System

Abstract

An object of a system according to an embodiment is to support a property search by utilizing a use history of a real estate site of a user.SOLUTION: A system according to an embodiment includes a log collection unit, a viewpoint suggestion unit, and an advertisement display unit. The log collection part collects a browsing log and a retrieval log of a real estate site of a user. The viewpoint proposal unit proposes a viewpoint of property searching on the basis of the browsing log and the search log collected by the log collection unit. The advertisement display unit displays a real estate advertisement suitable for the user based on the real estate information searched by the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has room for improvement in terms of effectively utilizing users' usage history of real estate sites to support property searches.

[0005] The system according to the embodiment aims to support a user's search for a property by utilizing the user's usage history of a real estate website. [Means for solving the problem]

[0006] The system according to the embodiment includes a log collection unit, a viewpoint suggestion unit, and an advertisement display unit. The log collection unit collects browsing logs and search logs of users' real estate sites. The viewpoint suggestion unit suggests viewpoints for property searches based on the browsing logs and search logs collected by the log collection unit. The advertisement display unit displays real estate advertisements appropriate for the user based on the real estate information the user has researched. [Effects of the Invention]

[0007] The system according to the embodiment can support a property search by utilizing a user's usage history of a real estate site. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The property search support system according to the embodiment of the present invention is a system in which a generation AI proposes perspectives for property searches based on a user's browsing log and search log of real estate websites, and displays appropriate real estate advertisements. This allows the property search support system to improve profits and deepen user understanding.

[0029] A property search support system according to an embodiment includes a log collection unit, a viewpoint suggestion unit, and an advertisement display unit. The log collection unit collects browsing logs and search logs of users' real estate websites. For example, it collects page view and clickstream data. The log collection unit can also collect search query and search result click data. For example, if a user frequently searches for properties in a specific area or price range, it collects that information. The viewpoint suggestion unit suggests property search viewpoints based on the browsing logs and search logs collected by the log collection unit. For example, the generation AI proposes specific viewpoints such as "This area has good transportation access, making it convenient for commuting" or "Many properties in this price range have been renovated." The generation AI also generates viewpoints based on prompts including the user's browsing log and search log. The advertisement display unit displays real estate advertisements appropriate for the user based on real estate information searched by the user. For example, if a user searches for properties in a specific area or price range, it displays advertisements related to that area and price range. This allows advertisements tailored to the user's interests to be displayed. As a result, the property search support system according to an embodiment can improve revenue and deepen user understanding. For example, it can improve ad click rates and conversion rates, and by gaining a deeper understanding of users' interests, it can provide more personalized services.

[0030] The log collection unit also collects the user's social media activity and online shopping history to generate a more detailed user profile. For example, the log collection unit analyzes the user's social media activity to understand their interests. For example, it collects the content of posts that the user frequently "likes" or shares, and uses this information as reference for property searches. The log collection unit also collects the user's online shopping history and analyzes their purchase history and browsing history. For example, it suggests perspectives for property searches based on information about products the user has purchased in the past. In this way, by collecting the user's social media activity and online shopping history, a more detailed user profile can be generated.

[0031] The log collection unit analyzes the user's browsing log and search log in real time, and responds immediately when the user's interests change. For example, the log collection unit monitors the user's browsing log in real time and immediately detects changes in interests. For example, if the user suddenly starts searching for properties in a different area, property information in that area is displayed preferentially. The log collection unit also analyzes the search log in real time and responds immediately when the user's interests change. For example, it detects a change in the search query and immediately displays new search results. This allows the system to respond immediately when the user's interests change, thereby quickly meeting the user's needs.

[0032] The viewpoint suggestion unit reflects the user's past purchase history and lifestyle information to make more personalized suggestions. For example, the viewpoint suggestion unit analyzes the user's past purchase history, and the generation AI suggests viewpoints for property searches. For example, a user who has purchased luxury furniture will be suggested luxury properties. The viewpoint suggestion unit also reflects the user's lifestyle information to suggest viewpoints for property searches. For example, a user who enjoys the outdoors will be suggested properties in areas with abundant natural environments. In this way, more personalized suggestions can be made by reflecting the user's past purchase history and lifestyle information.

