System

Abstract

An object of a system according to an embodiment is to efficiently present an optimum property according to a desire of a shopkeeper.SOLUTION: A system includes a property information acquisition unit, a request analysis unit, a property presentation unit, and a zoning generation unit. The property information acquisition unit acquires information from a property owner. The desire analysis unit analyzes a desire of the shopkeeper on the basis of the information acquired by the property information acquisition unit. The property presentation unit presents an optimal property based on the need analyzed by the need analysis unit. A zoning generation part generates a zoning layout on the basis of the request of the tenant and an actual section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find and contract for properties that met the specific requirements of exhibitors, and selecting a property took a lot of time.

[0005] The system according to the embodiment aims to efficiently present the most suitable property that meets the needs of the store owner. [Means for solving the problem]

[0006] The system according to the embodiment includes a property information acquisition unit, a request analysis unit, a property presentation unit, and a zoning generation unit. The property information acquisition unit acquires information from property owners. The request analysis unit analyzes the requests of store owners based on the information acquired by the property information acquisition unit. The property presentation unit presents the most suitable property based on the requests analyzed by the request analysis unit. The zoning generation unit generates a zoning layout based on the requests of store owners and the actual plots. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently present the most suitable property that meets the needs of the store owner. [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) The support system according to an embodiment of the present invention is a system for enabling unique stores to open in niche, profitable locations, even as the number of brick-and-mortar channels continues to decline. This system presents properties that are difficult to find on regular real estate agent sites, based on information provided directly by property owners and in accordance with the store owner's detailed requests. As a result, the support system presents optimal properties based on the store owner's requests and generates a zoning layout, enabling efficient property selection.

[0029] The support system according to the embodiment includes a property information acquisition unit, a request analysis unit, a property presentation unit, and a zoning generation unit. The property information acquisition unit acquires information from property owners. For example, the property information acquisition unit collects information about the property's location, area, rent, and facilities provided by the property owners. The property information acquisition unit can also acquire detailed information not available on typical real estate agent websites through direct communication with the property owners. The request analysis unit analyzes the store owner's requests based on the information acquired by the property information acquisition unit. For example, the request analysis unit analyzes information entered by the store owner, such as the desired location, budget, and target customer demographic, and selects the optimal property. The request analysis unit can also analyze the store owner's business model and target customer demographic and suggest the optimal area based on customer behavior patterns. The property presentation unit presents optimal properties based on the requests analyzed by the request analysis unit. For example, the property presentation unit lists and presents optimal properties based on the store owner's detailed requests. The property presentation unit can also analyze the store owner's past property selection history and propose the most suitable property based on similar successful patterns. The zoning generation unit generates a zoning layout based on the store owner's requests and the actual zoning area. For example, the zoning generation unit generates the most suitable zoning layout based on the store owner's requests. The zoning generation unit can also analyze the store owner's past successful layout cases and propose the most suitable zoning layout. As a result, the support system according to the embodiment can present the most suitable property based on the store owner's requests and generate a zoning layout, thereby enabling efficient property selection.

[0030] The zoning generation unit can generate an optimal zoning layout based on the store owner's requests. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic lines of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes an optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, by generating an optimal zoning layout based on the store owner's requests, a layout that meets the store owner's needs can be provided.

[0031] The property presentation unit can list and present optimal properties based on the detailed requests of the store owner. The property presentation unit, for example, analyzes the store owner's business model and stores the behavioral patterns of the target customer base in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal areas. The property presentation unit also analyzes the behavioral patterns of the store owner's target customer base and identifies areas that customers frequently visit. For example, it lists optimal store locations based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer base, and builds a system that proposes optimal areas based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. This allows the store owner to list and present optimal properties based on the store owner's detailed requests, enabling the store owner to select a property that meets their needs.

[0032] The request analysis unit can analyze the store owner's business model and target customer demographic and propose the optimal area based on customer behavior patterns. The request analysis unit, for example, analyzes the store owner's emotional state in real time and proposes an area that elicits positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects an area based on the emotion score. The request analysis unit also uses an emotion estimation function to analyze the store owner's emotional state and build a system that proposes areas that elicit positive emotions. For example, it prioritizes proposing areas with a high store owner's emotion score. The request analysis unit also analyzes the store owner's emotional state and proposes areas that elicit positive emotions. For example, it lists areas that have previously elicited positive emotions based on the store owner's emotion score. This increases the store owner's chances of success by analyzing the store owner's business model and target customer demographic and proposing the optimal area based on customer behavior patterns.

[0033] The property information acquisition unit can analyze the property owner's past transaction history and prioritize collecting information from highly reliable owners. The property information acquisition unit, for example, stores the property owner's past transaction history in a database and identifies highly reliable owners. For example, it evaluates reliability based on the number of past transactions and ratings. The property information acquisition unit also analyzes the property owner's transaction history and builds a system that prioritizes collecting information from highly reliable owners. For example, it evaluates based on the length of the transaction history and the success rate of transactions. The property information acquisition unit also analyzes the property owner's past transaction history and prioritizes collecting information from highly reliable owners. For example, it calculates a reliability score based on the past transaction history and prioritizes owners with high scores. In this way, by analyzing the property owner's past transaction history and preferentially collecting information from highly reliable owners, it is possible to provide highly reliable property information.

[0034] The property information acquisition unit can analyze information provided by property owners to discover the hidden charms and potential value of properties. The property information acquisition unit, for example, analyzes information provided by property owners to build a system that discovers the hidden charms and potential value of properties. For example, it evaluates value based on information about the property's history and surrounding environment. The property information acquisition unit also analyzes information provided by property owners to discover the hidden charms of properties. For example, it evaluates the charm based on the property's architectural style and design features. The property information acquisition unit also analyzes information provided by property owners to discover the property's potential value. For example, it evaluates value based on the property's future development plans and information about surrounding infrastructure development. In this way, by analyzing the information provided by property owners and discovering the hidden charms and potential value of properties, it is possible to provide properties that are attractive to store owners.

[0035] The property information acquisition unit can receive information from property owners in real time via video calls or live streaming. The property information acquisition unit, for example, builds a system in which property owners provide information via video calls. For example, the owner explains the details of the property in real time, providing an environment in which store owners can ask questions. The property information acquisition unit also uses live streaming to allow property owners to provide property information in real time. For example, property viewings are streamed live, allowing store owners to check the information on the spot. The property information acquisition unit also builds a system in which property owners provide information in real time via video calls or live streaming. For example, the owner explains the features and appeal of the property in real time. As a result, property owners can provide information in real time via video calls or live streaming, allowing store owners to check the details of the property in real time.

[0036] The property information acquisition unit can obtain information from property owners through local community events and networking events. The property information acquisition unit, for example, builds a system in which property owners provide property information at local community events. For example, at local events, the owner explains the details of the property, providing an environment in which exhibitors can directly ask questions. The property information acquisition unit also allows property owners to provide property information through networking events. For example, an event is held in which exhibitors and the owner can directly interact, and the details of the property are explained. The property information acquisition unit also builds a system in which property owners provide information through local community events and networking events. For example, the owner explains the features and appeal of the property at the event. In this way, by having property owners provide information through local community events and networking events, exhibitors can directly check the details of the property.

