system

Abstract

The system according to this embodiment aims to streamline the process of posting real estate information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an extraction unit, a generation unit, and an upload unit. The reception unit uploads drawings or photographs. The analysis unit analyzes the drawings or photographs uploaded by the reception unit. The extraction unit extracts the information analyzed by the analysis unit. The generation unit generates a manuscript based on the information extracted by the extraction unit. The upload unit uploads the manuscript generated by the generation unit to multiple portal sites.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the business of posting real estate information requires labor and time and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of the business of posting real estate information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a generation unit, and an upload unit. The reception unit uploads drawings or photographs. The analysis unit analyzes the drawings or photographs uploaded by the reception unit. The extraction unit extracts the information analyzed by the analysis unit. The generation unit generates a document based on the information extracted by the extraction unit. The upload unit uploads the document generated by the generation unit to multiple portal sites. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the process of posting real estate information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The real estate information posting support system according to an embodiment of the present invention is a system that automatically reads, analyzes, and summarizes multiple real estate drawings and photographs to generate a draft for posting on portal sites and uploads it to multiple portal sites in a one-stop manner. In this system, the user uploads multiple real estate drawings and photographs, the AI ​​analyzes these drawings and photographs, extracts important information, and automatically creates a draft for posting on portal sites. Furthermore, the created draft can be uploaded to multiple portal sites in a one-stop manner. For example, the user uploads floor plans, exterior photos, interior photos, etc., of a property to the system. This information is input to the AI. Next, the AI ​​analyzes the uploaded drawings and photographs and extracts important information. For example, it extracts the size and layout of rooms from floor plans, the exterior features of the building from exterior photos, and the interior condition of rooms from interior photos. Based on the extracted information, the AI ​​automatically creates a draft for posting on portal sites. For example, it creates a draft that includes an overview and features of the property, equipment information, and the surrounding environment. This allows the user to create a high-quality draft without any effort. Furthermore, the created draft can be uploaded to multiple portal sites in a one-stop manner. For example, the same draft can be uploaded to multiple portal sites at once. This eliminates the need for users to individually upload listings to multiple portal sites. This system streamlines the real estate listing process. Users can simply upload multiple property drawings and photos, and the AI ​​will automatically create the listing and upload it to multiple portal sites. This improves efficiency and saves time and effort. The real estate listing support system allows users to simply upload multiple property drawings and photos, and the AI ​​will automatically create the listing and upload it to multiple portal sites. This improves efficiency and saves time and effort.

[0029] The real estate information listing support system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a generation unit, and an upload unit. The reception unit allows users to upload multiple real estate drawings and photographs. The drawings and photographs uploaded by users include, but are not limited to, floor plans, exterior photographs, and interior photographs of properties. The reception unit allows users to upload drawings and photographs by, for example, logging into the system and clicking the upload button to select files. The reception unit also provides a drag-and-drop function, allowing users to upload files by dragging them. The analysis unit analyzes the uploaded drawings and photographs. The analysis unit extracts important information from the drawings and photographs, for example, using an image analysis algorithm. For example, the analysis unit analyzes the size and layout of rooms from floor plans, the exterior features of buildings from exterior photographs, and the interior condition of rooms from interior photographs. The extraction unit extracts the information analyzed by the analysis unit. For example, the extraction unit extracts the size and layout of rooms from floor plans, the exterior features of buildings from exterior photographs, and the interior condition of rooms from interior photographs. The generation unit generates a manuscript based on the information extracted by the extraction unit. The generation unit generates a manuscript that includes, for example, an overview and features of the property, facility information, and the surrounding environment. The generation unit uses natural language generation technology to automatically create a high-quality manuscript based on the extracted information. The upload unit uploads the manuscript generated by the generation unit to multiple portal sites. The upload unit can upload the same manuscript to multiple portal sites at once, for example. The upload unit can automatically upload the manuscript using the portal site's API. As a result, the real estate information listing support system according to this embodiment can perform everything from uploading drawings and photographs to analysis, information extraction, manuscript generation, and uploading to portal sites in an integrated manner.

