system

Abstract

The system according to the embodiment aims to efficiently collect barrier-free information about facilities and provide it as a web page. [Solution] A system according to an embodiment includes an analysis unit, an extraction unit, a generation unit, and a feedback unit. The analysis unit analyzes images or videos. The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit. The generation unit generates a web page based on the information extracted by the extraction unit. The feedback unit feeds back the information generated by the generation unit to the facility.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to efficiently collect barrier-free information about facilities and provide it as a web page, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect barrier-free information about facilities and provide it as a web page. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an extraction unit, a generation unit, and a feedback unit. The analysis unit analyzes images or videos. The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit. The generation unit generates a web page based on the information extracted by the extraction unit. The feedback unit feeds back the information generated by the generation unit to the facility. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect barrier-free information about facilities and provide it as a web page. [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 barrier-free information generation system according to an embodiment of the present invention analyzes images and videos of hotels and tourist attractions, extracts barrier-free information, and generates a web page. This system takes images and videos taken by facility employees and others as input and outputs the barrier-free information as a web page. For example, images and videos of barrier-free facilities such as entrances, elevators, restrooms, and guest rooms are taken and uploaded to the service. The system then analyzes the uploaded images and videos to extract barrier-free information. For example, information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom is extracted. This analysis is performed using a generation AI. The generation AI automatically extracts barrier-free information from the images and videos and generates analysis results. Next, a web page is created based on the generated barrier-free information. The web page contains detailed information about each barrier-free facility. For example, information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom is included. This web page is then posted on the official website of the hotel or tourist attraction. Furthermore, the system accumulates and analyzes the barrier-free information and provides feedback to the facility. This allows the facility to understand the progress of its barrier-free efforts and identify areas for improvement. For example, if a step at the entrance is a problem, measures can be taken to eliminate the step. This allows the barrier-free information generating system to promote barrier-free access at hotels and tourist destinations, providing a more comfortable environment for users. This allows the barrier-free information generating system to promote barrier-free access at hotels and tourist destinations, providing a more comfortable environment for users. For example, it makes it easier for wheelchair users and the elderly to select facilities with ample barrier-free access information. It also makes it easier for facilities to grasp the progress of their barrier-free access efforts, allowing them to make improvements more efficiently.

[0029] A barrier-free information generation system according to an embodiment includes an analysis unit, an extraction unit, a generation unit, and a feedback unit. The analysis unit analyzes images or videos. Examples of images or videos include, but are not limited to, still images, continuous videos, and videos with different resolutions. The analysis unit analyzes images using, for example, image recognition technology. The analysis unit can also analyze videos using a video analysis algorithm. For example, the analysis unit can analyze the presence or absence of steps at an entrance using image recognition technology. The analysis unit can also analyze the size of an elevator using a video analysis algorithm. The analysis unit can also analyze images or videos using a generative AI to extract barrier-free information. For example, the generative AI can extract barrier-free information from images or videos using a generative adversarial network (GAN) or a Transformer model. The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit. The extraction unit generates specific barrier-free information based on, for example, the feature values ​​extracted by the analysis unit. For example, the extraction unit extracts information such as whether there are steps at the entrance, the size of the elevator, and the position of handrails in the restroom. The generation unit generates a web page based on the information extracted by the extraction unit. The generation unit creates the web page using, for example, HTML generation or a template engine. For example, the generation unit creates a web page including information such as whether there are steps at the entrance, the size of the elevator, and the position of handrails in the restroom based on the extracted barrier-free information. The feedback unit feeds back the information generated by the generation unit to the facility. The feedback unit provides the barrier-free information to the facility by, for example, adjusting the notification method or the content of the feedback. For example, the feedback unit notifies the facility of a link to the generated web page. The feedback unit can also provide the facility with the accumulated barrier-free information and the analysis results. As a result, the barrier-free information generation system according to the embodiment extracts barrier-free information from images and videos, generates web pages, and provides feedback to the facility, thereby contributing to the promotion of barrier-free access.

[0030] The analysis unit can analyze images and videos using a generative AI and extract barrier-free information. Examples of generative AI include, but are not limited to, GANs (generative adversarial networks) and Transformer models. The analysis unit can extract barrier-free information from images and videos using a generative AI. For example, the analysis unit can use a generative AI to analyze whether there are steps at an entrance. The analysis unit can also use a generative AI to analyze the size of an elevator. The analysis unit can also use a generative AI to analyze the position of handrails in a toilet. This improves the accuracy of analyzing images and videos by using a generative AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can extract barrier-free information from images and videos using a generative AI.

[0031] The extraction unit can generate specific barrier-free information based on the information extracted by the analysis unit using a generation AI. Examples of the generation AI include, but are not limited to, a GAN (generative artificial network) or a Transformer model. The extraction unit can generate specific barrier-free information based on the information extracted by the analysis unit using a generation AI. For example, the extraction unit can use a generation AI to extract whether or not there is a step at an entrance. The extraction unit can also use a generation AI to extract the size of an elevator. The extraction unit can also use a generation AI to extract the position of a handrail in a restroom. This improves the accuracy of generating specific barrier-free information by using a generation AI. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can use a generation AI to generate specific barrier-free information based on the information extracted by the analysis unit.

[0032] The generation unit can use a generation AI to create a web page based on the information generated by the extraction unit. Examples of generation AI include, but are not limited to, a generative artificial network (GAN) or a Transformer model. The generation unit can use a generation AI to create a web page based on the information generated by the extraction unit. For example, the generation unit can use a generation AI to create a web page that includes whether or not there is a step at the entrance. The generation unit can also use a generation AI to create a web page that includes the size of an elevator. The generation unit can also use a generation AI to create a web page that includes the location of handrails in a toilet. This improves the accuracy of web page creation by using a generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to create a web page based on the information generated by the extraction unit.

[0033] The feedback unit can feed back the barrier-free information generated using the generation AI to the facility side. Examples of the generation AI include, but are not limited to, a GAN (generative artificial network) or a Transformer model. The feedback unit, for example, uses the generation AI to feed back the generated barrier-free information to the facility side. For example, the feedback unit can use the generation AI to feed back the presence or absence of steps at the entrance to the facility side. The feedback unit can also use the generation AI to feed back the size of the elevator to the facility side. The feedback unit can also use the generation AI to feed back the position of handrails in the restroom to the facility side. In this way, the use of the generation AI improves the accuracy of the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can feed back the generated barrier-free information to the facility side using the generation AI.

[0034] The analysis unit can improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos. For example, the analysis unit can improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the analysis unit can obtain past barrier-free information about the facility from a database and reflect it in the analysis. The analysis unit can also complement the analysis results of the current images or videos based on the past barrier-free information. The analysis unit can also improve the reliability of the analysis results by referring to past barrier-free information about the facility. Thus, referring to past barrier-free information improves 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 improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos using a generation AI.

