on the estimated user's emotion. Methods for determining the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice analysis technology. For example, if the user is anxious, the reception unit can prioritize images uploaded earliest. If the user is relaxed, the reception unit can prioritize images with high resolution or quality. If the user is anxious, the reception unit can prioritize images that most clearly show the user's facial features. This improves user convenience by prioritizing images according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0084] The reception unit can preferentially accept highly relevant images based on the user's geographical location information. The reception unit, for example, preferentially accepts highly relevant images taking into account the user's geographical location information. Methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area within a facility, the reception unit may preferentially accept images taken in that area. Also, if the user is near an entrance to the facility, the reception unit may preferentially accept images taken near the entrance. Also, if the user is on a specific floor of the facility, the reception unit may preferentially accept images taken on that floor. This preferential acceptance of highly relevant images improves the accuracy of the analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant images.

[0085] The reception unit can analyze the user's social media activity and receive relevant images. The reception unit, for example, analyzes the user's social media activity and receives relevant images. Methods for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and comments. For example, the reception unit automatically receives images posted by the user on social media. The reception unit can also automatically select relevant images from the user's social media activity. The reception unit can also receive relevant images by referring to images posted by the user's friends on social media. This allows for efficient reception of relevant images by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select relevant images.

[0086] The reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, adjusts the reception method by reflecting the user's past feedback. Methods for obtaining past feedback include, but are not limited to, survey results and user ratings. For example, the reception unit may preferentially provide an image upload method that the user has previously preferred. The reception unit may also provide an optimal image upload interface based on the user's past feedback. The reception unit may also provide a customized image upload method by improving issues previously pointed out by the user. This improves user convenience by reflecting past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into the generation AI and cause the generation AI to adjust the reception method.

[0087] The analysis unit can estimate the user's emotion and change the accuracy of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and changes the accuracy of the analysis based on the estimated user's emotion. Methods for determining the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, if the user is anxious, the analysis unit can quickly perform an analysis and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and improve accuracy. If the user is anxious, the analysis unit can use multiple analysis methods in combination to increase the reliability of the analysis results. This improves the reliability of the analysis results by providing analysis accuracy according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] The analysis unit can analyze the subject's features from multiple angles and combine multiple features to improve the accuracy of the analysis. The analysis unit, for example, combines features such as the subject's facial contour, eye position, and hairstyle for analysis. Multifaceted analysis methods include, but are not limited to, combining multiple features and applying different analysis techniques. For example, the analysis unit analyzes the facial contour using edge detection technology, identifies the eye position based on the center point of the eyes and the distance between the eyes, and analyzes hairstyle features based on the length, color, and style of the hair. The analysis unit can also incorporate additional information such as the subject's clothing and belongings into the analysis. For example, the analysis unit analyzes the color and design of the subject's clothing to complement the features. The analysis unit can also analyze the bag or accessories the subject carries to complement the features. The analysis unit can also analyze the subject's movements and posture to dynamically extract features. For example, the analysis unit analyzes the subject's walking pattern to extract features. The analysis unit can also analyze the subject's posture and movements to dynamically extract features. The analysis unit can also analyze changes in the subject's movements in real time and extract features. This allows for combining multiple features to improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input images of the subject's face and movement data into the generation AI and have the generation AI perform feature analysis.

[0089] The analysis unit can analyze the subject's movements and posture and dynamically extract features. The analysis unit can, for example, analyze the subject's movements and posture and dynamically extract features. Methods for analyzing movements and posture include, but are not limited to, motion analysis algorithms and posture estimation techniques. For example, the analysis unit can analyze the subject's walking pattern and extract features. The analysis unit can also analyze the subject's posture and movements and dynamically extract features. The analysis unit can also analyze changes in the subject's movements in real time and extract features. In this way, features can be dynamically extracted by analyzing movements and posture. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data on the subject's movements and posture into the generation AI and cause the generation AI to dynamically extract features.

[0090] The analysis unit can analyze additional information about the subject's clothing and belongings to complement the characteristics. The analysis unit can analyze additional information, such as the subject's clothing and belongings, to complement the characteristics. Methods for analyzing clothing and belongings include, but are not limited to, color, shape, and brand. For example, the analysis unit can analyze the color and design of the subject's clothing to complement the characteristics. The analysis unit can also analyze bags and accessories carried by the subject to complement the characteristics. The analysis unit can also analyze changes in the subject's clothing and belongings to dynamically complement the characteristics. In this way, analyzing the additional information complements the characteristics and improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data about the subject's clothing and belongings into the generation AI and have the generation AI complement the characteristics.