[0030] The reception unit can analyze the user's past upload history and select an upload method. For example, the reception unit can automatically select an upload method that the user has frequently used in the past. The reception unit can also suggest the most efficient upload time based on the user's past upload history. Furthermore, the reception unit can suggest the optimal file format based on the file formats the user has uploaded in the past. This improves upload efficiency by selecting the optimal method based on past upload history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0031] The reception system can filter uploaded drawings and photographs based on the user's current projects and areas of interest. For example, the reception system can filter to upload only drawings and photographs related to the user's current ongoing projects. It can also prioritize the upload of highly relevant drawings and photographs based on the user's areas of interest. Furthermore, the reception system can automatically select drawings and photographs related to projects the user has previously shown interest in. This allows for the priority uploading of highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0032] The reception desk can prioritize uploading drawings and photos that are highly relevant to the user, taking into account the user's geographical location when the user uploads drawings and photos. For example, if the user is in a specific region, the reception desk will prioritize uploading drawings and photos related to that region. The reception desk can also prioritize uploading drawings and photos of properties close to the user's current location. Furthermore, the reception desk can automatically select drawings and photos related to regions the user has visited in the past. In this way, by considering geographical location, highly relevant information can be uploaded preferentially. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the selection of highly relevant drawings and photos.

[0033] The reception desk can analyze a user's social media activity when they upload drawings and photos, and upload relevant drawings and photos. For example, the reception desk can prioritize uploading drawings and photos related to properties the user has shared on social media. It can also upload highly relevant drawings and photos based on the user's interests on social media. Furthermore, the reception desk can automatically select drawings and photos from real estate agents that the user follows on social media. This allows for the priority uploading of highly relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant drawings and photos.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the drawings and photographs during the analysis. For example, the analysis unit will perform a detailed analysis on important drawings and photographs. It can also perform a simplified analysis on less important drawings and photographs. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the drawings and photographs. This allows for efficient analysis by adjusting the level of detail according to the importance of the drawings and photographs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of drawings and photographs into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the drawings and photographs during the analysis. For example, for floor plans, the analysis unit can apply an algorithm that analyzes the size and layout of rooms. It can also apply an algorithm that analyzes the exterior features of a building to exterior photographs. Furthermore, it can apply an algorithm that analyzes the interior condition of rooms to interior photographs. This improves the accuracy of the analysis by applying analysis algorithms appropriate to the category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of drawings and photographs into a generating AI and have the generating AI execute the application of analysis algorithms.

[0036] The analysis unit can determine the priority of analysis based on the submission dates of drawings and photographs during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted drawings and photographs. It can also postpone the analysis of older drawings and photographs. Furthermore, the analysis unit can adjust the analysis schedule based on the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of drawings and photographs into a generating AI and have the generating AI determine the priority of analysis.

[0037] The analysis unit can adjust the order of analysis based on the relationships between drawings and photographs during the analysis process. For example, the analysis unit can prioritize the analysis of drawings and photographs with high relevance. It can also postpone the analysis of drawings and photographs with low relevance. Furthermore, the analysis unit can determine the order of analysis based on the relationships between drawings and photographs. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relationship data between drawings and photographs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0038] The extraction unit can improve the accuracy of extraction based on the interrelationships between drawings and photographs during the extraction process. For example, the extraction unit can accurately extract the size and layout of rooms by considering the interrelationships between floor plans and interior photographs. It can also accurately extract the exterior features and interior condition of a building by considering the interrelationships between exterior and interior photographs. Furthermore, the extraction unit can analyze the interrelationships between drawings and photographs to improve the accuracy of extraction. This improves the accuracy of extraction by considering the interrelationships between drawings and photographs. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input interrelationship data between drawings and photographs into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0039] The extraction unit can perform extraction based on the attribute information of the submitter of drawings and photographs. For example, if the submitter is a real estate agent, the extraction unit will extract information considering the agent's reliability. Furthermore, if the submitter is an individual, the extraction unit can extract information considering the individual's reliability. In addition, the extraction unit can improve the accuracy of the extraction based on the submitter's attribute information. This improves the accuracy of the extraction by considering the submitter's attribute information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0040] The extraction unit can perform extraction while considering the geographical distribution of drawings and photographs. For example, the extraction unit can prioritize the extraction of drawings and photographs related to a specific region. It can also prioritize the extraction of drawings and photographs of properties that are geographically close. Furthermore, the extraction unit can improve the accuracy of the extraction by considering the geographical distribution. This allows for the priority extraction of highly relevant information by considering the geographical distribution. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the extraction.

[0041] The extraction unit can improve the accuracy of extraction by referring to related literature for drawings and photographs during the extraction process. For example, the extraction unit accurately extracts information from drawings and photographs by referring to related literature. The extraction unit can also improve the accuracy of extraction based on related literature. Furthermore, the extraction unit can improve the accuracy of extraction by analyzing related literature for drawings and photographs. As a result, the accuracy of extraction is improved by referring to related literature. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0042] The generation unit can adjust the level of detail in the manuscript based on the importance of the extracted information during generation. For example, the generation unit can generate a manuscript with detailed explanations for important information. It can also generate a manuscript with concise explanations for less important information. Furthermore, the generation unit can adjust the level of detail in the manuscript according to the importance of the extracted information. This allows for efficient manuscript generation by adjusting the level of detail in the manuscript according to the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the extracted information into a generation AI and have the generation AI perform the adjustment of the level of detail in the manuscript.