[0033] The viewpoint suggestion unit suggests safe and secure properties for the user, taking into account at least one social factor, such as the local crime rate or school ratings. The viewpoint suggestion unit, for example, analyzes local crime rate data, and the generation AI suggests safe properties. For example, it prioritizes suggesting properties in areas with low crime rates. The viewpoint suggestion unit also analyzes school rating data, and the generation AI suggests safe properties. For example, it prioritizes suggesting properties in areas with high school ratings. In this way, by including social factors such as the local crime rate and school ratings, it is possible to suggest safe and secure properties.

[0034] The perspective suggestion unit adapts the perspectives proposed by the generation AI to different languages ​​and cultural spheres, making them applicable to international users. For example, the perspective suggestion unit translates the perspectives proposed by the generation AI into different languages, making them applicable to international users. For example, it can support multiple languages ​​such as English, French, and Chinese. The perspective suggestion unit also adapts the perspectives proposed by the generation AI to different cultural spheres. For example, it makes suggestions taking into account cultural backgrounds such as Asia, Europe, the United States, and the Middle East. This makes it applicable to international users by adapting to different languages ​​and cultural spheres.

[0035] The viewpoint suggestion unit presents the viewpoints proposed by the generation AI visually and audibly, in a format that is easy for the user to intuitively understand. For example, the viewpoint suggestion unit presents the viewpoints proposed by the generation AI visually, in a format that is easy for the user to intuitively understand. For example, the viewpoints are displayed visually using graphs or charts. The viewpoint suggestion unit also presents the viewpoints proposed by the generation AI audibly. For example, the viewpoints are explained using narration. In this way, by presenting them visually and audibly, it is possible to create a format that is easy for the user to intuitively understand.

[0036] The advertisement display unit optimizes the timing of advertisement display based on the user's browsing log and search log, and displays advertisements at the moment when the user is most interested. The advertisement display unit, for example, analyzes the user's browsing log and builds a system that identifies the optimal timing of advertisement display. For example, it displays related advertisements while the user is browsing property information. The advertisement display unit also analyzes the search log and displays advertisements at the moment when the user is most interested. For example, it displays related advertisements immediately after the user searches for a specific property. This allows the advertisement display timing to be optimized and advertisements to be displayed at the moment when the user is most interested.

[0037] The advertisement display unit dynamically changes the content of the advertisement based on the user's past behavior and interests, and displays more personalized advertisements. The advertisement display unit, for example, builds a system that dynamically changes the content of the advertisement based on the user's past behavior data. For example, the advertisement displays advertisements that reflect the characteristics of properties that the user has searched for in the past. The advertisement display unit also changes the content of the advertisement based on the user's interests. For example, if the user is interested in luxury properties, the advertisement for luxury properties is displayed. This makes it possible to dynamically change the content of the advertisement and display more personalized advertisements.

[0038] The advertisement display unit optimizes the display of advertisements based on the user's geographical location information, and prioritizes the display of nearby property information. The advertisement display unit, for example, builds a system that optimizes the display of advertisements based on the user's geographical location information. For example, property information close to the user's current location is prioritized for display. The advertisement display unit also prioritizes the display of nearby property information based on the user's geographical location information. For example, if the user is in a specific area, property information for that area is displayed. This makes it possible to optimize the display of advertisements based on the user's geographical location information, and prioritize the display of nearby property information.

[0039] The ad display unit optimizes the display of ads according to the type of device or browser of the user and displays them in the optimal format. The ad display unit builds a system that optimizes the display of ads, for example, based on user device information. For example, the ad format is changed according to the device, such as a smartphone, tablet, or PC. The ad display unit also optimizes the display of ads according to the type of browser of the user. For example, ads compatible with browsers such as Chrome, Firefox, and Safari are displayed. This allows the display of ads to be optimized according to the type of device or browser of the user and displayed in the optimal format.

[0040] The advertisement display unit uses the generation AI to analyze user behavior data and identify the most profitable advertisement display pattern. The advertisement display unit, for example, uses the generation AI to build a system that analyzes user behavior data and identifies the most profitable advertisement display pattern. For example, the optimal display pattern is identified based on the advertisement click rate and conversion rate. The advertisement display unit also uses the generation AI to identify a profitable advertisement display pattern based on the user behavior data. For example, data on advertisements that the user has clicked in the past is analyzed to identify the optimal display pattern. In this way, the user behavior data can be analyzed and the most profitable advertisement display pattern can be identified.