[0091] The analysis unit can estimate the user's emotion and change the display method of the analysis results based on the estimated user's emotion. The analysis unit can, for example, estimate the user's emotion and change the display method of the analysis results based on the estimated user's emotion. Methods for determining the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves the visibility of the analysis results by providing a display method that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0092] The analysis unit can improve the accuracy of the analysis based on the subject's past visit history. The analysis unit improves the accuracy of the analysis based on, for example, the subject's past visit history. Methods for acquiring the past visit history include, but are not limited to, the date and time of visit and the number of visits. For example, the analysis unit complements characteristics based on the subject's past visit history. The analysis unit can also improve the accuracy of the analysis by referring to information on places the subject has visited in the past. The analysis unit can also incorporate the subject's past behavioral patterns into the analysis to improve accuracy. In this way, referring to the past visit history improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the subject's past visit history data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0093] The analysis unit can complement the characteristics by referring to literature and databases related to the subject. The analysis unit complements the characteristics by, for example, referring to literature and databases related to the subject. Methods for referencing related literature and databases include, but are not limited to, academic papers and patent databases. For example, the analysis unit complements the characteristics by referring to literature related to the subject. The analysis unit can also complement the characteristics by referring to database information about the subject. The analysis unit can also complement the characteristics by referring to past research data about the subject. This complements the characteristics by referring to related literature and databases, improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data from literature and databases related to the subject into the generation AI and have the generation AI complement the characteristics.

[0094] The analysis unit can perform analysis taking into account the market value and influence of the subject. The analysis unit performs analysis taking into account, for example, the market value and influence of the subject. Methods for evaluating market value and influence include, but are not limited to, sales revenue and number of followers. For example, the analysis unit determines the priority of analysis based on the market value of the subject. The analysis unit can also adjust the accuracy of analysis taking into account the influence of the subject. The analysis unit can also adjust the display method of the analysis results based on the market value and influence of the subject. This improves the priority and accuracy of analysis by taking market value and influence into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the market value and influence of the subject into the generation AI and have the generation AI adjust the priority and accuracy of the analysis.

[0095] The search unit can estimate a user's emotions and set search priorities based on the estimated user emotions. The search unit can estimate a user's emotions and set search priorities based on the estimated user emotions. Methods for determining a user's emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the search unit can estimate a user's emotions using facial expression recognition technology. The search unit can also estimate a user's emotions using voice analysis technology. For example, if a user is anxious, the search unit can quickly perform a search and provide results quickly. If a user is relaxed, the search unit can perform a detailed search to improve accuracy. If a user is anxious, the search unit can use multiple search methods in combination to increase the reliability of search results. This improves the reliability of search results by providing search priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The search unit can improve search accuracy by taking into account the image quality and angle of the security camera footage. The search unit improves search accuracy by taking into account, for example, the image quality and angle of the security camera footage. Methods for evaluating image quality and angle include, but are not limited to, resolution and camera installation angle. For example, the search unit prioritizes searching for security camera footage with high resolution. The search unit can also prioritize searching for footage in which the camera installation angle captures the target's face from the front. The search unit can also prioritize searching for security camera footage with good lighting conditions. This improves search accuracy by taking image quality and angle into account. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input data on the image quality and angle of the security camera footage into the generation AI and cause the generation AI to improve search accuracy.

[0097] The search unit can analyze the movement pattern of the subject and set a predicted location. The search unit, for example, analyzes the movement pattern of the subject and sets a predicted location. Methods for analyzing movement patterns include, but are not limited to, past movement history and movement speed. For example, the search unit can analyze the subject's past movement pattern and identify a predicted location. The search unit can also analyze the subject's current movement pattern in real time and identify a predicted location. The search unit can also analyze changes in the subject's movement pattern and identify a predicted location. This makes it easier to identify a predicted location by analyzing the movement pattern. Some or all of the above-mentioned processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input the subject's movement pattern data into the generation AI and cause the generation AI to identify a predicted location.

[0098] The search unit can analyze environmental information about the subject and improve search accuracy. The search unit can analyze, for example, environmental information about the subject's surroundings and improve search accuracy. Methods for analyzing environmental information include, but are not limited to, lighting conditions and surrounding objects. For example, the search unit can analyze lighting conditions about the subject and improve search accuracy. The search unit can also analyze the movements of people around the subject and improve search accuracy. The search unit can also analyze objects and backgrounds around the subject and improve search accuracy. In this way, analyzing environmental information improves search accuracy. Some or all of the above-described processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input environmental information data about the subject to the generation AI and cause the generation AI to improve search accuracy.