[0037] The zoning generation unit can analyze the store owner's past successful layout cases and propose the optimal zoning layout. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic flow of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes the optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, the store owner's past successful layout cases are analyzed and the optimal zoning layout is proposed, thereby increasing the store owner's chances of success.

[0038] The zoning generation unit can analyze the store owner's business model and target customer demographic and propose a zoning layout that takes customer movement lines into consideration. The zoning generation unit, for example, analyzes the store owner's business model and stores the movement lines of the target customer demographic in a database. For example, it analyzes customer purchasing behavior and movement patterns and proposes an optimal layout. The zoning generation unit also analyzes the movement lines of the store owner's target customer demographic and identifies a layout that customers can use comfortably. For example, it lists optimal seating arrangements and product placements based on customer movement line data. The zoning generation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes a zoning layout that takes customer movement lines into consideration. For example, it makes proposals based on customer purchasing behavior and movement patterns. In this way, by analyzing the store owner's business model and target customer demographic and proposing a zoning layout that takes customer movement lines into consideration, customer convenience is increased.

[0039] The zoning generation unit can consider the influence of seasons and weather when proposing a zoning layout and propose the optimal layout. The zoning generation unit, for example, builds a system that considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes the optimal layout based on past weather data. The zoning generation unit also considers the influence of seasons and weather when proposing the optimal layout. For example, it analyzes customer movement and usage patterns by season and lists the optimal layouts. The zoning generation unit also considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes a layout that will enable the store owner to make the most profit based on weather data. In this way, by proposing the optimal layout that considers the influence of seasons and weather, the store owner's chances of success are increased.

[0040] The zoning generation unit takes into account local culture and event information when proposing a zoning layout, and can propose a layout that is attractive to exhibitors. The zoning generation unit, for example, builds a system that takes into account local culture and event information when proposing a zoning layout. For example, it proposes a layout that is attractive to exhibitors based on information about local festivals and events. The zoning generation unit also takes into account local culture and event information when proposing a zoning layout. For example, it selects a layout based on information about local cultural facilities and tourist spots. The zoning generation unit also takes into account local culture and event information when proposing a zoning layout. For example, it proposes a layout that is most likely to make a profit for exhibitors based on a local event calendar. In this way, by proposing an attractive layout that takes into account local culture and event information, the exhibitor's chances of success are increased.

[0041] The property presentation unit can analyze the store owner's past property selection history and propose optimal properties based on similar successful patterns. For example, the property presentation unit stores the store owner's past property selection history in a database and analyzes successful property selection patterns. For example, it extracts the characteristics of properties that have been successful in the past and proposes similar properties. The property presentation unit also uses AI to automatically search for and propose similar properties based on the store owner's success stories. For example, it analyzes the locations and conditions of properties that have been successful in the past and lists properties with similar conditions. The property presentation unit also analyzes the store owner's past property selection history and builds a system that proposes optimal properties based on successful patterns. For example, it makes proposals based on information about the locations and conditions of successful properties. This increases the store owner's chances of success by analyzing the store owner's past property selection history and proposing optimal properties based on similar successful patterns.

[0042] The property presentation unit can analyze the store owner's business model and target customer demographic and propose optimal properties based on customer behavior patterns. The property presentation unit, for example, analyzes the store owner's business model and stores the behavior patterns of the target customer demographic in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal properties. The property presentation unit also analyzes the behavior patterns of the store owner's target customer demographic and identifies properties that customers frequently visit. For example, it lists optimal properties based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes optimal properties based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed and optimal properties are proposed based on customer behavior patterns, thereby increasing the store owner's chances of success.

[0043] The property presentation unit can consider the influence of seasons and weather when presenting properties and propose the most suitable property. The property presentation unit, for example, builds a system that considers the influence of seasons and weather when presenting properties. For example, it proposes the most suitable property based on past weather data. The property presentation unit also considers the influence of seasons and weather when presenting properties and proposes the most suitable property. For example, it analyzes customer purchasing patterns by season and lists the most suitable properties. The property presentation unit also considers the influence of seasons and weather when presenting properties. For example, it proposes the property that will make the store owner the most profitable based on weather data. In this way, by proposing the most suitable property taking into account the influence of seasons and weather, the store owner's chances of success are increased.

[0044] The property presentation unit can propose properties that are attractive to store owners by taking into account local culture and event information when presenting properties. The property presentation unit, for example, builds a system that considers local culture and event information when presenting properties. For example, it proposes properties that are attractive to store owners based on information about local festivals and events. The property presentation unit also considers local culture and event information when presenting properties and proposes properties that are attractive to store owners. For example, it selects properties based on information about local cultural facilities and tourist spots. The property presentation unit also considers local culture and event information when presenting properties. For example, it proposes properties that are most likely to make a profit for store owners based on a local event calendar. In this way, by proposing attractive properties that take into account local culture and event information, the store owner's chances of success are increased.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The property information acquisition unit not only acquires information from property owners, but also collects infrastructure development plans and future development plans for the surrounding area. For example, the property information acquisition unit collects information from local governments and developers to identify properties that have the potential to increase in value in the future. The property information acquisition unit can also list properties that are advantageous for store owners based on development plans for surrounding transportation infrastructure and public facilities. Furthermore, the property information acquisition unit can collect development plans for local commercial facilities and tourist spots and propose properties that are attractive to store owners. In this way, the property information acquisition unit increases the store owner's chances of success by providing property information that takes into account not only information from property owners but also future development plans for the surrounding area.

[0047] The zoning generation unit not only generates an optimal zoning layout based on the store owner's requests, but can also propose a customizable layout that suits the store owner's business model. For example, the zoning generation unit proposes a layout that optimizes in-store traffic flow and product placement according to the store owner's business model. The zoning generation unit can also propose a layout that suits the store owner's target customer demographic. For example, for a family-oriented store, the zoning generation unit proposes a layout that allows customers with children to spend time comfortably. The zoning generation unit can also propose layouts that suit the season or event according to the store owner's business model. For example, during the Christmas season, the zoning generation unit proposes a layout that incorporates Christmas decorations. In this way, the zoning generation unit increases the store owner's chances of success by proposing a customizable layout that suits the store owner's business model.

[0048] The property presentation unit not only lists optimal properties based on the store owner's detailed requests, but also makes customization proposals for the properties in accordance with the store owner's business model. For example, the property presentation unit makes customization proposals for the interior and facilities of the properties in accordance with the store owner's business model. The property presentation unit can also make customization proposals for the properties in accordance with the store owner's target customer demographic. For example, if a store owner is opening a cafe, the property presentation unit can make proposals for interior and facilities suitable for a cafe. The property presentation unit can also make proposals for the exterior and sign design of the properties in accordance with the store owner's business model. For example, if a store owner is opening a restaurant, the property presentation unit can make proposals for the exterior and sign design suitable for a restaurant. In this way, the property presentation unit can increase the store owner's chances of success by making customization proposals for the properties in accordance with the store owner's business model.

