System

Abstract

An object of a system according to an embodiment is to quickly identify an optimum candidate site for a new store based on a trading area and competition information.SOLUTION: A system includes a reception unit, an analysis unit, a specification unit, and a display unit. The reception part inputs a trading area and competition information in an interactive form. The analysis unit analyzes the information input by the reception unit in real time. The specification part specifies a candidate site for the store on the basis of a result analyzed by the analysis part. The display part displays the candidate site specified by the specification part on an online map.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly identify the optimal candidate location for a store based on trade areas and competitive information.

[0005] The system according to the embodiment aims to quickly identify the most suitable candidate site for opening a store based on commercial areas and competitive information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a display unit. The reception unit interactively inputs commercial area and competitive information. The analysis unit analyzes the information input by the reception unit in real time. The identification unit identifies a potential store location based on the results of the analysis by the analysis unit. The display unit displays the potential store location identified by the identification unit on an online map. [Effects of the Invention]

[0007] The system according to the embodiment can quickly identify the most suitable candidate site for opening a store based on commercial areas and competitive information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A store location candidate identification system according to an embodiment of the present invention interactively inputs trade area and competitor information, analyzes it in real time, identifies optimal store location candidates, and displays it on an online map. The store location candidate identification system interactively inputs trade area and competitor information, analyzes it using real-time data analysis and a geographic information system, identifies optimal store location candidates, and displays it on an online map. For example, a user interactively inputs trade area and competitor information into the store location candidate identification system. For example, the user answers questions such as, "What is the size of the trade area in this area?" and "What kind of competitors exist in this area?" This information is entered into the system. Next, the store location candidate identification system analyzes the input information using real-time data analysis and a geographic information system. For example, the system analyzes the trade area size, the location of competitors, demographic data, and other information. This allows the system to understand the characteristics of the trade area and the impact of competitors. Next, the store location candidate identification system uses a unique algorithm to identify optimal store location candidates based on the analysis results. For example, the system identifies areas within the trade area with fewer competitors and a larger population. This algorithm incorporates trade area and competitive information, allowing the user to quickly identify the optimal store location. The store location identification system then displays the identified store location on an online map. The user can view the location on the map and view detailed information, such as the demographic data surrounding the location and the location of competitors. This allows retailers and companies considering opening new stores to quickly and effectively analyze the market and select a store location. This allows the store location identification system to optimize business development strategies and increase the chances of success. For example, the store location identification system can quickly and accurately identify store locations and provide feedback to users to support business success.

[0029] A store location candidate identification system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a display unit. The reception unit interactively inputs information about a trade area and competitors. For example, a user can input the trade area's size, the location of competitors, demographic data, and other information by answering questions such as, "What is the size of the trade area in this area?" and "What kind of competitors exist in this area?" The analysis unit analyzes the information input by the reception unit using real-time data analysis and a geographic information system. For example, the analysis unit analyzes the trade area's size, the location of competitors, demographic data, and other information to understand the characteristics of the trade area and the impact of competitors. The identification unit uses a proprietary algorithm to identify an optimal store location candidate based on the results of the analysis by the analysis unit. For example, the analysis unit identifies an area within the trade area with few competitors and a large population. The display unit displays the store location candidate identified by the identification unit on an online map. For example, a user can view the candidate location on the map and view detailed information. As a result, the store opening candidate site identification system according to the embodiment can interactively input trade area and competitive information, analyze it in real time, identify the most suitable store opening candidate site, and display it on an online map.

[0030] The reception unit can receive the range of the trade area, the location of competitors, and demographic data. The range of the trade area includes, for example, a geographical area, population density, purchasing power, etc., but is not limited to these examples. The reception unit can receive, for example, the range of the trade area as a number of kilometers in radius or a specific region. Furthermore, the location of competitors includes, for example, GPS data and address information, but is not limited to these examples. The reception unit can receive, for example, the location of competitors as GPS data or address information. Furthermore, the demographic data includes, for example, age group, gender, income level, etc., but is not limited to these examples. The reception unit can receive, for example, demographic data as age group, gender, income level, etc. In this way, the range of the trade area, the location of competitors, demographic data, etc. can be received.

[0031] The analysis unit can analyze information by utilizing real-time data analysis and a geographic information system. Examples of real-time data analysis include, but are not limited to, streaming data processing and real-time analysis tools. The analysis unit can analyze data in real time using, for example, streaming data processing. Examples of geographic information systems include, but are not limited to, GIS software and types of map data. The analysis unit can analyze geographic information using, for example, GIS software. This allows information to be analyzed by utilizing real-time data analysis and a geographic information system.