[0035] The analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos. For example, the analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the analysis unit can integrate images and videos taken from different viewpoints to analyze overall barrier-free information. The analysis unit can also combine data from multiple viewpoints to obtain more detailed analysis results. The analysis unit can also integrate information from different viewpoints to improve the accuracy of the analysis results. Integrating information from different viewpoints improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos using a generation AI.

[0036] The analysis unit can focus its analysis on a specific area of ​​the facility when analyzing images or videos. For example, the analysis unit can focus its analysis on a specific area of ​​the facility (such as the entrance or restroom) when analyzing images or videos. Specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the analysis unit can analyze images or videos of the entrance to check whether there are steps or whether ramps are installed. The analysis unit can also analyze images or videos of the restroom to check the location and width of handrails. The analysis unit can also analyze images or videos of the elevator to check the size and location of the operation panel. By focusing on a specific area, detailed barrier-free information can be provided. Some or all of the above-described processing by 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 use a generation AI to focus its analysis on a specific area of ​​the facility when analyzing images or videos.

[0037] The analysis unit may take into account the geographical location information of a facility when analyzing images or videos. For example, the analysis unit may take into account the geographical location information of a facility when analyzing images or videos. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the analysis unit may incorporate the barrier-free status of the surrounding environment into the analysis based on the geographical location information of the facility. The analysis unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The analysis unit may also improve the reliability of the analysis results based on the geographical location information of the facility. Thus, taking the geographical location information into account improves the reliability of the analysis results. 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 may use a generation AI to take into account the geographical location information of a facility when analyzing images or videos.

[0038] The analysis unit can perform analysis by taking into account attribute information of facility users when analyzing images and videos. For example, when analyzing images and videos, the analysis unit can perform analysis by taking into account attribute information of facility users (age, type of disability, etc.). Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the analysis unit analyzes barrier-free information by taking into account the user's age and type of disability. The analysis unit can also customize the analysis results based on the user's attribute information. The analysis unit can also improve the reliability of the analysis results by taking into account the user's attribute information. In this way, the reliability of the analysis results is improved by taking into account the user's attribute information. 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 perform analysis by using a generation AI while taking into account attribute information of facility users when analyzing images and videos.

[0039] The analysis unit can customize the analysis method by reflecting past facility feedback when analyzing images or videos. For example, the analysis unit can customize the analysis method by reflecting past facility feedback when analyzing images or videos. Past feedback includes, but is not limited to, user reviews and survey results. For example, the analysis unit adjusts the analysis method based on the facility's past feedback. The analysis unit can also improve the accuracy of the analysis results by reflecting past feedback. The analysis unit can also customize the analysis method by taking into account the facility's past feedback. In this way, the accuracy of the analysis method is improved by reflecting past feedback. 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 use a generation AI to customize the analysis method by reflecting past facility feedback when analyzing images or videos.

[0040] The extraction unit can improve extraction accuracy by referring to past barrier-free information of the facility during extraction. For example, the extraction unit can improve extraction accuracy by referring to past barrier-free information of the facility during extraction. Past barrier-free information includes, but is not limited to, database usage and past reports. For example, the extraction unit obtains past barrier-free information of the facility from a database and reflects it in the extraction. The extraction unit can also complement current information based on past barrier-free information. The extraction unit can also improve the reliability of the extraction results by referring to past barrier-free information. Thus, by referring to past barrier-free information, extraction accuracy is improved. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can improve extraction accuracy by using a generation AI to refer to past barrier-free information of the facility during extraction.

[0041] The extraction unit may extract barrier-free information by integrating multiple images and videos taken from different viewpoints during extraction. For example, the extraction unit may extract barrier-free information by integrating multiple images and videos taken from different viewpoints during extraction. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the extraction unit may integrate images and videos taken from different viewpoints to extract overall barrier-free information. The extraction unit may also combine data from multiple viewpoints to extract more detailed information. The extraction unit may also integrate information from different viewpoints to improve the accuracy of the extraction results. In this way, integrating information from different viewpoints improves extraction accuracy. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may use a generation AI to integrate multiple images and videos taken from different viewpoints during extraction to extract barrier-free information.

[0042] The extraction unit can extract barrier-free information by focusing on a specific area of ​​the facility during extraction. For example, the extraction unit can extract barrier-free information by focusing on a specific area of ​​the facility (such as an entrance or restroom) during extraction. Specific areas include, but are not limited to, entrances, restrooms, and hallways. For example, the extraction unit can analyze images or videos of the entrance to extract whether there are steps or whether ramps are installed. The extraction unit can also analyze images or videos of restrooms to extract the location and width of handrails. The extraction unit can also analyze images or videos of elevators to extract their size and the location of their operation panels. This allows for detailed barrier-free information to be provided by focusing on specific areas. Some or all of the above-described processing by the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can use a generation AI to extract barrier-free information by focusing on a specific area of ​​the facility during extraction.

[0043] The extraction unit may extract barrier-free information by taking into account the geographical location information of the facility during extraction. For example, the extraction unit may extract the barrier-free information by taking into account the geographical location information of the facility during extraction. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the extraction unit may incorporate the barrier-free status of the surrounding environment into the extraction based on the geographical location information of the facility. The extraction unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The extraction unit may also improve the reliability of the extraction results based on the geographical location information of the facility. Taking into account the geographical location information improves the reliability of the extraction results. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may extract barrier-free information by taking into account the geographical location information of the facility during extraction using a generation AI.

[0044] The extraction unit can extract barrier-free information by taking into account attribute information of facility users during extraction. For example, the extraction unit extracts barrier-free information by taking into account attribute information of facility users (such as age and type of disability). Attribute information includes, but is not limited to, age, type of disability, and gender. For example, the extraction unit extracts barrier-free information by taking into account the user's age and type of disability. The extraction unit can also customize the extraction results based on the user's attribute information. The extraction unit can also improve the reliability of the extraction results by taking into account the user's attribute information. This improves the reliability of the extraction results by taking into account the user's attribute information. Some or all of the above-described processing in the extraction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the extraction unit can extract barrier-free information by taking into account the attribute information of facility users during extraction using a generation AI.

[0045] The extraction unit can customize the extraction method by reflecting past facility feedback at the time of extraction. For example, the extraction unit customizes the extraction method by reflecting past facility feedback at the time of extraction. Past feedback includes, but is not limited to, user reviews and survey results. For example, the extraction unit adjusts the extraction method based on past facility feedback. The extraction unit can also improve the accuracy of the extraction results by reflecting past feedback. The extraction unit can also customize the extraction method by taking past facility feedback into consideration. In this way, the accuracy of the extraction method is improved by reflecting past feedback. Some or all of the above-described processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can customize the extraction method by reflecting past facility feedback at the time of extraction using a generation AI.

