system

Abstract

The system according to this embodiment aims to provide optimal property information based on the user's desired conditions. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, and a notification unit. The reception unit receives the user's desired conditions. The generation unit analyzes the desired conditions received by the reception unit and generates optimal property information. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The notification unit notifies when a new property matching the conditions proposed by the proposal unit is registered.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently provide property information that meets the user's desired conditions, and often satisfactory property information cannot be obtained.

[0005] The system according to the embodiment aims to provide optimal property information based on the user's desired conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a notification unit. The reception unit receives the user's desired conditions. The generation unit analyzes the desired conditions received by the reception unit and generates optimal property information. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The notification unit notifies the user when a new property matching the conditions proposed by the proposal unit is registered. [Effects of the Invention]

[0007] The system according to this embodiment can provide optimal property information based on the user's desired conditions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The property matching system according to an embodiment of the present invention is a property matching tool that uses a generative AI. This property matching system is a system that understands the user's desired conditions and lifestyle in detail through conversational AI chat and provides optimal property information. When a user enters conditions such as "10 minutes walk from the station," the generative AI analyzes those conditions and proposes the most suitable properties. Furthermore, it makes suggestions to relax the conditions (e.g., extending "10 minutes walk from the station" to "11 minutes walk," changing "south-facing" to "southwest or southeast-facing") to provide the best possible options within the budget. In addition, when a new property matching the conditions is registered, the generative AI notifies the user via a messaging app. First, the user enters their desired conditions. For example, they enter conditions such as "within 10 minutes walk from the station, south-facing, budget under 10 million yen." This information is input to the generative AI, which then begins its analysis. Next, the generative AI analyzes the user's desired conditions and provides optimal property information. For example, if a user enters "within 10 minutes walk from the station, south-facing, budget under 10 million yen," the generative AI searches for and lists properties that match those conditions. Furthermore, the generating AI suggests relaxing the search criteria. For example, it might suggest expanding the criteria from "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing," providing users with more options. This allows users to find the most suitable property within their budget. Additionally, when a new property matching the criteria is registered, the generating AI notifies the user via the messaging app. This allows users to receive the latest property information in real time. This system ensures that even users with strong preferences and limited budgets can find satisfactory property information. For example, if a user enters criteria such as "within 10 minutes walk from the station, south-facing, and under 10 million yen," the generating AI will list properties that meet those criteria and suggest further relaxing the criteria, providing users with more options. Additionally, when a new property matching the criteria is registered, the generating AI notifies the user via the messaging app. This allows users to receive the latest property information in real time. As a result, the property matching system can understand the user's desired conditions in detail and provide optimal property information.

[0029] The property matching system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a notification unit. The reception unit receives the user's desired conditions. The user's desired conditions include, for example, price, location, and floor plan, but are not limited to these examples. The reception unit, for example, stores the desired conditions entered by the user in a database. The reception unit can also update the user's desired conditions in real time. The generation unit uses a generation AI to analyze the desired conditions received by the reception unit and generate optimal property information. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate optimal property information. The generation unit can also use a generation AI to generate property information based on the user's desired conditions. For example, the generation AI takes the user's desired conditions as input and outputs optimal property information. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The proposal unit makes suggestions to relax the user's desired conditions. For example, the suggestion unit can make suggestions such as expanding the "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." The suggestion unit can also make suggestions to relax the user's desired conditions. For example, the suggestion unit takes the user's desired conditions as input and outputs relaxed conditions. The notification unit notifies the user when a new property matching the conditions suggested by the suggestion unit is registered. For example, the notification unit notifies the user when a new property matching the conditions is registered. For example, the notification unit sends a notification via a messaging app. The notification unit can also notify the user by email when a new property matching the conditions is registered. For example, the notification unit sends an email when a new property matching the conditions is registered. As a result, the property matching system according to this embodiment can understand the user's desired conditions in detail and provide optimal property information.

[0030] The reception desk receives user preferences. These preferences include, but are not limited to, price, location, and floor plan. The reception desk stores the user's preferences in a database. Specifically, information entered by the user through web forms or applications is saved to the database in real time. This ensures that the user's preferences are immediately reflected in the system. The reception desk can also update user preferences in real time. For example, if a user changes their preferences, the change is immediately reflected in the database. This ensures that users always receive property information based on their most up-to-date preferences. Furthermore, the reception desk provides an interface to assist user input. For example, it can provide options such as price range, location, and floor plan in the form of pull-down menus or sliders, making it easy for users to enter their preferences. The reception desk also analyzes user input, detecting errors and inconsistencies and prompting corrections. This allows users to enter their preferences accurately and efficiently. Additionally, the reception desk can suggest recommended preferences based on the user's past input history. For example, based on previously entered criteria and search history, the system automatically suggests criteria that the user might be interested in. This allows users to set their desired criteria more quickly and accurately.

