system

Abstract

The system according to the embodiment aims to quickly respond to inquiries from customers and propose suitable properties. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a search unit, a proposal unit, and a provision unit. The reception unit receives questions from customers. The analysis unit analyzes the questions received by the reception unit. The search unit searches for properties based on the questions analyzed by the analysis unit. The proposal unit proposes properties searched for by the search unit. The provision unit provides property information proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to respond quickly to customer questions and make appropriate property suggestions.

[0005] The system according to the embodiment aims to quickly respond to inquiries from customers and propose suitable properties. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, a proposal unit, and a provision unit. The reception unit receives questions from customers. The analysis unit analyzes the questions received by the reception unit. The search unit searches for properties based on the questions analyzed by the analysis unit. The proposal unit proposes properties searched for by the search unit. The provision unit provides property information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly respond to inquiries from customers and propose suitable properties. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication platform for real estate agents according to an embodiment of the present invention is a system that utilizes generative AI to facilitate communication between real estate agents and customers. This system accepts customer questions, and the generative AI generates appropriate answers and suggests properties based on the customer's needs. This reduces the burden on real estate agents and allows customers to efficiently search for properties based on their needs. For example, when a customer inputs a question, a reception unit accepts the question. Next, an analysis unit analyzes the question accepted by the reception unit, and the generative AI understands the question and generates an appropriate answer. Furthermore, a search unit searches for properties based on instructions from the analysis unit, and a suggestion unit suggests appropriate properties based on the search results. Finally, a provision unit provides the suggested property information to the customer. This allows the communication platform for real estate agents utilizing generative AI to facilitate communication between real estate agents and customers, reduce the burden on real estate agents, and enable customers to efficiently search for properties. This allows the communication platform for real estate agents to respond to customer questions 24 hours a day, and the generative AI suggests appropriate properties, enabling quick and accurate responses.

[0029] A communication platform for real estate agents according to an embodiment includes a reception unit, an analysis unit, a search unit, a proposal unit, and a provision unit. The reception unit accepts questions from customers. For example, if a customer asks, "Are there any 3LDK properties in this area?", the question is input to the reception unit. The analysis unit uses a generation AI to analyze the question accepted by the reception unit. For example, the generation AI understands the question, "Are there any 3LDK properties in this area?" and generates instructions for searching for 3LDK property information in that area. The search unit searches for properties based on the instructions from the analysis unit. For example, if the generation AI issues an instruction to "Search for 3LDK properties in this area," the search unit searches for 3LDK properties in that area. The proposal unit proposes appropriate properties based on the search results from the search unit. For example, the search unit passes a list of 3LDK properties found to the proposal unit, and the proposal unit makes proposals to the customer based on the list. The provision unit provides the proposed property information to the customer. For example, the provision unit provides detailed information about 3LDK properties found by the proposal unit to the customer. As a result, the communication platform for real estate agents according to the embodiment can facilitate communication between real estate agents and customers by accepting and analyzing customer questions, searching for properties, proposing and providing them.

[0030] The reception unit can accept questions from customers at any time. Specific time ranges and conditions for "always" include, but are not limited to, 24 hours a day, 365 days a year, and specific time periods. For example, the reception unit can accept questions even if a customer inputs the question late at night. The reception unit can also accept questions on weekends and holidays. This allows questions from customers to be accepted 24 hours a day, making it possible to respond at any time. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input questions from customers into AI and have the AI ​​accept the questions.

[0031] The analysis unit can analyze customer questions and generate answers. The analysis unit can analyze customer questions using, for example, a generation AI. For example, the generation AI can understand the content of the question using natural language processing technology and generate an appropriate answer. The analysis unit can also analyze question patterns using a machine learning algorithm and generate an answer. For example, the analysis unit can learn data on past questions and answers and generate an appropriate answer for a new question. Furthermore, the analysis unit can search for related information based on the content of the question and generate an answer. For example, the analysis unit can search literature or databases related to the question and generate an answer based on that information. This enables quick and accurate responses by analyzing customer questions and generating appropriate answers. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer questions into a generation AI and have the generation AI generate an answer.

[0032] The search unit can search for properties based on the customer's specific needs. Specific needs include, but are not limited to, budget, location, and floor plan. For example, if a customer inputs needs such as "budget of less than 30 million yen, location in Tokyo, and floor plan of 3LDK," the search unit searches for properties based on those needs. The search unit can also set filtering conditions to search for properties. For example, the search unit can set conditions such as price range, area, and property type and search for properties that match those conditions. Furthermore, the search unit can search for properties using keyword search. For example, the search unit can input keywords such as "pets allowed" or "near a station" and search for properties related to those keywords. This allows the search for properties based on the customer's needs, thereby providing the customer with the most suitable property. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input the customer's needs into the generation AI and have the generation AI perform a property search.

[0033] The suggestion unit can suggest properties based on the search results. Specific content and criteria of the search results include, but are not limited to, display order, filtering conditions, and the like. The suggestion unit can suggest properties that are optimal for the customer based on, for example, a list of properties found by the search unit. The suggestion unit can also suggest properties using a ranking algorithm. For example, the suggestion unit can create a ranking based on property ratings and popularity and suggest properties based on the ranking. The suggestion unit can also suggest properties using a recommendation system. For example, the suggestion unit can recommend properties that are optimal for the customer based on the customer's past search history and preferences. This allows the customer to be provided with the optimal property by suggesting appropriate properties based on the search results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input search results to a generation AI and have the generation AI execute property suggestions.

[0034] The providing unit can provide the proposed property information to the customer. Specific content and format of the property information include, but are not limited to, price, location, and floor plan. The providing unit can provide the customer with detailed information about the property found by the suggesting unit. The providing unit can also provide the property information via email, chat, dashboard, or other methods. For example, the providing unit can send the property information to the customer by email. The providing unit can also provide the property information using a chatbot. The providing unit can also provide the property information through a dashboard. For example, the providing unit can prepare a dashboard dedicated to the customer and display the property information there. This allows the proposed property information to be provided to the customer, thereby quickly providing the information to the customer. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the property information into the generation AI and have the generation AI provide the information.