[0046] The generation unit can improve the generation accuracy by referring to past barrier-free information of the facility when generating the web page. For example, the generation unit can improve the generation accuracy by referring to past barrier-free information of the facility when generating the web page. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the generation unit can obtain past barrier-free information of the facility from a database and reflect it in the generation of the web page. The generation unit can also complement current information based on the past barrier-free information. The generation unit can also improve the reliability of the generated results by referring to the past barrier-free information of the facility. Thus, the generation accuracy is improved by referring to the past barrier-free information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can improve the generation accuracy by referring to the past barrier-free information of the facility when generating the web page using a generation AI.

[0047] The generation unit can integrate multiple images and videos taken from different viewpoints to display information when generating a web page. For example, the generation unit can integrate multiple images and videos taken from different viewpoints to display information when generating a web page. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the generation unit can integrate images and videos taken from different viewpoints to display overall barrier-free information. The generation unit can also combine data from multiple viewpoints to display more detailed information. The generation unit can also integrate information from different viewpoints to improve the accuracy of the display results. Integrating information from different viewpoints improves display accuracy. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can use a generation AI to integrate multiple images and videos taken from different viewpoints to display information when generating a web page.

[0048] The generation unit can display information by focusing on a specific area of ​​the facility when generating a web page. For example, the generation unit can display information by focusing on a specific area of ​​the facility (such as an entrance or restroom) when generating a web page. Specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the generation unit can analyze images and videos of the entrance to display whether there are steps and whether a ramp is installed. The generation unit can also analyze images and videos of the restroom to display the location and width of handrails. The generation unit can also analyze images and videos of the elevator to display the size and the location of the operation panel. By focusing on a specific area, detailed barrier-free information can be provided. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can use a generation AI to display information by focusing on a specific area of ​​the facility when generating a web page.

[0049] The generation unit can display information taking into account the geographical location information of the facility when generating a web page. For example, the generation unit can display information taking into account the geographical location information of the facility when generating a web page. Geographical location information includes, but is not limited to, GPS data and map information. For example, the generation unit can display the barrier-free status of the surrounding environment based on the geographical location information of the facility. The generation unit can also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The generation unit can also improve the reliability of the display result based on the geographical location information of the facility. As a result, the reliability of the display result is improved by taking into account the geographical location information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can display information taking into account the geographical location information of the facility when generating a web page using a generation AI.

[0050] The generation unit can display information taking into account attribute information of facility users when generating a web page. For example, the generation unit can display information taking into account attribute information of facility users (such as age and type of disability) when generating a web page. Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the generation unit displays barrier-free information taking into account the user's age and type of disability. The generation unit can also customize the display result based on the user's attribute information. The generation unit can also improve the reliability of the display result by taking into account the user's attribute information. As a result, the reliability of the display result is improved by taking into account the user's attribute information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can display information taking into account the attribute information of facility users when generating a web page using a generation AI.

[0051] The generation unit can customize the display method by reflecting past facility feedback when generating a web page. For example, the generation unit customizes the display method by reflecting past facility feedback when generating a web page. Past feedback includes, but is not limited to, user reviews and survey results. For example, the generation unit adjusts the display method based on the facility's past feedback. The generation unit can also improve the accuracy of the display results by reflecting past feedback. The generation unit can also customize the display method by taking into account the facility's past feedback. In this way, the accuracy of the display method is improved by reflecting past feedback. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can customize the display method by reflecting past facility feedback when generating a web page using a generation AI.

[0052] The feedback unit can improve the feedback accuracy by referring to past barrier-free information of the facility when providing feedback. For example, the feedback unit can improve the feedback accuracy by referring to past barrier-free information of the facility when providing feedback. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the feedback unit can obtain past barrier-free information of the facility from a database and reflect it in the feedback. The feedback unit can also complement current information based on the past barrier-free information. The feedback unit can also improve the reliability of the feedback results by referring to the past barrier-free information. Thus, by referring to the past barrier-free information, the feedback accuracy is improved. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can improve the feedback accuracy by using a generation AI to refer to past barrier-free information of the facility when providing feedback.

[0053] The feedback unit may provide information by integrating multiple images and videos taken from different viewpoints during feedback. For example, the feedback unit may provide information by integrating multiple images and videos taken from different viewpoints during feedback. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the feedback unit may integrate images and videos taken from different viewpoints to provide overall barrier-free information. The feedback unit may also combine data from multiple viewpoints to provide more detailed information. The feedback unit may also integrate information from different viewpoints to improve the accuracy of the feedback results. In this way, integrating information from different viewpoints improves feedback accuracy. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit may use a generation AI to integrate multiple images and videos taken from different viewpoints during feedback to provide information.

[0054] The feedback unit can provide information by focusing on a specific area of ​​the facility when providing feedback. For example, the feedback unit can provide information by focusing on a specific area of ​​the facility (such as the entrance or restroom) when providing feedback. Examples of specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the feedback unit can analyze images or videos of the entrance and provide feedback on the presence or absence of steps and the installation status of ramps. The feedback unit can also analyze images or videos of the restroom and provide feedback on the position and width of handrails. The feedback unit can also analyze images or videos of the elevator and provide feedback on the size and position of the operation panel. In this way, detailed feedback can be provided by focusing on a specific area. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can use a generation AI to provide information by focusing on a specific area of ​​the facility when providing feedback.

[0055] The feedback unit may provide information taking into account the geographical location information of the facility when providing feedback. For example, the feedback unit may provide information taking into account the geographical location information of the facility when providing feedback. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the feedback unit may reflect the barrier-free status of the surrounding environment in the feedback based on the geographical location information of the facility. The feedback unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The feedback unit may also improve the reliability of the feedback result based on the geographical location information of the facility. As a result, the reliability of the feedback result is improved by taking into account the geographical location information. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit may provide information taking into account the geographical location information of the facility when providing feedback using a generation AI.

[0056] The feedback unit may provide information while taking into consideration attribute information of the facility user when providing feedback. For example, the feedback unit may provide information while taking into consideration attribute information of the facility user (such as age and type of disability) when providing feedback. Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the feedback unit may provide feedback on barrier-free access information while taking into consideration the user's age and type of disability. The feedback unit may also customize the feedback results based on the user's attribute information. The feedback unit may also improve the reliability of the feedback results by taking into consideration the user's attribute information. This improves the reliability of the feedback results by taking into consideration the user's attribute information. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit may provide information while taking into consideration the attribute information of the facility user when providing feedback using a generation AI.

[0057] The feedback unit can customize the provision method by reflecting past facility feedback when providing feedback. For example, the feedback unit customizes the provision method by reflecting past facility feedback when providing feedback. Past feedback includes, but is not limited to, user reviews and survey results. For example, the feedback unit adjusts the provision method based on the facility's past feedback. The feedback unit can also improve the accuracy of the feedback results by reflecting past feedback. The feedback unit can also customize the provision method by taking into account the facility's past feedback. In this way, the accuracy of the provision method is improved by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can customize the provision method by reflecting past facility feedback when providing feedback using a generation AI.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] When analyzing images and videos, the analysis unit can analyze the user's movement patterns within a facility and extract barrier-free information. For example, the analysis unit can analyze the user's movement patterns within a facility and identify routes that are easy for wheelchair users to access. The analysis unit can also identify areas that are prone to congestion based on the user's movement patterns and determine the priority of barrier-free measures. Furthermore, the analysis unit can analyze the user's movement patterns and evaluate whether the placement of barrier-free facilities is appropriate. This makes it possible to provide more practical barrier-free information by taking the user's movement patterns into consideration.