[0031] The generation unit uses a generation AI to analyze the user's desired conditions received by the reception unit and generate the most suitable property information. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate the most suitable property information. Specifically, the generation AI receives the user's desired conditions as input and generates the most suitable property information based on past property data and market trends. For example, the generation AI analyzes the user's desired price range, location, floor plan, and other conditions and generates property information that matches them. The generation AI can also generate property information based on the user's desired conditions. For example, the generation AI takes the user's desired conditions as input and outputs the most suitable property information. The generation AI uses natural language processing technology to analyze the user's desired conditions in detail and generate the most suitable property information. This allows the generation unit to quickly and accurately generate property information that best suits the user's desired conditions. Furthermore, the generation unit can periodically update the generation AI's learning data to reflect the latest market trends and property information. This allows the generation unit to always provide the most suitable property information based on the latest information. Furthermore, the generation unit can improve the AI ​​algorithm based on user feedback, enabling it to generate more accurate property information. This allows the generation unit to improve user satisfaction.

[0032] The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the proposal unit makes suggestions to relax the user's desired conditions. Specifically, the proposal unit analyzes the user's desired conditions and proposes more properties by slightly relaxing the conditions. For example, the proposal unit may suggest expanding "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." This allows the user to consider more properties that are close to their desired conditions. The proposal unit can also make suggestions to relax the user's desired conditions. For example, the proposal unit takes the user's desired conditions as input and outputs relaxed conditions. The proposal unit automatically generates relaxed conditions based on the user's desired conditions and proposes them to the user. This allows the user to consider more properties that are close to their desired conditions. Furthermore, the proposal unit can improve its suggestions based on user feedback. For example, it analyzes how the user reacted to the proposed conditions and reflects this in the next suggestion. This allows the proposal unit to make suggestions that are better suited to the user's needs. Furthermore, the suggestion department can provide more personalized suggestions based on the user's past search history and preferences. This allows the suggestion department to improve user satisfaction.

[0033] The notification unit notifies users when a new property matching the criteria proposed by the proposal unit is registered. For example, the notification unit notifies the user when a new property matching the criteria is registered. Specifically, the notification unit immediately notifies the user when a new property matching the criteria is registered, based on the notification method set by the user. For example, the notification unit sends notifications via messaging apps. The notification unit can also notify users via email when a new property matching the criteria is registered. For example, the notification unit sends email notifications when a new property matching the criteria is registered. This allows users to immediately know when a new property matching their desired criteria is registered. Furthermore, the notification unit allows users to flexibly change their notification settings. For example, if a user wants to change the frequency or method of notifications, an interface is provided that allows them to easily change these settings. In addition, the notification unit can combine multiple notification methods to ensure that information is delivered to the user. For example, by using messaging app notifications and email notifications together, important information can be reliably delivered to the user. This allows the notification unit to provide information to users quickly and reliably, enabling them to consider properties that match their desired criteria without missing anything. Furthermore, the notification unit can improve its notification content and methods based on user feedback, enabling it to deliver more effective notifications. This allows the notification unit to improve user satisfaction.

[0034] The proposal unit can make suggestions to relax the conditions. For example, the proposal unit can make suggestions to relax the user's desired conditions. For example, the proposal unit can suggest expanding "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." The proposal unit can also make suggestions to relax the user's desired conditions. For example, the proposal unit can take the user's desired conditions as input and output relaxed conditions. In this way, by making suggestions to relax the conditions, more options can be provided to the user.

[0035] The generation unit can generate optimal property information using a generation AI. For example, the generation unit uses a generation AI to analyze the user's desired conditions and generate optimal property information. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate optimal property information. Furthermore, the generation unit can also generate property information based on the user's desired conditions using the generation AI. For example, the generation AI takes the user's desired conditions as input and outputs optimal property information. Thus, by using the generation AI, optimal property information can be generated.

[0036] The notification unit can notify users when a new property matching the specified criteria is registered. For example, the notification unit will notify users when a new property matching the criteria is registered. For example, the notification unit will send notifications via messaging apps. The notification unit can also notify users via email when a new property matching the criteria is registered. For example, the notification unit will send email notifications when a new property matching the criteria is registered. This allows users to receive the latest property information in real time by notifying them when a new property matching the criteria is registered.

[0037] The reception desk can analyze the user's past preference history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past preference history. In this way, by analyzing the past preference history, the reception desk can suggest the optimal input method to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past preference history into a generating AI and have the generating AI suggest the optimal input method.

[0038] The reception desk can filter the user's current living situation and areas of interest when receiving their desired conditions. For example, if the user enters their family structure, the reception desk will prioritize displaying properties suitable for families. It can also prioritize displaying pet-friendly properties if the user owns pets. Furthermore, if the reception desk enters hobbies or areas of interest, it can suggest relevant properties based on that information. This allows for more appropriate property information to be provided by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI and have the generating AI perform the filtering.

[0039] The reception desk can prioritize highly relevant conditions when receiving user requests, taking into account the user's geographical location. For example, if a user lives in a specific area, the reception desk can prioritize displaying properties related to that area. Similarly, if a user desires a location near a specific train station, the reception desk can prioritize displaying properties around that station. Furthermore, if a user desires a location within a specific school district, the reception desk can prioritize displaying properties within that school district. This allows for the provision of more relevant property information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the AI ​​prioritize highly relevant conditions.

[0040] The reception desk can analyze the user's social media activity when receiving their desired conditions and suggest relevant conditions. For example, the reception desk can suggest relevant properties based on places and events the user has shared on social media. It can also suggest relevant properties based on accounts the user follows on social media. Furthermore, it can suggest relevant properties based on photos and comments the user has posted on social media. This allows for the provision of more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant conditions.