[0035] When receiving a question, the reception unit can select a reception method by referring to the customer's past inquiry history. Specific content and reference methods of the past inquiry history include, but are not limited to, past question content and response history. For example, the reception unit can automatically display as candidates content that the customer has frequently inquired about in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest question content that will be used during a specific time period based on the customer's past inquiry history. This makes it possible to select the optimal reception method by referring to the customer's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the past inquiry history into the generation AI and have the generation AI select a reception method.

[0036] When receiving a question, the reception unit can filter the questions based on the customer's current situation and areas of interest. Specific details and criteria of the current situation include, but are not limited to, the customer's current place of residence, occupation, and family structure. Specific details and criteria of the areas of interest include, but are not limited to, hobbies and property types of interest. For example, when a customer inputs their current situation, the reception unit prioritizes receiving questions related to that situation. The reception unit can also automatically filter related questions based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the reception unit can suggest questions that are best suited to that situation. This allows for filtering based on the customer's current situation and areas of interest, thereby receiving more appropriate questions. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0037] When accepting questions, the reception unit can prioritize relevant questions by taking into account the customer's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the customer is in a specific area, the reception unit prioritizes questions related to that area. The reception unit can also automatically filter relevant questions based on the customer's geographical location information. Furthermore, if the customer is on the move, the reception unit can suggest optimal questions based on the customer's current location. This allows for prioritized acceptance of relevant questions by taking the customer's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's geographical location information into the generation AI and have the generation AI perform question filtering.

[0038] When receiving a question, the reception unit can analyze the customer's social media activity and receive related questions. Specific content and analysis methods of social media activity include, but are not limited to, post content, number of likes, and number of followers. The reception unit, for example, extracts topics of interest from the customer's social media activity and prioritizes receiving questions related to those topics. The reception unit can also suggest related questions based on information shared by the customer on social media. Furthermore, the reception unit can analyze the customer's social media activity and receive optimal questions. In this way, related questions can be received by analyzing the customer's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's social media activity data into the generation AI and have the generation AI receive the question.

[0039] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the specific importance of the question. Specific evaluation criteria for the importance of a question include, but are not limited to, the customer's urgency and the content of the question. For example, the analysis unit performs a detailed analysis for a question with a high level of importance. The analysis unit can also perform a concise analysis for a question with a low level of importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance of the question. This allows for adjusting the level of detail of the analysis based on the importance of the question, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Specific types and classification criteria of categories include, but are not limited to, residential, commercial property, and land. For example, the analysis unit can apply an analysis algorithm dedicated to residential properties to questions about residential properties. The analysis unit can also apply an analysis algorithm dedicated to commercial properties to questions about commercial properties. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the category of the question. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the category of the question. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input question category data into the generation AI and have the generation AI select an analysis algorithm.

[0041] When analyzing a question, the analysis unit can determine the analysis priority based on the time of submission of the question. Specific evaluation criteria for the submission time include, but are not limited to, the submission date and time, the elapsed time since submission, etc. The analysis unit can determine the analysis priority based on, for example, the time period in which the question was submitted. The analysis unit can also adjust the analysis order depending on the time of submission of the question. Furthermore, the analysis unit can set the analysis priority based on the time of submission of the question. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the time of submission of the question. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the analysis unit can input question submission time data into the generation AI and have the generation AI determine the analysis priority.

[0042] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. Specific evaluation criteria for relevance include, but are not limited to, similarity of question content and past inquiry history. The analysis unit determines the order of analysis based on, for example, the relevance of the question. The analysis unit can also set an analysis priority according to the relevance of the question. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the question. In this way, by adjusting the order of analysis based on the relevance of the question, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The search unit can improve search accuracy by taking into account specific interrelationships between properties. Specific content and evaluation criteria of interrelationships include, for example, proximity of properties and shared facilities, but are not limited to such examples. The search unit, for example, searches for the most suitable property by taking into account the location of properties. The search unit can also search for related properties by taking into account the price range of the properties. Furthermore, the search unit can search for the most suitable property by taking into account the characteristics of the properties. In this way, by taking into account the interrelationships between properties, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input property interrelationship data into the generation AI and have the generation AI improve search accuracy.

[0044] When searching for a property, the search unit can perform the search while taking into account the attribute information of the property provider. Specific content and acquisition methods of the provider's attribute information include, for example, the provider's reliability and past transaction history, but are not limited to these examples. The search unit, for example, searches for the most suitable property while taking into account the reliability of the property provider. The search unit can also search for related properties while taking into account the property provider's past transaction history. Furthermore, the search unit can search for the most suitable property while taking into account the property provider's evaluation. This makes it possible to search for a more suitable property by taking into account the property provider's attribute information. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input the property provider's attribute information into the generation AI and have the generation AI perform the search.

[0045] When searching for properties, the search unit can perform the search while taking into account the geographic distribution of the properties. Specific details and evaluation criteria of the geographic distribution include, for example, the number of properties per region and ease of access, but are not limited to these examples. The search unit, for example, searches for the most suitable property by taking into account the geographic distribution of the properties. The search unit can also search for related properties by taking into account the locational relationships of the properties. Furthermore, the search unit can search for the most suitable property by taking into account the geographic characteristics of the property. This makes it possible to search for a more suitable property by taking into account the geographic distribution of the property. Some or all of the above-mentioned processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input property geographic distribution data into the generation AI and have the generation AI execute the search.

[0046] When searching for a property, the search unit can improve the accuracy of the search by referring to literature related to the property. Specific types and reference methods of related literature include, but are not limited to, past transaction data and market reports. The search unit, for example, refers to literature related to the property to search for the optimal property. The search unit can also refer to the property's past transaction history to search for related properties. Furthermore, the search unit can also refer to property evaluations to search for the optimal property. In this way, by referring to literature related to the property, the accuracy of the search is improved. Some or all of the above-mentioned processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input literature data related to the property into the generation AI and have the generation AI improve the accuracy of the search.