[0060] The extraction unit can collect feedback from facility users in real time and reflect it in the extraction of barrier-free information. For example, the extraction unit can collect feedback provided by users through a smartphone app to improve the accuracy of the barrier-free information. The extraction unit can also update the barrier-free information for a specific area based on the user feedback. Furthermore, the extraction unit can analyze the user feedback and identify areas for improvement in barrier-free access. In this way, by utilizing user feedback, more accurate and up-to-date barrier-free information can be provided.

[0061] The generating unit can use infographics to display barrier-free information in a visually easy-to-understand manner. For example, the generating unit can create infographics that indicate whether or not there are steps at the entrance. The generating unit can also create infographics that visually display the size of an elevator and the location of its operation panel. Furthermore, the generating unit can also create infographics that indicate the location and size of handrails in a restroom. In this way, by providing visually easy-to-understand information, users can easily understand the barrier-free information.

[0062] The feedback unit can provide barrier-free information about facilities in cooperation with social media. For example, the feedback unit automatically posts the generated barrier-free information on social media such as Twitter (registered trademark) and Facebook (registered trademark). The feedback unit can also collect user comments and feedback on social media and reflect them in improving the barrier-free information. Furthermore, the feedback unit can encourage the sharing of barrier-free information on social media, providing the information to more users. In this way, the use of social media can promote the dissemination and improvement of barrier-free information.

[0063] The analysis unit can compare the facility's barrier-free information with other facilities and perform a relative evaluation. For example, the analysis unit can compare the barrier-free information of other hotels and tourist destinations in the same area to evaluate the facility's progress toward barrier-free access. The analysis unit can also identify areas for improvement based on the barrier-free information of other facilities and propose specific measures. Furthermore, the analysis unit can provide feedback to the facility regarding the results of the comparison with other facilities, improving the facility's competitiveness in barrier-free access. This allows the facility's progress toward barrier-free access to be objectively evaluated through comparison with other facilities.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The analysis unit analyzes the image or video. Images and videos can be still images, continuous videos, or videos with different resolutions. The analysis unit uses image recognition technology and video analysis algorithms to perform the analysis. For example, it can analyze whether there are steps at the entrance or the size of the elevator. It can also use generative AI to extract barrier-free information from images and videos. Step 2: The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit, such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom. Step 3: The generator generates a web page based on the information extracted by the extractor. For example, it uses HTML generation or a template engine to create a web page that includes information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom. Step 4: The feedback unit feeds back the information generated by the generation unit to the facility. For example, it notifies the facility of a link to the generated web page. It can also provide the facility with the accumulated and analyzed barrier-free information.

[0066] (Example 2) A barrier-free information generation system according to an embodiment of the present invention analyzes images and videos of hotels and tourist attractions, extracts barrier-free information, and generates a web page. This system takes images and videos taken by facility employees and others as input and outputs the barrier-free information as a web page. For example, images and videos of barrier-free facilities such as entrances, elevators, restrooms, and guest rooms are taken and uploaded to the service. The system then analyzes the uploaded images and videos to extract barrier-free information. For example, information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom is extracted. This analysis is performed using a generation AI. The generation AI automatically extracts barrier-free information from the images and videos and generates analysis results. Next, a web page is created based on the generated barrier-free information. The web page contains detailed information about each barrier-free facility. For example, information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom is included. This web page is then posted on the official website of the hotel or tourist attraction. Furthermore, the system accumulates and analyzes the barrier-free information and provides feedback to the facility. This allows the facility to understand the progress of its barrier-free efforts and identify areas for improvement. For example, if a step at the entrance is a problem, measures can be taken to eliminate the step. This allows the barrier-free information generating system to promote barrier-free access at hotels and tourist destinations, providing a more comfortable environment for users. This allows the barrier-free information generating system to promote barrier-free access at hotels and tourist destinations, providing a more comfortable environment for users. For example, it makes it easier for wheelchair users and the elderly to select facilities with ample barrier-free access information. It also makes it easier for facilities to grasp the progress of their barrier-free access efforts, allowing them to make improvements more efficiently.

[0067] A barrier-free information generation system according to an embodiment includes an analysis unit, an extraction unit, a generation unit, and a feedback unit. The analysis unit analyzes images or videos. Examples of images or videos include, but are not limited to, still images, continuous videos, and videos with different resolutions. The analysis unit analyzes images using, for example, image recognition technology. The analysis unit can also analyze videos using a video analysis algorithm. For example, the analysis unit can analyze the presence or absence of steps at an entrance using image recognition technology. The analysis unit can also analyze the size of an elevator using a video analysis algorithm. The analysis unit can also analyze images or videos using a generative AI to extract barrier-free information. For example, the generative AI can extract barrier-free information from images or videos using a generative adversarial network (GAN) or a Transformer model. The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit. The extraction unit generates specific barrier-free information based on, for example, the feature values ​​extracted by the analysis unit. For example, the extraction unit extracts information such as whether there are steps at the entrance, the size of the elevator, and the position of handrails in the restroom. The generation unit generates a web page based on the information extracted by the extraction unit. The generation unit creates the web page using, for example, HTML generation or a template engine. For example, the generation unit creates a web page including information such as whether there are steps at the entrance, the size of the elevator, and the position of handrails in the restroom based on the extracted barrier-free information. The feedback unit feeds back the information generated by the generation unit to the facility. The feedback unit provides the barrier-free information to the facility by, for example, adjusting the notification method or the content of the feedback. For example, the feedback unit notifies the facility of a link to the generated web page. The feedback unit can also provide the facility with the accumulated barrier-free information and the analysis results. As a result, the barrier-free information generation system according to the embodiment extracts barrier-free information from images and videos, generates web pages, and provides feedback to the facility, thereby contributing to the promotion of barrier-free access.

[0068] The analysis unit can analyze images and videos using a generative AI and extract barrier-free information. Examples of generative AI include, but are not limited to, GANs (generative adversarial networks) and Transformer models. The analysis unit can extract barrier-free information from images and videos using a generative AI. For example, the analysis unit can use a generative AI to analyze whether there are steps at an entrance. The analysis unit can also use a generative AI to analyze the size of an elevator. The analysis unit can also use a generative AI to analyze the position of handrails in a toilet. This improves the accuracy of analyzing images and videos by using a generative AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can extract barrier-free information from images and videos using a generative AI.