[0032] The identification unit can identify a candidate store site using a unique algorithm. Examples of the unique algorithm include, but are not limited to, a machine learning algorithm and a heuristic algorithm. The identification unit can identify a candidate store site using, for example, a machine learning algorithm. The identification unit can also identify a candidate store site using a heuristic algorithm. This makes it possible to identify the optimal candidate store site using a unique algorithm.

[0033] The display unit can display the candidate locations on an online map. Examples of online maps include, but are not limited to, Google (registered trademark) Maps, Yahoo! (registered trademark) Maps, OpenStreetMap, and the like. The display unit can display the candidate locations using, for example, Google Maps. The display unit can also display the candidate locations using OpenStreetMap. This allows the candidate locations to be displayed on the online map.

[0034] The display unit can display demographic data around the candidate site and the location of competitors. Demographic data includes, for example, age group, gender, income level, etc., but is not limited to these examples. The display unit can display, for example, age group, gender, income level around the candidate site. Furthermore, the location of competitors includes, for example, GPS data, address information, etc., but is not limited to these examples. The display unit can display, for example, the location of competitors around the candidate site as GPS data or address information. This makes it possible to display demographic data around the candidate site and the location of competitors, etc.

[0035] The reception unit can analyze past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the trade area or competitive information that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the trade area or competitive information to be used in a specific time period based on the user's past input history. This makes it possible to analyze past input history and suggest the optimal input method.

[0036] The reception unit can perform filtering based on the user's current business situation when entering trade area and competitor information. For example, if the user is considering opening a new store, the reception unit can prioritize displaying areas with less competition. Furthermore, if the user is considering expanding an existing store, the reception unit can also prioritize displaying trade area information near the existing store. Furthermore, if the user belongs to a specific industry, the reception unit can also prioritize displaying competitor information related to that industry. This allows filtering to be performed based on the user's current business situation.

[0037] When entering trade area information or competitor information, the reception unit can select the optimal input means depending on the user's input method. For example, when the user enters trade area information by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user enters competitor information by text, the reception unit can also support the input using text analysis technology. Furthermore, when the user enters trade area information by image, the reception unit can also support the input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method.

[0038] When inputting trade area and competitor information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize inputting trade area information related to that region. Furthermore, if the user is in a specific city, the reception unit can also prioritize inputting competitor information related to that city. Furthermore, if the user is in a specific country, the reception unit can also prioritize inputting demographic data related to that country. This makes it possible to prioritize inputting highly relevant information in consideration of the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related information when entering trade area and competitor information. For example, the reception unit inputs trade area information related to a location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and input related competitor information. The reception unit can also input related trade area information by referring to the activities of the user's friends on social media. This makes it possible to analyze the user's social media activity and input related information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when entering trade area or competitor information. The reception unit can improve the input interface, for example, based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by referring to the user's past feedback. The reception unit can also customize the input method by reflecting the user's past feedback. This makes it possible to customize the input method by reflecting the user's past feedback.

[0041] During the analysis, the analysis unit can acquire additional data for a detailed analysis of the characteristics of the trade area and the impact of competition. For example, the analysis unit acquires additional demographic data to analyze the characteristics of the trade area in detail. The analysis unit can also acquire additional sales data of competitors to analyze the impact of competition in detail. The analysis unit can also acquire additional regional economic data to analyze the characteristics of the trade area in detail. This makes it possible to acquire additional data for a detailed analysis of the characteristics of the trade area and the impact of competition.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of trade area or competitive information. For example, the analysis unit applies an algorithm based on demographic data to analyze trade area information. The analysis unit can also apply an algorithm based on competitor sales data to analyze competitive information. The analysis unit can also apply a composite algorithm to analyze the interrelationship between trade area information and competitive information. This makes it possible to apply different analysis algorithms depending on the category of trade area or competitive information.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy of trade area information based on past analysis results. The analysis unit can also improve the analysis accuracy of competitive information based on past analysis results. The analysis unit can also improve the analysis accuracy of the interrelationship between trade area information and competitive information based on past analysis results. This makes it possible to improve the accuracy of analysis by referring to past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of trade area and competitor information. For example, the analysis unit prioritizes analysis of the most recent trade area information. The analysis unit can also prioritize analysis of the most recent competitor information. The analysis unit can also lower the analysis priority of information that was submitted earlier. This makes it possible to determine the priority of analysis based on the time of submission of trade area and competitor information.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the trade area and competitor information. For example, the analysis unit prioritizes analysis when the relevance between the trade area information and competitor information is high. The analysis unit can also lower the priority of analysis when the relevance between the trade area information and competitor information is low. The analysis unit can also adjust the order of analysis based on the relevance between the trade area information and competitor information. This makes it possible to adjust the order of analysis based on the relevance between the trade area information and competitor information.