[0047] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the specific importance of the property. Specific evaluation criteria for the importance of a property include, but are not limited to, price, location, etc. The proposal unit, for example, makes a detailed proposal for a property with a high importance. The proposal unit can also make a concise proposal for a property with a low importance. Furthermore, the proposal unit can adjust the depth of the proposal depending on the importance of the property. This allows for adjusting the level of detail of the proposal based on the importance of the property, thereby providing a more appropriate proposal. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input property importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the property category. Specific types and implementation methods of the proposal algorithm include, but are not limited to, recommendation systems and ranking algorithms. For example, the proposal unit can apply a proposal algorithm dedicated to housing to proposals related to housing. The proposal unit can also apply a proposal algorithm dedicated to commercial properties to proposals related to commercial properties. Furthermore, the proposal unit can select an optimal proposal algorithm depending on the property category. This allows for more appropriate proposals to be provided by applying different proposal algorithms depending on the property category. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input property category data into the generation AI and have the generation AI select a proposal algorithm.

[0049] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the property. Specific evaluation criteria for the time of submission include, but are not limited to, for example, the submission date and time, the elapsed time since submission, etc. For example, if a property is newly submitted, the proposal unit can prioritize the property. The proposal unit can also adjust the order of proposals depending on the time of submission of the property. Furthermore, the proposal unit can also set the priority of proposals based on the time of submission of the property. This allows more appropriate proposals to be provided by determining the priority of proposals based on the time of submission of the property. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input property submission date data into the generation AI and have the generation AI determine the priority of proposals.

[0050] When making a proposal, the proposal unit can adjust the order of proposals based on the specific relevance of the properties. Specific evaluation criteria for relevance include, but are not limited to, for example, the similarity of the properties and the needs of the customer. The proposal unit, for example, determines the order of proposals based on the relevance of the properties. The proposal unit can also set a priority of the proposals according to the relevance of the properties. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the properties. In this way, by adjusting the order of proposals based on the relevance of the properties, more appropriate proposals can be provided. Some or all of the above-described processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input property relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0051] When providing information, the providing unit can select the optimal information providing method by referring to the customer's past inquiry history. Specific content and reference methods of the past inquiry history include, but are not limited to, past question content and response history. The providing unit can provide optimal information, for example, based on the customer's frequent past inquiries. The providing unit can also provide related information from the customer's past inquiry history. Furthermore, the providing unit can preferentially suggest information providing methods (email, chat, etc.) that the customer has used in the past. This allows the optimal information providing method to be selected by referring to the customer's past inquiry history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input past inquiry history data into the generation AI and have the generation AI select an information providing method.

[0052] When providing information, the providing unit may filter the information based on the customer's current situation and areas of interest. Specific content and criteria of the current situation include, but are not limited to, the customer's current place of residence, occupation, and family structure. Specific content and criteria of areas of interest include, but are not limited to, hobbies and property types of interest. For example, when a customer inputs their current situation, the providing unit may prioritize providing information related to that situation. The providing unit may also automatically filter related information based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the providing unit may provide information that is optimal for that situation. This allows for more appropriate information to be provided by filtering based on the customer's current situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input data on the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0053] When providing information, the providing unit can prioritize providing specific, highly relevant information by taking into account the customer's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the customer is in a specific area, the providing unit can prioritize providing information related to that area. The providing unit can also automatically filter relevant information based on the customer's geographical location information. Furthermore, if the customer is on the move, the providing unit can provide optimal information based on the customer's current location. This allows for prioritized provision of highly relevant information by taking the customer's geographical location information into account. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the customer's geographical location information into the generation AI and have the generation AI perform information filtering.

[0054] When providing information, the providing unit can analyze the customer's social media activity and provide relevant information. Specific content and analysis methods of social media activity include, but are not limited to, post content, number of likes, and number of followers. The providing unit can, for example, extract topics of interest from the customer's social media activity and prioritize providing information related to those topics. The providing unit can also suggest related information based on information shared by the customer on social media. Furthermore, the providing unit can analyze the customer's social media activity and provide optimal information. In this way, relevant information can be provided by analyzing the customer's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the customer's social media activity data into the generation AI and have the generation AI provide the information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can refer to a customer's past search history and automatically suggest similar questions. For example, if a customer previously searched for "3LDK properties in Tokyo," the reception unit will automatically suggest similar conditions the next time the customer asks a question. The reception unit can also suggest related questions based on the characteristics of properties the customer previously searched for. Furthermore, the reception unit can analyze a customer's past search history and prioritize frequently searched conditions. This makes it possible to receive questions more efficiently by utilizing a customer's past search history.

[0057] The analysis unit can refer to the opinions of external experts when generating answers to customer questions. For example, for questions about law, the analysis unit can refer to the opinions of lawyers to generate answers. For questions about architecture, the analysis unit can also refer to the opinions of architects. Furthermore, the analysis unit can generate more accurate answers based on the opinions of external experts. In this way, by utilizing the opinions of external experts, it is possible to provide more reliable answers.

[0058] The reception unit can prioritize receiving information about the nearest property based on the customer's current location information. For example, if the customer is in a specific area, it will prioritize receiving information about properties related to that area. The reception unit can also automatically suggest related questions based on the customer's location information. Furthermore, if the customer is on the move, the reception unit can suggest the most suitable property information based on the customer's current location. This makes it possible to receive more appropriate questions by utilizing the customer's location information.

[0059] When generating answers to customer questions, the analysis unit can refer to a database of similar questions from the past. For example, if a similar question has been asked in the past, a new answer can be generated based on that answer. The analysis unit can also learn from data on past questions and answers to generate more accurate answers. Furthermore, the analysis unit can analyze past question data and prepare templates for frequently asked questions. This makes it possible to provide faster and more accurate answers by utilizing past data.