[0041] The generation unit can generate optimal property information by referring to the user's past search history when generating property information. For example, the generation unit can generate relevant property information based on the characteristics of properties the user has searched for in the past. The generation unit can also extract preferred areas and conditions from the user's past search history and generate optimal property information. Furthermore, the generation unit can generate relevant property information by referring to property information previously saved by the user. This allows for the provision of more appropriate property information by referring to the user's past search history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past search history data into a generation AI and have the generation AI execute the generation of optimal property information.

[0042] The generation unit can customize property information based on the user's current living situation when generating property information. For example, if the user enters their family structure, the generation unit can provide property information suitable for families. It can also provide pet-friendly property information if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the generation unit can provide relevant property information based on that information. This allows for the provision of more appropriate property information by customizing it based on the user's living situation. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's living situation data into a generation AI and have the generation AI perform the property information customization.

[0043] The generation unit can generate optimal property information by considering the user's geographical location when generating property information. For example, if the user lives in a specific area, the generation unit can provide property information related to that area. Furthermore, if the user desires a location near a specific train station, the generation unit can provide property information around that station. Additionally, if the user desires a location within a specific school district, the generation unit can provide property information within that school district. This allows for the provision of more appropriate property information by considering the user's geographical location. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI generate optimal property information.

[0044] The generation unit can generate property information by analyzing the user's social media activity. For example, the generation unit can provide relevant property information based on places and events shared by the user on social media. It can also provide relevant property information based on accounts followed by the user on social media. Furthermore, it can provide relevant property information based on photos and comments posted by the user on social media. This allows for the provision of more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of property information.

[0045] The proposal unit can analyze the user's past compromises to make the optimal proposal when suggesting a relaxation of conditions. For example, the proposal unit can suggest related relaxations based on conditions the user has compromised on in the past. It can also suggest the optimal relaxation of conditions based on the user's past compromises. Furthermore, the proposal unit can suggest related relaxations by referring to conditions the user has compromised on in the past. In this way, by analyzing the user's past compromises, it is possible to make more appropriate proposals for relaxation of conditions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past compromise data into a generating AI and have the generating AI perform the generation of the optimal proposal.

[0046] The suggestion unit can customize the suggested conditions based on the user's current living situation when proposing relaxed conditions. For example, if the user enters their family structure, the suggestion unit will propose relaxed conditions for families. Furthermore, if the user owns a pet, the suggestion unit can also propose relaxed conditions related to pet-friendly properties. Additionally, if the user enters their hobbies or areas of interest, the suggestion unit can propose related relaxed conditions based on that information. This allows for more appropriate suggestions by customizing the suggestions based on the user's living situation. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI customize the suggested conditions.

[0047] The suggestion unit can make optimal suggestions by considering the user's geographical location when proposing relaxations of conditions. For example, if the user lives in a specific area, the suggestion unit can propose relaxations of conditions related to that area. Furthermore, if the user desires a location near a specific train station, the suggestion unit can propose relaxations of conditions around that station. Additionally, if the user desires a specific school district, the suggestion unit can propose relaxations of conditions within that school district. This allows for more appropriate suggestions of relaxations of conditions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI generate optimal suggestions.

[0048] The proposal unit can analyze the user's social media activity to determine the content of the proposal when suggesting a relaxation of conditions. For example, the proposal unit can make relevant condition relaxation suggestions based on places and events shared by the user on social media. It can also make relevant condition relaxation suggestions based on accounts followed by the user on social media. Furthermore, it can make relevant condition relaxation suggestions based on photos and comments posted by the user on social media. This allows for more appropriate condition relaxation suggestions to be made by analyzing the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI determine the content of the proposal.

[0049] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can select the optimal notification method based on the notification methods the user has preferred to receive in the past. The notification unit can also select the most effective notification method from the user's past notification history. Furthermore, the notification unit can select the optimal notification method by avoiding notification methods the user has ignored in the past. This allows for the selection of a more appropriate notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0050] The notification unit can customize notification content based on the user's current living situation when it sends a notification. For example, if the user enters their family structure, the notification unit can provide notifications tailored to the family. It can also provide pet-related notifications if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the notification unit can provide relevant notifications based on that information. This allows for more appropriate notifications by customizing them based on the user's living situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's living situation data into a generating AI and have the generating AI customize the notification content.

[0051] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location information. For example, if the user lives in a specific area, the notification unit can provide a notification method relevant to that area. It can also provide a notification method around a specific train station if the user prefers to be near that station. Furthermore, if the user prefers to be in a specific school district, the notification unit can provide a notification method within that school district. This allows for the selection of a more appropriate notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the most appropriate notification method.

[0052] The notification unit can analyze the user's social media activity to determine the content of the notification. For example, the notification unit can provide relevant notification content based on places and events shared by the user on social media. It can also provide relevant notification content based on accounts followed by the user on social media. Furthermore, it can provide relevant notification content based on photos and comments posted by the user on social media. This allows for the provision of more appropriate notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI determine the notification content.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can suggest the most suitable input method by referring to the user's past search history when receiving user requests. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use at a specific time of day based on their past request history. In this way, by analyzing past request history, the system can suggest the most suitable input method for the user.