[0069] The extraction unit can generate specific barrier-free information based on the information extracted by the analysis unit using a generation AI. Examples of the generation AI include, but are not limited to, a GAN (generative artificial network) or a Transformer model. The extraction unit can generate specific barrier-free information based on the information extracted by the analysis unit using a generation AI. For example, the extraction unit can use a generation AI to extract whether or not there is a step at an entrance. The extraction unit can also use a generation AI to extract the size of an elevator. The extraction unit can also use a generation AI to extract the position of a handrail in a restroom. This improves the accuracy of generating specific barrier-free information by using a generation AI. Some or all of the above-mentioned processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can use a generation AI to generate specific barrier-free information based on the information extracted by the analysis unit.

[0070] The generation unit can use a generation AI to create a web page based on the information generated by the extraction unit. Examples of generation AI include, but are not limited to, a generative artificial network (GAN) or a Transformer model. The generation unit can use a generation AI to create a web page based on the information generated by the extraction unit. For example, the generation unit can use a generation AI to create a web page that includes whether or not there is a step at the entrance. The generation unit can also use a generation AI to create a web page that includes the size of an elevator. The generation unit can also use a generation AI to create a web page that includes the location of handrails in a toilet. This improves the accuracy of web page creation by using a generation AI. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to create a web page based on the information generated by the extraction unit.

[0071] The feedback unit can feed back the barrier-free information generated using the generation AI to the facility side. Examples of the generation AI include, but are not limited to, a GAN (generative artificial network) or a Transformer model. The feedback unit, for example, uses the generation AI to feed back the generated barrier-free information to the facility side. For example, the feedback unit can use the generation AI to feed back the presence or absence of steps at the entrance to the facility side. The feedback unit can also use the generation AI to feed back the size of the elevator to the facility side. The feedback unit can also use the generation AI to feed back the position of handrails in the restroom to the facility side. In this way, the use of the generation AI improves the accuracy of the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can feed back the generated barrier-free information to the facility side using the generation AI.

[0072] The analysis unit can estimate a user's emotions and adjust the image and video analysis method based on the estimated user emotions. The analysis unit, for example, estimates a user's emotions and adjusts the image and video analysis method based on the estimated user emotions. The user emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the analysis unit can speed up the analysis to provide results more quickly. Furthermore, if the user is relaxed, the analysis unit can perform a more detailed analysis to provide more information. Furthermore, if the user is in a hurry, the analysis unit can focus on important barrier-free information. This allows for more appropriate analysis results to be provided by adjusting the analysis method according to the user's emotions. 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 estimate a user's emotions using a generation AI and adjust the image and video analysis method based on the estimated user emotions.

[0073] The analysis unit can improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos. For example, the analysis unit can improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the analysis unit can obtain past barrier-free information about the facility from a database and reflect it in the analysis. The analysis unit can also complement the analysis results of the current images or videos based on the past barrier-free information. The analysis unit can also improve the reliability of the analysis results by referring to past barrier-free information about the facility. Thus, referring to past barrier-free information improves 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 improve the accuracy of the analysis by referring to past barrier-free information about the facility when analyzing images or videos using a generation AI.

[0074] The analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos. For example, the analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the analysis unit can integrate images and videos taken from different viewpoints to analyze overall barrier-free information. The analysis unit can also combine data from multiple viewpoints to obtain more detailed analysis results. The analysis unit can also integrate information from different viewpoints to improve the accuracy of the analysis results. Integrating information from different viewpoints improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can integrate and analyze multiple images and videos taken from different viewpoints when analyzing images and videos using a generation AI.

[0075] The analysis unit can focus its analysis on a specific area of ​​the facility when analyzing images or videos. For example, the analysis unit can focus its analysis on a specific area of ​​the facility (such as the entrance or restroom) when analyzing images or videos. Specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the analysis unit can analyze images or videos of the entrance to check whether there are steps or whether ramps are installed. The analysis unit can also analyze images or videos of the restroom to check the location and width of handrails. The analysis unit can also analyze images or videos of the elevator to check the size and location of the operation panel. By focusing on a specific area, detailed barrier-free information can be provided. Some or all of the above-described processing by 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 use a generation AI to focus its analysis on a specific area of ​​the facility when analyzing images or videos.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. 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 estimate the user's emotions using a generation AI and adjust the display method of the analysis results based on the estimated user emotions.

[0077] The analysis unit may take into account the geographical location information of a facility when analyzing images or videos. For example, the analysis unit may take into account the geographical location information of a facility when analyzing images or videos. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the analysis unit may incorporate the barrier-free status of the surrounding environment into the analysis based on the geographical location information of the facility. The analysis unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The analysis unit may also improve the reliability of the analysis results based on the geographical location information of the facility. Thus, taking the geographical location information into account improves the reliability of the analysis results. 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 may use a generation AI to take into account the geographical location information of a facility when analyzing images or videos.

[0078] The analysis unit can perform analysis by taking into account attribute information of facility users when analyzing images and videos. For example, when analyzing images and videos, the analysis unit can perform analysis by taking into account attribute information of facility users (age, type of disability, etc.). Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the analysis unit analyzes barrier-free information by taking into account the user's age and type of disability. The analysis unit can also customize the analysis results based on the user's attribute information. The analysis unit can also improve the reliability of the analysis results by taking into account the user's attribute information. In this way, the reliability of the analysis results is improved by taking into account the user's attribute information. 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 perform analysis by using a generation AI while taking into account attribute information of facility users when analyzing images and videos.

[0079] The analysis unit can customize the analysis method by reflecting past facility feedback when analyzing images or videos. For example, the analysis unit can customize the analysis method by reflecting past facility feedback when analyzing images or videos. Past feedback includes, but is not limited to, user reviews and survey results. For example, the analysis unit adjusts the analysis method based on the facility's past feedback. The analysis unit can also improve the accuracy of the analysis results by reflecting past feedback. The analysis unit can also customize the analysis method by taking into account the facility's past feedback. In this way, the accuracy of the analysis method is improved by reflecting past feedback. 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 use a generation AI to customize the analysis method by reflecting past facility feedback when analyzing images or videos.

[0080] The extraction unit can estimate the user's emotions and adjust the barrier-free information extraction method based on the estimated user emotions. The extraction unit, for example, estimates the user's emotions and adjusts the barrier-free information extraction method based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the extraction unit prioritizes extraction of important barrier-free information when the user is stressed. The extraction unit can also extract detailed barrier-free information when the user is relaxed. The extraction unit can also adopt a method that can extract information quickly when the user is in a hurry. By adjusting the extraction method according to the user's emotions, more appropriate barrier-free information can be provided. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can estimate the user's emotions using a generation AI and adjust the barrier-free information extraction method based on the estimated user emotions.