[0043] The generation unit can apply different generation algorithms depending on the category of the extracted information during generation. For example, for property summaries, the generation unit can apply a generation algorithm that emphasizes the summary. It can also apply a generation algorithm that emphasizes the details of the equipment for equipment information. Furthermore, it can apply a generation algorithm that emphasizes the characteristics of the surrounding environment for the surrounding environment. This improves the accuracy of the generated document by applying a generation algorithm appropriate to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the extracted information into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of manuscripts based on the submission timing of the extracted information during generation. For example, the generation unit will prioritize the inclusion of recently extracted information in the manuscript. The generation unit can also postpone the inclusion of older information in the manuscript. Furthermore, the generation unit can determine the priority of manuscripts based on the submission timing. This enables efficient manuscript generation by prioritizing manuscripts based on submission timing. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing data of the extracted information into a generation AI and have the generation AI perform the task of determining the priority of manuscripts.

[0045] The generation unit can adjust the order of the manuscript based on the relevance of the extracted information during generation. For example, the generation unit can prioritize reflecting highly relevant information in the manuscript. It can also postpone reflecting less relevant information in the manuscript. Furthermore, the generation unit can determine the order of the manuscript based on the relevance of the extracted information. This allows for efficient manuscript generation by adjusting the order of the manuscript based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the extracted information into a generation AI and have the generation AI perform the adjustment of the manuscript order.

[0046] The upload unit can select the optimal upload method based on the characteristics of the portal site during the upload process. For example, the upload unit can automatically adjust the document to match the format of each portal site and upload it. The upload unit can also select the optimal image size and resolution based on the characteristics of the portal site. Furthermore, the upload unit can suggest the optimal upload time based on the characteristics of the portal site. This enables efficient uploading by selecting the optimal method based on the characteristics of the portal site. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input portal site characteristic data into a generating AI and have the generating AI select the optimal upload method.

[0047] The upload unit can customize the uploaded content according to the user base of the portal site during the upload process. For example, the upload unit can adjust the presentation of the document to suit the user base of each portal site. The upload unit can also customize the selection and placement of images according to the user base. Furthermore, the upload unit can adjust the length and level of detail of the document according to the user base. By customizing the uploaded content according to the user base, effective information provision becomes possible. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input user base data of the portal site into a generating AI and have the generating AI perform the customization of the uploaded content.

[0048] The upload unit can select an upload method based on the geographical distribution of portal sites during the upload process. For example, the upload unit may prioritize uploading portal sites related to a specific region. It can also prioritize uploading portal sites that are geographically close. Furthermore, the upload unit can select the optimal upload method by considering geographical distribution. This allows for the priority uploading of highly relevant information by considering geographical distribution. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input geographical distribution data of portal sites into a generating AI and have the generating AI select the optimal upload method.

[0049] The upload unit can improve the accuracy of uploads by referring to relevant literature on the portal site during the upload process. For example, the upload unit uploads the most suitable manuscript to the portal site by referring to relevant literature. The upload unit can also improve the accuracy of uploads based on the relevant literature. Furthermore, the upload unit can analyze the relevant literature on the portal site to improve the accuracy of uploads. As a result, the accuracy of uploads is improved by referring to relevant literature. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input relevant literature data into a generating AI and have the generating AI perform the upload accuracy improvement.

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

[0051] The reception desk can analyze a user's past upload history and suggest the optimal upload method. For example, it can automatically select the upload method the user has frequently used in the past. The reception desk can also suggest the most efficient upload time based on the user's past upload history. Furthermore, the reception desk can suggest the optimal file format based on the file formats the user has uploaded in the past. This allows for improved upload efficiency by selecting the most suitable method based on past upload history.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the drawings and photographs during the analysis. For example, it will perform a detailed analysis on important drawings and photographs. It can also perform a simplified analysis on less important drawings and photographs. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the drawings and photographs. This allows for efficient analysis by adjusting the level of detail according to the importance of the drawings and photographs.

[0053] The extraction unit can improve the accuracy of extraction based on the interrelationships between drawings and photographs. For example, it can accurately extract room sizes and layouts by considering the interrelationships between floor plans and interior photographs. Furthermore, the extraction unit can accurately extract building exterior features and interior conditions by considering the interrelationships between exterior and interior photographs. In addition, the extraction unit can improve extraction accuracy by analyzing the interrelationships between drawings and photographs. Thus, considering the interrelationships between drawings and photographs improves extraction accuracy.