[0041] The ad display unit collects user feedback to gain a deeper understanding of the user's interests and concerns, and uses it as learning data for the generation AI. The ad display unit, for example, builds a system that collects user feedback and uses it as learning data for the generation AI. For example, it collects how users responded to advertisements and uses this as learning data for the AI. The ad display unit also collects feedback to gain a deeper understanding of the user's interests and concerns. For example, it collects survey results and user reviews and uses this as learning data for the AI. In this way, by collecting user feedback and using it as learning data for the generation AI, it is possible to gain a deeper understanding of the user's interests and concerns.

[0042] The advertisement display unit cooperates with different advertisement networks to improve profits and displays the most effective advertisements. The advertisement display unit, for example, cooperates with different advertisement networks to build a system that displays the most effective advertisements. For example, it selects and displays the optimal advertisement from multiple advertisement networks. The advertisement display unit also cooperates with different advertisement networks to improve profits. For example, it selects the optimal advertisement based on the click rate or conversion rate of the advertisement. This makes it possible to improve profits by cooperating with different advertisement networks and displaying the most effective advertisements.

[0043] The advertisement display unit integrates the user's behavioral data with other data sets (e.g., social media data) to generate a more detailed user profile in order to gain a deeper understanding of the user. The advertisement display unit, for example, builds a system that integrates the user's behavioral data with social media data to generate a more detailed user profile. For example, it analyzes the content of the user's posts and "like" history to understand their interests and concerns. The advertisement display unit also integrates the user's behavioral data with other data sets to generate a more detailed user profile. For example, it generates a detailed profile based on the user's browsing history and purchase history. In this way, the user's behavioral data can be integrated with other data sets to generate a more detailed user profile, thereby deepening understanding of the user.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The log collection unit can also collect the user's health data and reflect it in the property search perspective. For example, if the user uses a fitness tracker, that data can be collected and used to suggest properties suitable for health-conscious users. Specifically, it can suggest properties with nearby gyms or parks, or properties in areas suitable for walking or running. It can also suggest properties in areas with good air quality or low noise levels based on the user's health data. This makes it possible to search for properties that suit the user's health condition and lifestyle.

[0046] The viewpoint suggestion unit can also suggest properties based on the user's hobbies and special skills. For example, for a user whose hobby is music, properties with soundproofing or properties with a nearby music studio can be suggested. For a user whose hobby is cooking, properties with spacious kitchens and the latest cooking equipment can be suggested. Furthermore, for a user whose hobby is gardening, properties with gardens or properties with nearby community gardens can be suggested. This makes it possible to search for properties that match the user's hobbies and special skills.

[0047] The advertisement display unit can also estimate a user's purchasing power and display advertisements based on that. For example, the purchasing power can be estimated by analyzing the user's income and expenditure data. For users with high purchasing power, advertisements for luxury properties and expensive real estate investments can be displayed. On the other hand, advertisements for affordable properties and rental properties can be displayed to users with low purchasing power. In addition, advertisements proposing mortgage and financing options can be displayed according to the user's purchasing power. This makes it possible to display advertisements according to the user's purchasing power.

[0048] The viewpoint suggestion unit can also suggest properties based on the user's family composition. For example, for a family with children, it can suggest properties that are close to schools or parks. For a family with elderly people, it can also suggest properties with barrier-free designs or properties that are close to medical facilities. Furthermore, for a family with pets, it can also suggest pet-friendly properties or properties with nearby pet facilities. This makes it possible to search for properties that suit the user's family composition.