[0049] The property information acquisition unit can analyze the property owner's past transaction history and prioritize collecting information from highly reliable owners. For example, the property information acquisition unit stores the property owner's past transaction history in a database and identifies highly reliable owners. For example, the property information acquisition unit evaluates reliability based on the number of past transactions and ratings. The property information acquisition unit also analyzes the property owner's transaction history and builds a system that prioritizes collecting information from highly reliable owners. For example, the evaluation is based on the length of the transaction history and the success rate of transactions. The property information acquisition unit also analyzes the property owner's past transaction history and prioritizes collecting information from highly reliable owners. For example, the property information acquisition unit calculates a reliability score based on the past transaction history and prioritizes owners with a high score. In this way, highly reliable property information can be provided by analyzing the property owner's past transaction history and prioritize collecting information from highly reliable owners.

[0050] The property information acquisition unit can analyze information provided by property owners to discover the hidden charms and potential value of properties. For example, the property information acquisition unit analyzes information provided by property owners to build a system that discovers the hidden charms and potential value of properties. For example, it evaluates value based on information about the property's history and surrounding environment. The property information acquisition unit also analyzes information provided by property owners to discover the hidden charms of properties. For example, it evaluates the charm based on the property's architectural style and design features. The property information acquisition unit also analyzes information provided by property owners to discover the property's potential value. For example, it evaluates value based on the property's future development plans and information about surrounding infrastructure development. In this way, by analyzing the information provided by property owners and discovering the hidden charms and potential value of properties, it is possible to provide properties that are attractive to store owners.

[0051] The property information acquisition unit can receive information from property owners in real time via video calls or live streaming. For example, the property information acquisition unit builds a system in which property owners provide information via video calls. For example, the owner explains the details of the property in real time, providing an environment in which store owners can ask questions. The property information acquisition unit also uses live streaming to allow property owners to provide property information in real time. For example, property viewings are streamed live, allowing store owners to check the information on the spot. The property information acquisition unit also builds a system in which property owners provide information in real time via video calls or live streaming. For example, the owner explains the features and appeal of the property in real time. As a result, property owners can provide information in real time via video calls or live streaming, allowing store owners to check the details of the property in real time.

[0052] The property information acquisition unit can obtain information from property owners through local community events and networking events. For example, the property information acquisition unit builds a system in which property owners provide property information at local community events. For example, the owner explains the details of the property at a local event, providing an environment in which exhibitors can directly ask questions. The property information acquisition unit also allows property owners to provide property information through networking events. For example, an event is held in which exhibitors and the owner can directly interact, and the details of the property are explained. The property information acquisition unit also builds a system in which property owners provide information through local community events and networking events. For example, the owner explains the features and appeal of the property at the event. In this way, by having property owners provide information through local community events and networking events, exhibitors can directly check the details of the property.

[0053] The zoning generation unit can analyze the store owner's past successful layout cases and propose the optimal zoning layout. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic flow of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes the optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, the store owner's past successful layout cases are analyzed and the optimal zoning layout is proposed, increasing the store owner's chances of success.

[0054] The zoning generation unit can analyze the store owner's business model and target customer demographic, and propose a zoning layout that takes customer movement lines into consideration. For example, the zoning generation unit analyzes the store owner's business model and stores the movement lines of the target customer demographic in a database. For example, it analyzes customer purchasing behavior and movement patterns and proposes an optimal layout. The zoning generation unit also analyzes the movement lines of the store owner's target customer demographic and identifies a layout that customers can use comfortably. For example, it lists optimal seating arrangements and product placements based on customer movement line data. The zoning generation unit also analyzes the store owner's business model and target customer demographic, and builds a system that proposes a zoning layout that takes customer movement lines into consideration. For example, it makes proposals based on customer purchasing behavior and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed, and a zoning layout that takes customer movement lines into consideration is proposed, thereby increasing customer convenience.

[0055] The zoning generation unit can consider the influence of seasons and weather when proposing a zoning layout and propose the optimal layout. For example, the zoning generation unit builds a system that considers the influence of seasons and weather when proposing a zoning layout. For example, the zoning generation unit proposes the optimal layout based on past weather data. The zoning generation unit also considers the influence of seasons and weather when proposing the optimal layout. For example, it analyzes customer movement and usage patterns by season and lists optimal layouts. The zoning generation unit also considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes a layout that will enable the store owner to make the most profit based on weather data. In this way, by proposing the optimal layout that considers the influence of seasons and weather, the store owner's chances of success are increased.

[0056] The zoning generation unit can propose a layout that is attractive to exhibitors by taking into account local culture and event information when proposing a zoning layout. For example, the zoning generation unit builds a system that considers local culture and event information when proposing a zoning layout. For example, it proposes a layout that is attractive to exhibitors based on information about local festivals and events. The zoning generation unit also considers local culture and event information when proposing a zoning layout. For example, it selects a layout based on information about local cultural facilities and tourist spots. The zoning generation unit also considers local culture and event information when proposing a zoning layout. For example, it proposes a layout that is most likely to make a profit for exhibitors based on a local event calendar. In this way, by proposing an attractive layout that takes into account local culture and event information, the exhibitor's chances of success are increased.

[0057] The property presentation unit can analyze the store owner's past property selection history and propose optimal properties based on similar successful patterns. For example, the property presentation unit stores the store owner's past property selection history in a database and analyzes successful property selection patterns. For example, it extracts the characteristics of properties that have been successful in the past and proposes similar properties. The property presentation unit also uses AI to automatically search for and propose similar properties based on the store owner's success stories. For example, it analyzes the locations and conditions of properties that have been successful in the past and lists properties with similar conditions. The property presentation unit also analyzes the store owner's past property selection history and builds a system that proposes optimal properties based on successful patterns. For example, it makes proposals based on information about the locations and conditions of successful properties. This increases the store owner's chances of success by analyzing the store owner's past property selection history and proposing optimal properties based on similar successful patterns.

[0058] The property presentation unit can analyze the store owner's business model and target customer demographic and propose optimal properties based on customer behavior patterns. For example, the property presentation unit analyzes the store owner's business model and stores the behavior patterns of the target customer demographic in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal properties. The property presentation unit also analyzes the behavior patterns of the store owner's target customer demographic and identifies properties that customers frequently visit. For example, it lists optimal properties based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes optimal properties based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed and optimal properties are proposed based on customer behavior patterns, thereby increasing the store owner's chances of success.

[0059] The property presentation unit can consider the influence of seasons and weather when presenting properties and propose the most suitable property. For example, the property presentation unit builds a system that considers the influence of seasons and weather when presenting properties. For example, the property presentation unit proposes the most suitable property based on past weather data. The property presentation unit also considers the influence of seasons and weather when presenting properties and proposes the most suitable property. For example, it analyzes customer purchasing patterns by season and lists the most suitable properties. The property presentation unit also considers the influence of seasons and weather when presenting properties. For example, it proposes the property that will make the store owner the most profit based on weather data. In this way, by proposing the most suitable property taking into account the influence of seasons and weather, the store owner's chances of success are increased.