[0046] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides detailed analysis results. Alternatively, if the user does not have expertise, the analysis unit can provide simple analysis results. Alternatively, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to adjust the level of detail of the analysis results according to the user's level of expertise.

[0047] During identification, the identification unit can perform a detailed analysis of areas within the trade area that have little competition and are densely populated. For example, the identification unit prioritizes identifying areas within the trade area that have little competition. The identification unit can also prioritize identifying areas within the trade area that have a dense population. The identification unit can also perform a detailed analysis of areas within the trade area that have little competition and are densely populated. This allows for a detailed analysis of areas within the trade area that have little competition and are densely populated.

[0048] During identification, the identification unit can improve the accuracy of identification by taking into account the interrelationships between commercial areas and competitive information. The identification unit improves the accuracy of identification by taking into account, for example, the interrelationships between commercial area information and competitive information. The identification unit can also identify the optimal candidate site based on the interrelationships between commercial area information and competitive information. The identification unit can also improve the accuracy of identification by analyzing the interrelationships between commercial area information and competitive information in detail. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between commercial areas and competitive information.

[0049] When identifying, the identification unit can take into consideration the attribute information of the submitter of the trade area and competitive information. For example, if the submitter is considering opening a new store, the identification unit can prioritize identifying areas with less competition. Furthermore, if the submitter is considering expanding an existing store, the identification unit can also prioritize identifying trade area information near the existing store. Furthermore, if the submitter belongs to a specific industry, the identification unit can also prioritize identifying competitive information related to that industry. This allows identification to be performed taking into consideration the attribute information of the submitter of the trade area and competitive information.

[0050] The identification unit can perform the identification while taking into consideration the geographic distribution of the trade area and the competitive information. For example, the identification unit identifies the optimal candidate site based on the geographic distribution of the trade area information. The identification unit can also identify the optimal candidate site based on the geographic distribution of the competitive information. The identification unit can also identify the optimal candidate site by analyzing the geographic distribution of the trade area information and the competitive information in detail. This allows the identification to be performed while taking into consideration the geographic distribution of the trade area and the competitive information.

[0051] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the trade area and competitive information. For example, the identification unit can improve the accuracy of identification by referring to literature related to the trade area information. The identification unit can also improve the accuracy of identification by referring to literature related to the competitive information. The identification unit can also improve the accuracy of identification by analyzing in detail literature related to the trade area information and competitive information. This makes it possible to improve the accuracy of identification by referring to literature related to the trade area and competitive information.

[0052] The identification unit can perform the identification taking into consideration the market value of the trade area and the competitive information. For example, the identification unit can identify the optimal candidate site based on the market value of the trade area information. The identification unit can also identify the optimal candidate site based on the market value of the competitive information. The identification unit can also identify the optimal candidate site by analyzing the market value of the trade area information and the competitive information in detail. This allows the identification to be performed taking into consideration the market value of the trade area and the competitive information.

[0053] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit provides the optimal display method based on, for example, a display method that the user has previously preferred. The display unit can also analyze the user's past operation history and suggest the optimal display method. The display unit can also customize the display method based on the user's past operation history. This makes it possible to select the optimal display method by referring to the user's past operation history.

[0054] The display unit can customize the display content according to the user's current task when displaying. For example, if the user is considering opening a new store, the display unit can prioritize displaying related information. Also, if the user is considering expanding an existing store, the display unit can also prioritize displaying related information. Also, the display unit can customize the display content according to the user's current task. This makes it possible to customize the display content according to the user's current task.

[0055] The display unit can improve the display method by reflecting user feedback during display. For example, the display unit improves the display method based on user feedback. The display unit can also customize the display content by referring to user feedback. The display unit can also optimize the display method by reflecting user feedback. This makes it possible to improve the display method by reflecting user feedback.

[0056] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can also display detailed information. This makes it possible to select the optimal display method in consideration of the user's device information.

[0057] The display unit can make the displayed content multilingual according to the user's language setting when displaying. The display unit can automatically translate the displayed content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the displayed content in a specific language when the user selects that language. This allows the displayed content to be multilingual according to the user's language setting.