[0049] The advertisement display unit can also display advertisements for properties related to travel destinations based on the user's past travel history. For example, it can collect data on travel destinations visited by the user in the past and display advertisements for properties in those areas. Specifically, it can display advertisements for properties in cities or resort areas frequently visited by the user. Furthermore, if the user shows interest in a particular country or region, it can display advertisements for real estate investments in that region. This makes it possible to display personalized advertisements based on the user's travel history.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The log collection unit collects the user's browsing and search logs for real estate websites. For example, it collects page views, clickstream data, search queries, and click data for search results. If the user frequently searches for properties in a specific area or price range, it also collects that information. Step 2: The perspective suggestion unit proposes perspectives for property searches based on the browsing and search logs collected by the log collection unit. For example, the generation AI proposes specific perspectives such as "This area has good transportation access, making it convenient for commuting" or "Many properties in this price range have been renovated." It also generates perspectives based on prompts including the user's browsing and search logs. Step 3: The advertisement display unit displays real estate advertisements appropriate for the user based on the real estate information the user has researched. For example, if the user has researched properties in a specific area or price range, advertisements related to that area and price range will be displayed. This allows advertisements that match the user's interests to be displayed.

[0052] (Example 2) The property search support system according to the embodiment of the present invention is a system in which a generation AI proposes perspectives for property searches based on a user's browsing log and search log of real estate websites, and displays appropriate real estate advertisements. This allows the property search support system to improve profits and deepen user understanding.

[0053] A property search support system according to an embodiment includes a log collection unit, a viewpoint suggestion unit, and an advertisement display unit. The log collection unit collects browsing logs and search logs of users' real estate websites. For example, it collects page view and clickstream data. The log collection unit can also collect search query and search result click data. For example, if a user frequently searches for properties in a specific area or price range, it collects that information. The viewpoint suggestion unit suggests property search viewpoints based on the browsing logs and search logs collected by the log collection unit. For example, the generation AI proposes specific viewpoints such as "This area has good transportation access, making it convenient for commuting" or "Many properties in this price range have been renovated." The generation AI also generates viewpoints based on prompts including the user's browsing log and search log. The advertisement display unit displays real estate advertisements appropriate for the user based on real estate information searched by the user. For example, if a user searches for properties in a specific area or price range, it displays advertisements related to that area and price range. This allows advertisements tailored to the user's interests to be displayed. As a result, the property search support system according to an embodiment can improve revenue and deepen user understanding. For example, it can improve ad click rates and conversion rates, and by gaining a deeper understanding of users' interests, it can provide more personalized services.

[0054] The log collection unit also collects the user's social media activity and online shopping history to generate a more detailed user profile. For example, the log collection unit analyzes the user's social media activity to understand their interests. For example, it collects the content of posts that the user frequently "likes" or shares, and uses this information as reference for property searches. The log collection unit also collects the user's online shopping history and analyzes their purchase history and browsing history. For example, it suggests perspectives for property searches based on information about products the user has purchased in the past. In this way, by collecting the user's social media activity and online shopping history, a more detailed user profile can be generated.

[0055] The log collection unit analyzes the user's browsing log and search log in real time, and responds immediately when the user's interests change. For example, the log collection unit monitors the user's browsing log in real time and immediately detects changes in interests. For example, if the user suddenly starts searching for properties in a different area, property information in that area is displayed preferentially. The log collection unit also analyzes the search log in real time and responds immediately when the user's interests change. For example, it detects a change in the search query and immediately displays new search results. This allows the system to respond immediately when the user's interests change, thereby quickly meeting the user's needs.

[0056] The log collection unit uses the emotion estimation function to estimate the emotion a user feels when viewing property information, and optimizes the log collection method for eliciting positive emotions. The log collection unit, for example, analyzes facial expressions and voice when the user views property information to estimate the emotion. For example, if a smile or an excited voice is detected, the log collection unit prioritizes collecting information related to that property. The log collection unit also uses the emotion estimation function to analyze the user's emotion in real time, and optimizes the log collection method for eliciting positive emotions. For example, the log collection unit prioritizes collecting property information for which the user expressed positive emotions. This makes it possible to estimate the user's emotion and optimize the log collection method for eliciting positive emotions.

[0057] The viewpoint suggestion unit reflects the user's past purchase history and lifestyle information to make more personalized suggestions. For example, the viewpoint suggestion unit analyzes the user's past purchase history, and the generation AI suggests viewpoints for property searches. For example, a user who has purchased luxury furniture will be suggested luxury properties. The viewpoint suggestion unit also reflects the user's lifestyle information to suggest viewpoints for property searches. For example, a user who enjoys the outdoors will be suggested properties in areas with abundant natural environments. In this way, more personalized suggestions can be made by reflecting the user's past purchase history and lifestyle information.