[0055] The generation unit can customize property information based on the user's current living situation when generating property information. For example, if the user enters their family structure, it can provide property information suitable for families. If the user owns a pet, it can also provide pet-friendly property information. Furthermore, if the user enters their hobbies or areas of interest, it can provide relevant property information based on that information. This allows for the provision of more appropriate property information by customizing it based on the user's living situation.

[0056] The proposal department can analyze the user's past compromises when proposing relaxations of conditions to make the most appropriate proposal. For example, it can propose related relaxations based on conditions the user has compromised on in the past. It can also propose the most appropriate relaxations based on the user's past compromises. Furthermore, it can propose related relaxations by referring to conditions the user has compromised on in the past. In this way, by analyzing the user's past compromises, it is possible to make more appropriate proposals for relaxations of conditions.

[0057] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, it can select the optimal notification method based on the notification methods the user has preferred to receive in the past. It can also select the most effective notification method from the user's past notification history. Furthermore, it can select the optimal notification method by avoiding notification methods the user has ignored in the past. In this way, by referring to the user's past notification history, a more appropriate notification method can be selected.

[0058] The generation unit can generate optimal property information by considering the user's geographical location. For example, if a user lives in a specific area, it can provide property information related to that area. If a user desires a location near a specific train station, it can provide property information around that station. Furthermore, if a user desires a location within a specific school district, it can provide property information within that school district. This allows for the provision of more appropriate property information by considering the user's geographical location.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives the user's preferences. These preferences include price, location, floor plan, etc. The reception desk can also save the user's preferences in a database and update it in real time. Step 2: The generation unit uses a generation AI to analyze the desired conditions received by the reception unit and generate the most suitable property information. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and output the most suitable property information. Step 3: The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, it may suggest expanding the range from "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." Step 4: The notification unit notifies the user when a new property matching the criteria proposed by the proposal unit is registered. For example, when a new property matching the criteria is registered, the notification is sent to the user via a messaging app or email.

[0061] (Example of form 2) The property matching system according to an embodiment of the present invention is a property matching tool that uses a generative AI. This property matching system is a system that understands the user's desired conditions and lifestyle in detail through conversational AI chat and provides optimal property information. When a user enters conditions such as "10 minutes walk from the station," the generative AI analyzes those conditions and proposes the most suitable properties. Furthermore, it makes suggestions to relax the conditions (e.g., extending "10 minutes walk from the station" to "11 minutes walk," changing "south-facing" to "southwest or southeast-facing") to provide the best possible options within the budget. In addition, when a new property matching the conditions is registered, the generative AI notifies the user via a messaging app. First, the user enters their desired conditions. For example, they enter conditions such as "within 10 minutes walk from the station, south-facing, budget under 10 million yen." This information is input to the generative AI, which then begins its analysis. Next, the generative AI analyzes the user's desired conditions and provides optimal property information. For example, if a user enters "within 10 minutes walk from the station, south-facing, budget under 10 million yen," the generative AI searches for and lists properties that match those conditions. Furthermore, the generating AI suggests relaxing the search criteria. For example, it might suggest expanding the criteria from "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing," providing users with more options. This allows users to find the most suitable property within their budget. Additionally, when a new property matching the criteria is registered, the generating AI notifies the user via the messaging app. This allows users to receive the latest property information in real time. This system ensures that even users with strong preferences and limited budgets can find satisfactory property information. For example, if a user enters criteria such as "within 10 minutes walk from the station, south-facing, and under 10 million yen," the generating AI will list properties that meet those criteria and suggest further relaxing the criteria, providing users with more options. Additionally, when a new property matching the criteria is registered, the generating AI notifies the user via the messaging app. This allows users to receive the latest property information in real time. As a result, the property matching system can understand the user's desired conditions in detail and provide optimal property information.

[0062] The property matching system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a notification unit. The reception unit receives the user's desired conditions. The user's desired conditions include, for example, price, location, and floor plan, but are not limited to these examples. The reception unit, for example, stores the desired conditions entered by the user in a database. The reception unit can also update the user's desired conditions in real time. The generation unit uses a generation AI to analyze the desired conditions received by the reception unit and generate optimal property information. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate optimal property information. The generation unit can also use a generation AI to generate property information based on the user's desired conditions. For example, the generation AI takes the user's desired conditions as input and outputs optimal property information. The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. The proposal unit makes suggestions to relax the user's desired conditions. For example, the suggestion unit can make suggestions such as expanding the "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." The suggestion unit can also make suggestions to relax the user's desired conditions. For example, the suggestion unit takes the user's desired conditions as input and outputs relaxed conditions. The notification unit notifies the user when a new property matching the conditions suggested by the suggestion unit is registered. For example, the notification unit notifies the user when a new property matching the conditions is registered. For example, the notification unit sends a notification via a messaging app. The notification unit can also notify the user by email when a new property matching the conditions is registered. For example, the notification unit sends an email when a new property matching the conditions is registered. As a result, the property matching system according to this embodiment can understand the user's desired conditions in detail and provide optimal property information.