[0099] The search unit can estimate a user's emotion and change the display method of search results based on the estimated user emotion. The search unit, for example, estimates a user's emotion and changes the display method of search results based on the estimated user emotion. Methods for determining a user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the search unit can estimate a user's emotion using facial expression recognition technology. The search unit can also estimate a user's emotion using voice analysis technology. For example, if a user is nervous, the search unit can provide a simple, highly visible display method. If a user is relaxed, the search unit can provide a display method that includes detailed information. If a user is in a hurry, the search unit can provide a display method that focuses on the main points. This improves the visibility of search results by providing a display method that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The search unit can perform a search based on the geographic distribution of the target. The search unit performs a search, for example, taking into account the geographic distribution of the target. Methods for acquiring the geographic distribution include, but are not limited to, GPS data and location information services. For example, if the target is in a specific area, the search unit can prioritize searching security camera footage within that area. Also, if the target is on a specific floor of a facility, the search unit can prioritize searching security camera footage within that floor. Also, if the target is near the entrance to a facility, the search unit can prioritize searching security camera footage near the entrance. This improves search accuracy by taking geographic distribution into account. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input geographic distribution data of the target into the generation AI and cause the generation AI to improve search accuracy.

[0101] The search unit can improve search accuracy by referring to literature and databases related to the subject. The search unit can improve search accuracy by, for example, referring to literature and databases related to the subject. Methods for referencing related literature and databases include, but are not limited to, academic papers and patent databases. For example, the search unit can improve search accuracy by referring to literature related to the subject. The search unit can also improve search accuracy by referring to database information about the subject. The search unit can also improve search accuracy by referring to past research data about the subject. In this way, by referring to related literature and databases, search accuracy is improved. Some or all of the above-mentioned processing in the search unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the search unit can input literature and database data related to the subject into the generation AI and have the generation AI improve search accuracy.

[0102] The search unit can conduct a search taking into account the market value and influence of the target person. The search unit, for example, conducts a search taking into account the market value and influence of the target person. Methods for evaluating market value and influence include, but are not limited to, sales revenue and number of followers. For example, the search unit determines search priority based on the market value of the target person. The search unit can also adjust search accuracy taking into account the influence of the target person. The search unit can also adjust the display method of search results based on the market value and influence of the target person. This improves search priority and accuracy by taking market value and influence into account. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input data on the market value and influence of the target person into the generation AI and have the generation AI adjust the search priority and accuracy.

[0103] The providing unit can estimate the user's emotion and change the presentation method of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and changes the presentation method of the information to be provided based on the estimated user's emotion. Methods for determining the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, if the user is nervous, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide information that focuses on the main points. This improves the visibility of the information by providing a presentation method that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0104] The providing unit can change the level of detail of the information provided based on the importance of the search results. The providing unit, for example, changes the level of detail of the information provided based on the importance of the search results. Methods for evaluating the importance include, but are not limited to, the relevance and urgency of the search results. For example, the providing unit provides detailed information for search results with high importance. The providing unit can also provide concise information for search results with low importance. The providing unit can also adjust the display order of the information based on the importance of the search results. This improves the visibility of the information by providing the level of detail of the information according to the importance. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the search results to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0105] The providing unit can apply an appropriate providing algorithm depending on the category of the search result. The providing unit applies an appropriate providing algorithm depending on, for example, the category of the search result. Examples of categorization methods include, but are not limited to, the type and relevance of the search result. For example, if the search result is related to a person, the providing unit can provide information that emphasizes facial features. Furthermore, if the search result is related to an object, the providing unit can provide detailed information about the object. Furthermore, if the search result is related to a place, the providing unit can provide detailed information about the place. This improves the visibility of the information by applying a providing algorithm depending on the category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input category data of the search result to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0106] The providing unit can improve the accuracy of the provision based on the user's past provision results. The providing unit improves the accuracy of the provision based on, for example, the user's past provision results. Methods for acquiring past provision results include, but are not limited to, user ratings and feedback. For example, the providing unit improves the accuracy of the provision based on information received by the user in the past. The providing unit can also improve the accuracy of the provision by referring to the user's past feedback. The providing unit can also analyze the user's past behavioral patterns and improve the accuracy of the provision. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0107] The providing unit can estimate the user's emotion and change the length of the information to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and changes the length of the information to be provided based on the estimated user's emotion. Methods for determining the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, if the user is nervous, the providing unit can provide short, concise information. If the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise, quick information. This improves the visibility of the information by providing the length of information according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's facial expression data to the generating AI and cause the generating AI to estimate the emotion.