[0058] The viewpoint suggestion unit suggests safe and secure properties for the user, taking into account at least one social factor, such as the local crime rate or school ratings. The viewpoint suggestion unit, for example, analyzes local crime rate data, and the generation AI suggests safe properties. For example, it prioritizes suggesting properties in areas with low crime rates. The viewpoint suggestion unit also analyzes school rating data, and the generation AI suggests safe properties. For example, it prioritizes suggesting properties in areas with high school ratings. In this way, by including social factors such as the local crime rate and school ratings, it is possible to suggest safe and secure properties.

[0059] The viewpoint suggestion unit uses the emotion estimation function to estimate what emotion the user will have toward the proposed viewpoint, and preferentially suggests viewpoints that elicit positive emotions. The viewpoint suggestion unit, for example, uses the emotion estimation function to build a system that estimates what emotion the user will have toward the proposed viewpoint. For example, it analyzes the user's facial expression and voice and calculates an emotion score. The viewpoint suggestion unit also uses the emotion estimation function to preferentially suggest viewpoints that indicate positive emotions for the user. For example, it preferentially suggests viewpoints that indicate an excited facial expression for the user. This makes it possible to estimate the user's emotions and preferentially suggest viewpoints that elicit positive emotions.

[0060] The perspective suggestion unit adapts the perspectives proposed by the generation AI to different languages ​​and cultural spheres, making them applicable to international users. For example, the perspective suggestion unit translates the perspectives proposed by the generation AI into different languages, making them applicable to international users. For example, it can support multiple languages ​​such as English, French, and Chinese. The perspective suggestion unit also adapts the perspectives proposed by the generation AI to different cultural spheres. For example, it makes suggestions taking into account cultural backgrounds such as Asia, Europe, the United States, and the Middle East. This makes it applicable to international users by adapting to different languages ​​and cultural spheres.

[0061] The viewpoint suggestion unit presents the viewpoints proposed by the generation AI visually and audibly, in a format that is easy for the user to intuitively understand. For example, the viewpoint suggestion unit presents the viewpoints proposed by the generation AI visually, in a format that is easy for the user to intuitively understand. For example, the viewpoints are displayed visually using graphs or charts. The viewpoint suggestion unit also presents the viewpoints proposed by the generation AI audibly. For example, the viewpoints are explained using narration. In this way, by presenting them visually and audibly, it is possible to create a format that is easy for the user to intuitively understand.

[0062] The viewpoint proposal unit uses the emotion estimation function to collect the user's emotional reactions to the proposed viewpoints in real time and dynamically adjusts the proposed content. The viewpoint proposal unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to the proposed viewpoints in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The viewpoint proposal unit also uses the emotion estimation function to dynamically adjust the proposed content based on the user's emotional reactions. For example, it preferentially suggests viewpoints for which the user has expressed positive emotions. This makes it possible to collect the user's emotional reactions in real time and dynamically adjust the proposed content.

[0063] The advertisement display unit optimizes the timing of advertisement display based on the user's browsing log and search log, and displays advertisements at the moment when the user is most interested. The advertisement display unit, for example, analyzes the user's browsing log and builds a system that identifies the optimal timing of advertisement display. For example, it displays related advertisements while the user is browsing property information. The advertisement display unit also analyzes the search log and displays advertisements at the moment when the user is most interested. For example, it displays related advertisements immediately after the user searches for a specific property. This allows the advertisement display timing to be optimized and advertisements to be displayed at the moment when the user is most interested.

[0064] The advertisement display unit dynamically changes the content of the advertisement based on the user's past behavior and interests, and displays more personalized advertisements. The advertisement display unit, for example, builds a system that dynamically changes the content of the advertisement based on the user's past behavior data. For example, the advertisement displays advertisements that reflect the characteristics of properties that the user has searched for in the past. The advertisement display unit also changes the content of the advertisement based on the user's interests. For example, if the user is interested in luxury properties, the advertisement for luxury properties is displayed. This makes it possible to dynamically change the content of the advertisement and display more personalized advertisements.