[0060] When searching for properties, the search unit can automatically set search conditions by referencing the customer's past search history. For example, if a customer previously searched for "3LDK properties in Tokyo," the same conditions will be automatically set the next time they search. The search unit can also prioritize displaying related properties based on the customer's past search history. Furthermore, the search unit can analyze the customer's past search history and prioritize displaying frequently searched conditions. This makes it possible to search for properties more efficiently by utilizing the customer's past search history.

[0061] The proposal unit can propose the most suitable property based on the customer's past preferences. For example, it can propose similar properties based on the characteristics of properties that the customer has previously selected. The proposal unit can also analyze the customer's past preferences and prioritize the proposal of related properties. Furthermore, the proposal unit can also propose the most suitable properties in a ranking format based on the customer's past preferences. This makes it possible to make more appropriate property proposals by utilizing the customer's past preferences.

[0062] The providing unit can filter information based on the customer's current situation and areas of interest and provide the most appropriate information. For example, when a customer inputs their current place of residence, information related to that area is preferentially provided. The providing unit can also automatically filter relevant information based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the providing unit can provide the most appropriate information for that situation. This makes it possible to provide more appropriate information by filtering information based on the customer's current situation and areas of interest.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception department accepts questions from customers. For example, if a customer asks, "Are there any 3LDK properties in this area?", the question is entered into the reception department. Step 2: The analysis unit uses the generation AI to analyze the question received by the reception unit. For example, the generation AI understands the question, "Are there any 3LDK properties in this area?" and generates instructions to search for 3LDK properties in that area. Step 3: The search unit searches for properties based on instructions from the analysis unit. For example, if the generation AI issues an instruction to "search for 3LDK properties in this area," the search unit will search for 3LDK properties in that area. Step 4: The proposal department proposes suitable properties based on the search results from the search department. For example, the search department passes a list of 3LDK properties found to the proposal department, which then makes proposals to the customer based on that list. Step 5: The offering department provides the proposed property information to the customer. For example, the offering department provides detailed information about a 3LDK property found by the offering department to the customer.

[0065] (Example 2) A communication platform for real estate agents according to an embodiment of the present invention is a system that utilizes generative AI to facilitate communication between real estate agents and customers. This system accepts customer questions, and the generative AI generates appropriate answers and suggests properties based on the customer's needs. This reduces the burden on real estate agents and allows customers to efficiently search for properties based on their needs. For example, when a customer inputs a question, a reception unit accepts the question. Next, an analysis unit analyzes the question accepted by the reception unit, and the generative AI understands the question and generates an appropriate answer. Furthermore, a search unit searches for properties based on instructions from the analysis unit, and a suggestion unit suggests appropriate properties based on the search results. Finally, a provision unit provides the suggested property information to the customer. This allows the communication platform for real estate agents utilizing generative AI to facilitate communication between real estate agents and customers, reduce the burden on real estate agents, and enable customers to efficiently search for properties. This allows the communication platform for real estate agents to respond to customer questions 24 hours a day, and the generative AI suggests appropriate properties, enabling quick and accurate responses.

[0066] A communication platform for real estate agents according to an embodiment includes a reception unit, an analysis unit, a search unit, a proposal unit, and a provision unit. The reception unit accepts questions from customers. For example, if a customer asks, "Are there any 3LDK properties in this area?", the question is input to the reception unit. The analysis unit uses a generation AI to analyze the question accepted by the reception unit. For example, the generation AI understands the question, "Are there any 3LDK properties in this area?" and generates instructions for searching for 3LDK property information in that area. The search unit searches for properties based on the instructions from the analysis unit. For example, if the generation AI issues an instruction to "Search for 3LDK properties in this area," the search unit searches for 3LDK properties in that area. The proposal unit proposes appropriate properties based on the search results from the search unit. For example, the search unit passes a list of 3LDK properties found to the proposal unit, and the proposal unit makes proposals to the customer based on the list. The provision unit provides the proposed property information to the customer. For example, the provision unit provides detailed information about 3LDK properties found by the proposal unit to the customer. As a result, the communication platform for real estate agents according to the embodiment can facilitate communication between real estate agents and customers by accepting and analyzing customer questions, searching for properties, proposing and providing them.

[0067] The reception unit can accept questions from customers at any time. Specific time ranges and conditions for "always" include, but are not limited to, 24 hours a day, 365 days a year, and specific time periods. For example, the reception unit can accept questions even if a customer inputs the question late at night. The reception unit can also accept questions on weekends and holidays. This allows questions from customers to be accepted 24 hours a day, making it possible to respond at any time. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input questions from customers into AI and have the AI ​​accept the questions.

[0068] The analysis unit can analyze customer questions and generate answers. The analysis unit can analyze customer questions using, for example, a generation AI. For example, the generation AI can understand the content of the question using natural language processing technology and generate an appropriate answer. The analysis unit can also analyze question patterns using a machine learning algorithm and generate an answer. For example, the analysis unit can learn data on past questions and answers and generate an appropriate answer for a new question. Furthermore, the analysis unit can search for related information based on the content of the question and generate an answer. For example, the analysis unit can search literature or databases related to the question and generate an answer based on that information. This enables quick and accurate responses by analyzing customer questions and generating appropriate answers. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input customer questions into a generation AI and have the generation AI generate an answer.

[0069] The search unit can search for properties based on the customer's specific needs. Specific needs include, but are not limited to, budget, location, and floor plan. For example, if a customer inputs needs such as "budget of less than 30 million yen, location in Tokyo, and floor plan of 3LDK," the search unit searches for properties based on those needs. The search unit can also set filtering conditions to search for properties. For example, the search unit can set conditions such as price range, area, and property type and search for properties that match those conditions. Furthermore, the search unit can search for properties using keyword search. For example, the search unit can input keywords such as "pets allowed" or "near a station" and search for properties related to those keywords. This allows the search for properties based on the customer's needs, thereby providing the customer with the most suitable property. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input the customer's needs into the generation AI and have the generation AI perform a property search.