[0063] The reception desk receives user preferences. These preferences include, but are not limited to, price, location, and floor plan. The reception desk stores the user's preferences in a database. Specifically, information entered by the user through web forms or applications is saved to the database in real time. This ensures that the user's preferences are immediately reflected in the system. The reception desk can also update user preferences in real time. For example, if a user changes their preferences, the change is immediately reflected in the database. This ensures that users always receive property information based on their most up-to-date preferences. Furthermore, the reception desk provides an interface to assist user input. For example, it can provide options such as price range, location, and floor plan in the form of pull-down menus or sliders, making it easy for users to enter their preferences. The reception desk also analyzes user input, detecting errors and inconsistencies and prompting corrections. This allows users to enter their preferences accurately and efficiently. Additionally, the reception desk can suggest recommended preferences based on the user's past input history. For example, based on previously entered criteria and search history, the system automatically suggests criteria that the user might be interested in. This allows users to set their desired criteria more quickly and accurately.

[0064] The generation unit uses a generation AI to analyze the user's desired conditions received by the reception unit and generate the most suitable property information. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate the most suitable property information. Specifically, the generation AI receives the user's desired conditions as input and generates the most suitable property information based on past property data and market trends. For example, the generation AI analyzes the user's desired price range, location, floor plan, and other conditions and generates property information that matches them. The generation AI can also generate property information based on the user's desired conditions. For example, the generation AI takes the user's desired conditions as input and outputs the most suitable property information. The generation AI uses natural language processing technology to analyze the user's desired conditions in detail and generate the most suitable property information. This allows the generation unit to quickly and accurately generate property information that best suits the user's desired conditions. Furthermore, the generation unit can periodically update the generation AI's learning data to reflect the latest market trends and property information. This allows the generation unit to always provide the most suitable property information based on the latest information. Furthermore, the generation unit can improve the AI ​​algorithm based on user feedback, enabling it to generate more accurate property information. This allows the generation unit to improve user satisfaction.

[0065] The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, the proposal unit makes suggestions to relax the user's desired conditions. Specifically, the proposal unit analyzes the user's desired conditions and proposes more properties by slightly relaxing the conditions. For example, the proposal unit may suggest expanding "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." This allows the user to consider more properties that are close to their desired conditions. The proposal unit can also make suggestions to relax the user's desired conditions. For example, the proposal unit takes the user's desired conditions as input and outputs relaxed conditions. The proposal unit automatically generates relaxed conditions based on the user's desired conditions and proposes them to the user. This allows the user to consider more properties that are close to their desired conditions. Furthermore, the proposal unit can improve its suggestions based on user feedback. For example, it analyzes how the user reacted to the proposed conditions and reflects this in the next suggestion. This allows the proposal unit to make suggestions that are better suited to the user's needs. Furthermore, the suggestion department can provide more personalized suggestions based on the user's past search history and preferences. This allows the suggestion department to improve user satisfaction.

[0066] The notification unit notifies users when a new property matching the criteria proposed by the proposal unit is registered. For example, the notification unit notifies the user when a new property matching the criteria is registered. Specifically, the notification unit immediately notifies the user when a new property matching the criteria is registered, based on the notification method set by the user. For example, the notification unit sends notifications via messaging apps. The notification unit can also notify users via email when a new property matching the criteria is registered. For example, the notification unit sends email notifications when a new property matching the criteria is registered. This allows users to immediately know when a new property matching their desired criteria is registered. Furthermore, the notification unit allows users to flexibly change their notification settings. For example, if a user wants to change the frequency or method of notifications, an interface is provided that allows them to easily change these settings. In addition, the notification unit can combine multiple notification methods to ensure that information is delivered to the user. For example, by using messaging app notifications and email notifications together, important information can be reliably delivered to the user. This allows the notification unit to provide information to users quickly and reliably, enabling them to consider properties that match their desired criteria without missing anything. Furthermore, the notification unit can improve its notification content and methods based on user feedback, enabling it to deliver more effective notifications. This allows the notification unit to improve user satisfaction.

[0067] The proposal unit can make suggestions to relax the conditions. For example, the proposal unit can make suggestions to relax the user's desired conditions. For example, the proposal unit can suggest expanding "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." The proposal unit can also make suggestions to relax the user's desired conditions. For example, the proposal unit can take the user's desired conditions as input and output relaxed conditions. In this way, by making suggestions to relax the conditions, more options can be provided to the user.

[0068] The generation unit can generate optimal property information using a generation AI. For example, the generation unit uses a generation AI to analyze the user's desired conditions and generate optimal property information. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and generate optimal property information. Furthermore, the generation unit can also generate property information based on the user's desired conditions using the generation AI. For example, the generation AI takes the user's desired conditions as input and outputs optimal property information. Thus, by using the generation AI, optimal property information can be generated.

[0069] The notification unit can notify users when a new property matching the specified criteria is registered. For example, the notification unit will notify users when a new property matching the criteria is registered. For example, the notification unit will send notifications via messaging apps. The notification unit can also notify users via email when a new property matching the criteria is registered. For example, the notification unit will send email notifications when a new property matching the criteria is registered. This allows users to receive the latest property information in real time by notifying them when a new property matching the criteria is registered.