[0065] The advertisement display unit uses the emotion estimation function to estimate the emotion a user will have when viewing an advertisement, and preferentially displays advertisements that elicit positive emotions. The advertisement display unit, for example, uses the emotion estimation function to build a system that estimates the emotion a user will have when viewing an advertisement. For example, the advertisement display unit analyzes the user's facial expressions and voice and calculates an emotion score. The advertisement display unit also uses the emotion estimation function to preferentially display advertisements that show the user positive emotions. For example, advertisements that show the user smiling are preferentially displayed. This makes it possible to estimate the user's emotions and preferentially display advertisements that elicit positive emotions.

[0066] The advertisement display unit optimizes the display of advertisements based on the user's geographical location information, and prioritizes the display of nearby property information. The advertisement display unit, for example, builds a system that optimizes the display of advertisements based on the user's geographical location information. For example, property information close to the user's current location is prioritized for display. The advertisement display unit also prioritizes the display of nearby property information based on the user's geographical location information. For example, if the user is in a specific area, property information for that area is displayed. This makes it possible to optimize the display of advertisements based on the user's geographical location information, and prioritize the display of nearby property information.

[0067] The ad display unit optimizes the display of ads according to the type of device or browser of the user and displays them in the optimal format. The ad display unit builds a system that optimizes the display of ads, for example, based on user device information. For example, the ad format is changed according to the device, such as a smartphone, tablet, or PC. The ad display unit also optimizes the display of ads according to the type of browser of the user. For example, ads compatible with browsers such as Chrome, Firefox, and Safari are displayed. This allows the display of ads to be optimized according to the type of device or browser of the user and displayed in the optimal format.

[0068] The advertisement display unit uses the emotion estimation function to collect emotional responses of users when they view advertisements in real time and dynamically adjust the content of the advertisements. The advertisement display unit, for example, uses the emotion estimation function to build a system that collects emotional responses of users when they view advertisements in real time. For example, the advertisement display unit analyzes the user's facial expressions and voice and calculates an emotion score. The advertisement display unit also uses the emotion estimation function to dynamically adjust the content of the advertisements based on the user's emotional responses. For example, advertisements for which the user shows positive emotions are preferentially displayed. This makes it possible to collect the user's emotional responses in real time and dynamically adjust the content of the advertisements.

[0069] The advertisement display unit uses the generation AI to analyze user behavior data and identify the most profitable advertisement display pattern. The advertisement display unit, for example, uses the generation AI to build a system that analyzes user behavior data and identifies the most profitable advertisement display pattern. For example, the optimal display pattern is identified based on the advertisement click rate and conversion rate. The advertisement display unit also uses the generation AI to identify a profitable advertisement display pattern based on the user behavior data. For example, data on advertisements that the user has clicked in the past is analyzed to identify the optimal display pattern. In this way, the user behavior data can be analyzed and the most profitable advertisement display pattern can be identified.

[0070] The ad display unit collects user feedback to gain a deeper understanding of the user's interests and concerns, and uses it as learning data for the generation AI. The ad display unit, for example, builds a system that collects user feedback and uses it as learning data for the generation AI. For example, it collects how users responded to advertisements and uses this as learning data for the AI. The ad display unit also collects feedback to gain a deeper understanding of the user's interests and concerns. For example, it collects survey results and user reviews and uses this as learning data for the AI. In this way, by collecting user feedback and using it as learning data for the generation AI, it is possible to gain a deeper understanding of the user's interests and concerns.

[0071] The advertisement display unit uses the emotion estimation function to analyze the emotion a user has when clicking on an advertisement, and optimizes an advertisement display method that elicits positive emotions. The advertisement display unit, for example, uses the emotion estimation function to build a system that analyzes the emotion a user has when clicking on an advertisement. For example, the advertisement display unit analyzes the user's facial expressions and voice and calculates an emotion score. The advertisement display unit also uses the emotion estimation function to optimize an advertisement display method that shows a user positive emotion. For example, advertisements that show a user smiling are preferentially displayed. This makes it possible to analyze the emotion a user has when clicking on an advertisement and optimize an advertisement display method that elicits positive emotions.

[0072] The advertisement display unit cooperates with different advertisement networks to improve profits and displays the most effective advertisements. The advertisement display unit, for example, cooperates with different advertisement networks to build a system that displays the most effective advertisements. For example, it selects and displays the optimal advertisement from multiple advertisement networks. The advertisement display unit also cooperates with different advertisement networks to improve profits. For example, it selects the optimal advertisement based on the click rate or conversion rate of the advertisement. This makes it possible to improve profits by cooperating with different advertisement networks and displaying the most effective advertisements.