[0070] The suggestion unit can suggest properties based on the search results. Specific content and criteria of the search results include, but are not limited to, display order, filtering conditions, and the like. The suggestion unit can suggest properties that are optimal for the customer based on, for example, a list of properties found by the search unit. The suggestion unit can also suggest properties using a ranking algorithm. For example, the suggestion unit can create a ranking based on property ratings and popularity and suggest properties based on the ranking. The suggestion unit can also suggest properties using a recommendation system. For example, the suggestion unit can recommend properties that are optimal for the customer based on the customer's past search history and preferences. This allows the customer to be provided with the optimal property by suggesting appropriate properties based on the search results. Some or all of the above-described processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input search results to a generation AI and have the generation AI execute property suggestions.

[0071] The providing unit can provide the proposed property information to the customer. Specific content and format of the property information include, but are not limited to, price, location, and floor plan. The providing unit can provide the customer with detailed information about the property found by the suggesting unit. The providing unit can also provide the property information via email, chat, dashboard, or other methods. For example, the providing unit can send the property information to the customer by email. The providing unit can also provide the property information using a chatbot. The providing unit can also provide the property information through a dashboard. For example, the providing unit can prepare a dashboard dedicated to the customer and display the property information there. This allows the proposed property information to be provided to the customer, thereby quickly providing the information to the customer. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the property information into the generation AI and have the generation AI provide the information.

[0072] The reception unit can estimate a customer's emotions and adjust the way questions are accepted based on the estimated customer emotions. For example, if a customer is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if a customer is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if a customer is in a hurry, the reception unit can prioritize voice input to allow the customer to quickly enter a question. This allows for more appropriate responses by adjusting the way questions are accepted based on the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI adjust the way questions are accepted.

[0073] When receiving a question, the reception unit can select a reception method by referring to the customer's past inquiry history. Specific content and reference methods of the past inquiry history include, but are not limited to, past question content and response history. For example, the reception unit can automatically display as candidates content that the customer has frequently inquired about in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the customer has used in the past. Furthermore, the reception unit can predict and suggest question content that will be used during a specific time period based on the customer's past inquiry history. This makes it possible to select the optimal reception method by referring to the customer's past inquiry history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the past inquiry history into the generation AI and have the generation AI select a reception method.

[0074] When receiving a question, the reception unit can filter the questions based on the customer's current situation and areas of interest. Specific details and criteria of the current situation include, but are not limited to, the customer's current place of residence, occupation, and family structure. Specific details and criteria of the areas of interest include, but are not limited to, hobbies and property types of interest. For example, when a customer inputs their current situation, the reception unit prioritizes receiving questions related to that situation. The reception unit can also automatically filter related questions based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the reception unit can suggest questions that are best suited to that situation. This allows for filtering based on the customer's current situation and areas of interest, thereby receiving more appropriate questions. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0075] The reception unit can estimate the customer's emotions and determine the order of questions to be received based on the estimated customer emotions. Specific criteria for determining the order of questions include, but are not limited to, urgency and importance. For example, if a customer has an urgent question, the reception unit can receive that question with priority. Furthermore, if a customer is relaxed, the reception unit can also receive a question with normal priority. Furthermore, if a customer is feeling stressed, the reception unit can raise the priority of that question to process it more quickly. This enables more appropriate responses by determining the priority of questions based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input customer emotion data into the generation AI and have the generation AI determine the priority of questions.

[0076] When accepting questions, the reception unit can prioritize relevant questions by taking into account the customer's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the customer is in a specific area, the reception unit prioritizes questions related to that area. The reception unit can also automatically filter relevant questions based on the customer's geographical location information. Furthermore, if the customer is on the move, the reception unit can suggest optimal questions based on the customer's current location. This allows for prioritized acceptance of relevant questions by taking the customer's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's geographical location information into the generation AI and have the generation AI perform question filtering.

[0077] When receiving a question, the reception unit can analyze the customer's social media activity and receive related questions. Specific content and analysis methods of social media activity include, but are not limited to, post content, number of likes, and number of followers. The reception unit, for example, extracts topics of interest from the customer's social media activity and prioritizes receiving questions related to those topics. The reception unit can also suggest related questions based on information shared by the customer on social media. Furthermore, the reception unit can analyze the customer's social media activity and receive optimal questions. In this way, related questions can be received by analyzing the customer's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the customer's social media activity data into the generation AI and have the generation AI receive the question.

[0078] The analysis unit can estimate the customer's emotions and adjust the presentation method of the analysis based on the estimated customer emotions. Specific content and standards of the presentation method of the analysis include, but are not limited to, text format and graph format. For example, the analysis unit can provide detailed analysis results when the customer is relaxed. Furthermore, the analysis unit can provide concise and to-the-point analysis results when the customer is in a hurry. Furthermore, the analysis unit can provide visually easy-to-understand analysis results when the customer is stressed. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0079] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the specific importance of the question. Specific evaluation criteria for the importance of a question include, but are not limited to, the customer's urgency and the content of the question. For example, the analysis unit performs a detailed analysis for a question with a high level of importance. The analysis unit can also perform a concise analysis for a question with a low level of importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance of the question. This allows for adjusting the level of detail of the analysis based on the importance of the question, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input question importance data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0080] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Specific types and classification criteria of categories include, but are not limited to, residential, commercial property, and land. For example, the analysis unit can apply an analysis algorithm dedicated to residential properties to questions about residential properties. The analysis unit can also apply an analysis algorithm dedicated to commercial properties to questions about commercial properties. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the category of the question. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the category of the question. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input question category data into the generation AI and have the generation AI select an analysis algorithm.