[0054] The generation unit can adjust the level of detail in the manuscript based on the importance of the extracted information during generation. For example, it can generate a manuscript with detailed explanations for important information. It can also generate a manuscript with concise explanations for less important information. Furthermore, the generation unit can adjust the level of detail in the manuscript according to the importance of the extracted information. This allows for efficient manuscript generation by adjusting the level of detail according to the importance of the information.

[0055] The upload unit can select the optimal upload method based on the characteristics of the portal site during the upload process. For example, it can automatically adjust the document to match the format of each portal site before uploading. The upload unit can also select the optimal image size and resolution based on the characteristics of the portal site. Furthermore, the upload unit can suggest the optimal upload time based on the characteristics of the portal site. This enables efficient uploads by selecting the optimal method based on the characteristics of each portal site.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk allows users to upload multiple property drawings and photos. Users can log in to the system and upload drawings and photos by clicking the upload button and selecting files, or by using the drag-and-drop function. Uploaded drawings and photos include floor plans, exterior photos, interior photos, etc. Step 2: The analysis unit analyzes the uploaded drawings and photographs. The analysis unit uses image analysis algorithms to analyze important information such as room size and layout from floor plans, exterior features of the building from exterior photographs, and the condition of the room interiors from interior photographs. Step 3: The extraction unit extracts the information analyzed by the analysis unit. The extraction unit extracts information such as room size and layout from floor plans, exterior features of the building from exterior photos, and interior condition of rooms from interior photos. Step 4: The generation unit generates the document based on the information extracted by the extraction unit. The generation unit automatically creates the document, which includes an overview of the property, its features, equipment information, and surrounding environment, using natural language generation technology. Step 5: The upload unit uploads the manuscript generated by the generation unit to multiple portal sites. The upload unit uses the portal site's API to automatically upload the same manuscript to multiple portal sites simultaneously.

[0058] (Example of form 2) The real estate information posting support system according to an embodiment of the present invention is a system that automatically reads, analyzes, and summarizes multiple real estate drawings and photographs to generate a draft for posting on portal sites and uploads it to multiple portal sites in a one-stop manner. In this system, the user uploads multiple real estate drawings and photographs, the AI ​​analyzes these drawings and photographs, extracts important information, and automatically creates a draft for posting on portal sites. Furthermore, the created draft can be uploaded to multiple portal sites in a one-stop manner. For example, the user uploads floor plans, exterior photos, interior photos, etc., of a property to the system. This information is input to the AI. Next, the AI ​​analyzes the uploaded drawings and photographs and extracts important information. For example, it extracts the size and layout of rooms from floor plans, the exterior features of the building from exterior photos, and the interior condition of rooms from interior photos. Based on the extracted information, the AI ​​automatically creates a draft for posting on portal sites. For example, it creates a draft that includes an overview and features of the property, equipment information, and the surrounding environment. This allows the user to create a high-quality draft without any effort. Furthermore, the created draft can be uploaded to multiple portal sites in a one-stop manner. For example, the same draft can be uploaded to multiple portal sites at once. This eliminates the need for users to individually upload listings to multiple portal sites. This system streamlines the real estate listing process. Users can simply upload multiple property drawings and photos, and the AI ​​will automatically create the listing and upload it to multiple portal sites. This improves efficiency and saves time and effort. The real estate listing support system allows users to simply upload multiple property drawings and photos, and the AI ​​will automatically create the listing and upload it to multiple portal sites. This improves efficiency and saves time and effort.

[0059] The real estate information listing support system according to this embodiment comprises a reception unit, an analysis unit, an extraction unit, a generation unit, and an upload unit. The reception unit allows users to upload multiple real estate drawings and photographs. The drawings and photographs uploaded by users include, but are not limited to, floor plans, exterior photographs, and interior photographs of properties. The reception unit allows users to upload drawings and photographs by, for example, logging into the system and clicking the upload button to select files. The reception unit also provides a drag-and-drop function, allowing users to upload files by dragging them. The analysis unit analyzes the uploaded drawings and photographs. The analysis unit extracts important information from the drawings and photographs, for example, using an image analysis algorithm. For example, the analysis unit analyzes the size and layout of rooms from floor plans, the exterior features of buildings from exterior photographs, and the interior condition of rooms from interior photographs. The extraction unit extracts the information analyzed by the analysis unit. For example, the extraction unit extracts the size and layout of rooms from floor plans, the exterior features of buildings from exterior photographs, and the interior condition of rooms from interior photographs. The generation unit generates a manuscript based on the information extracted by the extraction unit. The generation unit generates a manuscript that includes, for example, an overview and features of the property, facility information, and the surrounding environment. The generation unit uses natural language generation technology to automatically create a high-quality manuscript based on the extracted information. The upload unit uploads the manuscript generated by the generation unit to multiple portal sites. The upload unit can upload the same manuscript to multiple portal sites at once, for example. The upload unit can automatically upload the manuscript using the portal site's API. As a result, the real estate information listing support system according to this embodiment can perform everything from uploading drawings and photographs to analysis, information extraction, manuscript generation, and uploading to portal sites in an integrated manner.