[0073] The advertisement display unit integrates the user's behavioral data with other data sets (e.g., social media data) to generate a more detailed user profile in order to gain a deeper understanding of the user. The advertisement display unit, for example, builds a system that integrates the user's behavioral data with social media data to generate a more detailed user profile. For example, it analyzes the content of the user's posts and "like" history to understand their interests and concerns. The advertisement display unit also integrates the user's behavioral data with other data sets to generate a more detailed user profile. For example, it generates a detailed profile based on the user's browsing history and purchase history. In this way, the user's behavioral data can be integrated with other data sets to generate a more detailed user profile, thereby deepening understanding of the user.

[0074] The advertisement display unit uses the emotion estimation function to collect emotional responses in real time when a user clicks on an advertisement, and dynamically adjusts the advertisement display method. The advertisement display unit, for example, uses the emotion estimation function to build a system that collects emotional responses in real time when a user clicks on an advertisement. For example, the advertisement display unit analyzes the user's facial expressions and voice and calculates an emotion score. The advertisement display unit also uses the emotion estimation function to dynamically adjust the advertisement display method based on the user's emotional responses. For example, advertisements for which the user shows positive emotions are preferentially displayed. This makes it possible to collect the user's emotional responses in real time and dynamically adjust the advertisement display method.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The log collection unit can also collect the user's health data and reflect it in the property search perspective. For example, if the user uses a fitness tracker, that data can be collected and used to suggest properties suitable for health-conscious users. Specifically, it can suggest properties with nearby gyms or parks, or properties in areas suitable for walking or running. It can also suggest properties in areas with good air quality or low noise levels based on the user's health data. This makes it possible to search for properties that suit the user's health condition and lifestyle.

[0077] The viewpoint suggestion unit can also suggest properties based on the user's hobbies and special skills. For example, for a user whose hobby is music, properties with soundproofing or properties with a nearby music studio can be suggested. For a user whose hobby is cooking, properties with spacious kitchens and the latest cooking equipment can be suggested. Furthermore, for a user whose hobby is gardening, properties with gardens or properties with nearby community gardens can be suggested. This makes it possible to search for properties that match the user's hobbies and special skills.

[0078] The advertisement display unit can also estimate a user's purchasing power and display advertisements based on that. For example, the purchasing power can be estimated by analyzing the user's income and expenditure data. For users with high purchasing power, advertisements for luxury properties and expensive real estate investments can be displayed. On the other hand, advertisements for affordable properties and rental properties can be displayed to users with low purchasing power. In addition, advertisements proposing mortgage and financing options can be displayed according to the user's purchasing power. This makes it possible to display advertisements according to the user's purchasing power.

[0079] The viewpoint suggestion unit can also suggest properties based on the user's family composition. For example, for a family with children, it can suggest properties that are close to schools or parks. For a family with elderly people, it can also suggest properties with barrier-free designs or properties that are close to medical facilities. Furthermore, for a family with pets, it can also suggest pet-friendly properties or properties with nearby pet facilities. This makes it possible to search for properties that suit the user's family composition.

[0080] The advertisement display unit can also display advertisements for properties related to travel destinations based on the user's past travel history. For example, it can collect data on travel destinations visited by the user in the past and display advertisements for properties in those areas. Specifically, it can display advertisements for properties in cities or resort areas frequently visited by the user. Furthermore, if the user shows interest in a particular country or region, it can display advertisements for real estate investments in that region. This makes it possible to display personalized advertisements based on the user's travel history.

[0081] The viewpoint suggestion unit can also estimate the user's emotions and suggest properties that will help reduce stress. For example, if the user is feeling stressed, it can suggest properties in quiet environments or surrounded by nature. It can also suggest properties that are close to facilities that have a relaxing effect. Specifically, it can suggest properties that are close to spas, hot springs, or yoga studios. Furthermore, if the user expresses positive emotions, it can suggest properties that will help maintain those emotions. This makes it possible to suggest properties based on the user's emotions.