[0060] The property presentation unit can propose properties that are attractive to store owners by taking into account local culture and event information when presenting properties. For example, the property presentation unit builds a system that considers local culture and event information when presenting properties. For example, it proposes properties that are attractive to store owners based on information about local festivals and events. The property presentation unit also considers local culture and event information when presenting properties and proposes properties that are attractive to store owners. For example, it selects properties based on information about local cultural facilities and tourist spots. The property presentation unit also considers local culture and event information when presenting properties. For example, it proposes properties that are most likely to make a profit for store owners based on a local event calendar. In this way, by proposing attractive properties that take into account local culture and event information, the store owner's chances of success are increased.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The property information acquisition unit acquires information from the property owner. For example, the property information acquisition unit collects information such as the property's location, area, rent, and facilities provided by the property owner. The property information acquisition unit can also acquire detailed information that cannot be obtained from regular real estate agent websites through direct communication with the property owner. Step 2: The request analysis unit analyzes the store owner's requests based on the information acquired by the property information acquisition unit. For example, the request analysis unit analyzes information entered by the store owner, such as the desired location, budget, and target customer demographic, and selects the most suitable property. The request analysis unit can also analyze the store owner's business model and target customer demographic, and suggest the most suitable area based on customer behavior patterns. Step 3: The property presentation unit presents the most suitable property based on the requirements analyzed by the requirement analysis unit. For example, the property presentation unit lists and presents the most suitable property based on the store owner's detailed requirements. The property presentation unit can also analyze the store owner's past property selection history and suggest the most suitable property based on similar successful patterns. Step 4: The zoning generation unit generates a zoning layout based on the store owner's requests and the actual area. For example, the zoning generation unit generates an optimal zoning layout based on the store owner's requests. The zoning generation unit can also analyze the store owner's past successful layout examples and propose an optimal zoning layout.

[0063] (Example 2) The support system according to an embodiment of the present invention is a system for enabling unique stores to open in niche, profitable locations, even as the number of brick-and-mortar channels continues to decline. This system presents properties that are difficult to find on regular real estate agent sites, based on information provided directly by property owners and in accordance with the store owner's detailed requests. As a result, the support system presents optimal properties based on the store owner's requests and generates a zoning layout, enabling efficient property selection.

[0064] The support system according to the embodiment includes a property information acquisition unit, a request analysis unit, a property presentation unit, and a zoning generation unit. The property information acquisition unit acquires information from property owners. For example, the property information acquisition unit collects information about the property's location, area, rent, and facilities provided by the property owners. The property information acquisition unit can also acquire detailed information not available on typical real estate agent websites through direct communication with the property owners. The request analysis unit analyzes the store owner's requests based on the information acquired by the property information acquisition unit. For example, the request analysis unit analyzes information entered by the store owner, such as the desired location, budget, and target customer demographic, and selects the optimal property. The request analysis unit can also analyze the store owner's business model and target customer demographic and suggest the optimal area based on customer behavior patterns. The property presentation unit presents optimal properties based on the requests analyzed by the request analysis unit. For example, the property presentation unit lists and presents optimal properties based on the store owner's detailed requests. The property presentation unit can also analyze the store owner's past property selection history and propose the most suitable property based on similar successful patterns. The zoning generation unit generates a zoning layout based on the store owner's requests and the actual zoning area. For example, the zoning generation unit generates the most suitable zoning layout based on the store owner's requests. The zoning generation unit can also analyze the store owner's past successful layout cases and propose the most suitable zoning layout. As a result, the support system according to the embodiment can present the most suitable property based on the store owner's requests and generate a zoning layout, thereby enabling efficient property selection.

[0065] The zoning generation unit can generate an optimal zoning layout based on the store owner's requests. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic lines of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes an optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, by generating an optimal zoning layout based on the store owner's requests, a layout that meets the store owner's needs can be provided.

[0066] The property presentation unit can list and present optimal properties based on the detailed requests of the store owner. The property presentation unit, for example, analyzes the store owner's business model and stores the behavioral patterns of the target customer base in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal areas. The property presentation unit also analyzes the behavioral patterns of the store owner's target customer base and identifies areas that customers frequently visit. For example, it lists optimal store locations based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer base, and builds a system that proposes optimal areas based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. This allows the store owner to list and present optimal properties based on the store owner's detailed requests, enabling the store owner to select a property that meets their needs.

[0067] The request analysis unit can analyze the store owner's business model and target customer demographic and propose the optimal area based on customer behavior patterns. The request analysis unit, for example, analyzes the store owner's emotional state in real time and proposes an area that elicits positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects an area based on the emotion score. The request analysis unit also uses an emotion estimation function to analyze the store owner's emotional state and build a system that proposes areas that elicit positive emotions. For example, it prioritizes proposing areas with a high store owner's emotion score. The request analysis unit also analyzes the store owner's emotional state and proposes areas that elicit positive emotions. For example, it lists areas that have previously elicited positive emotions based on the store owner's emotion score. This increases the store owner's chances of success by analyzing the store owner's business model and target customer demographic and proposing the optimal area based on customer behavior patterns.

[0068] The request analysis unit can analyze the emotional state of the store owner and suggest areas that will elicit positive emotions. The request analysis unit, for example, analyzes the emotional state of the store owner in real time and suggests areas that will elicit positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects an area based on the emotion score. The request analysis unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and suggests areas that will elicit positive emotions. For example, it prioritizes suggesting areas with a high emotion score for the store owner. The request analysis unit also analyzes the emotional state of the store owner and suggests areas that will elicit positive emotions. For example, it lists areas that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's satisfaction can be increased by analyzing the emotional state of the store owner and suggesting areas that will elicit positive emotions.

[0069] The property information acquisition unit can analyze the property owner's past transaction history and prioritize collecting information from highly reliable owners. The property information acquisition unit, for example, stores the property owner's past transaction history in a database and identifies highly reliable owners. For example, it evaluates reliability based on the number of past transactions and ratings. The property information acquisition unit also analyzes the property owner's transaction history and builds a system that prioritizes collecting information from highly reliable owners. For example, it evaluates based on the length of the transaction history and the success rate of transactions. The property information acquisition unit also analyzes the property owner's past transaction history and prioritizes collecting information from highly reliable owners. For example, it calculates a reliability score based on the past transaction history and prioritizes owners with high scores. In this way, by analyzing the property owner's past transaction history and preferentially collecting information from highly reliable owners, it is possible to provide highly reliable property information.

[0070] The property information acquisition unit can analyze information provided by property owners to discover the hidden charms and potential value of properties. The property information acquisition unit, for example, analyzes information provided by property owners to build a system that discovers the hidden charms and potential value of properties. For example, it evaluates value based on information about the property's history and surrounding environment. The property information acquisition unit also analyzes information provided by property owners to discover the hidden charms of properties. For example, it evaluates the charm based on the property's architectural style and design features. The property information acquisition unit also analyzes information provided by property owners to discover the property's potential value. For example, it evaluates value based on the property's future development plans and information about surrounding infrastructure development. In this way, by analyzing the information provided by property owners and discovering the hidden charms and potential value of properties, it is possible to provide properties that are attractive to store owners.

[0071] The property information acquisition unit can analyze the emotional state of the property owner and preferentially collect information from owners who have positive emotions. The property information acquisition unit, for example, uses an emotion estimation function to analyze the emotional state of the property owner in real time and preferentially collect information from owners who have positive emotions. For example, it analyzes the owner's facial expressions and voice and selects information based on an emotion score. The property information acquisition unit also analyzes the emotional state of the property owner and builds a system that preferentially collects information from owners who have positive emotions. For example, it preferentially lists information about owners with high emotion scores. The property information acquisition unit also uses the emotion estimation function to analyze the emotional state of the property owner and preferentially collect information from owners who have positive emotions. For example, it selects owners based on past emotion data. In this way, by analyzing the emotional state of the property owner and preferentially collecting information from owners who have positive emotions, it is possible to provide highly reliable property information.