[0081] The extraction unit can improve extraction accuracy by referring to past barrier-free information of the facility during extraction. For example, the extraction unit can improve extraction accuracy by referring to past barrier-free information of the facility during extraction. Past barrier-free information includes, but is not limited to, database usage and past reports. For example, the extraction unit obtains past barrier-free information of the facility from a database and reflects it in the extraction. The extraction unit can also complement current information based on past barrier-free information. The extraction unit can also improve the reliability of the extraction results by referring to past barrier-free information. Thus, by referring to past barrier-free information, extraction accuracy is improved. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can improve extraction accuracy by using a generation AI to refer to past barrier-free information of the facility during extraction.

[0082] The extraction unit may extract barrier-free information by integrating multiple images and videos taken from different viewpoints during extraction. For example, the extraction unit may extract barrier-free information by integrating multiple images and videos taken from different viewpoints during extraction. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the extraction unit may integrate images and videos taken from different viewpoints to extract overall barrier-free information. The extraction unit may also combine data from multiple viewpoints to extract more detailed information. The extraction unit may also integrate information from different viewpoints to improve the accuracy of the extraction results. In this way, integrating information from different viewpoints improves extraction accuracy. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may use a generation AI to integrate multiple images and videos taken from different viewpoints during extraction to extract barrier-free information.

[0083] The extraction unit can extract barrier-free information by focusing on a specific area of ​​the facility during extraction. For example, the extraction unit can extract barrier-free information by focusing on a specific area of ​​the facility (such as an entrance or restroom) during extraction. Specific areas include, but are not limited to, entrances, restrooms, and hallways. For example, the extraction unit can analyze images or videos of the entrance to extract whether there are steps or whether ramps are installed. The extraction unit can also analyze images or videos of restrooms to extract the location and width of handrails. The extraction unit can also analyze images or videos of elevators to extract their size and the location of their operation panels. This allows for detailed barrier-free information to be provided by focusing on specific areas. Some or all of the above-described processing by the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can use a generation AI to extract barrier-free information by focusing on a specific area of ​​the facility during extraction.

[0084] The extraction unit can estimate the user's emotion and adjust the display method of the extraction result based on the estimated user's emotion. The extraction unit, for example, estimates the user's emotion and adjusts the display method of the extraction result based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the extraction unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the extraction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the extraction unit can provide a display method that focuses on the main points. This allows for more appropriate extraction results to be provided by adjusting the display method according to the user's emotion. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can estimate the user's emotion using a generation AI and adjust the display method of the extraction result based on the estimated user's emotion.

[0085] The extraction unit may extract barrier-free information by taking into account the geographical location information of the facility during extraction. For example, the extraction unit may extract the barrier-free information by taking into account the geographical location information of the facility during extraction. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the extraction unit may incorporate the barrier-free status of the surrounding environment into the extraction based on the geographical location information of the facility. The extraction unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The extraction unit may also improve the reliability of the extraction results based on the geographical location information of the facility. Taking into account the geographical location information improves the reliability of the extraction results. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may extract barrier-free information by taking into account the geographical location information of the facility during extraction using a generation AI.

[0086] The extraction unit can extract barrier-free information by taking into account attribute information of facility users during extraction. For example, the extraction unit extracts barrier-free information by taking into account attribute information of facility users (such as age and type of disability). Attribute information includes, but is not limited to, age, type of disability, and gender. For example, the extraction unit extracts barrier-free information by taking into account the user's age and type of disability. The extraction unit can also customize the extraction results based on the user's attribute information. The extraction unit can also improve the reliability of the extraction results by taking into account the user's attribute information. This improves the reliability of the extraction results by taking into account the user's attribute information. Some or all of the above-described processing in the extraction unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the extraction unit can extract barrier-free information by taking into account the attribute information of facility users during extraction using a generation AI.

[0087] The extraction unit can customize the extraction method by reflecting past facility feedback at the time of extraction. For example, the extraction unit customizes the extraction method by reflecting past facility feedback at the time of extraction. Past feedback includes, but is not limited to, user reviews and survey results. For example, the extraction unit adjusts the extraction method based on past facility feedback. The extraction unit can also improve the accuracy of the extraction results by reflecting past feedback. The extraction unit can also customize the extraction method by taking past facility feedback into consideration. In this way, the accuracy of the extraction method is improved by reflecting past feedback. Some or all of the above-described processing in the extraction unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the extraction unit can customize the extraction method by reflecting past facility feedback at the time of extraction using a generation AI.

[0088] The generation unit can estimate a user's emotion and adjust the web page generation method based on the estimated user's emotion. The generation unit, for example, estimates a user's emotion and adjusts the web page generation method based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the generation unit generates a simple, highly visible web page. Also, if the user is relaxed, the generation unit can generate a web page containing detailed information. Also, if the user is in a hurry, the generation unit can generate a web page that focuses on the main points. This allows for adjusting the generation method according to the user's emotion, thereby providing a more appropriate web page. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can estimate a user's emotion using a generation AI and adjust the web page generation method based on the estimated user's emotion.

[0089] The generation unit can improve the generation accuracy by referring to past barrier-free information of the facility when generating the web page. For example, the generation unit can improve the generation accuracy by referring to past barrier-free information of the facility when generating the web page. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the generation unit can obtain past barrier-free information of the facility from a database and reflect it in the generation of the web page. The generation unit can also complement current information based on the past barrier-free information. The generation unit can also improve the reliability of the generated results by referring to the past barrier-free information of the facility. Thus, the generation accuracy is improved by referring to the past barrier-free information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can improve the generation accuracy by referring to the past barrier-free information of the facility when generating the web page using a generation AI.

[0090] The generation unit can integrate multiple images and videos taken from different viewpoints to display information when generating a web page. For example, the generation unit can integrate multiple images and videos taken from different viewpoints to display information when generating a web page. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the generation unit can integrate images and videos taken from different viewpoints to display overall barrier-free information. The generation unit can also combine data from multiple viewpoints to display more detailed information. The generation unit can also integrate information from different viewpoints to improve the accuracy of the display results. Integrating information from different viewpoints improves display accuracy. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can use a generation AI to integrate multiple images and videos taken from different viewpoints to display information when generating a web page.

[0091] The generation unit can display information by focusing on a specific area of ​​the facility when generating a web page. For example, the generation unit can display information by focusing on a specific area of ​​the facility (such as an entrance or restroom) when generating a web page. Specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the generation unit can analyze images and videos of the entrance to display whether there are steps and whether a ramp is installed. The generation unit can also analyze images and videos of the restroom to display the location and width of handrails. The generation unit can also analyze images and videos of the elevator to display the size and the location of the operation panel. By focusing on a specific area, detailed barrier-free information can be provided. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can use a generation AI to display information by focusing on a specific area of ​​the facility when generating a web page.

[0092] The generation unit can estimate a user's emotion and adjust the display method of the web page based on the estimated user's emotion. The generation unit, for example, estimates a user's emotion and adjusts the display method of the web page based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that focuses on the main points. This allows for a more appropriate web page to be provided by adjusting the display method according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can estimate a user's emotion using a generation AI and adjust the display method of the web page based on the estimated user's emotion.