[0060] The reception desk can estimate the user's emotions and adjust the timing of uploads of drawings and photos based on the estimated emotions. For example, if the user is stressed, the system can automatically upload the files, reducing the user's effort. The reception desk can also provide an interface that allows the user to choose when to upload if they are relaxed. Furthermore, if the user is in a hurry, the reception desk can provide a simplified procedure for faster uploads. This reduces the user's burden by adjusting the upload timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0061] The reception unit can analyze the user's past upload history and select an upload method. For example, the reception unit can automatically select an upload method that the user has frequently used in the past. The reception unit can also suggest the most efficient upload time based on the user's past upload history. Furthermore, the reception unit can suggest the optimal file format based on the file formats the user has uploaded in the past. This improves upload efficiency by selecting the optimal method based on past upload history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0062] The reception system can filter uploaded drawings and photographs based on the user's current projects and areas of interest. For example, the reception system can filter to upload only drawings and photographs related to the user's current ongoing projects. It can also prioritize the upload of highly relevant drawings and photographs based on the user's areas of interest. Furthermore, the reception system can automatically select drawings and photographs related to projects the user has previously shown interest in. This allows for the priority uploading of highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0063] The reception desk can estimate the user's emotions and determine the priority of drawings and photos to be uploaded based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize uploading important drawings and photos. If the user is relaxed, the reception desk can also upload drawings and photos in an order chosen by the user. Furthermore, if the user is in a hurry, the reception desk can quickly upload the most important drawings and photos. This allows for the rapid uploading of important information by prioritizing uploads according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0064] The reception desk can prioritize uploading drawings and photos that are highly relevant to the user, taking into account the user's geographical location when the user uploads drawings and photos. For example, if the user is in a specific region, the reception desk will prioritize uploading drawings and photos related to that region. The reception desk can also prioritize uploading drawings and photos of properties close to the user's current location. Furthermore, the reception desk can automatically select drawings and photos related to regions the user has visited in the past. In this way, by considering geographical location, highly relevant information can be uploaded preferentially. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the selection of highly relevant drawings and photos.

[0065] The reception desk can analyze a user's social media activity when they upload drawings and photos, and upload relevant drawings and photos. For example, the reception desk can prioritize uploading drawings and photos related to properties the user has shared on social media. It can also upload highly relevant drawings and photos based on the user's interests on social media. Furthermore, the reception desk can automatically select drawings and photos from real estate agents that the user follows on social media. This allows for the priority uploading of highly relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant drawings and photos.

[0066] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the drawings and photographs during the analysis. For example, the analysis unit will perform a detailed analysis on important drawings and photographs. It can also perform a simplified analysis on less important drawings and photographs. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the drawings and photographs. This allows for efficient analysis by adjusting the level of detail according to the importance of the drawings and photographs. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of drawings and photographs into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0068] The analysis unit can apply different analysis algorithms depending on the category of the drawings and photographs during the analysis. For example, for floor plans, the analysis unit can apply an algorithm that analyzes the size and layout of rooms. It can also apply an algorithm that analyzes the exterior features of a building to exterior photographs. Furthermore, it can apply an algorithm that analyzes the interior condition of rooms to interior photographs. This improves the accuracy of the analysis by applying analysis algorithms appropriate to the category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of drawings and photographs into a generating AI and have the generating AI execute the application of analysis algorithms.

[0069] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the user with an appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The analysis unit can determine the priority of analysis based on the submission dates of drawings and photographs during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted drawings and photographs. It can also postpone the analysis of older drawings and photographs. Furthermore, the analysis unit can adjust the analysis schedule based on the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date data of drawings and photographs into a generating AI and have the generating AI determine the priority of analysis.