[0072] The property information acquisition unit can receive information from property owners in real time via video calls or live streaming. The property information acquisition unit, for example, builds a system in which property owners provide information via video calls. For example, the owner explains the details of the property in real time, providing an environment in which store owners can ask questions. The property information acquisition unit also uses live streaming to allow property owners to provide property information in real time. For example, property viewings are streamed live, allowing store owners to check the information on the spot. The property information acquisition unit also builds a system in which property owners provide information in real time via video calls or live streaming. For example, the owner explains the features and appeal of the property in real time. As a result, property owners can provide information in real time via video calls or live streaming, allowing store owners to check the details of the property in real time.

[0073] The property information acquisition unit can obtain information from property owners through local community events and networking events. The property information acquisition unit, for example, builds a system in which property owners provide property information at local community events. For example, at local events, the owner explains the details of the property, providing an environment in which exhibitors can directly ask questions. The property information acquisition unit also allows property owners to provide property information through networking events. For example, an event is held in which exhibitors and the owner can directly interact, and the details of the property are explained. The property information acquisition unit also builds a system in which property owners provide information through local community events and networking events. For example, the owner explains the features and appeal of the property at the event. In this way, by having property owners provide information through local community events and networking events, exhibitors can directly check the details of the property.

[0074] The property information acquisition unit can analyze the emotional responses of property owners and strengthen relationships with owners who have positive emotions. The property information acquisition unit, for example, uses an emotion estimation function to analyze the emotional responses of property owners in real time and strengthen relationships with owners who have positive emotions. For example, it analyzes the owner's facial expressions and voice and builds relationships based on emotion scores. The property information acquisition unit also analyzes the emotional responses of property owners and builds a system to strengthen relationships with owners who have positive emotions. For example, it prioritizes communication with owners with high emotion scores. The property information acquisition unit also uses the emotion estimation function to analyze the emotional responses of property owners and strengthen relationships with owners who have positive emotions. For example, it strengthens relationships with owners based on past emotion data. In this way, by analyzing the emotional responses of property owners and strengthening relationships with owners who have positive emotions, it is possible to provide highly reliable property information.

[0075] The zoning generation unit can analyze the store owner's past successful layout cases and propose the optimal zoning layout. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic flow of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes the optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, the store owner's past successful layout cases are analyzed and the optimal zoning layout is proposed, thereby increasing the store owner's chances of success.

[0076] The zoning generation unit can analyze the store owner's business model and target customer demographic and propose a zoning layout that takes customer movement lines into consideration. The zoning generation unit, for example, analyzes the store owner's business model and stores the movement lines of the target customer demographic in a database. For example, it analyzes customer purchasing behavior and movement patterns and proposes an optimal layout. The zoning generation unit also analyzes the movement lines of the store owner's target customer demographic and identifies a layout that customers can use comfortably. For example, it lists optimal seating arrangements and product placements based on customer movement line data. The zoning generation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes a zoning layout that takes customer movement lines into consideration. For example, it makes proposals based on customer purchasing behavior and movement patterns. In this way, by analyzing the store owner's business model and target customer demographic and proposing a zoning layout that takes customer movement lines into consideration, customer convenience is increased.

[0077] The zoning generation unit can analyze the emotional state of the store owner and propose a zoning layout that elicits positive emotions. The zoning generation unit, for example, analyzes the emotional state of the store owner in real time and proposes a layout that elicits positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects a layout based on an emotion score. The zoning generation unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and proposes a layout that elicits positive emotions. For example, it preferentially proposes layouts with high emotion scores for the store owner. The zoning generation unit also analyzes the emotional state of the store owner and proposes a layout that elicits positive emotions. For example, it lists layouts that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's satisfaction is increased by analyzing the emotional state of the store owner and proposing a zoning layout that elicits positive emotions.

[0078] The zoning generation unit can consider the influence of seasons and weather when proposing a zoning layout and propose the optimal layout. The zoning generation unit, for example, builds a system that considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes the optimal layout based on past weather data. The zoning generation unit also considers the influence of seasons and weather when proposing the optimal layout. For example, it analyzes customer movement and usage patterns by season and lists the optimal layouts. The zoning generation unit also considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes a layout that will enable the store owner to make the most profit based on weather data. In this way, by proposing the optimal layout that considers the influence of seasons and weather, the store owner's chances of success are increased.

[0079] The zoning generation unit takes into account local culture and event information when proposing a zoning layout, and can propose a layout that is attractive to exhibitors. The zoning generation unit, for example, builds a system that takes into account local culture and event information when proposing a zoning layout. For example, it proposes a layout that is attractive to exhibitors based on information about local festivals and events. The zoning generation unit also takes into account local culture and event information when proposing a zoning layout. For example, it selects a layout based on information about local cultural facilities and tourist spots. The zoning generation unit also takes into account local culture and event information when proposing a zoning layout. For example, it proposes a layout that is most likely to make a profit for exhibitors based on a local event calendar. In this way, by proposing an attractive layout that takes into account local culture and event information, the exhibitor's chances of success are increased.

[0080] The zoning generation unit can analyze customers' emotional responses to zoning layouts and propose layouts that generate many positive responses. The zoning generation unit, for example, uses an emotion estimation function to build a system that analyzes customers' emotional responses to zoning layouts. For example, it analyzes customers' facial expressions and voices and selects a layout based on an emotion score. The zoning generation unit also analyzes customers' emotional responses and proposes layouts that generate many positive responses. For example, it prioritizes listing layouts with high customer emotion scores. The zoning generation unit also uses the emotion estimation function to analyze customers' emotional responses to zoning layouts and proposes layouts that generate many positive responses. For example, it selects a layout based on past customer emotion data. In this way, customer satisfaction is increased by analyzing customers' emotional responses and proposing layouts that generate many positive responses.

[0081] The property presentation unit can analyze the store owner's past property selection history and propose optimal properties based on similar successful patterns. For example, the property presentation unit stores the store owner's past property selection history in a database and analyzes successful property selection patterns. For example, it extracts the characteristics of properties that have been successful in the past and proposes similar properties. The property presentation unit also uses AI to automatically search for and propose similar properties based on the store owner's success stories. For example, it analyzes the locations and conditions of properties that have been successful in the past and lists properties with similar conditions. The property presentation unit also analyzes the store owner's past property selection history and builds a system that proposes optimal properties based on successful patterns. For example, it makes proposals based on information about the locations and conditions of successful properties. This increases the store owner's chances of success by analyzing the store owner's past property selection history and proposing optimal properties based on similar successful patterns.

[0082] The property presentation unit can analyze the store owner's business model and target customer demographic and propose optimal properties based on customer behavior patterns. The property presentation unit, for example, analyzes the store owner's business model and stores the behavior patterns of the target customer demographic in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal properties. The property presentation unit also analyzes the behavior patterns of the store owner's target customer demographic and identifies properties that customers frequently visit. For example, it lists optimal properties based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes optimal properties based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed and optimal properties are proposed based on customer behavior patterns, thereby increasing the store owner's chances of success.