[0070] The reception desk can estimate the user's emotions and adjust the input interface for desired conditions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of desired conditions. By adjusting the input interface according to the user's emotions, it is possible to reduce user stress and provide a more comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past preference history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest conditions to be used during specific time periods based on the user's past preference history. In this way, by analyzing the past preference history, the reception desk can suggest the optimal input method to the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past preference history into a generating AI and have the generating AI suggest the optimal input method.

[0072] The reception desk can filter the user's current living situation and areas of interest when receiving their desired conditions. For example, if the user enters their family structure, the reception desk will prioritize displaying properties suitable for families. It can also prioritize displaying pet-friendly properties if the user owns pets. Furthermore, if the reception desk enters hobbies or areas of interest, it can suggest relevant properties based on that information. This allows for more appropriate property information to be provided by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's living situation data into a generating AI and have the generating AI perform the filtering.

[0073] The reception desk can estimate the user's emotions and prioritize desired conditions based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying important conditions and postpone other conditions. If the user is relaxed, the reception desk can also display all conditions equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying the most important conditions, allowing for quick selection. This allows for the provision of more appropriate property information by prioritizing desired conditions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception desk can prioritize highly relevant conditions when receiving user requests, taking into account the user's geographical location. For example, if a user lives in a specific area, the reception desk can prioritize displaying properties related to that area. Similarly, if a user desires a location near a specific train station, the reception desk can prioritize displaying properties around that station. Furthermore, if a user desires a location within a specific school district, the reception desk can prioritize displaying properties within that school district. This allows for the provision of more relevant property information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the AI ​​prioritize highly relevant conditions.

[0075] The reception desk can analyze the user's social media activity when receiving their desired conditions and suggest relevant conditions. For example, the reception desk can suggest relevant properties based on places and events the user has shared on social media. It can also suggest relevant properties based on accounts the user follows on social media. Furthermore, it can suggest relevant properties based on photos and comments the user has posted on social media. This allows for the provision of more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant conditions.

[0076] The generation unit can estimate the user's emotions and adjust the method of generating property information based on the estimated user emotions. For example, if the user is relaxed, the generation unit can provide detailed property information. If the user is in a hurry, the generation unit can also provide concise property information that gets straight to the point. Furthermore, if the user is excited, the generation unit can provide visually appealing property information. In this way, by adjusting the method of generating property information according to the user's emotions, more appropriate property information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The generation unit can generate optimal property information by referring to the user's past search history when generating property information. For example, the generation unit can generate relevant property information based on the characteristics of properties the user has searched for in the past. The generation unit can also extract preferred areas and conditions from the user's past search history and generate optimal property information. Furthermore, the generation unit can generate relevant property information by referring to property information previously saved by the user. This allows for the provision of more appropriate property information by referring to the user's past search history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past search history data into a generation AI and have the generation AI execute the generation of optimal property information.

[0078] The generation unit can customize property information based on the user's current living situation when generating property information. For example, if the user enters their family structure, the generation unit can provide property information suitable for families. It can also provide pet-friendly property information if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the generation unit can provide relevant property information based on that information. This allows for the provision of more appropriate property information by customizing it based on the user's living situation. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's living situation data into a generation AI and have the generation AI perform the property information customization.

[0079] The generation unit can estimate the user's emotions and determine the priority of property information to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize displaying important property information. If the user is relaxed, the generation unit can also display all property information equally. Furthermore, if the user is in a hurry, the generation unit can prioritize displaying the most important property information. This allows for the provision of more appropriate property information by prioritizing property information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The generation unit can generate optimal property information by considering the user's geographical location when generating property information. For example, if the user lives in a specific area, the generation unit can provide property information related to that area. Furthermore, if the user desires a location near a specific train station, the generation unit can provide property information around that station. Additionally, if the user desires a location within a specific school district, the generation unit can provide property information within that school district. This allows for the provision of more appropriate property information by considering the user's geographical location. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI generate optimal property information.

[0081] The generation unit can generate property information by analyzing the user's social media activity. For example, the generation unit can provide relevant property information based on places and events shared by the user on social media. It can also provide relevant property information based on accounts followed by the user on social media. Furthermore, it can provide relevant property information based on photos and comments posted by the user on social media. This allows for the provision of more appropriate property information by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of property information.

[0082] The suggestion unit can estimate the user's emotions and adjust the method of suggesting condition mitigation based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can offer simple and easy-to-understand condition mitigation suggestions. If the user is relaxed, the suggestion unit can also offer detailed condition mitigation suggestions. Furthermore, if the user is in a hurry, the suggestion unit can offer rapid condition mitigation suggestions. By adjusting the method of suggesting condition mitigation according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The proposal unit can analyze the user's past compromises to make the optimal proposal when suggesting a relaxation of conditions. For example, the proposal unit can suggest related relaxations based on conditions the user has compromised on in the past. It can also suggest the optimal relaxation of conditions based on the user's past compromises. Furthermore, the proposal unit can suggest related relaxations by referring to conditions the user has compromised on in the past. In this way, by analyzing the user's past compromises, it is possible to make more appropriate proposals for relaxation of conditions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past compromise data into a generating AI and have the generating AI perform the generation of the optimal proposal.