[0108] The providing unit can set the priority of provision based on the submission time of the search results. The providing unit sets the priority of provision based on, for example, the submission time of the search results. Methods for obtaining the submission time include, but are not limited to, for example, the submission date and time or the submission frequency. For example, the providing unit prioritizes the provision of the most recent search results. The providing unit can also provide concise information for search results that were submitted earlier. The providing unit can also adjust the display order of information based on the submission time. This improves the visibility of information by providing priority according to the submission time. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the submission time of the search results to the generation AI and cause the generation AI to set the priority of provision.

[0109] The providing unit can change the order of providing the search results based on the relevance of the search results. The providing unit changes the order of providing the search results based on, for example, the relevance of the search results. Methods for evaluating the relevance include, but are not limited to, the degree of match or relevance of the search results. For example, the providing unit prioritizes providing highly relevant search results. The providing unit can also provide concise information for less relevant search results. The providing unit can also adjust the display order of the information based on the relevance of the search results. This improves the visibility of the information by providing the order based on the relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the search results to the generation AI and cause the generation AI to change the order of providing the information.

[0110] The providing unit can change the use of provided terminology depending on the user's level of expertise. The providing unit, for example, changes the use of provided terminology depending on the user's level of expertise. Methods for evaluating the level of expertise include, but are not limited to, the user's occupation, educational background, and years of experience. For example, if the user has specialized knowledge, the providing unit can provide information using specialized terminology. If the user does not have specialized knowledge, the providing unit can also provide information in simple language. The providing unit can also adjust the level of detail of the information depending on the user's level of expertise. This improves understanding of the information by providing terminology appropriate to the level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to change the use of terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives images of the subject from a guardian or relative. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the images received by the reception unit and security camera footage using a generation AI to extract features of the subject. For example, the search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for the subject in the security camera footage based on the features extracted by the analysis unit. For example, the provision unit is realized by the output device 40 of the smart device 14 and notifies the guardian or facility staff of the search results. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives an image of the subject from a guardian or relative. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image received by the reception unit and security camera footage using a generation AI to extract features of the subject. For example, the search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for the subject in the security camera footage based on the features extracted by the analysis unit. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and notifies the guardian or facility staff of the search results. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives an image of the subject from a guardian or relative. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image received by the reception unit and security camera footage using a generation AI to extract features of the subject. For example, the search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for the subject in the security camera footage based on the features extracted by the analysis unit. For example, the provision unit is realized by the display 343 of the headset-type terminal 314 and notifies the guardian or facility staff of the search results. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, search unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives an image of the subject from a guardian or relative. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the image received by the reception unit and security camera footage using a generative AI to extract features of the subject. For example, the search unit is realized by the identification processing unit 290 of the data processing device 12 and searches for the subject in the security camera footage based on the features extracted by the analysis unit. For example, the provision unit is realized by the speaker 240 of the robot 414 and notifies the guardian or facility staff of the search results.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The reception unit can estimate the user's emotions and change the image reception method based on the estimated user emotions. For example, if the user is anxious, the reception unit can provide a simple and intuitive interface to enable the user to quickly upload images. If the user is relaxed, the reception unit can provide detailed explanations and options to carefully support the user in selecting and uploading images. If the user is feeling anxious, the reception unit can display reassuring messages and guides to enable the user to smoothly upload images. This improves user convenience by providing an image reception method that corresponds to the user's emotions.

[0113] The analysis unit can analyze the subject's movements and posture and dynamically extract features. For example, it can analyze the subject's walking pattern and extract features. It can also analyze the subject's posture and movements and dynamically extract features. It can also analyze changes in the subject's movements in real time and extract features. This allows features to be dynamically extracted by analyzing movements and posture.

[0114] The search unit can estimate the user's emotions and set search priorities based on the estimated user emotions. For example, if the user is anxious, the search unit can quickly perform a search and provide results quickly. If the user is relaxed, the search unit can perform a detailed search to improve accuracy. If the user is anxious, the search unit can use multiple search methods in combination to increase the reliability of the search results. This improves the reliability of the search results by providing search priorities according to the user's emotions.

[0115] The providing unit can estimate the user's emotions and change the way the information is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can also provide detailed information. If the user is in a hurry, the providing unit can also provide information that focuses on the main points. In this way, the visibility of the information is improved by providing a way of presenting information according to the user's emotions.