[0058] The display unit can prioritize displaying highly relevant information in consideration of the user's geographical location information when displaying information. For example, when the user is in a specific region, the display unit can prioritize displaying information related to that region. Furthermore, when the user is in a specific city, the display unit can prioritize displaying information related to that city. Furthermore, when the user is in a specific country, the display unit can prioritize displaying information related to that country. This makes it possible to prioritize displaying highly relevant information in consideration of the user's geographical location information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the trade area or competitor information that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the trade area or competitor information to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and suggest the optimal input method.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the category of trade area or competitive information. For example, an algorithm based on demographic data can be applied to analyze trade area information. The analysis unit can also apply an algorithm based on competitor sales data to analyze competitive information. Furthermore, the analysis unit can apply a composite algorithm to analyze the interrelationship between trade area information and competitive information. This makes it possible to apply different analysis algorithms depending on the category of trade area or competitive information.

[0062] The identification unit can perform the identification taking into consideration the market value of the trade area and competitive information. For example, the identification unit can identify the optimal candidate site based on the market value of the trade area information. The identification unit can also identify the optimal candidate site based on the market value of the competitive information. Furthermore, the identification unit can perform a detailed analysis of the market value of the trade area information and competitive information to identify the optimal candidate site. This allows the identification to be performed taking into consideration the market value of the trade area and competitive information.

[0063] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can also display detailed information. This makes it possible to select the optimal display method in consideration of the user's device information.

[0064] The reception unit can filter the trade area and competitor information based on the user's current business situation when inputting the trade area and competitor information. For example, if the user is considering opening a new store, areas with less competition can be displayed preferentially. Also, if the user is considering expanding an existing store, the reception unit can preferentially display trade area information near the existing store. Furthermore, if the user belongs to a specific industry, the reception unit can preferentially display competitor information related to that industry. This allows filtering to be performed based on the user's current business situation.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit interactively inputs trade area and competitor information. For example, by answering questions such as "What is the trade area of ​​this region?" and "What kind of competitors exist in this area?", the user can input the trade area's range, competitor locations, demographic data, and so on. Step 2: The analysis unit uses real-time data analysis and geographic information systems to analyze the information entered by the reception unit. For example, it analyzes the scope of the trade area, the location of competitors, demographic data, etc., to understand the characteristics of the trade area and the impact of competitors. Step 3: The identification unit uses a proprietary algorithm to identify the optimal candidate location for a store based on the results of the analysis by the analysis unit. For example, it identifies areas within the trade area that have little competition and a large population. Step 4: The display unit displays the candidate locations identified by the identification unit on an online map. For example, the user can check the candidate locations on the map and view detailed information.

[0067] (Example 2) A store location candidate identification system according to an embodiment of the present invention interactively inputs trade area and competitor information, analyzes it in real time, identifies optimal store location candidates, and displays it on an online map. The store location candidate identification system interactively inputs trade area and competitor information, analyzes it using real-time data analysis and a geographic information system, identifies optimal store location candidates, and displays it on an online map. For example, a user interactively inputs trade area and competitor information into the store location candidate identification system. For example, the user answers questions such as, "What is the size of the trade area in this area?" and "What kind of competitors exist in this area?" This information is entered into the system. Next, the store location candidate identification system analyzes the input information using real-time data analysis and a geographic information system. For example, the system analyzes the trade area size, the location of competitors, demographic data, and other information. This allows the system to understand the characteristics of the trade area and the impact of competitors. Next, the store location candidate identification system uses a unique algorithm to identify optimal store location candidates based on the analysis results. For example, the system identifies areas within the trade area with fewer competitors and a larger population. This algorithm incorporates trade area and competitive information, allowing the user to quickly identify the optimal store location. The store location identification system then displays the identified store location on an online map. The user can view the location on the map and view detailed information, such as the demographic data surrounding the location and the location of competitors. This allows retailers and companies considering opening new stores to quickly and effectively analyze the market and select a store location. This allows the store location identification system to optimize business development strategies and increase the chances of success. For example, the store location identification system can quickly and accurately identify store locations and provide feedback to users to support business success.