[0093] The generation unit can display information taking into account the geographical location information of the facility when generating a web page. For example, the generation unit can display information taking into account the geographical location information of the facility when generating a web page. Geographical location information includes, but is not limited to, GPS data and map information. For example, the generation unit can display the barrier-free status of the surrounding environment based on the geographical location information of the facility. The generation unit can also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The generation unit can also improve the reliability of the display result based on the geographical location information of the facility. As a result, the reliability of the display result is improved by taking into account the geographical location information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can display information taking into account the geographical location information of the facility when generating a web page using a generation AI.

[0094] The generation unit can display information taking into account attribute information of facility users when generating a web page. For example, the generation unit can display information taking into account attribute information of facility users (such as age and type of disability) when generating a web page. Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the generation unit displays barrier-free information taking into account the user's age and type of disability. The generation unit can also customize the display result based on the user's attribute information. The generation unit can also improve the reliability of the display result by taking into account the user's attribute information. As a result, the reliability of the display result is improved by taking into account the user's attribute information. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can display information taking into account the attribute information of facility users when generating a web page using a generation AI.

[0095] The generation unit can customize the display method by reflecting past facility feedback when generating a web page. For example, the generation unit customizes the display method by reflecting past facility feedback when generating a web page. Past feedback includes, but is not limited to, user reviews and survey results. For example, the generation unit adjusts the display method based on the facility's past feedback. The generation unit can also improve the accuracy of the display results by reflecting past feedback. The generation unit can also customize the display method by taking into account the facility's past feedback. In this way, the accuracy of the display method is improved by reflecting past feedback. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can customize the display method by reflecting past facility feedback when generating a web page using a generation AI.

[0096] The feedback unit can estimate the user's emotion and adjust the feedback method based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion and adjusts the feedback method based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, the feedback unit can provide concise and to-the-point feedback when the user is stressed. The feedback unit can also provide detailed feedback when the user is relaxed. The feedback unit can also provide quick feedback when the user is in a hurry. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the user's emotion. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can estimate the user's emotion using a generation AI and adjust the feedback method based on the estimated user's emotion.

[0097] The feedback unit can improve the feedback accuracy by referring to past barrier-free information of the facility when providing feedback. For example, the feedback unit can improve the feedback accuracy by referring to past barrier-free information of the facility when providing feedback. Past barrier-free information includes, but is not limited to, the use of a database or past reports. For example, the feedback unit can obtain past barrier-free information of the facility from a database and reflect it in the feedback. The feedback unit can also complement current information based on the past barrier-free information. The feedback unit can also improve the reliability of the feedback results by referring to the past barrier-free information. Thus, by referring to the past barrier-free information, the feedback accuracy is improved. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit can improve the feedback accuracy by using a generation AI to refer to past barrier-free information of the facility when providing feedback.

[0098] The feedback unit may provide information by integrating multiple images and videos taken from different viewpoints during feedback. For example, the feedback unit may provide information by integrating multiple images and videos taken from different viewpoints during feedback. Different viewpoints include, but are not limited to, camera positions and shooting angles. For example, the feedback unit may integrate images and videos taken from different viewpoints to provide overall barrier-free information. The feedback unit may also combine data from multiple viewpoints to provide more detailed information. The feedback unit may also integrate information from different viewpoints to improve the accuracy of the feedback results. In this way, integrating information from different viewpoints improves feedback accuracy. Some or all of the above-described processing in the feedback unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback unit may use a generation AI to integrate multiple images and videos taken from different viewpoints during feedback to provide information.

[0099] The feedback unit can provide information by focusing on a specific area of ​​the facility when providing feedback. For example, the feedback unit can provide information by focusing on a specific area of ​​the facility (such as the entrance or restroom) when providing feedback. Examples of specific areas include, but are not limited to, the entrance, restroom, and hallway. For example, the feedback unit can analyze images or videos of the entrance and provide feedback on the presence or absence of steps and the installation status of ramps. The feedback unit can also analyze images or videos of the restroom and provide feedback on the position and width of handrails. The feedback unit can also analyze images or videos of the elevator and provide feedback on the size and position of the operation panel. In this way, detailed feedback can be provided by focusing on a specific area. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can use a generation AI to provide information by focusing on a specific area of ​​the facility when providing feedback.

[0100] The feedback unit can estimate the user's emotion and adjust the feedback display method based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion and adjusts the feedback display method based on the estimated user's emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the feedback unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the feedback unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the feedback unit can provide a display method that focuses on the main points. This allows for more appropriate feedback to be provided by adjusting the display method according to the user's emotion. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can estimate the user's emotion using a generation AI, and adjust the feedback display method based on the estimated user's emotion.

[0101] The feedback unit may provide information taking into account the geographical location information of the facility when providing feedback. For example, the feedback unit may provide information taking into account the geographical location information of the facility when providing feedback. Examples of geographical location information include, but are not limited to, GPS data and map information. For example, the feedback unit may reflect the barrier-free status of the surrounding environment in the feedback based on the geographical location information of the facility. The feedback unit may also integrate barrier-free information inside and outside the facility by taking into account the geographical location information. The feedback unit may also improve the reliability of the feedback result based on the geographical location information of the facility. As a result, the reliability of the feedback result is improved by taking into account the geographical location information. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit may provide information taking into account the geographical location information of the facility when providing feedback using a generation AI.

[0102] The feedback unit may provide information while taking into consideration attribute information of the facility user when providing feedback. For example, the feedback unit may provide information while taking into consideration attribute information of the facility user (such as age and type of disability) when providing feedback. Attribute information includes, but is not limited to, age, type of disability, and gender, for example. For example, the feedback unit may provide feedback on barrier-free access information while taking into consideration the user's age and type of disability. The feedback unit may also customize the feedback results based on the user's attribute information. The feedback unit may also improve the reliability of the feedback results by taking into consideration the user's attribute information. This improves the reliability of the feedback results by taking into consideration the user's attribute information. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit may provide information while taking into consideration the attribute information of the facility user when providing feedback using a generation AI.