[0081] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated customer emotions. Specific criteria for adjusting the length of the analysis include, but are not limited to, the number of characters and time. For example, if the customer is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the customer is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the customer is stressed, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the customer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input customer emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] When analyzing a question, the analysis unit can determine the analysis priority based on the time of submission of the question. Specific evaluation criteria for the submission time include, but are not limited to, the submission date and time, the elapsed time since submission, etc. The analysis unit can determine the analysis priority based on, for example, the time period in which the question was submitted. The analysis unit can also adjust the analysis order depending on the time of submission of the question. Furthermore, the analysis unit can set the analysis priority based on the time of submission of the question. This allows for more appropriate analysis results to be provided by determining the analysis priority based on the time of submission of the question. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the analysis unit can input question submission time data into the generation AI and have the generation AI determine the analysis priority.

[0083] When analyzing a question, the analysis unit can adjust the order of analysis based on the relevance of the question. Specific evaluation criteria for relevance include, but are not limited to, similarity of question content and past inquiry history. The analysis unit determines the order of analysis based on, for example, the relevance of the question. The analysis unit can also set an analysis priority according to the relevance of the question. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the question. In this way, by adjusting the order of analysis based on the relevance of the question, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] The search unit can estimate a customer's emotions and adjust search criteria based on the estimated customer emotions. Specific content and adjustment methods of the search criteria include, but are not limited to, keywords and filtering conditions. For example, if a customer is relaxed, the search unit can provide detailed search criteria. Furthermore, if a customer is in a hurry, the search unit can provide concise and to-the-point search criteria. Furthermore, if a customer is stressed, the search unit can provide visually easy-to-understand search criteria. This allows for adjusting the search criteria according to the customer's emotions, thereby providing more appropriate search results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the search unit can be performed using, for example, the generation AI. For example, the search unit can input customer emotion data into the generation AI and have the generation AI adjust the search criteria.

[0085] The search unit can improve search accuracy by taking into account specific interrelationships between properties. Specific content and evaluation criteria of interrelationships include, for example, proximity of properties and shared facilities, but are not limited to such examples. The search unit, for example, searches for the most suitable property by taking into account the location of properties. The search unit can also search for related properties by taking into account the price range of the properties. Furthermore, the search unit can search for the most suitable property by taking into account the characteristics of the properties. In this way, by taking into account the interrelationships between properties, search accuracy is improved. Some or all of the above-mentioned processing in the search unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the search unit can input property interrelationship data into the generation AI and have the generation AI improve search accuracy.

[0086] When searching for a property, the search unit can perform the search while taking into account the attribute information of the property provider. Specific content and acquisition methods of the provider's attribute information include, for example, the provider's reliability and past transaction history, but are not limited to these examples. The search unit, for example, searches for the most suitable property while taking into account the reliability of the property provider. The search unit can also search for related properties while taking into account the property provider's past transaction history. Furthermore, the search unit can search for the most suitable property while taking into account the property provider's evaluation. This makes it possible to search for a more suitable property by taking into account the property provider's attribute information. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input the property provider's attribute information into the generation AI and have the generation AI perform the search.

[0087] The search unit can estimate a customer's emotions and adjust the order in which search results are displayed based on the estimated customer emotions. Specific criteria for determining the display order of search results include, but are not limited to, customer priority and property popularity. For example, the search unit can provide detailed search results when a customer is relaxed. Furthermore, the search unit can provide concise and to-the-point search results when a customer is in a hurry. Furthermore, the search unit can provide visually easy-to-understand search results when a customer is stressed. This allows for more appropriate search results to be provided by adjusting the display order of search results according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit can be performed using, for example, the generation AI. For example, the search unit can input customer emotion data into the generation AI and have the generation AI adjust the display order of search results.

[0088] When searching for properties, the search unit can perform the search while taking into account the geographic distribution of the properties. Specific details and evaluation criteria of the geographic distribution include, for example, the number of properties per region and ease of access, but are not limited to these examples. The search unit, for example, searches for the most suitable property by taking into account the geographic distribution of the properties. The search unit can also search for related properties by taking into account the locational relationships of the properties. Furthermore, the search unit can search for the most suitable property by taking into account the geographic characteristics of the property. This makes it possible to search for a more suitable property by taking into account the geographic distribution of the property. Some or all of the above-mentioned processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input property geographic distribution data into the generation AI and have the generation AI execute the search.

[0089] When searching for a property, the search unit can improve the accuracy of the search by referring to literature related to the property. Specific types and reference methods of related literature include, but are not limited to, past transaction data and market reports. The search unit, for example, refers to literature related to the property to search for the optimal property. The search unit can also refer to the property's past transaction history to search for related properties. Furthermore, the search unit can also refer to property evaluations to search for the optimal property. In this way, by referring to literature related to the property, the accuracy of the search is improved. Some or all of the above-mentioned processing in the search unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the search unit can input literature data related to the property into the generation AI and have the generation AI improve the accuracy of the search.

[0090] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. Specific content and standards for the way the suggestion is presented include, but are not limited to, text format and visual format. For example, the suggestion unit can provide detailed suggestions when the customer is relaxed. Furthermore, the suggestion unit can provide concise and to-the-point suggestions when the customer is in a hurry. Furthermore, the suggestion unit can provide visually easy-to-understand suggestions when the customer is stressed. This allows the suggestion to be tailored to the customer's emotions, resulting in more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input customer emotion data into the generation AI and have the generation AI adjust the way the suggestion is presented.

[0091] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the specific importance of the property. Specific evaluation criteria for the importance of a property include, but are not limited to, price, location, etc. The proposal unit, for example, makes a detailed proposal for a property with a high importance. The proposal unit can also make a concise proposal for a property with a low importance. Furthermore, the proposal unit can adjust the depth of the proposal depending on the importance of the property. This allows for adjusting the level of detail of the proposal based on the importance of the property, thereby providing a more appropriate proposal. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input property importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0092] When making a proposal, the proposal unit can apply different proposal algorithms depending on the property category. Specific types and implementation methods of the proposal algorithm include, but are not limited to, recommendation systems and ranking algorithms. For example, the proposal unit can apply a proposal algorithm dedicated to housing to proposals related to housing. The proposal unit can also apply a proposal algorithm dedicated to commercial properties to proposals related to commercial properties. Furthermore, the proposal unit can select an optimal proposal algorithm depending on the property category. This allows for more appropriate proposals to be provided by applying different proposal algorithms depending on the property category. Some or all of the above-described processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input property category data into the generation AI and have the generation AI select a proposal algorithm.