[0068] A store location candidate identification system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a display unit. The reception unit interactively inputs information about a trade area and competitors. For example, a user can input the trade area's size, the location of competitors, demographic data, and other information by answering questions such as, "What is the size of the trade area in this area?" and "What kind of competitors exist in this area?" The analysis unit analyzes the information input by the reception unit using real-time data analysis and a geographic information system. For example, the analysis unit analyzes the trade area's size, the location of competitors, demographic data, and other information to understand the characteristics of the trade area and the impact of competitors. The identification unit uses a proprietary algorithm to identify an optimal store location candidate based on the results of the analysis by the analysis unit. For example, the analysis unit identifies an area within the trade area with few competitors and a large population. The display unit displays the store location candidate identified by the identification unit on an online map. For example, a user can view the candidate location on the map and view detailed information. As a result, the store opening candidate site identification system according to the embodiment can interactively input trade area and competitive information, analyze it in real time, identify the most suitable store opening candidate site, and display it on an online map.

[0069] The reception unit can receive the range of the trade area, the location of competitors, and demographic data. The range of the trade area includes, for example, a geographical area, population density, purchasing power, etc., but is not limited to these examples. The reception unit can receive, for example, the range of the trade area as a number of kilometers in radius or a specific region. Furthermore, the location of competitors includes, for example, GPS data and address information, but is not limited to these examples. The reception unit can receive, for example, the location of competitors as GPS data or address information. Furthermore, the demographic data includes, for example, age group, gender, income level, etc., but is not limited to these examples. The reception unit can receive, for example, demographic data as age group, gender, income level, etc. In this way, the range of the trade area, the location of competitors, demographic data, etc. can be received.

[0070] The analysis unit can analyze information by utilizing real-time data analysis and a geographic information system. Examples of real-time data analysis include, but are not limited to, streaming data processing and real-time analysis tools. The analysis unit can analyze data in real time using, for example, streaming data processing. Examples of geographic information systems include, but are not limited to, GIS software and types of map data. The analysis unit can analyze geographic information using, for example, GIS software. This allows information to be analyzed by utilizing real-time data analysis and a geographic information system.

[0071] The identification unit can identify a candidate store site using a unique algorithm. Examples of the unique algorithm include, but are not limited to, a machine learning algorithm and a heuristic algorithm. The identification unit can identify a candidate store site using, for example, a machine learning algorithm. The identification unit can also identify a candidate store site using a heuristic algorithm. This makes it possible to identify the optimal candidate store site using a unique algorithm.

[0072] The display unit can display the candidate locations on an online map. Examples of online maps include, but are not limited to, Google Maps and Yahoo! Maps. The display unit can display the candidate locations using, for example, Google Maps. The display unit can also display the candidate locations using OpenStreetMap. This allows the candidate locations to be displayed on the online map.

[0073] The display unit can display demographic data around the candidate site and the location of competitors. Demographic data includes, for example, age group, gender, income level, etc., but is not limited to these examples. The display unit can display, for example, age group, gender, income level around the candidate site. Furthermore, the location of competitors includes, for example, GPS data, address information, etc., but is not limited to these examples. The display unit can display, for example, the location of competitors around the candidate site as GPS data or address information. This makes it possible to display demographic data around the candidate site and the location of competitors, etc.

[0074] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly enter business area and competitive information. This allows the display method of the input interface to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze past input history and suggest the optimal input method. For example, the reception unit automatically displays as candidates the trade area or competitive information that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the trade area or competitive information to be used in a specific time period based on the user's past input history. This makes it possible to analyze past input history and suggest the optimal input method.

[0076] The reception unit can perform filtering based on the user's current business situation when entering trade area and competitor information. For example, if the user is considering opening a new store, the reception unit can prioritize displaying areas with less competition. Furthermore, if the user is considering expanding an existing store, the reception unit can also prioritize displaying trade area information near the existing store. Furthermore, if the user belongs to a specific industry, the reception unit can also prioritize displaying competitor information related to that industry. This allows filtering to be performed based on the user's current business situation.

[0077] When entering trade area information or competitor information, the reception unit can select the optimal input means depending on the user's input method. For example, when the user enters trade area information by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user enters competitor information by text, the reception unit can also support the input using text analysis technology. Furthermore, when the user enters trade area information by image, the reception unit can also support the input using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method.