[0083] The property presentation unit can analyze the emotional state of the store owner and suggest properties that elicit positive emotions. The property presentation unit, for example, analyzes the emotional state of the store owner in real time and suggests properties that elicit positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects properties based on an emotion score. The property presentation unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and suggests properties that elicit positive emotions. For example, it prioritizes suggesting properties with a high emotion score for the store owner. The property presentation unit also analyzes the emotional state of the store owner and suggests properties that elicit positive emotions. For example, it lists properties that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's satisfaction is increased by analyzing the emotional state of the store owner and suggesting properties that elicit positive emotions.

[0084] The property presentation unit can consider the influence of seasons and weather when presenting properties and propose the most suitable property. The property presentation unit, for example, builds a system that considers the influence of seasons and weather when presenting properties. For example, it proposes the most suitable property based on past weather data. The property presentation unit also considers the influence of seasons and weather when presenting properties and proposes the most suitable property. For example, it analyzes customer purchasing patterns by season and lists the most suitable properties. The property presentation unit also considers the influence of seasons and weather when presenting properties. For example, it proposes the property that will make the store owner the most profitable based on weather data. In this way, by proposing the most suitable property taking into account the influence of seasons and weather, the store owner's chances of success are increased.

[0085] The property presentation unit can propose properties that are attractive to store owners by taking into account local culture and event information when presenting properties. The property presentation unit, for example, builds a system that considers local culture and event information when presenting properties. For example, it proposes properties that are attractive to store owners based on information about local festivals and events. The property presentation unit also considers local culture and event information when presenting properties and proposes properties that are attractive to store owners. For example, it selects properties based on information about local cultural facilities and tourist spots. The property presentation unit also considers local culture and event information when presenting properties. For example, it proposes properties that are most likely to make a profit for store owners based on a local event calendar. In this way, by proposing attractive properties that take into account local culture and event information, the store owner's chances of success are increased.

[0086] The property presentation unit can analyze customers' emotional responses to properties and suggest properties that have a high number of positive responses. The property presentation unit, for example, uses an emotion estimation function to build a system that analyzes customers' emotional responses to properties. For example, it analyzes customers' facial expressions and voices and selects properties based on an emotion score. The property presentation unit also analyzes customers' emotional responses and suggests properties that have a high number of positive responses. For example, it prioritizes listing properties that have a high customer emotion score. The property presentation unit also uses the emotion estimation function to analyze customers' emotional responses to properties and suggests properties that have a high number of positive responses. For example, it selects properties based on past customer emotion data. In this way, customer satisfaction is increased by analyzing customers' emotional responses and suggesting properties that have a high number of positive responses.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The property information acquisition unit not only acquires information from property owners, but also collects infrastructure development plans and future development plans for the surrounding area. For example, the property information acquisition unit collects information from local governments and developers to identify properties that have the potential to increase in value in the future. The property information acquisition unit can also list properties that are advantageous for store owners based on development plans for surrounding transportation infrastructure and public facilities. Furthermore, the property information acquisition unit can collect development plans for local commercial facilities and tourist spots and propose properties that are attractive to store owners. In this way, the property information acquisition unit increases the store owner's chances of success by providing property information that takes into account not only information from property owners but also future development plans for the surrounding area.

[0089] The zoning generation unit not only generates an optimal zoning layout based on the store owner's requests, but can also propose a customizable layout that suits the store owner's business model. For example, the zoning generation unit proposes a layout that optimizes in-store traffic flow and product placement according to the store owner's business model. The zoning generation unit can also propose a layout that suits the store owner's target customer demographic. For example, for a family-oriented store, the zoning generation unit proposes a layout that allows customers with children to spend time comfortably. The zoning generation unit can also propose layouts that suit the season or event according to the store owner's business model. For example, during the Christmas season, the zoning generation unit proposes a layout that incorporates Christmas decorations. In this way, the zoning generation unit increases the store owner's chances of success by proposing a customizable layout that suits the store owner's business model.

[0090] The property presentation unit not only lists optimal properties based on the store owner's detailed requests, but also makes customization proposals for the properties in accordance with the store owner's business model. For example, the property presentation unit makes customization proposals for the interior and facilities of the properties in accordance with the store owner's business model. The property presentation unit can also make customization proposals for the properties in accordance with the store owner's target customer demographic. For example, if a store owner is opening a cafe, the property presentation unit can make proposals for interior and facilities suitable for a cafe. The property presentation unit can also make proposals for the exterior and sign design of the properties in accordance with the store owner's business model. For example, if a store owner is opening a restaurant, the property presentation unit can make proposals for the exterior and sign design suitable for a restaurant. In this way, the property presentation unit can increase the store owner's chances of success by making customization proposals for the properties in accordance with the store owner's business model.

[0091] The request analysis unit not only analyzes the store owner's business model and target customer demographic, but also analyzes the store owner's emotional state and can suggest areas that elicit positive emotions. For example, the request analysis unit analyzes the store owner's facial expressions and voice and selects an area based on the emotion score. The request analysis unit also uses an emotion estimation function to analyze the store owner's emotional state and build a system that suggests areas that elicit positive emotions. For example, it prioritizes suggesting areas with a high emotion score for the store owner. The request analysis unit also analyzes the store owner's emotional state and suggests areas that elicit positive emotions. For example, it lists areas that have elicited positive emotions in the past based on the store owner's emotion score. This allows the store owner's business model and target customer demographic to be analyzed, and the optimal area to be suggested based on customer behavior patterns, thereby increasing the store owner's chances of success.

[0092] The request analysis unit can analyze the emotional state of the store owner and suggest areas that will elicit positive emotions. For example, the request analysis unit analyzes the emotional state of the store owner in real time and suggests areas that will elicit positive emotions. For example, the request analysis unit analyzes the store owner's facial expressions and voice and selects an area based on the emotion score. The request analysis unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and suggests areas that will elicit positive emotions. For example, it prioritizes suggesting areas with a high emotion score for the store owner. The request analysis unit also analyzes the emotional state of the store owner and suggests areas that will elicit positive emotions. For example, it lists areas that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's satisfaction can be increased by analyzing the emotional state of the store owner and suggesting areas that will elicit positive emotions.

[0093] The property information acquisition unit can analyze the property owner's past transaction history and prioritize collecting information from highly reliable owners. For example, the property information acquisition unit stores the property owner's past transaction history in a database and identifies highly reliable owners. For example, the property information acquisition unit evaluates reliability based on the number of past transactions and ratings. The property information acquisition unit also analyzes the property owner's transaction history and builds a system that prioritizes collecting information from highly reliable owners. For example, the evaluation is based on the length of the transaction history and the success rate of transactions. The property information acquisition unit also analyzes the property owner's past transaction history and prioritizes collecting information from highly reliable owners. For example, the property information acquisition unit calculates a reliability score based on the past transaction history and prioritizes owners with a high score. In this way, highly reliable property information can be provided by analyzing the property owner's past transaction history and prioritize collecting information from highly reliable owners.