[0082] The advertisement display unit can also estimate the user's emotions and display advertisements according to the emotions. For example, if the user is excited, it can display property advertisements that will further increase the user's excitement. Specifically, it can display advertisements that include luxurious properties or special offers. Also, if the user is relaxed, it can display property advertisements that will maintain that sense of relaxation. For example, it can display advertisements for properties that are in a quiet environment or are close to facilities that have a relaxing effect. This makes it possible to display advertisements according to the user's emotions.

[0083] The viewpoint suggestion unit can also estimate the user's emotions and suggest a property viewing schedule based on the emotions. For example, if the user is expressing positive emotions, the viewpoint suggestion unit can suggest a property viewing schedule to maintain those emotions. Specifically, the viewpoint suggestion unit can suggest a property viewing schedule during a time when the user is relaxed. Also, if the user is feeling stressed, the viewpoint suggestion unit can adjust the property viewing schedule to reduce that stress. This makes it possible to suggest a property viewing schedule based on the user's emotions.

[0084] The advertisement display unit can also estimate the user's emotions and dynamically change the design of the advertisement based on the emotions. For example, if the user is expressing positive emotions, the advertisement display unit can display an advertisement with bright colors and designs that further enhance those emotions. Also, if the user is relaxed, the advertisement display unit can display an advertisement with calm colors and designs that maintain that relaxed feeling. This makes it possible to dynamically change the advertisement design based on the user's emotions.

[0085] The viewpoint suggestion unit can also estimate the user's emotions and select property photos and videos based on the emotions. For example, if the user is expressing positive emotions, bright and attractive photos and videos can be selected to maintain that emotion. Also, if the user is relaxed, quiet and calm photos and videos can be selected to maintain that relaxed feeling. This makes it possible to visually suggest properties based on the user's emotions.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The log collection unit collects the user's browsing and search logs for real estate websites. For example, it collects page views, clickstream data, search queries, and click data for search results. If the user frequently searches for properties in a specific area or price range, it also collects that information. Step 2: The perspective suggestion unit proposes perspectives for property searches based on the browsing and search logs collected by the log collection unit. For example, the generation AI proposes specific perspectives such as "This area has good transportation access, making it convenient for commuting" or "Many properties in this price range have been renovated." It also generates perspectives based on prompts including the user's browsing and search logs. Step 3: The advertisement display unit displays real estate advertisements appropriate for the user based on the real estate information the user has researched. For example, if the user has researched properties in a specific area or price range, advertisements related to that area and price range will be displayed. This allows advertisements that match the user's interests to be displayed.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0129] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0138] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0139] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0140] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0143] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0144] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0145] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0146] 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.

[0147] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0148] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0149] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0150] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0151] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0152] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0153] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a log collection unit that collects browsing logs and search logs of users' real estate sites; a viewpoint suggestion unit that suggests a viewpoint for searching for a property based on the browsing log and the search log collected by the log collection unit; an advertisement display unit that displays real estate advertisements suitable for the user based on the real estate information that the user has searched for; A system characterized by:

2. The log collection unit The user's social media activity and online shopping history are also collected to generate a more detailed user profile. The system of claim 1 .

3. The viewpoint suggestion unit Reflecting the user's past purchase history and lifestyle information to provide more personalized suggestions The system of claim 1 .

4. The advertisement display unit The timing of displaying advertisements is optimized based on the user's browsing log and search log, and the advertisements are displayed at the moment when the user is most interested. The system of claim 1 .

5. The log collection unit Using the emotion estimation function, the emotion felt by the user when viewing property information is estimated, and the log collection method is optimized to elicit positive emotions. The system of claim 1 .

6. The viewpoint suggestion unit Using an emotion estimation function, the user's feelings about the proposed viewpoints are estimated, and viewpoints that elicit positive emotions are preferentially suggested. The system of claim 1 .

7. The advertisement display unit Using an emotion estimation function, the emotion that the user felt when viewing the advertisement is estimated, and the advertisement that elicits positive emotions is preferentially displayed. The system of claim 1 .

8. The advertisement display unit Using the emotion estimation function, the emotion felt by the user when clicking on the advertisement is analyzed, and an advertisement display method that elicits the positive emotion is optimized. The system of claim 1 .

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