[0078] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input important information first. If the user is relaxed, the reception unit can also prompt the user to input detailed information. If the user is in a hurry, the reception unit can also prompt the user to input minimal information. This makes it possible to determine the priority of information to be input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] When inputting trade area and competitor information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize inputting trade area information related to that region. Furthermore, if the user is in a specific city, the reception unit can also prioritize inputting competitor information related to that city. Furthermore, if the user is in a specific country, the reception unit can also prioritize inputting demographic data related to that country. This makes it possible to prioritize inputting highly relevant information in consideration of the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity and input related information when entering trade area and competitor information. For example, the reception unit inputs trade area information related to a location where the user checked in on social media. The reception unit can also analyze the content of the user's social media posts and input related competitor information. The reception unit can also input related trade area information by referring to the activities of the user's friends on social media. This makes it possible to analyze the user's social media activity and input related information.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when entering trade area or competitor information. The reception unit can improve the input interface, for example, based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by referring to the user's past feedback. The reception unit can also customize the input method by reflecting the user's past feedback. This makes it possible to customize the input method by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit prioritizes analyzing important information. The analysis unit can also analyze detailed information if the user is relaxed. The analysis unit can also prioritize analyzing minimal information if the user is in a hurry. This allows the analysis priority to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] During the analysis, the analysis unit can acquire additional data for a detailed analysis of the characteristics of the trade area and the impact of competition. For example, the analysis unit acquires additional demographic data to analyze the characteristics of the trade area in detail. The analysis unit can also acquire additional sales data of competitors to analyze the impact of competition in detail. The analysis unit can also acquire additional regional economic data to analyze the characteristics of the trade area in detail. This makes it possible to acquire additional data for a detailed analysis of the characteristics of the trade area and the impact of competition.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of trade area or competitive information. For example, the analysis unit applies an algorithm based on demographic data to analyze trade area information. The analysis unit can also apply an algorithm based on competitor sales data to analyze competitive information. The analysis unit can also apply a composite algorithm to analyze the interrelationship between trade area information and competitive information. This makes it possible to apply different analysis algorithms depending on the category of trade area or competitive information.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit improves the analysis accuracy of trade area information based on past analysis results. The analysis unit can also improve the analysis accuracy of competitive information based on past analysis results. The analysis unit can also improve the analysis accuracy of the interrelationship between trade area information and competitive information based on past analysis results. This makes it possible to improve the accuracy of analysis by referring to past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of trade area and competitor information. For example, the analysis unit prioritizes analysis of the most recent trade area information. The analysis unit can also prioritize analysis of the most recent competitor information. The analysis unit can also lower the analysis priority of information that was submitted earlier. This makes it possible to determine the priority of analysis based on the time of submission of trade area and competitor information.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the trade area and competitor information. For example, the analysis unit prioritizes analysis when the relevance between the trade area information and competitor information is high. The analysis unit can also lower the priority of analysis when the relevance between the trade area information and competitor information is low. The analysis unit can also adjust the order of analysis based on the relevance between the trade area information and competitor information. This makes it possible to adjust the order of analysis based on the relevance between the trade area information and competitor information.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides detailed analysis results. Alternatively, if the user does not have expertise, the analysis unit can provide simple analysis results. Alternatively, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This makes it possible to adjust the level of detail of the analysis results according to the user's level of expertise.

[0090] The identification unit can estimate the user's emotions and determine the priority of candidate locations to be identified based on the estimated user emotions. For example, when the user is feeling stressed, the identification unit can prioritize displaying important candidate locations. Furthermore, when the user is relaxed, the identification unit can also display detailed candidate locations. Furthermore, when the user is in a hurry, the identification unit can prioritize displaying a minimum number of candidate locations. This allows the priority of candidate locations to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] During identification, the identification unit can perform a detailed analysis of areas within the trade area that have little competition and are densely populated. For example, the identification unit prioritizes identifying areas within the trade area that have little competition. The identification unit can also prioritize identifying areas within the trade area that have a dense population. The identification unit can also perform a detailed analysis of areas within the trade area that have little competition and are densely populated. This allows for a detailed analysis of areas within the trade area that have little competition and are densely populated.

[0092] During identification, the identification unit can improve the accuracy of identification by taking into account the interrelationships between commercial areas and competitive information. The identification unit improves the accuracy of identification by taking into account, for example, the interrelationships between commercial area information and competitive information. The identification unit can also identify the optimal candidate site based on the interrelationships between commercial area information and competitive information. The identification unit can also improve the accuracy of identification by analyzing the interrelationships between commercial area information and competitive information in detail. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between commercial areas and competitive information.

[0093] When identifying, the identification unit can take into consideration the attribute information of the submitter of the trade area and competitive information. For example, if the submitter is considering opening a new store, the identification unit can prioritize identifying areas with less competition. Furthermore, if the submitter is considering expanding an existing store, the identification unit can also prioritize identifying trade area information near the existing store. Furthermore, if the submitter belongs to a specific industry, the identification unit can also prioritize identifying competitive information related to that industry. This allows identification to be performed taking into consideration the attribute information of the submitter of the trade area and competitive information.