[0084] The suggestion unit can customize the suggested conditions based on the user's current living situation when proposing relaxed conditions. For example, if the user enters their family structure, the suggestion unit will propose relaxed conditions for families. Furthermore, if the user owns a pet, the suggestion unit can also propose relaxed conditions related to pet-friendly properties. Additionally, if the user enters their hobbies or areas of interest, the suggestion unit can propose related relaxed conditions based on that information. This allows for more appropriate suggestions by customizing the suggestions based on the user's living situation. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI customize the suggested conditions.

[0085] The suggestion unit can estimate the user's emotions and determine the priority of condition relaxations based on the estimated user emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting important condition relaxations. If the user is relaxed, the suggestion unit can also suggest all condition relaxations equally. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting the most important condition relaxations. This allows for more appropriate suggestions by determining the priority of condition relaxations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The suggestion unit can make optimal suggestions by considering the user's geographical location when proposing relaxations of conditions. For example, if the user lives in a specific area, the suggestion unit can propose relaxations of conditions related to that area. Furthermore, if the user desires a location near a specific train station, the suggestion unit can propose relaxations of conditions around that station. Additionally, if the user desires a specific school district, the suggestion unit can propose relaxations of conditions within that school district. This allows for more appropriate suggestions of relaxations of conditions by considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI generate optimal suggestions.

[0087] The proposal unit can analyze the user's social media activity to determine the content of the proposal when suggesting a relaxation of conditions. For example, the proposal unit can make relevant condition relaxation suggestions based on places and events shared by the user on social media. It can also make relevant condition relaxation suggestions based on accounts followed by the user on social media. Furthermore, it can make relevant condition relaxation suggestions based on photos and comments posted by the user on social media. This allows for more appropriate condition relaxation suggestions to be made by analyzing the user's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the user's social media activity data into a generating AI and have the generating AI determine the content of the proposal.

[0088] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and easy-to-understand notification. If the user is relaxed, the notification unit can also provide a more detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a quick and concise notification. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can select the optimal notification method based on the notification methods the user has preferred to receive in the past. The notification unit can also select the most effective notification method from the user's past notification history. Furthermore, the notification unit can select the optimal notification method by avoiding notification methods the user has ignored in the past. This allows for the selection of a more appropriate notification method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0090] The notification unit can customize notification content based on the user's current living situation when it sends a notification. For example, if the user enters their family structure, the notification unit can provide notifications tailored to the family. It can also provide pet-related notifications if the user owns pets. Furthermore, if the user enters their hobbies or areas of interest, the notification unit can provide relevant notifications based on that information. This allows for more appropriate notifications by customizing them based on the user's living situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's living situation data into a generating AI and have the generating AI customize the notification content.

[0091] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize displaying important notifications. If the user is relaxed, the notification unit can also display all notifications equally. Furthermore, if the user is in a hurry, the notification unit can prioritize displaying the most important notifications. This allows for more appropriate notifications by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The notification unit can select the most appropriate notification method when sending a notification, taking into account the user's geographical location information. For example, if the user lives in a specific area, the notification unit can provide a notification method relevant to that area. It can also provide a notification method around a specific train station if the user prefers to be near that station. Furthermore, if the user prefers to be in a specific school district, the notification unit can provide a notification method within that school district. This allows for the selection of a more appropriate notification method by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI select the most appropriate notification method.

[0093] The notification unit can analyze the user's social media activity to determine the content of the notification. For example, the notification unit can provide relevant notification content based on places and events shared by the user on social media. It can also provide relevant notification content based on accounts followed by the user on social media. Furthermore, it can provide relevant notification content based on photos and comments posted by the user on social media. This allows for the provision of more appropriate notification content by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's social media activity data into a generating AI and have the generating AI determine the notification content.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can suggest the most suitable input method by referring to the user's past search history when receiving user requests. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use at a specific time of day based on their past request history. In this way, by analyzing past request history, the system can suggest the most suitable input method for the user.

[0096] The generation unit can customize property information based on the user's current living situation when generating property information. For example, if the user enters their family structure, it can provide property information suitable for families. If the user owns a pet, it can also provide pet-friendly property information. Furthermore, if the user enters their hobbies or areas of interest, it can provide relevant property information based on that information. This allows for the provision of more appropriate property information by customizing it based on the user's living situation.

[0097] The proposal department can analyze the user's past compromises when proposing relaxations of conditions to make the most appropriate proposal. For example, it can propose related relaxations based on conditions the user has compromised on in the past. It can also propose the most appropriate relaxations based on the user's past compromises. Furthermore, it can propose related relaxations by referring to conditions the user has compromised on in the past. In this way, by analyzing the user's past compromises, it is possible to make more appropriate proposals for relaxations of conditions.

[0098] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, it can select the optimal notification method based on the notification methods the user has preferred to receive in the past. It can also select the most effective notification method from the user's past notification history. Furthermore, it can select the optimal notification method by avoiding notification methods the user has ignored in the past. In this way, by referring to the user's past notification history, a more appropriate notification method can be selected.

[0099] The reception desk can estimate the user's emotions and adjust the input interface for desired conditions based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of desired conditions. In this way, by adjusting the input interface according to the user's emotions, it is possible to reduce user stress and provide a more comfortable input experience.