[0094] The property information acquisition unit can analyze information provided by property owners to discover the hidden charms and potential value of properties. For example, the property information acquisition unit analyzes information provided by property owners to build a system that discovers the hidden charms and potential value of properties. For example, it evaluates value based on information about the property's history and surrounding environment. The property information acquisition unit also analyzes information provided by property owners to discover the hidden charms of properties. For example, it evaluates the charm based on the property's architectural style and design features. The property information acquisition unit also analyzes information provided by property owners to discover the property's potential value. For example, it evaluates value based on the property's future development plans and information about surrounding infrastructure development. In this way, by analyzing the information provided by property owners and discovering the hidden charms and potential value of properties, it is possible to provide properties that are attractive to store owners.

[0095] The property information acquisition unit can analyze the emotional state of the property owner and preferentially collect information from owners who have positive emotions. For example, the property information acquisition unit uses an emotion estimation function to analyze the emotional state of the property owner in real time and preferentially collect information from owners who have positive emotions. For example, the property information acquisition unit analyzes the owner's facial expressions and voice and selects information based on an emotion score. The property information acquisition unit also analyzes the emotional state of the property owner and builds a system that preferentially collects information from owners who have positive emotions. For example, it preferentially lists information about owners with high emotion scores. The property information acquisition unit also uses the emotion estimation function to analyze the emotional state of the property owner and preferentially collect information from owners who have positive emotions. For example, it selects owners based on past emotion data. In this way, by analyzing the emotional state of the property owner and preferentially collecting information from owners who have positive emotions, it is possible to provide highly reliable property information.

[0096] The property information acquisition unit can receive information from property owners in real time via video calls or live streaming. For example, the property information acquisition unit builds a system in which property owners provide information via video calls. For example, the owner explains the details of the property in real time, providing an environment in which store owners can ask questions. The property information acquisition unit also uses live streaming to allow property owners to provide property information in real time. For example, property viewings are streamed live, allowing store owners to check the information on the spot. The property information acquisition unit also builds a system in which property owners provide information in real time via video calls or live streaming. For example, the owner explains the features and appeal of the property in real time. As a result, property owners can provide information in real time via video calls or live streaming, allowing store owners to check the details of the property in real time.

[0097] The property information acquisition unit can obtain information from property owners through local community events and networking events. For example, the property information acquisition unit builds a system in which property owners provide property information at local community events. For example, the owner explains the details of the property at a local event, providing an environment in which exhibitors can directly ask questions. The property information acquisition unit also allows property owners to provide property information through networking events. For example, an event is held in which exhibitors and the owner can directly interact, and the details of the property are explained. The property information acquisition unit also builds a system in which property owners provide information through local community events and networking events. For example, the owner explains the features and appeal of the property at the event. In this way, by having property owners provide information through local community events and networking events, exhibitors can directly check the details of the property.

[0098] The property information acquisition unit can analyze the emotional responses of property owners and strengthen relationships with owners who have positive emotions. For example, the property information acquisition unit uses an emotion estimation function to analyze the emotional responses of property owners in real time and strengthen relationships with owners who have positive emotions. For example, the property information acquisition unit analyzes the owner's facial expressions and voice and builds relationships based on emotion scores. The property information acquisition unit also analyzes the emotional responses of property owners and builds a system to strengthen relationships with owners who have positive emotions. For example, it prioritizes communication with owners with high emotion scores. The property information acquisition unit also uses the emotion estimation function to analyze the emotional responses of property owners and strengthen relationships with owners who have positive emotions. For example, it strengthens relationships with owners based on past emotion data. In this way, by analyzing the emotional responses of property owners and strengthening relationships with owners who have positive emotions, it is possible to provide highly reliable property information.

[0099] The zoning generation unit can analyze the store owner's past successful layout cases and propose the optimal zoning layout. For example, the zoning generation unit stores the store owner's past successful layout cases in a database and analyzes successful layout patterns. For example, it extracts the characteristics of layouts that have been successful in the past and proposes similar layouts. The zoning generation unit also uses AI to automatically search for and propose similar layouts based on the store owner's successful cases. For example, it analyzes the seating arrangements and traffic flow of layouts that have been successful in the past and lists layouts with similar conditions. The zoning generation unit also analyzes the store owner's past successful layout cases and builds a system that proposes the optimal zoning layout. For example, it makes proposals based on information about the arrangement and design of successful layouts. In this way, the store owner's past successful layout cases are analyzed and the optimal zoning layout is proposed, increasing the store owner's chances of success.

[0100] The zoning generation unit can analyze the store owner's business model and target customer demographic, and propose a zoning layout that takes customer movement lines into consideration. For example, the zoning generation unit analyzes the store owner's business model and stores the movement lines of the target customer demographic in a database. For example, it analyzes customer purchasing behavior and movement patterns and proposes an optimal layout. The zoning generation unit also analyzes the movement lines of the store owner's target customer demographic and identifies a layout that customers can use comfortably. For example, it lists optimal seating arrangements and product placements based on customer movement line data. The zoning generation unit also analyzes the store owner's business model and target customer demographic, and builds a system that proposes a zoning layout that takes customer movement lines into consideration. For example, it makes proposals based on customer purchasing behavior and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed, and a zoning layout that takes customer movement lines into consideration is proposed, thereby increasing customer convenience.

[0101] The zoning generation unit can analyze the emotional state of the store owner and propose a zoning layout that elicits positive emotions. For example, the zoning generation unit analyzes the emotional state of the store owner in real time and proposes a layout that elicits positive emotions. For example, it analyzes the store owner's facial expressions and voice and selects a layout based on the emotion score. The zoning generation unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and proposes a layout that elicits positive emotions. For example, it preferentially proposes layouts with high emotion scores for the store owner. The zoning generation unit also analyzes the emotional state of the store owner and proposes a layout that elicits positive emotions. For example, it lists layouts that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's emotional state is analyzed and a zoning layout that elicits positive emotions is proposed, thereby increasing the store owner's satisfaction.

[0102] The zoning generation unit can consider the influence of seasons and weather when proposing a zoning layout and propose the optimal layout. For example, the zoning generation unit builds a system that considers the influence of seasons and weather when proposing a zoning layout. For example, the zoning generation unit proposes the optimal layout based on past weather data. The zoning generation unit also considers the influence of seasons and weather when proposing the optimal layout. For example, it analyzes customer movement and usage patterns by season and lists optimal layouts. The zoning generation unit also considers the influence of seasons and weather when proposing a zoning layout. For example, it proposes a layout that will enable the store owner to make the most profit based on weather data. In this way, by proposing the optimal layout that considers the influence of seasons and weather, the store owner's chances of success are increased.

[0103] The zoning generation unit can propose a layout that is attractive to exhibitors by taking into account local culture and event information when proposing a zoning layout. For example, the zoning generation unit builds a system that considers local culture and event information when proposing a zoning layout. For example, it proposes a layout that is attractive to exhibitors based on information about local festivals and events. The zoning generation unit also considers local culture and event information when proposing a zoning layout. For example, it selects a layout based on information about local cultural facilities and tourist spots. The zoning generation unit also considers local culture and event information when proposing a zoning layout. For example, it proposes a layout that is most likely to make a profit for exhibitors based on a local event calendar. In this way, by proposing an attractive layout that takes into account local culture and event information, the exhibitor's chances of success are increased.