[0094] The identification unit can estimate the user's emotions and adjust the display method of the identified candidate locations based on the estimated user emotions. For example, when the user is feeling stressed, the identification unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the identification unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This allows the display method of the identified candidate locations to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The identification unit can perform the identification while taking into consideration the geographic distribution of the trade area and the competitive information. For example, the identification unit identifies the optimal candidate site based on the geographic distribution of the trade area information. The identification unit can also identify the optimal candidate site based on the geographic distribution of the competitive information. The identification unit can also identify the optimal candidate site by analyzing the geographic distribution of the trade area information and the competitive information in detail. This allows the identification to be performed while taking into consideration the geographic distribution of the trade area and the competitive information.

[0096] During identification, the identification unit can improve the accuracy of identification by referring to literature related to the trade area and competitive information. For example, the identification unit can improve the accuracy of identification by referring to literature related to the trade area information. The identification unit can also improve the accuracy of identification by referring to literature related to the competitive information. The identification unit can also improve the accuracy of identification by analyzing in detail literature related to the trade area information and competitive information. This makes it possible to improve the accuracy of identification by referring to literature related to the trade area and competitive information.

[0097] The identification unit can perform the identification taking into consideration the market value of the trade area and the competitive information. For example, the identification unit can identify the optimal candidate site based on the market value of the trade area information. The identification unit can also identify the optimal candidate site based on the market value of the competitive information. The identification unit can also identify the optimal candidate site by analyzing the market value of the trade area information and the competitive information in detail. This allows the identification to be performed taking into consideration the market value of the trade area and the competitive information.

[0098] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is stressed, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. This allows the display method to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The display unit can select the optimal display method by referring to the user's past operation history when displaying. The display unit provides the optimal display method based on, for example, a display method that the user has previously preferred. The display unit can also analyze the user's past operation history and suggest the optimal display method. The display unit can also customize the display method based on the user's past operation history. This makes it possible to select the optimal display method by referring to the user's past operation history.

[0100] The display unit can customize the display content according to the user's current task when displaying. For example, if the user is considering opening a new store, the display unit can prioritize displaying related information. Also, if the user is considering expanding an existing store, the display unit can also prioritize displaying related information. Also, the display unit can customize the display content according to the user's current task. This makes it possible to customize the display content according to the user's current task.

[0101] The display unit can improve the display method by reflecting user feedback during display. For example, the display unit improves the display method based on user feedback. The display unit can also customize the display content by referring to user feedback. The display unit can also optimize the display method by reflecting user feedback. This makes it possible to improve the display method by reflecting user feedback.

[0102] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated user emotions. For example, when the user is stressed, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can also display detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that summarizes the main points. This allows the priority of information to be displayed to be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0103] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can also display detailed information. This makes it possible to select the optimal display method in consideration of the user's device information.

[0104] The display unit can make the displayed content multilingual according to the user's language setting when displaying. The display unit can automatically translate the displayed content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide the displayed content in a specific language when the user selects that language. This allows the displayed content to be multilingual according to the user's language setting.

[0105] The display unit can prioritize displaying highly relevant information in consideration of the user's geographical location information when displaying information. For example, when the user is in a specific region, the display unit can prioritize displaying information related to that region. Furthermore, when the user is in a specific city, the display unit can prioritize displaying information related to that city. Furthermore, when the user is in a specific country, the display unit can prioritize displaying information related to that country. This makes it possible to prioritize displaying highly relevant information in consideration of the user's geographical location information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, identification unit, and display unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, and trade area and competitive information is input interactively using a touch panel 38A and a microphone 38B. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information using real-time data analysis and a geographic information system. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal store location using a unique algorithm. The display unit is implemented, for example, by the output device 40 of the smart device 14, and displays the identified store location on an online map. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, identification unit, and display unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and interactively inputs commercial area and competitive information using voice input. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the input information using real-time data analysis and a geographic information system. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the optimal store location using a unique algorithm. The display unit is realized, for example, by the speaker 240 of the smart glasses 214 and notifies the user of the identified store location by voice. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, identification unit, and display unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and uses voice input to interactively input information about the trade area and competitive information. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information using real-time data analysis and a geographic information system. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal candidate site for a store using a unique algorithm. The display unit is realized, for example, by the display 343 of the headset terminal 314, and displays the identified candidate site on an online map. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, identification unit, and display unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and uses voice input to interactively input information about the commercial area and competitive information. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information using real-time data analysis and a geographic information system. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal candidate store location using a unique algorithm. The display unit is realized, for example, by the display and speaker 240 of the robot 414, and displays the identified candidate store location on an online map.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the trade area or competitor information that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the trade area or competitor information to be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and suggest the optimal input method.