[0093] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated customer emotions. Specific criteria for adjusting the length of the suggestion include, but are not limited to, the number of characters and time. For example, if the customer is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. Furthermore, if the customer is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the customer is stressed, the suggestion unit can provide a visually easy-to-understand suggestion. This allows for adjusting the length of the suggestion according to the customer's emotions, thereby providing a more appropriate suggestion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input customer emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0094] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the property. Specific evaluation criteria for the time of submission include, but are not limited to, for example, the submission date and time, the elapsed time since submission, etc. For example, if a property is newly submitted, the proposal unit can prioritize the property. The proposal unit can also adjust the order of proposals depending on the time of submission of the property. Furthermore, the proposal unit can also set the priority of proposals based on the time of submission of the property. This allows more appropriate proposals to be provided by determining the priority of proposals based on the time of submission of the property. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input property submission date data into the generation AI and have the generation AI determine the priority of proposals.

[0095] When making a proposal, the proposal unit can adjust the order of proposals based on the specific relevance of the properties. Specific evaluation criteria for relevance include, but are not limited to, for example, the similarity of the properties and the needs of the customer. The proposal unit, for example, determines the order of proposals based on the relevance of the properties. The proposal unit can also set a priority of the proposals according to the relevance of the properties. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the properties. In this way, by adjusting the order of proposals based on the relevance of the properties, more appropriate proposals can be provided. Some or all of the above-described processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input property relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0096] The providing unit can estimate the customer's emotions and adjust the way the information is presented based on the estimated customer emotions. Specific content and standards for the way the information is presented include, but are not limited to, text format and visual format. For example, if the customer is relaxed, the providing unit can provide detailed information. If the customer is in a hurry, the providing unit can also provide concise, to-the-point information. Furthermore, if the customer is stressed, the providing unit can also provide visually easy-to-understand information. This allows for more appropriate information to be provided by adjusting the way the information is presented based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input customer emotion data into the generation AI and have the generation AI adjust the way the information is presented.

[0097] When providing information, the providing unit can select the optimal information providing method by referring to the customer's past inquiry history. Specific content and reference methods of the past inquiry history include, but are not limited to, past question content and response history. The providing unit can provide optimal information, for example, based on the customer's frequent past inquiries. The providing unit can also provide related information from the customer's past inquiry history. Furthermore, the providing unit can preferentially suggest information providing methods (email, chat, etc.) that the customer has used in the past. This allows the optimal information providing method to be selected by referring to the customer's past inquiry history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input past inquiry history data into the generation AI and have the generation AI select an information providing method.

[0098] When providing information, the providing unit may filter the information based on the customer's current situation and areas of interest. Specific content and criteria of the current situation include, but are not limited to, the customer's current place of residence, occupation, and family structure. Specific content and criteria of areas of interest include, but are not limited to, hobbies and property types of interest. For example, when a customer inputs their current situation, the providing unit may prioritize providing information related to that situation. The providing unit may also automatically filter related information based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the providing unit may provide information that is optimal for that situation. This allows for more appropriate information to be provided by filtering based on the customer's current situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input data on the customer's current situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0099] The providing unit can estimate the customer's emotions and determine the priority of information to provide based on the estimated customer emotions. Specific criteria for determining the priority of information include, but are not limited to, the customer's level of urgency and the importance of the information. For example, if the customer requests urgent information, the providing unit can provide that information with priority. Furthermore, if the customer is relaxed, the providing unit can also provide information with normal priority. Furthermore, if the customer is feeling stressed, the providing unit can increase the priority of the information to provide it more quickly. This allows for more appropriate information to be provided by determining the priority of information based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input customer emotion data into the generation AI and have the generation AI determine the priority of the information.

[0100] When providing information, the providing unit can prioritize providing specific, highly relevant information by taking into account the customer's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data, address information, etc. For example, if the customer is in a specific area, the providing unit can prioritize providing information related to that area. The providing unit can also automatically filter relevant information based on the customer's geographical location information. Furthermore, if the customer is on the move, the providing unit can provide optimal information based on the customer's current location. This allows for prioritized provision of highly relevant information by taking the customer's geographical location information into account. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the customer's geographical location information into the generation AI and have the generation AI perform information filtering.

[0101] When providing information, the providing unit can analyze the customer's social media activity and provide relevant information. Specific content and analysis methods of social media activity include, but are not limited to, post content, number of likes, and number of followers. The providing unit can, for example, extract topics of interest from the customer's social media activity and prioritize providing information related to those topics. The providing unit can also suggest related information based on information shared by the customer on social media. Furthermore, the providing unit can analyze the customer's social media activity and provide optimal information. In this way, relevant information can be provided by analyzing the customer's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the customer's social media activity data into the generation AI and have the generation AI provide the information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, proposal unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from customers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for properties based on instructions from the analysis unit. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes appropriate properties based on the search results. The provision unit is realized by the output device 40 of the smart device 14 and provides the proposed property information to the customer. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts questions from customers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for properties based on instructions from the analysis unit. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate properties based on the search results. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the suggested property information to the customer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, proposal unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives questions from customers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for properties based on instructions from the analysis unit. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes appropriate properties based on the search results. The provision unit is realized by the display 343 of the headset terminal 314 and provides proposed property information to the customer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, search unit, proposal unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions from customers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for properties based on instructions from the analysis unit. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes appropriate properties based on the search results. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides proposed property information to the customer.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can refer to a customer's past search history and automatically suggest similar questions. For example, if a customer previously searched for "3LDK properties in Tokyo," the reception unit will automatically suggest similar conditions the next time the customer asks a question. The reception unit can also suggest related questions based on the characteristics of properties the customer previously searched for. Furthermore, the reception unit can analyze a customer's past search history and prioritize frequently searched conditions. This makes it possible to receive questions more efficiently by utilizing a customer's past search history.