[0116] The analysis unit can analyze additional information about the subject's clothing and belongings to complement the characteristics. For example, it can analyze the color and design of the subject's clothing to complement the characteristics. It can also analyze the bags and accessories the subject carries to complement the characteristics. It can also analyze changes in the subject's clothing and belongings to dynamically complement the characteristics. In this way, by analyzing additional information, the characteristics can be complemented and the accuracy of the analysis can be improved.

[0117] The search unit can analyze the movement pattern of the subject and set a predicted location. For example, it can analyze the subject's past movement pattern to identify the predicted location. It can also analyze the subject's current movement pattern in real time to identify the predicted location. It can also analyze changes in the subject's movement pattern to identify the predicted location. This makes it easier to identify the predicted location by analyzing the movement pattern.

[0118] The providing unit can estimate the user's emotions and change the length of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can provide detailed information. If the user is in a hurry, the providing unit can provide concise, quick information. This improves the visibility of the information by providing the length of information according to the user's emotions.

[0119] The analysis unit can improve the accuracy of the analysis based on the subject's past visit history. For example, it complements features based on the subject's past visit history. It can also improve the accuracy of the analysis by referring to information about places the subject has visited in the past. It can also improve accuracy by incorporating the subject's past behavioral patterns into the analysis. In this way, the accuracy of the analysis can be improved by referring to the past visit history.

[0120] The search unit can improve search accuracy by taking into account the image quality and angle of security camera footage. For example, it can prioritize searches for security camera footage with high resolution. It can also prioritize searches for footage where the camera's installation angle captures the target's face from the front. It can also prioritize searches for security camera footage with good lighting conditions. In this way, by taking image quality and angle into account, search accuracy is improved.

[0121] The providing unit can change the order of providing search results based on the relevance of the search results. For example, highly relevant search results are provided preferentially. For less relevant search results, concise information can be provided. Furthermore, the display order of information can be adjusted according to the relevance of the search results. This improves the visibility of information by providing an order according to relevance.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives an image of the subject from a guardian or relative. Examples of guardians or relatives include, but are not limited to, parents, grandparents, siblings, etc. The reception unit can receive, for example, photos taken with a smartphone or photos taken in the past. The reception unit can also automatically evaluate the resolution and quality of the images and select the optimal image. For example, the reception unit can automatically select the image with the highest resolution from multiple images uploaded by the user. Step 2: The analysis unit uses the generation AI to analyze the images and security camera footage received by the reception unit and extract features of the subject. The features of the subject include, but are not limited to, facial contours, eye position, and hairstyle. For example, the analysis unit extracts facial contours using edge detection technology. The analysis unit can also identify the eye position based on the center point of the eyes or the distance between the eyes. The analysis unit can also analyze hairstyle features based on hair length, color, and style. Step 3: The search unit searches for the target person in the security camera video based on the features extracted by the analysis unit. The search may include, but is not limited to, an image recognition algorithm and setting a search range. For example, the search unit may use cosine similarity to search for parts of the security camera video that match the target person's features. The search unit may also use Jaccard coefficients to search for parts that match the target person's features. The search unit may also use TF-IDF to search for parts that match the target person's features. Step 4: The providing unit provides the search results obtained by the search unit. Providing the results includes, but is not limited to, notifying parents or facility staff. For example, the providing unit displays the search results to parents or facility staff through a web application or a mobile application. The providing unit can also send the search results by email.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0172] 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.

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image of the subject from a guardian or relative; an analysis unit that analyzes the image and security camera footage received by the reception unit and extracts features of the target person; a search unit that searches for a target person in security camera footage based on the features extracted by the analysis unit; a providing unit that provides the search results obtained by the searching unit. A system characterized by:

2. The reception unit Accepts images that clearly show the subject's face 2. The system of claim 1.

3. The analysis unit Analyze facial contours, eye position, and hairstyle features 2. The system of claim 1.

4. The search unit Analyzes footage from multiple security cameras in a shopping center and identifies the location where the target person appears.

2. The system of claim 1.

5. The providing unit Provide search results to parents or facility staff 2. The system of claim 1.

6. The reception unit Regularly capture security camera footage and collect attribute data of facility visitors 2. The system of claim 1.

7. The analysis unit Analyze data on the number of visitors, gender, and age 2. The system of claim 1.

8. The providing unit The analysis results will be provided as data to be used to improve operational efficiency or increase sales at the facility.

2. The system of claim 1.

Images