[0071] The analysis unit can adjust the order of analysis based on the relationships between drawings and photographs during the analysis process. For example, the analysis unit can prioritize the analysis of drawings and photographs with high relevance. It can also postpone the analysis of drawings and photographs with low relevance. Furthermore, the analysis unit can determine the order of analysis based on the relationships between drawings and photographs. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relationship data between drawings and photographs into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0072] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit will prioritize extracting important information. If the user is relaxed, the extraction unit can also extract information in an order chosen by the user. Furthermore, if the user is in a hurry, the extraction unit can quickly extract the most important information. This allows for the rapid extraction of important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The extraction unit can improve the accuracy of extraction based on the interrelationships between drawings and photographs during the extraction process. For example, the extraction unit can accurately extract the size and layout of rooms by considering the interrelationships between floor plans and interior photographs. It can also accurately extract the exterior features and interior condition of a building by considering the interrelationships between exterior and interior photographs. Furthermore, the extraction unit can analyze the interrelationships between drawings and photographs to improve the accuracy of extraction. This improves the accuracy of extraction by considering the interrelationships between drawings and photographs. Some or all of the above-described processes in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input interrelationship data between drawings and photographs into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0074] The extraction unit can perform extraction based on the attribute information of the submitter of drawings and photographs. For example, if the submitter is a real estate agent, the extraction unit will extract information considering the agent's reliability. Furthermore, if the submitter is an individual, the extraction unit can extract information considering the individual's reliability. In addition, the extraction unit can improve the accuracy of the extraction based on the submitter's attribute information. This improves the accuracy of the extraction by considering the submitter's attribute information. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0075] The extraction unit can estimate the user's emotions and adjust the display method of the extracted information based on the estimated user emotions. For example, if the user is tense, the extraction unit can provide a simple and highly visible display method. If the user is relaxed, the extraction unit can also 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 gets straight to the point. In this way, by adjusting the display method of information according to the user's emotions, information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The extraction unit can perform extraction while considering the geographical distribution of drawings and photographs. For example, the extraction unit can prioritize the extraction of drawings and photographs related to a specific region. It can also prioritize the extraction of drawings and photographs of properties that are geographically close. Furthermore, the extraction unit can improve the accuracy of the extraction by considering the geographical distribution. This allows for the priority extraction of highly relevant information by considering the geographical distribution. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the extraction.

[0077] The extraction unit can improve the accuracy of extraction by referring to related literature for drawings and photographs during the extraction process. For example, the extraction unit accurately extracts information from drawings and photographs by referring to related literature. The extraction unit can also improve the accuracy of extraction based on related literature. Furthermore, the extraction unit can improve the accuracy of extraction by analyzing related literature for drawings and photographs. As a result, the accuracy of extraction is improved by referring to related literature. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input related literature data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0078] The generation unit can estimate the user's emotions and adjust the expression of the generated manuscript based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a manuscript with detailed explanations. If the user is in a hurry, the generation unit can also generate a concise and to-the-point manuscript. Furthermore, if the user is excited, the generation unit can generate a manuscript with visually stimulating effects. By adjusting the expression of the manuscript according to the user's emotions, it is possible to provide a manuscript that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0079] The generation unit can adjust the level of detail in the manuscript based on the importance of the extracted information during generation. For example, the generation unit can generate a manuscript with detailed explanations for important information. It can also generate a manuscript with concise explanations for less important information. Furthermore, the generation unit can adjust the level of detail in the manuscript according to the importance of the extracted information. This allows for efficient manuscript generation by adjusting the level of detail in the manuscript according to the importance of the information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance data of the extracted information into a generation AI and have the generation AI perform the adjustment of the level of detail in the manuscript.

[0080] The generation unit can apply different generation algorithms depending on the category of the extracted information during generation. For example, for property summaries, the generation unit can apply a generation algorithm that emphasizes the summary. It can also apply a generation algorithm that emphasizes the details of the equipment for equipment information. Furthermore, it can apply a generation algorithm that emphasizes the characteristics of the surrounding environment for the surrounding environment. This improves the accuracy of the generated document by applying a generation algorithm appropriate to the category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the extracted information into a generation AI and have the generation AI execute the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the generated document based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise document. If the user is relaxed, the generation unit can also generate a longer document with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a document with visually stimulating effects. By adjusting the length of the document according to the user's emotions, the system can provide the user with a document of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the user's emotion estimation.

[0082] The generation unit can determine the priority of manuscripts based on the submission timing of the extracted information during generation. For example, the generation unit will prioritize the inclusion of recently extracted information in the manuscript. The generation unit can also postpone the inclusion of older information in the manuscript. Furthermore, the generation unit can determine the priority of manuscripts based on the submission timing. This enables efficient manuscript generation by prioritizing manuscripts based on submission timing. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission timing data of the extracted information into a generation AI and have the generation AI perform the task of determining the priority of manuscripts.

[0083] The generation unit can adjust the order of the manuscript based on the relevance of the extracted information during generation. For example, the generation unit can prioritize reflecting highly relevant information in the manuscript. It can also postpone reflecting less relevant information in the manuscript. Furthermore, the generation unit can determine the order of the manuscript based on the relevance of the extracted information. This allows for efficient manuscript generation by adjusting the order of the manuscript based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance data of the extracted information into a generation AI and have the generation AI perform the adjustment of the manuscript order.