[0108] During analysis, the analysis unit can apply different analysis algorithms depending on the category of trade area or competitive information. For example, an algorithm based on demographic data can be applied to analyze trade area information. The analysis unit can also apply an algorithm based on competitor sales data to analyze competitive information. Furthermore, the analysis unit can apply a composite algorithm to analyze the interrelationship between trade area information and competitive information. This makes it possible to apply different analysis algorithms depending on the category of trade area or competitive information.

[0109] The identification unit can perform the identification taking into consideration the market value of the trade area and competitive information. For example, the identification unit can identify the optimal candidate site based on the market value of the trade area information. The identification unit can also identify the optimal candidate site based on the market value of the competitive information. Furthermore, the identification unit can perform a detailed analysis of the market value of the trade area information and competitive information to identify the optimal candidate site. This allows the identification to be performed taking into consideration the market value of the trade area and competitive information.

[0110] The display unit can select the optimal display method in consideration of the user's device information when displaying. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the display unit can also display detailed information. This makes it possible to select the optimal display method in consideration of the user's device information.

[0111] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. In addition, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly enter business area and competitor information. In this way, the display method of the input interface can be adjusted based on the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, important information is prioritized for analysis. The analysis unit can also analyze detailed information if the user is relaxed. Furthermore, the analysis unit can also prioritize analysis of minimal information if the user is in a hurry. This makes it possible to adjust the analysis priority based on the user's emotions.

[0113] The identification unit can estimate the user's emotions and determine the priority of candidate locations to be identified based on the estimated user's emotions. For example, if the user is feeling stressed, important candidate locations are preferentially displayed. The identification unit can also display detailed candidate locations when the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can also display a minimum number of candidate locations preferentially. In this way, the priority of candidate locations to be identified can be determined based on the user's emotions.

[0114] The display unit can estimate the user's emotions and adjust the display method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method is provided. If the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the display unit can also provide a display method that focuses on the main points. In this way, the display method can be adjusted based on the user's emotions.

[0115] The display unit can estimate the user's emotions and determine the priority of information to be displayed based on the estimated user's emotions. For example, if the user is feeling stressed, important information is displayed with priority. The display unit can also display detailed information when the user is relaxed. Furthermore, if the user is in a hurry, the display unit can also display information that focuses on the main points with priority. This makes it possible to determine the priority of information to be displayed based on the user's emotions.

[0116] The reception unit can filter the trade area and competitor information based on the user's current business situation when inputting the trade area and competitor information. For example, if the user is considering opening a new store, areas with less competition can be displayed preferentially. Also, if the user is considering expanding an existing store, the reception unit can preferentially display trade area information near the existing store. Furthermore, if the user belongs to a specific industry, the reception unit can preferentially display competitor information related to that industry. This allows filtering to be performed based on the user's current business situation.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit interactively inputs trade area and competitor information. For example, by answering questions such as "What is the trade area of ​​this region?" and "What kind of competitors exist in this area?", the user can input the trade area's range, competitor locations, demographic data, and so on. Step 2: The analysis unit uses real-time data analysis and geographic information systems to analyze the information entered by the reception unit. For example, it analyzes the scope of the trade area, the location of competitors, demographic data, etc., to understand the characteristics of the trade area and the impact of competitors. Step 3: The identification unit uses a proprietary algorithm to identify the optimal candidate location for a store based on the results of the analysis by the analysis unit. For example, it identifies areas within the trade area that have little competition and a large population. Step 4: The display unit displays the candidate locations identified by the identification unit on an online map. For example, the user can check the candidate locations on the map and view detailed information.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0121] 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.

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

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

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

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section where trade area and competitor information is entered interactively, an analysis unit that analyzes the information input by the reception unit in real time; an identification unit that identifies a potential store location based on the results of the analysis by the analysis unit; a display unit that displays the store candidate site identified by the identification unit on an online map; Equipped with A system characterized by:

2. The reception unit Accepts trade area limits, competitor locations, and demographic data 2. The system of claim 1.

3. The analysis unit Utilizing real-time data analytics and geographic information systems to analyze information 2. The system of claim 1.

4. The identification unit Identifying potential locations for new stores using a unique algorithm 2. The system of claim 1.

5. The display unit Display potential locations on an online map 2. The system of claim 1.

6. The display unit View demographic data and competitor locations around your proposed site 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes past input history and suggests optimal input methods 2. The system of claim 1.

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