[0100] The generation unit can estimate the user's emotions and adjust the property information generation method based on the estimated emotions. For example, if the user is relaxed, it can provide detailed property information. If the user is in a hurry, it can provide concise property information that gets straight to the point. Furthermore, if the user is excited, it can provide visually appealing property information. In this way, by adjusting the property information generation method according to the user's emotions, more appropriate property information can be provided.

[0101] The suggestion function can estimate the user's emotions and adjust the method of suggesting condition mitigation based on those emotions. For example, if the user is stressed, it can offer simple and easy-to-understand condition mitigation suggestions. If the user is relaxed, it can offer more detailed suggestions. Furthermore, if the user is in a hurry, it can offer quick suggestions. By adjusting the method of suggesting condition mitigation according to the user's emotions, it can provide more appropriate suggestions.

[0102] The notification unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand notification. If the user is relaxed, it can provide a more detailed notification. Furthermore, if the user is in a hurry, it can provide a quick and concise notification. By adjusting the notification method according to the user's emotions, it is possible to provide more appropriate notifications.

[0103] The suggestion function can estimate the user's emotions and determine the priority of condition mitigation based on those emotions. For example, if the user is stressed, it will prioritize suggesting important condition mitigation. If the user is relaxed, it can suggest all condition mitigation equally. Furthermore, if the user is in a hurry, it can prioritize suggesting the most important condition mitigation. By prioritizing condition mitigation according to the user's emotions, it can provide more appropriate suggestions.

[0104] The generation unit can generate optimal property information by considering the user's geographical location. For example, if a user lives in a specific area, it can provide property information related to that area. If a user desires a location near a specific train station, it can provide property information around that station. Furthermore, if a user desires a location within a specific school district, it can provide property information within that school district. This allows for the provision of more appropriate property information by considering the user's geographical location.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives the user's preferences. These preferences include price, location, floor plan, etc. The reception desk can also save the user's preferences in a database and update it in real time. Step 2: The generation unit uses a generation AI to analyze the desired conditions received by the reception unit and generate the most suitable property information. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's desired conditions and output the most suitable property information. Step 3: The proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit. For example, it may suggest expanding the range from "10 minutes walk from the station" to "11 minutes walk," or changing "south-facing" to "southwest or southeast-facing." Step 4: The notification unit notifies the user when a new property matching the criteria proposed by the proposal unit is registered. For example, when a new property matching the criteria is registered, the notification is sent to the user via a messaging app or email.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's desired conditions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the desired conditions and generate optimal property information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The notification unit is implemented by the output device 40 of the smart device 14 and notifies the user when a new property matching the conditions is registered. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's desired conditions. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generation AI to analyze the desired conditions and generate optimal property information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The notification unit is implemented by the speaker 240 of the smart glasses 214 and notifies the user when a new property matching the conditions is registered. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and notification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's desired conditions. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the desired conditions and generate optimal property information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The notification unit is implemented by, for example, the speaker 240 of the headset terminal 314 and notifies the user when a new property matching the conditions is registered. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's desired conditions. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the desired conditions and generate optimal property information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes suggestions to relax the conditions. The notification unit is implemented by, for example, the speaker 240 of the robot 414 and notifies when a new property matching the conditions is registered. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception desk that accepts user requests and conditions, A generation unit analyzes the desired conditions received by the reception unit and generates optimal property information, A proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit, The system includes a notification unit that notifies when a property matching the conditions proposed by the proposal unit is newly registered. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose to relax the conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates optimal property information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, We will notify you when a new property matching your criteria is registered. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for desired conditions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze the user's past preference history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving user requests, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of desired conditions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving user requests, the system prioritizes requests based on the user's geographical location, ensuring that most relevant conditions are considered. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving user requests, we analyze their social media activity and suggest relevant conditions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We estimate the user's emotions and adjust the property information generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating property information, the system references the user's past search history to generate the most suitable property information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating property information, customize the property information based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and determines the priority of property information to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating property information, the system takes the user's geographical location into consideration to generate the most suitable property information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating property information, the system analyzes the user's social media activity to generate the property information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the proposed condition relaxation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a relaxation of conditions, we analyze the user's past compromises to make the optimal proposal. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing a relaxation of conditions, the proposal will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system estimates the user's emotions and determines the priority of condition relaxations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing relaxations of conditions, we will make the optimal proposal by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When proposing a relaxation of conditions, we analyze users' social media activity to determine the content of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending a notification, the content of the notification is determined by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user requests and conditions, A generation unit analyzes the desired conditions received by the reception unit and generates optimal property information, A proposal unit makes suggestions to relax the conditions based on the property information generated by the generation unit, The system includes a notification unit that notifies when a property matching the conditions proposed by the proposal unit is newly registered. A system characterized by the following features.

2. The aforementioned proposal section is, We propose to relax the conditions. The system according to feature 1.

3. The generating unit is The AI ​​generates optimal property information. The system according to feature 1.

4. The aforementioned notification unit, We will notify you when a new property matching your criteria is registered. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for desired conditions based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze the user's past preference history and suggest the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When receiving user requests, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of desired conditions based on those estimated emotions. The system according to feature 1.

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