[0104] The analysis unit can refer to the opinions of external experts when generating answers to customer questions. For example, for questions about law, the analysis unit can refer to the opinions of lawyers to generate answers. For questions about architecture, the analysis unit can also refer to the opinions of architects. Furthermore, the analysis unit can generate more accurate answers based on the opinions of external experts. In this way, by utilizing the opinions of external experts, it is possible to provide more reliable answers.

[0105] The search unit can estimate a customer's emotions and adjust the way search results are displayed based on the estimated customer emotions. For example, if a customer is feeling stressed, search results can be displayed in a simple list format. If the customer is relaxed, search results can be displayed in a card format with detailed information. Furthermore, if the customer is in a hurry, the most relevant properties can be displayed preferentially. This allows for more appropriate information to be provided by adjusting the way search results are displayed according to the customer's emotions.

[0106] The suggestion unit can estimate the customer's emotions and adjust the number of properties to suggest based on the estimated customer emotions. For example, if the customer is feeling stressed, a small number of carefully selected properties can be suggested. Alternatively, if the customer is relaxed, a large number of properties can be suggested. Furthermore, if the customer is in a hurry, a small number of the most relevant properties can be suggested. In this way, more appropriate suggestions can be made by adjusting the number of properties to suggest according to the customer's emotions.

[0107] The providing unit can estimate the customer's emotions and adjust the way information is provided based on the estimated customer emotions. For example, if the customer is feeling stressed, concise, to-the-point information can be provided. If the customer is relaxed, detailed information can be provided. Furthermore, if the customer is in a hurry, visually easy-to-understand information can be provided. In this way, by adjusting the way information is provided according to the customer's emotions, more appropriate information can be provided.

[0108] The reception unit can prioritize receiving information about the nearest property based on the customer's current location information. For example, if the customer is in a specific area, it will prioritize receiving information about properties related to that area. The reception unit can also automatically suggest related questions based on the customer's location information. Furthermore, if the customer is on the move, the reception unit can suggest the most suitable property information based on the customer's current location. This makes it possible to receive more appropriate questions by utilizing the customer's location information.

[0109] When generating answers to customer questions, the analysis unit can refer to a database of similar questions from the past. For example, if a similar question has been asked in the past, a new answer can be generated based on that answer. The analysis unit can also learn from data on past questions and answers to generate more accurate answers. Furthermore, the analysis unit can analyze past question data and prepare templates for frequently asked questions. This makes it possible to provide faster and more accurate answers by utilizing past data.

[0110] When searching for properties, the search unit can automatically set search conditions by referencing the customer's past search history. For example, if a customer previously searched for "3LDK properties in Tokyo," the same conditions will be automatically set the next time they search. The search unit can also prioritize displaying related properties based on the customer's past search history. Furthermore, the search unit can analyze the customer's past search history and prioritize displaying frequently searched conditions. This makes it possible to search for properties more efficiently by utilizing the customer's past search history.

[0111] The proposal unit can propose the most suitable property based on the customer's past preferences. For example, it can propose similar properties based on the characteristics of properties that the customer has previously selected. The proposal unit can also analyze the customer's past preferences and prioritize the proposal of related properties. Furthermore, the proposal unit can also propose the most suitable properties in a ranking format based on the customer's past preferences. This makes it possible to make more appropriate property proposals by utilizing the customer's past preferences.

[0112] The providing unit can filter information based on the customer's current situation and areas of interest and provide the most appropriate information. For example, when a customer inputs their current place of residence, information related to that area is preferentially provided. The providing unit can also automatically filter relevant information based on the customer's areas of interest. Furthermore, when a customer is in a specific situation, the providing unit can provide the most appropriate information for that situation. This makes it possible to provide more appropriate information by filtering information based on the customer's current situation and areas of interest.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception department accepts questions from customers. For example, if a customer asks, "Are there any 3LDK properties in this area?", the question is entered into the reception department. Step 2: The analysis unit uses the generation AI to analyze the question received by the reception unit. For example, the generation AI understands the question, "Are there any 3LDK properties in this area?" and generates instructions to search for 3LDK properties in that area. Step 3: The search unit searches for properties based on instructions from the analysis unit. For example, if the generation AI issues an instruction to "search for 3LDK properties in this area," the search unit will search for 3LDK properties in that area. Step 4: The proposal department proposes suitable properties based on the search results from the search department. For example, the search department passes a list of 3LDK properties found to the proposal department, which then makes proposals to the customer based on that list. Step 5: The offering department provides the proposed property information to the customer. For example, the offering department provides detailed information about a 3LDK property found by the offering department to the customer.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception desk for accepting questions from customers; an analysis unit that analyzes the question received by the reception unit; a search unit that searches for properties based on the question analyzed by the analysis unit; a proposal unit that proposes properties searched by the search unit; a providing unit that provides property information proposed by the proposing unit; Equipped with A system characterized by:

2. The reception unit Always available to answer customer questions 2. The system of claim 1.

3. The analysis unit Analyze customer questions and generate answers 2. The system of claim 1.

4. The search unit Search for properties based on your specific needs 2. The system of claim 1.

5. The proposal unit Suggest properties based on search results 2. The system of claim 1.

6. The providing unit Providing proposed property information to customers 2. The system of claim 1.

7. The reception unit Estimate customer sentiment and adjust how questions are received based on that sentiment 2. The system of claim 1.

8. The reception unit When accepting a question, select the acceptance method by referring to the customer's past inquiry history 2. The system of claim 1.

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