[0084] The upload unit can estimate the user's emotions and adjust the upload timing based on the estimated emotions. For example, if the user is stressed, the system can automatically upload, reducing the user's effort. The upload unit can also provide an interface that allows the user to choose the upload timing when the user is relaxed. Furthermore, if the user is in a hurry, the upload unit can provide a simplified procedure for faster uploading. This reduces the user's burden by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0085] The upload unit can select the optimal upload method based on the characteristics of the portal site during the upload process. For example, the upload unit can automatically adjust the document to match the format of each portal site and upload it. The upload unit can also select the optimal image size and resolution based on the characteristics of the portal site. Furthermore, the upload unit can suggest the optimal upload time based on the characteristics of the portal site. This enables efficient uploading by selecting the optimal method based on the characteristics of the portal site. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input portal site characteristic data into a generating AI and have the generating AI select the optimal upload method.

[0086] The upload unit can customize the uploaded content according to the user base of the portal site during the upload process. For example, the upload unit can adjust the presentation of the document to suit the user base of each portal site. The upload unit can also customize the selection and placement of images according to the user base. Furthermore, the upload unit can adjust the length and level of detail of the document according to the user base. By customizing the uploaded content according to the user base, effective information provision becomes possible. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input user base data of the portal site into a generating AI and have the generating AI perform the customization of the uploaded content.

[0087] The upload unit can estimate the user's emotions and determine the priority of portal sites to upload based on the estimated emotions. For example, if the user is stressed, the upload unit will prioritize uploading important portal sites. If the user is relaxed, the upload unit can also upload to portal sites in an order chosen by the user. Furthermore, if the user is in a hurry, the upload unit can quickly upload the most important portal sites. This allows for the rapid uploading of important information by prioritizing portal sites according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The upload unit can select an upload method based on the geographical distribution of portal sites during the upload process. For example, the upload unit may prioritize uploading portal sites related to a specific region. It can also prioritize uploading portal sites that are geographically close. Furthermore, the upload unit can select the optimal upload method by considering geographical distribution. This allows for the priority uploading of highly relevant information by considering geographical distribution. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input geographical distribution data of portal sites into a generating AI and have the generating AI select the optimal upload method.

[0089] The upload unit can improve the accuracy of uploads by referring to relevant literature on the portal site during the upload process. For example, the upload unit uploads the most suitable manuscript to the portal site by referring to relevant literature. The upload unit can also improve the accuracy of uploads based on the relevant literature. Furthermore, the upload unit can analyze the relevant literature on the portal site to improve the accuracy of uploads. As a result, the accuracy of uploads is improved by referring to relevant literature. Some or all of the above processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input relevant literature data into a generating AI and have the generating AI perform the upload accuracy improvement. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, generation unit, and upload unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing users to upload real estate drawings and photographs. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the uploaded drawings and photographs. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and extracts the analyzed information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a document based on the extracted information. The upload unit is implemented by, for example, the control unit 46A of the smart device 14, and uploads the generated document to multiple portal sites. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, generation unit, and upload unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to upload real estate drawings and photographs. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the uploaded drawings and photographs. The extraction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and extracts the analyzed information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a document based on the extracted information. The upload unit is implemented, for example, by the control unit 46A of the smart glasses 214, and uploads the generated document to multiple portal sites. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, generation unit, and upload unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing users to upload real estate drawings and photographs. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the uploaded drawings and photographs. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and extracts the analyzed information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a document based on the extracted information. The upload unit is implemented by, for example, the control unit 46A of the headset terminal 314, and uploads the generated document to multiple portal sites. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, extraction unit, generation unit, and upload unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing users to upload real estate drawings and photographs. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the uploaded drawings and photographs. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and extracts the analyzed information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a document based on the extracted information. The upload unit is implemented by, for example, the control unit 46A of the robot 414, and uploads the generated document to multiple portal sites.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can analyze a user's past upload history and suggest the optimal upload method. For example, it can automatically select the upload method the user has frequently used in the past. The reception desk can also suggest the most efficient upload time based on the user's past upload history. Furthermore, the reception desk can suggest the optimal file format based on the file formats the user has uploaded in the past. This allows for improved upload efficiency by selecting the most suitable method based on past upload history.

[0092] The reception desk can estimate the user's emotions and adjust the timing of uploads of drawings and photos based on those emotions. For example, if the user is stressed, the system can automatically upload the files, reducing the user's effort. The reception desk can also provide an interface that allows the user to choose when to upload if they are relaxed. Furthermore, if the user is in a hurry, the reception desk can provide a simplified procedure for faster uploads. This reduces the user's burden by adjusting the upload timing according to their emotions.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the drawings and photographs during the analysis. For example, it will perform a detailed analysis on important drawings and photographs. It can also perform a simplified analysis on less important drawings and photographs. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the drawings and photographs. This allows for efficient analysis by adjusting the level of detail according to the importance of the drawings and photographs.