[0103] The feedback unit can customize the provision method by reflecting past facility feedback when providing feedback. For example, the feedback unit customizes the provision method by reflecting past facility feedback when providing feedback. Past feedback includes, but is not limited to, user reviews and survey results. For example, the feedback unit adjusts the provision method based on the facility's past feedback. The feedback unit can also improve the accuracy of the feedback results by reflecting past feedback. The feedback unit can also customize the provision method by taking into account the facility's past feedback. In this way, the accuracy of the provision method is improved by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the feedback unit can customize the provision method by reflecting past facility feedback when providing feedback using a generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, extraction unit, generation unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit acquires images and videos using the camera 42 of the smart device 14, and the acquired images and videos are analyzed by the specific processing unit 290 of the data processing device 12. The extraction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, extracts barrier-free information from the analyzed information. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a web page based on the extracted barrier-free information. The feedback unit, realized, for example, by the control unit 46A of the smart device 14, notifies the facility of the link to the generated web page and the barrier-free information. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, generation unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit acquires images and videos using the camera 42 of the smart glasses 214, and the images and videos are analyzed by the specific processing unit 290 of the data processing device 12. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts barrier-free information from the analyzed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a web page based on the extracted barrier-free information. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the facility of the link to the generated web page and the barrier-free information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, generation unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit acquires images and videos using the camera 42 of the headset type terminal 314, and the images and videos are analyzed by the specific processing unit 290 of the data processing device 12. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts barrier-free information from the analyzed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a web page based on the extracted barrier-free information. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the facility of the link to the generated web page and the barrier-free information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, extraction unit, generation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit acquires images and videos using the camera 42 of the robot 414, and the images and videos are analyzed by the specific processing unit 290 of the data processing device 12. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts barrier-free information from the analyzed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a web page based on the extracted barrier-free information. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and notifies the facility of the link to the generated web page and the barrier-free information.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When analyzing images and videos, the analysis unit can analyze the user's movement patterns within a facility and extract barrier-free information. For example, the analysis unit can analyze the user's movement patterns within a facility and identify routes that are easy for wheelchair users to access. The analysis unit can also identify areas that are prone to congestion based on the user's movement patterns and determine the priority of barrier-free measures. Furthermore, the analysis unit can analyze the user's movement patterns and evaluate whether the placement of barrier-free facilities is appropriate. This makes it possible to provide more practical barrier-free information by taking the user's movement patterns into consideration.

[0106] The extraction unit can collect feedback from facility users in real time and reflect it in the extraction of barrier-free information. For example, the extraction unit can collect feedback provided by users through a smartphone app to improve the accuracy of the barrier-free information. The extraction unit can also update the barrier-free information for a specific area based on the user feedback. Furthermore, the extraction unit can analyze the user feedback and identify areas for improvement in barrier-free access. In this way, by utilizing user feedback, more accurate and up-to-date barrier-free information can be provided.

[0107] The generating unit can use infographics to display barrier-free information in a visually easy-to-understand manner. For example, the generating unit can create infographics that indicate whether or not there are steps at the entrance. The generating unit can also create infographics that visually display the size of an elevator and the location of its operation panel. Furthermore, the generating unit can also create infographics that indicate the location and size of handrails in a restroom. In this way, by providing visually easy-to-understand information, users can easily understand the barrier-free information.

[0108] The feedback unit can provide barrier-free information about facilities in cooperation with social media. For example, the feedback unit can automatically post the generated barrier-free information to social media such as Twitter and Facebook. The feedback unit can also collect user comments and feedback on social media and reflect this in improving the barrier-free information. Furthermore, the feedback unit can encourage the sharing of barrier-free information on social media, providing the information to more users. In this way, the use of social media can promote the dissemination and improvement of barrier-free information.

[0109] The analysis unit can compare the facility's barrier-free information with other facilities and perform a relative evaluation. For example, the analysis unit can compare the barrier-free information of other hotels and tourist destinations in the same area to evaluate the facility's progress toward barrier-free access. The analysis unit can also identify areas for improvement based on the barrier-free information of other facilities and propose specific measures. Furthermore, the analysis unit can provide feedback to the facility regarding the results of the comparison with other facilities, improving the facility's competitiveness in barrier-free access. This allows the facility's progress toward barrier-free access to be objectively evaluated through comparison with other facilities.

[0110] The analysis unit can estimate the user's emotions and adjust the priority of barrier-free information based on the estimated user emotions. For example, if the user feels anxious, the analysis unit can prioritize providing important barrier-free information. Also, if the user feels reassured, the analysis unit can provide detailed barrier-free information. Furthermore, if the user is in a hurry, the analysis unit can prioritize providing information that can be accessed quickly. In this way, by adjusting the method of providing barrier-free information according to the user's emotions, more appropriate information can be provided.

[0111] The extraction unit can estimate the user's emotions and adjust the accuracy of the barrier-free information extraction based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit can prioritize the extraction of important barrier-free information. Also, if the user is relaxed, the extraction unit can extract detailed barrier-free information. Furthermore, if the user is in a hurry, the extraction unit can adopt a method that can extract information quickly. In this way, by adjusting the extraction method according to the user's emotions, more appropriate barrier-free information can be provided.

[0112] The generation unit can estimate the user's emotions and adjust the design of the web page based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide a simple, highly visible design. If the user is relaxed, the generation unit can also provide a design that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can also provide a design that focuses on the main points. In this way, by adjusting the design of the web page according to the user's emotions, more appropriate information can be provided.

[0113] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback unit can provide concise and to-the-point feedback. If the user is feeling relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can also provide quick feedback. In this way, by adjusting the content of the feedback according to the user's emotions, more appropriate feedback can be provided.

[0114] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotions, more appropriate analysis results can be provided.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The analysis unit analyzes the image or video. Images and videos can be still images, continuous videos, or videos with different resolutions. The analysis unit uses image recognition technology and video analysis algorithms to perform the analysis. For example, it can analyze whether there are steps at the entrance or the size of the elevator. It can also use generative AI to extract barrier-free information from images and videos. Step 2: The extraction unit extracts barrier-free information based on the information analyzed by the analysis unit, such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom. Step 3: The generator generates a web page based on the information extracted by the extractor. For example, it uses HTML generation or a template engine to create a web page that includes information such as whether there are steps at the entrance, the size of the elevator, and the location of handrails in the restroom. Step 4: The feedback unit feeds back the information generated by the generation unit to the facility. For example, it notifies the facility of a link to the generated web page. It can also provide the facility with the accumulated and analyzed barrier-free information.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes an image or video; an extraction unit that extracts barrier-free information based on the information analyzed by the analysis unit; a generation unit that generates a web page based on the information extracted by the extraction unit; a feedback unit that feeds back the information generated by the generation unit to the facility side. A system characterized by:

2. The analysis unit Analyze images and videos using generative AI to extract barrier-free information 2. The system of claim 1.

3. The extraction unit Generate specific barrier-free information based on the information extracted by the analysis unit using AI generation.

2. The system of claim 1.

4. The generation unit Using generation AI to create a web page based on the information generated by the extraction unit 2. The system of claim 1.

5. The feedback unit Barrier-free information generated using AI is fed back to the facility.

2. The system of claim 1.

6. The analysis unit Estimate user emotions and adjust image and video analysis methods based on the estimated user emotions 2. The system of claim 1.

7. The analysis unit When analyzing images and videos, refer to the facility's past barrier-free information to improve the accuracy of the analysis.

2. The system of claim 1.

8. The analysis unit When analyzing images or videos, multiple images or videos taken from different viewpoints are integrated and analyzed.

2. The system of claim 1.

Images