[0104] The zoning generation unit can analyze customers' emotional responses to zoning layouts and propose layouts that generate many positive responses. For example, the zoning generation unit uses an emotion estimation function to build a system that analyzes customers' emotional responses to zoning layouts. For example, it analyzes customers' facial expressions and voices and selects a layout based on an emotion score. The zoning generation unit also analyzes customers' emotional responses and proposes layouts that generate many positive responses. For example, it prioritizes listing layouts with high customer emotion scores. The zoning generation unit also uses the emotion estimation function to analyze customers' emotional responses to zoning layouts and proposes layouts that generate many positive responses. For example, it selects a layout based on past customer emotion data. In this way, customer satisfaction is increased by analyzing customers' emotional responses and proposing layouts that generate many positive responses.

[0105] The property presentation unit can analyze the store owner's past property selection history and propose optimal properties based on similar successful patterns. For example, the property presentation unit stores the store owner's past property selection history in a database and analyzes successful property selection patterns. For example, it extracts the characteristics of properties that have been successful in the past and proposes similar properties. The property presentation unit also uses AI to automatically search for and propose similar properties based on the store owner's success stories. For example, it analyzes the locations and conditions of properties that have been successful in the past and lists properties with similar conditions. The property presentation unit also analyzes the store owner's past property selection history and builds a system that proposes optimal properties based on successful patterns. For example, it makes proposals based on information about the locations and conditions of successful properties. This increases the store owner's chances of success by analyzing the store owner's past property selection history and proposing optimal properties based on similar successful patterns.

[0106] The property presentation unit can analyze the store owner's business model and target customer demographic and propose optimal properties based on customer behavior patterns. For example, the property presentation unit analyzes the store owner's business model and stores the behavior patterns of the target customer demographic in a database. For example, it analyzes customer purchasing histories and movement patterns to propose optimal properties. The property presentation unit also analyzes the behavior patterns of the store owner's target customer demographic and identifies properties that customers frequently visit. For example, it lists optimal properties based on location information from customers' smartphones. The property presentation unit also analyzes the store owner's business model and target customer demographic and builds a system that proposes optimal properties based on customer behavior patterns. For example, it makes proposals based on customer purchasing histories and movement patterns. In this way, the store owner's business model and target customer demographic are analyzed and optimal properties are proposed based on customer behavior patterns, thereby increasing the store owner's chances of success.

[0107] The property presentation unit can analyze the emotional state of the store owner and suggest properties that elicit positive emotions. For example, the property presentation unit analyzes the emotional state of the store owner in real time and suggests properties that elicit positive emotions. For example, the property presentation unit analyzes the store owner's facial expressions and voice and selects properties based on an emotion score. The property presentation unit also uses an emotion estimation function to build a system that analyzes the emotional state of the store owner and suggests properties that elicit positive emotions. For example, it prioritizes suggesting properties with a high emotion score for the store owner. The property presentation unit also analyzes the emotional state of the store owner and suggests properties that elicit positive emotions. For example, it lists properties that have elicited positive emotions in the past based on the store owner's emotion score. In this way, the store owner's satisfaction is increased by analyzing the emotional state of the store owner and suggesting properties that elicit positive emotions.

[0108] The property presentation unit can consider the influence of seasons and weather when presenting properties and propose the most suitable property. For example, the property presentation unit builds a system that considers the influence of seasons and weather when presenting properties. For example, the property presentation unit proposes the most suitable property based on past weather data. The property presentation unit also considers the influence of seasons and weather when presenting properties and proposes the most suitable property. For example, it analyzes customer purchasing patterns by season and lists the most suitable properties. The property presentation unit also considers the influence of seasons and weather when presenting properties. For example, it proposes the property that will make the store owner the most profit based on weather data. In this way, by proposing the most suitable property taking into account the influence of seasons and weather, the store owner's chances of success are increased.

[0109] The property presentation unit can propose properties that are attractive to store owners by taking into account local culture and event information when presenting properties. For example, the property presentation unit builds a system that considers local culture and event information when presenting properties. For example, it proposes properties that are attractive to store owners based on information about local festivals and events. The property presentation unit also considers local culture and event information when presenting properties and proposes properties that are attractive to store owners. For example, it selects properties based on information about local cultural facilities and tourist spots. The property presentation unit also considers local culture and event information when presenting properties. For example, it proposes properties that are most likely to make a profit for store owners based on a local event calendar. In this way, by proposing attractive properties that take into account local culture and event information, the store owner's chances of success are increased.

[0110] The property presentation unit can analyze customers' emotional responses to properties and suggest properties that have a high number of positive responses. For example, the property presentation unit uses an emotion estimation function to build a system that analyzes customers' emotional responses to properties. For example, it analyzes customers' facial expressions and voices and selects properties based on an emotion score. The property presentation unit also analyzes customers' emotional responses and suggests properties that have a high number of positive responses. For example, it prioritizes listing properties that have a high customer emotion score. The property presentation unit also uses the emotion estimation function to analyze customers' emotional responses to properties and suggests properties that have a high number of positive responses. For example, it selects properties based on past customer emotion data. In this way, customer satisfaction is increased by analyzing customers' emotional responses and suggesting properties that have a high number of positive responses.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The property information acquisition unit acquires information from the property owner. For example, the property information acquisition unit collects information such as the property's location, area, rent, and facilities provided by the property owner. The property information acquisition unit can also acquire detailed information that cannot be obtained from regular real estate agent websites through direct communication with the property owner. Step 2: The request analysis unit analyzes the store owner's requests based on the information acquired by the property information acquisition unit. For example, the request analysis unit analyzes information entered by the store owner, such as the desired location, budget, and target customer demographic, and selects the most suitable property. The request analysis unit can also analyze the store owner's business model and target customer demographic, and suggest the most suitable area based on customer behavior patterns. Step 3: The property presentation unit presents the most suitable property based on the requirements analyzed by the requirement analysis unit. For example, the property presentation unit lists and presents the most suitable property based on the store owner's detailed requirements. The property presentation unit can also analyze the store owner's past property selection history and suggest the most suitable property based on similar successful patterns. Step 4: The zoning generation unit generates a zoning layout based on the store owner's requests and the actual area. For example, the zoning generation unit generates an optimal zoning layout based on the store owner's requests. The zoning generation unit can also analyze the store owner's past successful layout examples and propose an optimal zoning layout.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, 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 robot 414 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 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 property information acquisition unit that acquires information from property owners; a request analysis unit that analyzes requests from shop owners based on the information acquired by the property information acquisition unit; a property presentation unit that presents optimal properties based on the needs analyzed by the need analysis unit; a zoning generation unit that generates a zoning layout based on the requests of the store owner and the actual divisions. A system characterized by:

2. The zoning generation unit Generate an optimal zoning layout based on the store owner's requests 2. The system of claim 1.

3. The property presentation unit Based on the detailed requirements of the store owner, we will list and present the most suitable properties.

2. The system of claim 1.

4. The request analysis unit Analyze the business model and target customer demographic of the store owner and propose the optimal area based on customer behavior patterns.

2. The system of claim 1.

5. The request analysis unit Analyze the emotional state of the exhibitors and suggest areas that will elicit positive emotions 2. The system of claim 1.

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