[0094] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it can provide analysis results that are easy for the user to understand.

[0095] The extraction unit can improve the accuracy of extraction based on the interrelationships between drawings and photographs. For example, it can accurately extract room sizes and layouts by considering the interrelationships between floor plans and interior photographs. Furthermore, the extraction unit can accurately extract building exterior features and interior conditions by considering the interrelationships between exterior and interior photographs. In addition, the extraction unit can improve extraction accuracy by analyzing the interrelationships between drawings and photographs. Thus, considering the interrelationships between drawings and photographs improves extraction accuracy.

[0096] The extraction unit can estimate the user's emotions and determine the priority of information to extract based on those emotions. For example, if the user is stressed, it will prioritize extracting important information. If the user is relaxed, the extraction unit can also extract information in the order chosen by the user. Furthermore, if the user is in a hurry, the extraction unit can quickly extract the most important information. This allows for the rapid extraction of important information by prioritizing it according to the user's emotions.

[0097] The generation unit can adjust the level of detail in the manuscript based on the importance of the extracted information during generation. For example, it can generate a manuscript with detailed explanations for important information. It can also generate a manuscript with concise explanations for less important information. Furthermore, the generation unit can adjust the level of detail in the manuscript according to the importance of the extracted information. This allows for efficient manuscript generation by adjusting the level of detail according to the importance of the information.

[0098] The generation unit can estimate the user's emotions and adjust the way the generated document is presented based on those emotions. For example, if the user is relaxed, it can generate a document with detailed explanations. If the user is in a hurry, it can generate a concise and to-the-point document. Furthermore, if the user is excited, it can generate a document with visually stimulating effects. By adjusting the presentation of the document according to the user's emotions, it can provide a document that is easy for the user to understand.

[0099] The upload unit can select the optimal upload method based on the characteristics of the portal site during the upload process. For example, it can automatically adjust the document to match the format of each portal site before uploading. The upload unit can also select the optimal image size and resolution based on the characteristics of the portal site. Furthermore, the upload unit can suggest the optimal upload time based on the characteristics of the portal site. This enables efficient uploads by selecting the optimal method based on the characteristics of each portal site.

[0100] The upload function can estimate the user's emotions and prioritize which portal sites to upload based on those emotions. For example, if the user is stressed, it will prioritize uploading important portal sites. If the user is relaxed, the upload function can also upload to portal sites in the order the user chooses. Furthermore, if the user is in a hurry, the upload function can quickly upload the most important portal sites. This allows for the rapid uploading of important information by prioritizing portal sites according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk allows users to upload multiple property drawings and photos. Users can log in to the system and upload drawings and photos by clicking the upload button and selecting files, or by using the drag-and-drop function. Uploaded drawings and photos include floor plans, exterior photos, interior photos, etc. Step 2: The analysis unit analyzes the uploaded drawings and photographs. The analysis unit uses image analysis algorithms to analyze important information such as room size and layout from floor plans, exterior features of the building from exterior photographs, and the condition of the room interiors from interior photographs. Step 3: The extraction unit extracts the information analyzed by the analysis unit. The extraction unit extracts information such as room size and layout from floor plans, exterior features of the building from exterior photos, and interior condition of rooms from interior photos. Step 4: The generation unit generates the document based on the information extracted by the extraction unit. The generation unit automatically creates the document, which includes an overview of the property, its features, equipment information, and surrounding environment, using natural language generation technology. Step 5: The upload unit uploads the manuscript generated by the generation unit to multiple portal sites. The upload unit uses the portal site's API to automatically upload the same manuscript to multiple portal sites simultaneously.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk for uploading drawings or photos, An analysis unit that analyzes drawings or photographs uploaded by the reception unit, An extraction unit that extracts information analyzed by the aforementioned analysis unit, A generation unit that generates a document based on the information extracted by the extraction unit, The system includes an upload unit that uploads the manuscript generated by the generation unit to multiple portal sites. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of drawing and photo uploads based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past upload history and select the appropriate upload method. The system according to feature 1.

4. The aforementioned reception unit is When uploading drawings or photos, the system filters them based on the user's current project or area of ​​interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of drawings and photos to upload based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When uploading drawings and photos, the system prioritizes uploading highly relevant drawings and photos based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users upload drawings or photos, the system analyzes their social media activity and uploads relevant drawings and photos. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of the drawings and photographs. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of drawings and photographs. The system according to feature 1.

Images